IntroductionIBM Predictive Customer Intelligence gives you the information and insight that you need to provide proactive service to your customers. The information can help you to develop a consistent customer contact strategy and improve your relationship with your customers.IBM Predictive Customer Intelligence brings together, in a single solution, the ability to do the following tasks:Determine the best offer for a customer.Retain customers that are likely to churn.Segment your customers, for example, by family status and salary.Identify the most appropriate channel to deliver an offer, for example, by email, telephone call, or application.This solution ensures that all interactions with customers are coordinated and optimized. IBM Predictive Customer Intelligence gives you the ability to sift quickly through millions of customers and know who to contact, when, and with what action.The following steps define the process:Understand the customer. Predictive modeling helps you to understand what market segments each customer falls into, what products they are interested in, and what offers they are most likely to respond to.Define possible actions and the rules and models that determine which customers are eligible for which offers.After the best action is identified, deliver the recommendation to the customer. AudienceThis guide is intended to provide users with an understanding of how the IBM Predictive Customer Intelligence solution works. It is designed to help people who are planning to implement IBM Predictive Customer Intelligence know what tasks are involved. Finding information To find product documentation on the web, including all translated documentation, access IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSCJHT_1.1.1). PDF versions of the documents are available from the Predictive Customer Intelligence version 1.1.1 product documentation page (www.ibm.com/support/docview.wss?uid=swg27048306). Accessibility features Accessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products. Some of the components included in the IBM Predictive Customer Intelligence have accessibility features.IBM Predictive Customer Intelligence HTML documentation has accessibility features. PDF documents are supplemental and, as such, include no added accessibility features. Forward-looking statements This documentation describes the current functionality of the product. References to items that are not currently available may be included. No implication of any future availability should be inferred. Any such references are not a commitment, promise, or legal obligation to deliver any material, code, or functionality. The development, release, and timing of features or functionality remain at the sole discretion of IBM. Samples disclaimer Sample files may contain fictional data manually or machine generated, factual data compiled from academic or public sources, or data used with permission of the copyright holder, for use as sample data to develop sample applications. Product names referenced may be the trademarks of their respective owners. Unauthorized duplication is prohibited.

Introduction

IBM® Predictive Customer Intelligence gives you the information and insight that you need to provide proactive service to your customers. The information can help you to develop a consistent customer contact strategy and improve your relationship with your customers.

IBM Predictive Customer Intelligence brings together, in a single solution, the ability to do the following tasks:

Determine the best offer for a customer.

Retain customers that are likely to churn.

Segment your customers, for example, by family status and salary.

Identify the most appropriate channel to deliver an offer, for example, by email, telephone call, or application.

This solution ensures that all interactions with customers are coordinated and optimized. IBM Predictive Customer Intelligence gives you the ability to sift quickly through millions of customers and know who to contact, when, and with what action.

The following steps define the process:

Understand the customer. Predictive modeling helps you to understand what market segments each customer falls into, what products they are interested in, and what offers they are most likely to respond to.

Define possible actions and the rules and models that determine which customers are eligible for which offers.

After the best action is identified, deliver the recommendation to the customer.

Audience

This guide is intended to provide users with an understanding of how the IBM Predictive Customer Intelligence solution works. It is designed to help people who are planning to implement IBM Predictive Customer Intelligence know what tasks are involved.

Finding information

To find product documentation on the web, including all translated documentation, access IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSCJHT_1.1.1).

PDF versions of the documents are available from the Predictive Customer Intelligence version 1.1.1 product documentation page (www.ibm.com/support/docview.wss?uid=swg27048306).

Accessibility features

Accessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products. Some of the components included in the IBM Predictive Customer Intelligence have accessibility features.

IBM Predictive Customer Intelligence HTML documentation has accessibility features. PDF documents are supplemental and, as such, include no added accessibility features.

Forward-looking statements

This documentation describes the current functionality of the product. References to items that are not currently available may be included. No implication of any future availability should be inferred. Any such references are not a commitment, promise, or legal obligation to deliver any material, code, or functionality. The development, release, and timing of features or functionality remain at the sole discretion of IBM.

Samples disclaimer

Sample files may contain fictional data manually or machine generated, factual data compiled from academic or public sources, or data used with permission of the copyright holder, for use as sample data to develop sample applications. Product names referenced may be the trademarks of their respective owners. Unauthorized duplication is prohibited.

Inject Next Best Action Recommendations into every Insurance Call Center InteractionBy using IBM Predictive Customer Intelligence, insurers can deliver the most appropriate action to each customer during each interaction by analyzing customer data. Insurance companies have access to large amounts of customer data, and building long term customer relationships by making use of this data is key to their success. The type of data insurers can use is customer profile data, customer policy data, customer claims data, customer complaints data and other transactions, as well as social, and web interaction data.Drive cross-sell opportunitiesA multi-line insurance company is facing a problem of declining new premiums from its Life Insurance line of business.Define the offers to be presented to insurance customersMonica is a marketing manager who is responsible for defining her company's marketing strategy. She determines which offers individual customers can receive based on their customer profile data and their real-time interaction data.Determine the best offers for insurance customers by creating business rulesAlex, a business analyst, uses IBM Analytical Decision Management to bring together the company's business rules and predictive models to determine the best possible offer for a customer.Build predictive models for an insurance companyDavid, the data scientist (or data engineer) builds models using IBM SPSS Modeler to achieve the following results:Industry accelerator artifactsThe IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator includes the following artifacts.Extend the accelerator with the IBM Predictive Customer Intelligence Usage ReportOptionally, you can monitor the effectiveness of your solution by using the IBM Predictive Customer Intelligence Usage Report. The IBM Predictive Customer Intelligence Usage Report displays the number of offers that are presented to customers and can be configured to show the number of offers that are accepted and rejected.

Inject Next Best Action Recommendations into every Insurance Call Center Interaction

By using IBM® Predictive Customer Intelligence, insurers can deliver the most appropriate action to each customer during each interaction by analyzing customer data.

Insurance companies have access to large amounts of customer data, and building long term customer relationships by making use of this data is key to their success. The type of data insurers can use is customer profile data, customer policy data, customer claims data, customer complaints data and other transactions, as well as social, and web interaction data.

Drive cross-sell opportunities
A multi-line insurance company is facing a problem of declining new premiums from its Life Insurance line of business.

Define the offers to be presented to insurance customers
Monica is a marketing manager who is responsible for defining her company's marketing strategy. She determines which offers individual customers can receive based on their customer profile data and their real-time interaction data.

Determine the best offers for insurance customers by creating business rules
Alex, a business analyst, uses IBM Analytical Decision Management to bring together the company's business rules and predictive models to determine the best possible offer for a customer.

Build predictive models for an insurance company
David, the data scientist (or data engineer) builds models using IBM SPSS® Modeler to achieve the following results:

Industry accelerator artifacts
The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator includes the following artifacts.

Extend the accelerator with the IBM Predictive Customer Intelligence Usage Report
Optionally, you can monitor the effectiveness of your solution by using the IBM Predictive Customer Intelligence Usage Report. The IBM Predictive Customer Intelligence Usage Report displays the number of offers that are presented to customers and can be configured to show the number of offers that are accepted and rejected.

Drive cross-sell opportunitiesA multi-line insurance company is facing a problem of declining new premiums from its Life Insurance line of business.Alex, the business analyst, analyzes the year-to-date data, and identifies that this decline is attributed mainly to the fall in the sale of Term Policies. Further analysis also reveals that there is a large proportion of existing Auto and Home Insurance customers who do not own a Term Policy. Monica is a marketing manager who is responsible for defining her company's marketing strategy. Monica decides to launches a cross-sell campaign to target existing Auto and Homeowner customers for selling Term Policies. David, the data scientist (or data engineer), builds models by using IBM SPSS Modeler to derive valuable insights from the customer's demographic, transactional, and behavioral data. IBM Predictive Customer Intelligence uses the real-time predictive insights, customer profile data, and current interaction data to recommend the best offer during each customer interaction. IBM Enterprise Marketing Management (IBM EMM) is integrated with the Customer Relationship Management (CRM) software to deliver the campaign offers at the Customer touchpoint.Customer profileGrace Wright calls the Insurance company and requests a quotation for a new Life-term policy, with a policy tenure of 25 years. Carolyn, a call center agent, opens her Customer Relationship Management (CRM) system and looks at Grace's profile. She sees that Grace is 32 years old, and is married with one child. Grace is a homeowner, and the household has two insured cars and three policies. Carolyn can also see other information. For example, Carolyn can view complaints that Grace made about the insurance policies. Carolyn can request to see more insights about Grace. IBM Predictive Customer Intelligence uses rich predictive modeling capabilities to view the segment that Grace is in (financially sophisticated), the customer lifetime value (high) and the churn propensity (high). The churn propensity is predicted in real-time based on the latest customer data. ActionsCarolyn selects the type of interaction (Get Quote) and the product line (policy type of life-term). Carolyn updates the customer interaction data, and requests a quotation for a policy tenure of 25 years and a coverage amount of $200,000. IBM Predictive Customer Intelligence provides two offers that are based on customer profile data, predictive insights, and current interaction data:Standard premium regardless of health, raised from $300000 to $600000. Ultra Select Pricing.Grace then requests a quotation for a policy tenure of 35 years. Using the new interaction data, IBM Predictive Customer Intelligence recommends an extra offer of the last two years of premium waived. Grace accepts the offer and decides to buy the policy. When the offer is accepted, the predictive data is rescored in real time. Grace's churn propensity is reduced.Inject Next Best Action Recommendations into every Insurance Call Center InteractionBy using IBM Predictive Customer Intelligence, insurers can deliver the most appropriate action to each customer during each interaction by analyzing customer data.

Drive cross-sell opportunities

A multi-line insurance company is facing a problem of declining new premiums from its Life Insurance line of business.

Alex, the business analyst, analyzes the year-to-date data, and identifies that this decline is attributed mainly to the fall in the sale of Term Policies. Further analysis also reveals that there is a large proportion of existing Auto and Home Insurance customers who do not own a Term Policy.

Monica is a marketing manager who is responsible for defining her company's marketing strategy. Monica decides to launches a cross-sell campaign to target existing Auto and Homeowner customers for selling Term Policies.

David, the data scientist (or data engineer), builds models by using IBM® SPSS® Modeler to derive valuable insights from the customer's demographic, transactional, and behavioral data. IBM Predictive Customer Intelligence uses the real-time predictive insights, customer profile data, and current interaction data to recommend the best offer during each customer interaction. IBM Enterprise Marketing Management (IBM EMM) is integrated with the Customer Relationship Management (CRM) software to deliver the campaign offers at the Customer touchpoint.

Customer profile

Grace Wright calls the Insurance company and requests a quotation for a new Life-term policy, with a policy tenure of 25 years. Carolyn, a call center agent, opens her Customer Relationship Management (CRM) system and looks at Grace's profile. She sees that Grace is 32 years old, and is married with one child. Grace is a homeowner, and the household has two insured cars and three policies. Carolyn can also see other information. For example, Carolyn can view complaints that Grace made about the insurance policies. Carolyn can request to see more insights about Grace. IBM Predictive Customer Intelligence uses rich predictive modeling capabilities to view the segment that Grace is in (financially sophisticated), the customer lifetime value (high) and the churn propensity (high). The churn propensity is predicted in real-time based on the latest customer data.

Actions

Carolyn selects the type of interaction (Get Quote) and the product line (policy type of life-term).

Carolyn updates the customer interaction data, and requests a quotation for a policy tenure of 25 years and a coverage amount of $200,000. IBM Predictive Customer Intelligence provides two offers that are based on customer profile data, predictive insights, and current interaction data:

Standard premium regardless of health, raised from $300000 to $600000.

Ultra Select Pricing.

Grace then requests a quotation for a policy tenure of 35 years. Using the new interaction data, IBM Predictive Customer Intelligence recommends an extra offer of the last two years of premium waived. Grace accepts the offer and decides to buy the policy. When the offer is accepted, the predictive data is rescored in real time. Grace's churn propensity is reduced.

Parent topic: Inject Next Best Action Recommendations into every Insurance Call Center Interaction

Define the offers to be presented to insurance customersMonica is a marketing manager who is responsible for defining her company's marketing strategy. She determines which offers individual customers can receive based on their customer profile data and their real-time interaction data.Monica determines which offers individual customers should receive based on the customer profile data and the real-time interaction data. This customer profile data can include the following:Customer claims data.Complaints data. Policies data. Customer demographic data. Predictive Insights such as a customer's segment, and likelihood of churn. Real-time data, for example, context of the call – Get a quote for an insurance product, complaint, and so on.The campaignMonica creates a campaign that is named Cross-Sell Campaign by using IBM Campaign, part of the IBM Enterprise Marketing Management (EMM) suite. The purpose of the campaign is to cross-sell life insurance policies to existing auto insurance customers.The Cross-Sell Campaign contains offers to be presented within the insurance call center application. The details of the offer vary based on individual customer characteristics. The results of the offer, whether accepted or rejected, are recorded in the customer's interaction history, and that information can be used for follow-up marketing offers and campaigns.Customers are segmented into categories in real time that are based on the call center interaction point, such as the context of the call and real time scoring, and the campaign's business rules.For example, you might have a flowchart with a decision node that assigns customers to five segments:High value customers.Loyal customers.High risk customers.Low risk customers.High value customers seeking long tenure.In this example, customers are assigned to the High Value Customers segment if they have a customer lifetime value (CTLV) of high, if their customer segment is Financially Sophisticated, and if they are a home owner.Customers are segmented based on off-line and real-time customer data, and offers are associated to customer segments.After each offer is evaluated, there might be more offers eligible than there is time or space to present. For example, a customer is eligible for three offers, but you want to present only the top two offers. In this case, the offers would be presented based on the marketer's score values. The marketer's score can be based on a calculation of an expression by using customer and offer attributes, a real-time learning algorithm, or a combination of these methods.Optimization of offersYou can further optimize offers by using the External Callout connector.Using the External Callout connector, you can override a single offer's score by calling an IBM SPSS Scoring Service. Also, by using the External Callout connector, you can invoke the IBM SPSS score to present eligible offers.In the insurance case study, you can invoke the IBM SPSS Scoring Services in real time to get the latest Churn Propensity based on the most current data. The Churn Propensity score is then used as the marketer's score.Inject Next Best Action Recommendations into every Insurance Call Center InteractionBy using IBM Predictive Customer Intelligence, insurers can deliver the most appropriate action to each customer during each interaction by analyzing customer data.

Define the offers to be presented to insurance customers

Monica is a marketing manager who is responsible for defining her company's marketing strategy. She determines which offers individual customers can receive based on their customer profile data and their real-time interaction data.

Monica determines which offers individual customers should receive based on the customer profile data and the real-time interaction data. This customer profile data can include the following:

Customer claims data.

Complaints data.

Policies data.

Customer demographic data.

Predictive Insights such as a customer's segment, and likelihood of churn.

Real-time data, for example, context of the call – Get a quote for an insurance product, complaint, and so on.

The campaign

Monica creates a campaign that is named Cross-Sell Campaign by using IBM® Campaign, part of the IBM Enterprise Marketing Management (EMM) suite. The purpose of the campaign is to cross-sell life insurance policies to existing auto insurance customers.

The Cross-Sell Campaign contains offers to be presented within the insurance call center application. The details of the offer vary based on individual customer characteristics. The results of the offer, whether accepted or rejected, are recorded in the customer's interaction history, and that information can be used for follow-up marketing offers and campaigns.

Customers are segmented into categories in real time that are based on the call center interaction point, such as the context of the call and real time scoring, and the campaign's business rules.

For example, you might have a flowchart with a decision node that assigns customers to five segments:

High value customers.

Loyal customers.

High risk customers.

Low risk customers.

High value customers seeking long tenure.

In this example, customers are assigned to the High Value Customers segment if they have a customer lifetime value (CTLV) of high, if their customer segment is Financially Sophisticated, and if they are a home owner.

Customers are segmented based on off-line and real-time customer data, and offers are associated to customer segments.

After each offer is evaluated, there might be more offers eligible than there is time or space to present. For example, a customer is eligible for three offers, but you want to present only the top two offers. In this case, the offers would be presented based on the marketer's score values. The marketer's score can be based on a calculation of an expression by using customer and offer attributes, a real-time learning algorithm, or a combination of these methods.

Optimization of offers

You can further optimize offers by using the External Callout connector.

Using the External Callout connector, you can override a single offer's score by calling an IBM SPSS® Scoring Service. Also, by using the External Callout connector, you can invoke the IBM SPSS score to present eligible offers.

In the insurance case study, you can invoke the IBM SPSS Scoring Services in real time to get the latest Churn Propensity based on the most current data. The Churn Propensity score is then used as the marketer's score.

Parent topic: Inject Next Best Action Recommendations into every Insurance Call Center Interaction

Determine the best offers for insurance customers by creating business rulesAlex, a business analyst, uses IBM Analytical Decision Management to bring together the company's business rules and predictive models to determine the best possible offer for a customer.The insurance company has a cross-sell campaign. Alex creates rules to target offers to customers depending on which segment they fall in.The following examples show offers that are targeted to customers in different segments:Young family with high churn propensityLife term insurance with free double indemnity riderYoung family with browsing targeted policiesLife term insuranceYoung family with high churn and browsing specific targeted policiesJuvenile life insuranceDivorced and no home ownershipRenter's insuranceOffers are allocated so that the first offer in the list that applies is presented to the customer.Example of a rule in IBM Analytical Decision ManagementThe rule "Young family with high churn propensity" takes into consideration the following points:Current life stage is "Young Family".Product recently browsed is "Life Term".Insurance Policy Recommendation is "Life Term".Churn propensity score that is returned from a real-time model is greater than or equal to 0.7.Young family with high churn propensity ruleLifestage Current Segment(Current Segment -) Young FamilyPRODUCT_BROWSED_1 = Life termInsurance_Policy_Recommendation(RECOMMENDATIONS -) =Life TermAuto Churn($RRP-CHURN -Propensity) >=0.7Inject Next Best Action Recommendations into every Insurance Call Center InteractionBy using IBM Predictive Customer Intelligence, insurers can deliver the most appropriate action to each customer during each interaction by analyzing customer data.

Determine the best offers for insurance customers by creating business rules

Alex, a business analyst, uses IBM® Analytical Decision Management to bring together the company's business rules and predictive models to determine the best possible offer for a customer.

The insurance company has a cross-sell campaign. Alex creates rules to target offers to customers depending on which segment they fall in.

The following examples show offers that are targeted to customers in different segments:

Young family with high churn propensity

Life term insurance with free double indemnity rider

Young family with browsing targeted policies

Life term insurance

Young family with high churn and browsing specific targeted policies

Juvenile life insurance

Divorced and no home ownership

Renter's insurance

Offers are allocated so that the first offer in the list that applies is presented to the customer.

Example of a rule in IBM Analytical Decision Management

The rule "Young family with high churn propensity" takes into consideration the following points:

Current life stage is "Young Family".

Product recently browsed is "Life Term".

Insurance Policy Recommendation is "Life Term".

Churn propensity score that is returned from a real-time model is greater than or equal to 0.7.

Young family with high churn propensity rule

Lifestage Current Segment(Current Segment -) Young Family

PRODUCT_BROWSED_1 = Life term

Insurance_Policy_Recommendation(RECOMMENDATIONS -) =Life Term

Auto Churn($RRP-CHURN -Propensity) >=0.7

Parent topic: Inject Next Best Action Recommendations into every Insurance Call Center Interaction

Build predictive models for an insurance companyDavid, the data scientist (or data engineer) builds models using IBM SPSS Modeler to achieve the following results:Segment customers based on financial sophistication, by using financial and demographic data. In this example, financially sophisticated customers have a relatively high credit score, three types of insurance policies, a relatively high income, and are mostly home owners between 30-35 years of age.Predict a customer's propensity to churn, by using information about the customer, such as household and financial data, transactional data, and behavioral data.Predict customer lifetime value (CLTV) based on the revenue that the customer brings to the company, the cost of maintaining the policies, the cost of retention, and the likelihood of the customer surrendering the policies in the near future.Predict the life policies likely to be bought by customers at different life stages by using historical purchase data. Segment the customers based on their current life stage and predict the life policies most relevant to their life stage.David uses IBM Social Media Analytics to identify life events by analyzing social media posts to detect life events with potential business value for the Insurance company. Life events such as "New baby born" can act as triggers for cross-selling life policies.David also uses IBM SPSS Modeler Text Analytics to analyze the customer comments in real time to predict the latest customer sentiment score. IBM SPSS Modeler Text Analytics extracts negative words (sentiment) from customer comments to get a negative sentiment score.The scores, segments, and results from these models are used as inputs within IBM Analytical Decision Management by Alex the business analyst as he creates his business rules.Inject Next Best Action Recommendations into every Insurance Call Center InteractionBy using IBM Predictive Customer Intelligence, insurers can deliver the most appropriate action to each customer during each interaction by analyzing customer data.

Build predictive models for an insurance company

David, the data scientist (or data engineer) builds models using IBM® SPSS® Modeler to achieve the following results:

Segment customers based on financial sophistication, by using financial and demographic data. In this example, financially sophisticated customers have a relatively high credit score, three types of insurance policies, a relatively high income, and are mostly home owners between 30-35 years of age.

Predict a customer's propensity to churn, by using information about the customer, such as household and financial data, transactional data, and behavioral data.

Predict customer lifetime value (CLTV) based on the revenue that the customer brings to the company, the cost of maintaining the policies, the cost of retention, and the likelihood of the customer surrendering the policies in the near future.

Predict the life policies likely to be bought by customers at different life stages by using historical purchase data. Segment the customers based on their current life stage and predict the life policies most relevant to their life stage.

David uses IBM Social Media Analytics to identify life events by analyzing social media posts to detect life events with potential business value for the Insurance company. Life events such as "New baby born" can act as triggers for cross-selling life policies.

David also uses IBM SPSS Modeler Text Analytics to analyze the customer comments in real time to predict the latest customer sentiment score. IBM SPSS Modeler Text Analytics extracts negative words (sentiment) from customer comments to get a negative sentiment score.

The scores, segments, and results from these models are used as inputs within IBM Analytical Decision Management by Alex the business analyst as he creates his business rules.

Parent topic: Inject Next Best Action Recommendations into every Insurance Call Center Interaction

Industry accelerator artifactsThe IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator includes the following artifacts.IBM Cognos Business Intelligence reportsPCI_Ins_CallCenter_CognosContent.zip PCI_Ins_CallCenter_FMProject.zip PCI_Images.zip The reports are described in Industry accelerator reportsAn IBM Cognos Business Intelligence report is provided with the IBM Predictive Customer Intelligence industry accelerator..IBM Analytical Decision Management applications and templatesPCI_Ins_CallCenter_App.zipPredictive modelsPCI_Ins_CallCenter_CDS_Archive.pes The individual stream files that are contained in the pes file are available in the Streams folder. The following streams are included: Auto Churn.strBuying_Propensity_Model.strCampaign Response Model.strChurn.strCLTV.strDataProcessing Stream.strExternalLearning.strInsurance_Policy_Recommendation.strLifestage Current Segment.strSegmentation.strSentiment.strSMA_Text_Analytics.str The Predictive Models are described in Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator. .IBM DB2 databasePCI_Ins_CallCenter_Data.zip The insurance data model is described in Data modelThe historic data that is used for predictive modeling in IBM Predictive Customer Intelligence is stored in an IBM DB2 database..Inject Next Best Action Recommendations into every Insurance Call Center InteractionBy using IBM Predictive Customer Intelligence, insurers can deliver the most appropriate action to each customer during each interaction by analyzing customer data.

Industry accelerator artifacts

The IBM® Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator includes the following artifacts.

IBM Cognos® Business Intelligence reports

PCI_Ins_CallCenter_CognosContent.zip

PCI_Ins_CallCenter_FMProject.zip

PCI_Images.zip

The reports are described in Industry accelerator reports.

IBM Analytical Decision Management applications and templates

PCI_Ins_CallCenter_App.zip

Predictive models

PCI_Ins_CallCenter_CDS_Archive.pes

The individual stream files that are contained in the pes file are available in the Streams folder.

The following streams are included:

Auto Churn.str

Buying_Propensity_Model.str

Campaign Response Model.str

Churn.str

CLTV.str

DataProcessing Stream.str

ExternalLearning.str

Insurance_Policy_Recommendation.str

Lifestage Current Segment.str

Segmentation.str

Sentiment.str

SMA_Text_Analytics.str

The Predictive Models are described in Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

IBM DB2® database

PCI_Ins_CallCenter_Data.zip

The insurance data model is described in Data model.

Parent topic: Inject Next Best Action Recommendations into every Insurance Call Center Interaction

Extend the accelerator with the IBM Predictive Customer Intelligence Usage ReportOptionally, you can monitor the effectiveness of your solution by using the IBM Predictive Customer Intelligence Usage Report. The IBM Predictive Customer Intelligence Usage Report displays the number of offers that are presented to customers and can be configured to show the number of offers that are accepted and rejected.You can download the IBM Predictive Customer Intelligence Usage Report from IBM AnalyticsZone (www.ibm.com/analyticszone).Inject Next Best Action Recommendations into every Insurance Call Center InteractionBy using IBM Predictive Customer Intelligence, insurers can deliver the most appropriate action to each customer during each interaction by analyzing customer data.

Extend the accelerator with the IBM Predictive Customer Intelligence Usage Report

Optionally, you can monitor the effectiveness of your solution by using the IBM® Predictive Customer Intelligence Usage Report. The IBM Predictive Customer Intelligence Usage Report displays the number of offers that are presented to customers and can be configured to show the number of offers that are accepted and rejected.

You can download the IBM Predictive Customer Intelligence Usage Report from IBM AnalyticsZone (www.ibm.com/analyticszone).

Parent topic: Inject Next Best Action Recommendations into every Insurance Call Center Interaction

Industry accelerator installationThe Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator is for use with IBM Predictive Customer Intelligence.The industry accelerator package contains the following parts:IBM DB2 databases.IBM Analytical Decision Management templates and applications.IBM SPSS project streams, models, and rules.IBM Cognos Business Intelligence reports and Framework Manager models and packages.To install the industry accelerator, you must perform the following steps:Download the industry accelerator from IBM AnalyticsZone (www.ibm.com/analyticszone).Create the sample databases on the data node computer.Install the Analytical Decision Management templates and applications on the Predictive Analytics node.Import the SPSS project streams, models, and rules on the Predictive Analytics node.Configure the data view for SPSS models on the Predictive Analytics node.Install the IBM Cognos Content on the Cognos Analytics node.Industry accelerator prerequisitesBefore you install the industry accelerator, you must have a fully configured environment.Download the industry acceleratorYou must download the IBM Predictive Customer Intelligence accelerators from IBM AnalyticsZone.Creating the databaseTo use the IBM Predictive Customer Intelligence industry accelerator, you must create a database.Installing Analytical Decision Management templates and applicationsThe IBM Predictive Customer Intelligence industry accelerator includes IBM Analytical Decision Management templates and applications. You must copy the template and application files to the Predictive Analytics node computer.Importing IBM SPSS project streams, models, and rulesIBM SPSS project streams, models, rules, and other artifacts are contained in a repository export file (.pes) for the IBM Predictive Customer Intelligence industry accelerator. If you want to modify or view these artifacts, you must copy the export file to the computer where IBM SPSS Collaboration and Deployment Services Deployment Manager is installed, and open the file.Configuring the data view for IBM SPSS modelsTo configure the data view, IBM SPSS Modeler must be connected to the IBM Predictive Customer Intelligence industry accelerator database through an ODBC data source connection.Deploy the IBM Cognos contentFor IBM Cognos Business Intelligence, you must catalog the database, create a data source connection, and then deploy the content files for the IBM Predictive Customer Intelligence industry accelerator.Copying the industry accelerator license files to each computerAfter you install the industry accelerator, you must copy the license folder to each computer on which you use the IBM Predictive Customer Intelligence industry accelerator.

Industry accelerator installation

The Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator is for use with IBM® Predictive Customer Intelligence.

The industry accelerator package contains the following parts:

IBM DB2® databases.

IBM Analytical Decision Management templates and applications.

IBM SPSS® project streams, models, and rules.

IBM Cognos® Business Intelligence reports and Framework Manager models and packages.

To install the industry accelerator, you must perform the following steps:

Download the industry accelerator from IBM AnalyticsZone (www.ibm.com/analyticszone).

Create the sample databases on the data node computer.

Install the Analytical Decision Management templates and applications on the Predictive Analytics node.

Import the SPSS project streams, models, and rules on the Predictive Analytics node.

Configure the data view for SPSS models on the Predictive Analytics node.

Install the IBM Cognos Content on the Cognos Analytics node.

Industry accelerator prerequisites
Before you install the industry accelerator, you must have a fully configured environment.

Download the industry accelerator
You must download the IBM Predictive Customer Intelligence accelerators from IBM AnalyticsZone.

Creating the database
To use the IBM Predictive Customer Intelligence industry accelerator, you must create a database.

Installing Analytical Decision Management templates and applications
The IBM Predictive Customer Intelligence industry accelerator includes IBM Analytical Decision Management templates and applications. You must copy the template and application files to the Predictive Analytics node computer.

Importing IBM SPSS project streams, models, and rules
IBM SPSS project streams, models, rules, and other artifacts are contained in a repository export file (.pes) for the IBM Predictive Customer Intelligence industry accelerator. If you want to modify or view these artifacts, you must copy the export file to the computer where IBM SPSS Collaboration and Deployment Services Deployment Manager is installed, and open the file.

Configuring the data view for IBM SPSS models
To configure the data view, IBM SPSS Modeler must be connected to the IBM Predictive Customer Intelligence industry accelerator database through an ODBC data source connection.

Deploy the IBM Cognos content
For IBM Cognos Business Intelligence, you must catalog the database, create a data source connection, and then deploy the content files for the IBM Predictive Customer Intelligence industry accelerator.

Copying the industry accelerator license files to each computer
After you install the industry accelerator, you must copy the license folder to each computer on which you use the IBM Predictive Customer Intelligence industry accelerator.

Industry accelerator prerequisitesBefore you install the industry accelerator, you must have a fully configured environment.You must have administration rights and have the ability to copy files between computers.Industry accelerator installationThe Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator is for use with IBM Predictive Customer Intelligence.

Industry accelerator prerequisites

Before you install the industry accelerator, you must have a fully configured environment.

You must have administration rights and have the ability to copy files between computers.

Parent topic: Industry accelerator installation

Download the industry acceleratorYou must download the IBM Predictive Customer Intelligence accelerators from IBM AnalyticsZone.Go to IBM AnalyticsZone (www.ibm.com/analyticszone).Click Downloads, and under Predictive Customer Intelligence Accelerators, click View all PCI downloads.Click More details for the accelerator that you want to download.If you are not signed in, click Sign In to Download. You must enter your IBM ID. If you do not have an IBM ID, you must register to create one.Click Download.Go to the directory where you downloaded the industry accelerator.Decompress the file.Industry accelerator installationThe Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator is for use with IBM Predictive Customer Intelligence.

Download the industry accelerator

You must download the IBM® Predictive Customer Intelligence accelerators from IBM AnalyticsZone.

Procedure

Go to IBM AnalyticsZone (www.ibm.com/analyticszone).

Click Downloads, and under Predictive Customer Intelligence Accelerators, click View all PCI downloads.

Click More details for the accelerator that you want to download.

If you are not signed in, click Sign In to Download. You must enter your IBM ID. If you do not have an IBM ID, you must register to create one.

Click Download.

Go to the directory where you downloaded the industry accelerator.

Decompress the file.

Parent topic: Industry accelerator installation

Creating the databaseTo use the IBM Predictive Customer Intelligence industry accelerator, you must create a database. You run one script to create the database, and then run another script to populate the database.Copy the industry accelerator database content file from the computer where you downloaded them to the data node computer: The Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator database file is PCI_1.0_Ins_CallCenter\Database\PCI_Ins_CallCenter_Data.zip. A database that is named INSURNC is created. On the data node computer, decompress the file.On Microsoft Windows operating systems, do the following steps:Log on to the data node computer as the DB2 instance owner user.Go to the folder where you decompressed the industry accelerator content files.In the uncompressed folder, double-click Install_DB.bat.Double-click Load_Data.bat.On Linux operating systems, do the following steps:Log on to the data node computer as root user.Open a terminal window, and go to the directory where you decompressed the industry accelerator content files.If you copied the content files to the home directory for the root user, you might have to move the files to another directory that is not in the root home directory so that you can run the scripts. Type the following command to change the permissions for the files:chmod -R 755 *sh Change to the database instance owner.For example, su db2inst1In the uncompressed folder, run sh ./Install_DB.sh.Run sh ./Load_Data.sh.Verify that the tables are created and the data is successfully loaded into the input tables by checking the out.log file. On Microsoft Windows operating systems, the log file is in the industry accelerator name folder. On Linux operating systems, the log file is in the db2inst1 home folder. Search for rows were rejected in the log file. The value should be zero, if it is not, there are data load issues.Industry accelerator installationThe Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator is for use with IBM Predictive Customer Intelligence.

Creating the database

To use the IBM® Predictive Customer Intelligence industry accelerator, you must create a database.

You run one script to create the database, and then run another script to populate the database.

Procedure

Copy the industry accelerator database content file from the computer where you downloaded them to the data node computer:

The Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator database file is PCI_1.0_Ins_CallCenter\Database\PCI_Ins_CallCenter_Data.zip. A database that is named INSURNC is created.

On the data node computer, decompress the file.

On Microsoft Windows operating systems, do the following steps:

Log on to the data node computer as the DB2® instance owner user.

Go to the folder where you decompressed the industry accelerator content files.

In the uncompressed folder, double-click Install_DB.bat.

Double-click Load_Data.bat.

On Linux operating systems, do the following steps:

Log on to the data node computer as root user.

Open a terminal window, and go to the directory where you decompressed the industry accelerator content files.

Note: If you copied the content files to the home directory for the root user, you might have to move the files to another directory that is not in the root home directory so that you can run the scripts.

Type the following command to change the permissions for the files:

chmod -R 755 *

sh

Change to the database instance owner. For example, su db2inst1

In the uncompressed folder, run sh ./Install_DB.sh.

Run sh ./Load_Data.sh.

What to do next

Verify that the tables are created and the data is successfully loaded into the input tables by checking the out.log file.

On Microsoft Windows operating systems, the log file is in the industry accelerator name folder. On Linux operating systems, the log file is in the db2inst1 home folder.

Search for "rows were rejected" in the log file. The value should be zero, if it is not, there are data load issues.

Parent topic: Industry accelerator installation

Installing Analytical Decision Management templates and applicationsThe IBM Predictive Customer Intelligence industry accelerator includes IBM Analytical Decision Management templates and applications. You must copy the template and application files to the Predictive Analytics node computer.Log on to the Predictive Analytics node computer.From the computer where you downloaded the industry accelerator files, copy the Analytical Decision Management applications and templates files. Application: PCI_1.0_Ins_CallCenter\Analytics\Applications\PCI_Ins_CallCenter_App.zipTemplate: PCI_1.0_Ins_CallCenter\Analytics\Templates\Insurance.xml Copy the files to the Applications or Templates folder as appropriate on the Predictive Analytics node computer.On Microsoft Windows operating systems, the default locations are C:\Program Files\IBM\SPSS\Deployment\6.0\Server\components\decision-management\Applications and C:\Program Files\IBM\SPSS\Deployment\6.0\Server\components\decision-management\Templates. On Linux operating systems, the default locations are /opt/IBM/SPSS/Deployment/6.0/Server/components/decision-management/Applications and /opt/IBM/SPSS/Deployment/6.0/Server/components/decision-management/Templates.Decompress the files in the Applications folder.In a browser, go to the IBM Analytical Decision Management launch page: http://analytics_node_name:port_number/DMIf you used the default values, the port_number is 9080.Enter the log in credentials.Click Add application, and select Insurance.Click Save.Industry accelerator installationThe Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator is for use with IBM Predictive Customer Intelligence.

Installing Analytical Decision Management templates and applications

The IBM® Predictive Customer Intelligence industry accelerator includes IBM Analytical Decision Management templates and applications. You must copy the template and application files to the Predictive Analytics node computer.

Procedure

Log on to the Predictive Analytics node computer.

From the computer where you downloaded the industry accelerator files, copy the Analytical Decision Management applications and templates files.

Application: PCI_1.0_Ins_CallCenter\Analytics\Applications\PCI_Ins_CallCenter_App.zip

Template: PCI_1.0_Ins_CallCenter\Analytics\Templates\Insurance.xml

Copy the files to the Applications or Templates folder as appropriate on the Predictive Analytics node computer.

On Microsoft Windows operating systems, the default locations are C:\Program Files\IBM\SPSS\Deployment\6.0\Server\components\decision-management\Applications and C:\Program Files\IBM\SPSS\Deployment\6.0\Server\components\decision-management\Templates.

On Linux operating systems, the default locations are /opt/IBM/SPSS/Deployment/6.0/Server/components/decision-management/Applications and /opt/IBM/SPSS/Deployment/6.0/Server/components/decision-management/Templates.

Decompress the files in the Applications folder.

In a browser, go to the IBM Analytical Decision Management launch page: http://analytics_node_name:port_number/DM

If you used the default values, the port_number is 9080.

Enter the log in credentials.

Click Add application, and select Insurance.

Click Save.

Parent topic: Industry accelerator installation

Importing IBM SPSS project streams, models, and rulesIBM SPSS project streams, models, rules, and other artifacts are contained in a repository export file (.pes) for the IBM Predictive Customer Intelligence industry accelerator. If you want to modify or view these artifacts, you must copy the export file to the computer where IBM SPSS Collaboration and Deployment Services Deployment Manager is installed, and open the file.From the computer where you downloaded the industry accelerator, copy the .pes file to the computer where IBM SPSS Collaboration and Deployment Services Deployment Manager is installed. The Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator file is PCI_1.0_Ins_CallCenter\Analytics\PCI_Ins_CallCenter_CDS_Archive.pes. In IBM SPSS Collaboration and Deployment Service Deployment Manager, right-click Content Repository, and click Import.Browse to the .pes file.Select the following options:Resolve conflicts globallyAdd new version of target item or rename source item, Use labels from source.Continue import even if some objects cannot be imported due to locking conflicts.Resolve Invalid Version Conflicts, Import.Resource Definitions, Recommended - Import if there are no Duplicate ID conflicts or Duplicate Name conflicts.Click OK.Content folders and resource definitions are added to the repository alongside any existing content. Industry accelerator installationThe Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator is for use with IBM Predictive Customer Intelligence.

Importing IBM SPSS project streams, models, and rules

IBM® SPSS® project streams, models, rules, and other artifacts are contained in a repository export file (.pes) for the IBM Predictive Customer Intelligence industry accelerator. If you want to modify or view these artifacts, you must copy the export file to the computer where IBM SPSS Collaboration and Deployment Services Deployment Manager is installed, and open the file.

Procedure

From the computer where you downloaded the industry accelerator, copy the .pes file to the computer where IBM SPSS Collaboration and Deployment Services Deployment Manager is installed.

The Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator file is PCI_1.0_Ins_CallCenter\Analytics\PCI_Ins_CallCenter_CDS_Archive.pes.

In IBM SPSS Collaboration and Deployment Service Deployment Manager, right-click Content Repository, and click Import.

Browse to the .pes file.

Select the following options:

Resolve conflicts globally

Add new version of target item or rename source item, Use labels from source.

Continue import even if some objects cannot be imported due to locking conflicts.

Resolve Invalid Version Conflicts, Import.

Resource Definitions, Recommended - Import if there are no Duplicate ID conflicts or Duplicate Name conflicts.

Click OK.

Results

Content folders and resource definitions are added to the repository alongside any existing content.

Parent topic: Industry accelerator installation

Configuring the data view for IBM SPSS modelsTo configure the data view, IBM SPSS Modeler must be connected to the IBM Predictive Customer Intelligence industry accelerator database through an ODBC data source connection. If your IBM Predictive Customer Intelligence environment uses the IBM SPSS Modeler client logged in to a Modeler server, perform the steps on the Predictive Analytics node computer (where IBM SPSS Modeler Server is installed). If your IBM Predictive Customer Intelligence environment uses the IBM SPSS Modeler Client in a stand-alone environment, perform the steps on the client computer where IBM SPSS Modeler client is installed. The Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Analytic Data View and JDBC source connections for accessing IBM Analytical Decision Manager. When the industry accelerator files are imported through IBM SPSS Collaboration and Deployment Services Deployment Manager, a new version of the Enterprise View is created that contains the tables for the industry accelerator.Catalog the database on the client computer.Click StartIBM DB2DB2COPY1 (Default)DB2 Command Window - Administrator.Enter the following command to catalog the database node:db2 catalog tcpip node NODE_NAME remote data_node_name server PORT_NUMBER NODE_NAME can be any value. PORT_NUMBER is 50000 by default.Enter the following command to catalog the PCI database:db2 catalog database INSURNC at node NODE_NAME authentication server You must use the same node_name that you used in the db2 catalog database command.Create an ODBC DSN to point to the industry accelerator database. The database account that is provided in the ODBC connection must be the same user that was used for creating the tables.On Microsoft Windows operating systems, in the Windows Control Panel, select Administrative Tools and click Data Sources. Click the System DSN tab.In the Deployment Manager client, under Resource Definitions, Credentials, open the db2admin credentials and update the user ID and password to the DB2 administrator account of the data node computer.In Resource Definitions, Data Sources, open INSURANCE_JDBC, and update the IP address and port number to point to the IBM Predictive Customer Intelligence data node computer. The default DB2 port number is 50000. Open the data source, INSURANCE_ODBC, and verify that the data source name corresponds to the ODBC name created for the industry accelerator.For example, INSURNC.Verify that the Application View and the Data Provider Definition are working properly.Open INSURANCE_DPD.Click Validate.To retrieve a previous version of the Enterprise View, right-click Enterprise View in Deployment Manager and click Open Version.To create a single view that combines the solution tables with any tables that existed previously, right-click Enterprise View in Deployment Manager and click Merge Versions.Industry accelerator installationThe Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator is for use with IBM Predictive Customer Intelligence.Configuring ODBC for IBM SPSS Modeler Server on Linux operating systemsTo use an ODBC data source with IBM SPSS Modeler Server on a Linux operating system, you must configure the environment.

Configuring the data view for IBM SPSS models

To configure the data view, IBM® SPSS® Modeler must be connected to the IBM Predictive Customer Intelligence industry accelerator database through an ODBC data source connection.

If your IBM Predictive Customer Intelligence environment uses the IBM SPSS Modeler client logged in to a Modeler server, perform the steps on the Predictive Analytics node computer (where IBM SPSS Modeler Server is installed).

If your IBM Predictive Customer Intelligence environment uses the IBM SPSS Modeler Client in a stand-alone environment, perform the steps on the client computer where IBM SPSS Modeler client is installed.

The Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Analytic Data View and JDBC source connections for accessing IBM Analytical Decision Manager. When the industry accelerator files are imported through IBM SPSS Collaboration and Deployment Services Deployment Manager, a new version of the Enterprise View is created that contains the tables for the industry accelerator.

Procedure

Catalog the database on the client computer.

Click Start > IBM DB2 > DB2COPY1 (Default) > DB2 Command Window - Administrator.

Enter the following command to catalog the database node:

db2 catalog tcpip node NODE_NAME remote data_node_name server PORT_NUMBER

NODE_NAME can be any value. PORT_NUMBER is 50000 by default.

Enter the following command to catalog the PCI database:

db2 catalog database INSURNC at node NODE_NAME authentication server

You must use the same node_name that you used in the db2 catalog database command.

Create an ODBC DSN to point to the industry accelerator database. The database account that is provided in the ODBC connection must be the same user that was used for creating the tables.

Tip: On Microsoft Windows operating systems, in the Windows Control Panel, select Administrative Tools and click Data Sources. Click the System DSN tab.

In the Deployment Manager client, under Resource Definitions, Credentials, open the db2admin credentials and update the user ID and password to the DB2® administrator account of the data node computer.

In Resource Definitions, Data Sources, open INSURANCE_JDBC, and update the IP address and port number to point to the IBM Predictive Customer Intelligence data node computer. The default DB2 port number is 50000.

Open the data source, INSURANCE_ODBC, and verify that the data source name corresponds to the ODBC name created for the industry accelerator. For example, INSURNC.

Verify that the Application View and the Data Provider Definition are working properly.

Open INSURANCE_DPD.

Click Validate.

To retrieve a previous version of the Enterprise View, right-click Enterprise View in Deployment Manager and click Open Version.

To create a single view that combines the solution tables with any tables that existed previously, right-click Enterprise View in Deployment Manager and click Merge Versions.

Configuring ODBC for IBM SPSS Modeler Server on Linux operating systems
To use an ODBC data source with IBM SPSS Modeler Server on a Linux operating system, you must configure the environment.

Parent topic: Industry accelerator installation

Configuring ODBC for IBM SPSS Modeler Server on Linux operating systemsTo use an ODBC data source with IBM SPSS Modeler Server on a Linux operating system, you must configure the environment.Stop the IBM SPSS Modeler Server.Go to the /root/SDAP71 directory. The driver files are installed as part of the IBM Predictive Customer Intelligence Server deployment. Run the setodbcpath.sh script to update the ODBC path in the scripts.Edit the odbc.sh script to add the definition for ODBCINI to the bottom of the script. For example:ODBCINI=/usr/spss/odbc/odbc.ini; export ODBCINIODBCINI must point to the full file path of the odbc.ini file for IBM SPSS Modeler. The odbc.ini file lists the ODBC data sources that you want to connect to. A default odbc.ini file is installed with the drivers.In the odbc.ini file, add the data source and specify the driver in the [ODBC Data Sources] section of the file. For example, add the data source as: [ODBC Data Sources] INSURNC=IBM DB2 ODBC Driver In the odbc.ini file, create an ODBC data source connection for the industry accelerator database. For example, include the following content: [INSURNC] Driver=/opt/ibm/db2/V10.1/lib64/libdb2o.so DriverUnicodeType=1 Description=IBM DB2 ODBC Driver ApplicationUsingThreads=1 AuthenticationMethod=0 BulkBinaryThreshold=32 BulkCharacterThreshold=-1 BulkLoadBatchSize=1024 CharsetFor65535=0 #Database applies to DB2 UDB only Database=INSURNC DefaultIsolationLevel=1 DynamicSections=200 EnableBulkLoad=0 EncryptionMethod=0 FailoverGranularity=0 FailoverMode=0 FailoverPreconnect=0 GrantAuthid=PUBLIC GrantExecute=1 GSSClient=native HostNameInCertificate= IpAddress=IP_Address_of_DB_server KeyPassword= KeyStore= KeyStorePassword= LoadBalanceTimeout=0 LoadBalancing=0 LogonID=db2inst1 MaxPoolSize=100 MinPoolSize=0 Password=password PackageCollection=NULLID PackageNamePrefix=DD PackageOwner= Pooling=0 ProgramID= QueryTimeout=0 ReportCodePageConversionErrors=0 TcpPort=50000 TrustStore= TrustStorePassword= UseCurrentSchema=0 ValidateServerCertificate=1 WithHold=1 XMLDescribeType=-10 You must use the driver library libdb2o.so with IBM SPSS Modeler. Ensure that you set DriverUnicodeType=1 to avoid buffer overflow errors when you connect to the database. If you are using the 64-bit version of IBM SPSS Modeler Server, define and export LD_LIBRARY_PATH_64 in the odbc.sh script: if [ "$LD_LIBRARY_PATH_64" = "" ]; then LD_LIBRARY_PATH_64=<library_path> else LD_LIBRARY_PATH_64=<library_path>:$LD_LIBRARY_PATH_64 fi export LD_LIBRARY_PATH_64 Where <library_path> is the same as for the LD_LIBRARY_PATH definition in the script that was initialized with the installation path. For example, /usr/spss/odbc/lib. You can copy the if and export statements for LD_LIBRARY_PATH in the odbc.sh file, append them to the end of the file. Then, replace the LD_LIBRARY_PATH strings in the newly appended if and export statements with LD_LIBRARY_PATH_64. Here is an example of the odbc.sh file for a 64-bit IBM SPSS Modeler Server installation:if [ "$LD_LIBRARY_PATH" = "" ]; then LD_LIBRARY_PATH=/usr/spss/odbc/lib else LD_LIBRARY_PATH=/usr/spss/odbc/lib:$LD_LIBRARY_PATH fi export LD_LIBRARY_PATH if [ "$LD_LIBRARY_PATH_64" = "" ]; then LD_LIBRARY_PATH_64=/usr/spss/odbc/lib else LD_LIBRARY_PATH_64=/usr/spss/odbc/lib:$LD_LIBRARY_PATH_64 fi export LD_LIBRARY_PATH_64 ODBCINI=/usr/spss/odbc/odbc.ini; export ODBCINIEnsure that you export LD_LIBRARY_PATH_64, and define it with the if loop. Configure IBM SPSS Modeler Server to use the driver. Edit modelersrv.sh and add the following line immediately below the line that defines SCLEMDNAME:. <odbc.sh_path>Where odbc.sh_path is the full path to the odbc.sh file.For example: . /usr/spss/odbc/odbc.shEnsure that you leave a space between the first period and the file path.Save modelersrv.sh.Configure the IBM SPSS Modeler Server to use the ODBC wrapper named libspssodbc_datadirect.so.Go to the /usr/IBM/SPSS/ModelerServer/16.0/bin directory.Remove the existing libspssodbc.so soft link by using the following command:rm –fr libspssodbc.soLink the new wrapper to libspssodbc.so by using the following command:ln –s libspssodbc_datadirect_utf16.so libspssodbc.soConfigure the db2cli.ini file in <db2 instance home>/sqllib/cfg/db2cli.ini to add the sections for each database. [INSURNC] Database=INSURNC Protocol=TCPIP DriverUnicodeType=1 Port=50000 Hostname=ip or hostname UID=username PWD=password Save odbc.ini.To test the connection, do the following steps:Restart IBM SPSS Modeler Server.Connect to IBM SPSS Modeler Server from a client.Add a database source node to the canvas.Open the node and verify that you can see the data source names that you defined in the odbc.ini file.For additional information and troubleshooting tips for connecting to data sources, see the SPSS Modeler documentation (www.ibm.com/support/knowledgecenter/SS3RA7_16.0.0)Configuring the data view for IBM SPSS modelsTo configure the data view, IBM SPSS Modeler must be connected to the IBM Predictive Customer Intelligence industry accelerator database through an ODBC data source connection.

Configuring ODBC for IBM SPSS Modeler Server on Linux operating systems

To use an ODBC data source with IBM® SPSS® Modeler Server on a Linux operating system, you must configure the environment.

Procedure

Stop the IBM SPSS Modeler Server.

Go to the /root/SDAP71 directory.

The driver files are installed as part of the IBM Predictive Customer Intelligence Server deployment.

Run the setodbcpath.sh script to update the ODBC path in the scripts.

Edit the odbc.sh script to add the definition for ODBCINI to the bottom of the script. For example:

ODBCINI=/usr/spss/odbc/odbc.ini; export ODBCINI

ODBCINI must point to the full file path of the odbc.ini file for IBM SPSS Modeler. The odbc.ini file lists the ODBC data sources that you want to connect to. A default odbc.ini file is installed with the drivers.

In the odbc.ini file, add the data source and specify the driver in the [ODBC Data Sources] section of the file.

For example, add the data source as:

[ODBC Data Sources]
INSURNC=IBM DB2 ODBC Driver

In the odbc.ini file, create an ODBC data source connection for the industry accelerator database.

For example, include the following content:

[INSURNC]
Driver=/opt/ibm/db2/V10.1/lib64/libdb2o.so
DriverUnicodeType=1
Description=IBM DB2 ODBC Driver
ApplicationUsingThreads=1
AuthenticationMethod=0
BulkBinaryThreshold=32
BulkCharacterThreshold=-1
BulkLoadBatchSize=1024
CharsetFor65535=0
#Database applies to DB2 UDB only
Database=INSURNC
DefaultIsolationLevel=1
DynamicSections=200
EnableBulkLoad=0
EncryptionMethod=0
FailoverGranularity=0
FailoverMode=0
FailoverPreconnect=0
GrantAuthid=PUBLIC
GrantExecute=1
GSSClient=native
HostNameInCertificate=
IpAddress=IP_Address_of_DB_server
KeyPassword=
KeyStore=
KeyStorePassword=
LoadBalanceTimeout=0
LoadBalancing=0
LogonID=db2inst1
MaxPoolSize=100
MinPoolSize=0
Password=password
PackageCollection=NULLID
PackageNamePrefix=DD
PackageOwner=
Pooling=0
ProgramID=
QueryTimeout=0
ReportCodePageConversionErrors=0
TcpPort=50000
TrustStore=
TrustStorePassword=
UseCurrentSchema=0
ValidateServerCertificate=1
WithHold=1
XMLDescribeType=-10

Note: You must use the driver library libdb2o.so with IBM SPSS Modeler.

Ensure that you set DriverUnicodeType=1 to avoid buffer overflow errors when you connect to the database.

If you are using the 64-bit version of IBM SPSS Modeler Server, define and export LD_LIBRARY_PATH_64 in the odbc.sh script:

if [ "$LD_LIBRARY_PATH_64" = "" ]; then
LD_LIBRARY_PATH_64=<library_path>
else
LD_LIBRARY_PATH_64=<library_path>:$LD_LIBRARY_PATH_64
fi
export LD_LIBRARY_PATH_64

Where <library_path> is the same as for the LD_LIBRARY_PATH definition in the script that was initialized with the installation path. For example, /usr/spss/odbc/lib.

Tip: You can copy the if and export statements for LD_LIBRARY_PATH in the odbc.sh file, append them to the end of the file. Then, replace the LD_LIBRARY_PATH strings in the newly appended if and export statements with LD_LIBRARY_PATH_64. Here is an example of the odbc.sh file for a 64-bit IBM SPSS Modeler Server installation:

if [ "$LD_LIBRARY_PATH" = "" ]; then
		LD_LIBRARY_PATH=/usr/spss/odbc/lib
else
		LD_LIBRARY_PATH=/usr/spss/odbc/lib:$LD_LIBRARY_PATH
fi
export LD_LIBRARY_PATH
if [ "$LD_LIBRARY_PATH_64" = "" ]; then
		LD_LIBRARY_PATH_64=/usr/spss/odbc/lib
else
		LD_LIBRARY_PATH_64=/usr/spss/odbc/lib:$LD_LIBRARY_PATH_64
fi
export LD_LIBRARY_PATH_64
ODBCINI=/usr/spss/odbc/odbc.ini; export ODBCINI

Ensure that you export LD_LIBRARY_PATH_64, and define it with the if loop.

Configure IBM SPSS Modeler Server to use the driver. Edit modelersrv.sh and add the following line immediately below the line that defines SCLEMDNAME:

. <odbc.sh_path>

Where odbc.sh_path is the full path to the odbc.sh file.

For example: . /usr/spss/odbc/odbc.sh

Ensure that you leave a space between the first period and the file path.

Save modelersrv.sh.

Configure the IBM SPSS Modeler Server to use the ODBC wrapper named libspssodbc_datadirect.so.

Go to the /usr/IBM/SPSS/ModelerServer/16.0/bin directory.

Remove the existing libspssodbc.so soft link by using the following command:

rm –fr libspssodbc.so

Link the new wrapper to libspssodbc.so by using the following command:

ln –s libspssodbc_datadirect_utf16.so libspssodbc.so

Configure the db2cli.ini file in <db2 instance home>/sqllib/cfg/db2cli.ini to add the sections for each database.

[INSURNC]
Database=INSURNC
Protocol=TCPIP
DriverUnicodeType=1
Port=50000
Hostname=ip or hostname
UID=username
PWD=password

Save odbc.ini.

What to do next

To test the connection, do the following steps:

Restart IBM SPSS Modeler Server.

Connect to IBM SPSS Modeler Server from a client.

Add a database source node to the canvas.

Open the node and verify that you can see the data source names that you defined in the odbc.ini file.

For additional information and troubleshooting tips for connecting to data sources, see the SPSS Modeler documentation (www.ibm.com/support/knowledgecenter/SS3RA7_16.0.0)

Parent topic: Configuring the data view for IBM SPSS models

Deploy the IBM Cognos contentFor IBM Cognos Business Intelligence, you must catalog the database, create a data source connection, and then deploy the content files for the IBM Predictive Customer Intelligence industry accelerator.Industry accelerator installationThe Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator is for use with IBM Predictive Customer Intelligence.Moving the IBM Cognos contentYou must copy the IBM Cognos content for the IBM Predictive Customer Intelligence industry accelerator to the appropriate locations in your IBM Cognos installation to be able to deploy the content.Creating a data source connection to the industry accelerator databaseYou must create a data source connection to the IBM Predictive Customer Intelligence industry accelerator database.Deploy the IBM Cognos reportsYou must deploy the IBM Predictive Customer Intelligence industry accelerator reports using IBM Cognos Administration.

Deploy the IBM Cognos content

For IBM® Cognos® Business Intelligence, you must catalog the database, create a data source connection, and then deploy the content files for the IBM Predictive Customer Intelligence industry accelerator.

Moving the IBM Cognos content
You must copy the IBM Cognos content for the IBM Predictive Customer Intelligence industry accelerator to the appropriate locations in your IBM Cognos installation to be able to deploy the content.

Creating a data source connection to the industry accelerator database
You must create a data source connection to the IBM Predictive Customer Intelligence industry accelerator database.

Deploy the IBM Cognos reports
You must deploy the IBM Predictive Customer Intelligence industry accelerator reports using IBM Cognos Administration.

Parent topic: Industry accelerator installation

Moving the IBM Cognos contentYou must copy the IBM Cognos content for the IBM Predictive Customer Intelligence industry accelerator to the appropriate locations in your IBM Cognos installation to be able to deploy the content.Copy the IBM Cognos content from the computer where you downloaded the industry accelerator to the Cognos_Install_location\Deployment folder on the Cognos Analytics node computer. The Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator IBM Cognos content file is PCI_1.0_Ins_CallCenter\BI\PCI_Ins_CallCenter_CognosContent.zip Decompress the IBM Cognos report image file where you downloaded the industry accelerator. The report images file is PCI_1.0_Ins_CallCenter\BI\PCI_Images.zip If you are installing more than one accelerator, you do not have to replace the images. The PCI_Images.zip files contains all of the images that are used in all of the accelerator reports.Copy the PCI_Images folder to the Cognos_Install_location\webcontent\bi\PCI_Images folder that contains report images files on the Cognos Analytics node computer.Copy the IBM Cognos Framework Manager model files from the computer where you downloaded theindustry accelerator to the computer where you installed IBM Cognos Framework Manager, and decompress the file. The Insurance industry accelerator Framework Manager file is compressed in PCI_1.0_Ins_CallCenter\BI\PCI_Ins_CallCenter_FMProject.zip If you want to edit the Framework Manager models, you must catalog the industry accelerator database on the computer where you installed Framework Manager.Click StartIBM DB2DB2COPY1 (Default)DB2 Command Window - Administrator.Enter the following command to catalog the database node:db2 catalog tcpip node NODE_NAME remote data_node_name server PORT_NUMBER NODE_NAME can be any value. PORT_NUMBER is 50000 by default.Enter the following command to catalog the PCI database:db2 catalog database INSURNC at node NODE_NAME authentication server You must use the same node_name that you used in the db2 catalog database command.Deploy the IBM Cognos contentFor IBM Cognos Business Intelligence, you must catalog the database, create a data source connection, and then deploy the content files for the IBM Predictive Customer Intelligence industry accelerator.

Moving the IBM Cognos content

You must copy the IBM® Cognos® content for the IBM Predictive Customer Intelligence industry accelerator to the appropriate locations in your IBM Cognos installation to be able to deploy the content.

Procedure

Copy the IBM Cognos content from the computer where you downloaded the industry accelerator to the Cognos_Install_location\Deployment folder on the Cognos Analytics node computer.

The Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator IBM Cognos content file is PCI_1.0_Ins_CallCenter\BI\PCI_Ins_CallCenter_CognosContent.zip

Decompress the IBM Cognos report image file where you downloaded the industry accelerator.

The report images file is PCI_1.0_Ins_CallCenter\BI\PCI_Images.zip

Note: If you are installing more than one accelerator, you do not have to replace the images. The PCI_Images.zip files contains all of the images that are used in all of the accelerator reports.

Copy the PCI_Images folder to the Cognos_Install_location\webcontent\bi\PCI_Images folder that contains report images files on the Cognos Analytics node computer.

Copy the IBM Cognos Framework Manager model files from the computer where you downloaded theindustry accelerator to the computer where you installed IBM Cognos Framework Manager, and decompress the file.

The Insurance industry accelerator Framework Manager file is compressed in PCI_1.0_Ins_CallCenter\BI\PCI_Ins_CallCenter_FMProject.zip

If you want to edit the Framework Manager models, you must catalog the industry accelerator database on the computer where you installed Framework Manager.

Click Start > IBM DB2 > DB2COPY1 (Default) > DB2 Command Window - Administrator.

Enter the following command to catalog the database node:

db2 catalog tcpip node NODE_NAME remote data_node_name server PORT_NUMBER

NODE_NAME can be any value. PORT_NUMBER is 50000 by default.

Enter the following command to catalog the PCI database:

db2 catalog database INSURNC at node NODE_NAME authentication server

You must use the same node_name that you used in the db2 catalog database command.

Parent topic: Deploy the IBM Cognos content

Creating a data source connection to the industry accelerator databaseYou must create a data source connection to the IBM Predictive Customer Intelligence industry accelerator database.Open a web browser.Go to the IBM Cognos BI portal URL.For example, go to http://bi_node_name/ibmcognos/.On the Welcome page, click Administer IBM Cognos Content.Click the Configuration tab, and click Data Source Connections.Click the New Data Source button New data source icon.In the Name box, type INSURNC, and then click Next.In the connection page, select IBM DB2, ensure that Configure JDBC connection is selected, and click Next.In the DB2 database name field, type INSURNC.Leave DB2 connect string blank.Under Signons, select both Password and Create a signon that the Everyone group can use, and then type the user ID and password for the DB2 instance owner user that you used to create the database, and click Next.To test whether the parameters are correct, click Test the connection. After you test the connection, click OK to return to the connection page.In the Server name box, enter the name or IP address of your data node computer.In the Port number box, enter the DB2 port number. The default is 50000.In Database name, type INSURNC.To test whether the parameters are correct, click Test the connection. After you test the connection, click OK to return to the connection page.Click Finish.Deploy the IBM Cognos contentFor IBM Cognos Business Intelligence, you must catalog the database, create a data source connection, and then deploy the content files for the IBM Predictive Customer Intelligence industry accelerator.

Creating a data source connection to the industry accelerator database

You must create a data source connection to the IBM® Predictive Customer Intelligence industry accelerator database.

Procedure

Open a web browser.

Go to the IBM Cognos® BI portal URL. For example, go to http://bi_node_name/ibmcognos/.

On the Welcome page, click Administer IBM Cognos Content.

Click the Configuration tab, and click Data Source Connections.

Click the New Data Source button New data source icon.

In the Name box, type INSURNC, and then click Next.

In the connection page, select IBM DB2, ensure that Configure JDBC connection is selected, and click Next.

In the DB2 database name field, type INSURNC.

Leave DB2 connect string blank.

Under Signons, select both Password and Create a signon that the Everyone group can use, and then type the user ID and password for the DB2® instance owner user that you used to create the database, and click Next.

Tip: To test whether the parameters are correct, click Test the connection. After you test the connection, click OK to return to the connection page.

In the Server name box, enter the name or IP address of your data node computer.

In the Port number box, enter the DB2 port number. The default is 50000.

In Database name, type INSURNC.

Tip: To test whether the parameters are correct, click Test the connection. After you test the connection, click OK to return to the connection page.

Click Finish.

Parent topic: Deploy the IBM Cognos content

Deploy the IBM Cognos reportsYou must deploy the IBM Predictive Customer Intelligence industry accelerator reports using IBM Cognos Administration.Open a web browser.Go to the IBM Cognos BI portal URL.For example, go to http://bi_node_name/ibmcognos/.On the Welcome page, click Administer IBM Cognos Content.On the Configuration tab, click Content Administration.On the toolbar, click the New Import button.In the Deployment Archive pane, select deployment archive, and click Next. The deployment archive is named PCI_Ins_CallCenter_CognosContent In the Specify a name and description pane, accept the default or enter a new name, and click Next.In the Select the public folders and directory dontent pane, select all of the packages in the table, leave the Options as default, and click Next.Ensure that the Disable after import option is cleared.In the Specify the general options pane, accept the defaults, and click Next.On the Review the summary page, click Next.On the Select an action page, select Save and run once, and click Finish.On the Run with options page, accept the defaults, and click Run, and then click OK.Select View the details of this import after closing this dialog and click OK.In IBM Cognos Administration, click the Home button.The content is available in Public FoldersPCI Industry Accelerators 1.0.Deploy the IBM Cognos contentFor IBM Cognos Business Intelligence, you must catalog the database, create a data source connection, and then deploy the content files for the IBM Predictive Customer Intelligence industry accelerator.

Deploy the IBM Cognos reports

You must deploy the IBM® Predictive Customer Intelligence industry accelerator reports using IBM Cognos® Administration.

Procedure

Open a web browser.

Go to the IBM Cognos BI portal URL. For example, go to http://bi_node_name/ibmcognos/.

On the Welcome page, click Administer IBM Cognos Content.

On the Configuration tab, click Content Administration.

On the toolbar, click the New Import button.

In the Deployment Archive pane, select deployment archive, and click Next.

The deployment archive is named PCI_Ins_CallCenter_CognosContent

In the Specify a name and description pane, accept the default or enter a new name, and click Next.

In the Select the public folders and directory dontent pane, select all of the packages in the table, leave the Options as default, and click Next.

Note: Ensure that the Disable after import option is cleared.

In the Specify the general options pane, accept the defaults, and click Next.

On the Review the summary page, click Next.

On the Select an action page, select Save and run once, and click Finish.

On the Run with options page, accept the defaults, and click Run, and then click OK.

Select View the details of this import after closing this dialog and click OK.

In IBM Cognos Administration, click the Home button.

The content is available in Public Folders > PCI Industry Accelerators 1.0.

Parent topic: Deploy the IBM Cognos content

Copying the industry accelerator license files to each computerAfter you install the industry accelerator, you must copy the license folder to each computer on which you use the IBM Predictive Customer Intelligence industry accelerator. Do not rename the folders or files.Copy the license folder from the folder where you decompressed the industry accelerator to each computer on which an IBM Predictive Customer Intelligence component is installed.For example, copy the folder and contents so that you have a C:\IBM\PCI_IndustryAccelerators\1.0\license folder on Microsoft Windows operating systems or an /opt/IBM/PCI_IndustryAccelerators/1.0/license folder on Linux operating systems on each node computer.The folder contains the license files. The folder should exist on each server and client node computer.Industry accelerator installationThe Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator is for use with IBM Predictive Customer Intelligence.

Copying the industry accelerator license files to each computer

After you install the industry accelerator, you must copy the license folder to each computer on which you use the IBM® Predictive Customer Intelligence industry accelerator.

Important: Do not rename the folders or files.

Procedure

Copy the license folder from the folder where you decompressed the industry accelerator to each computer on which an IBM Predictive Customer Intelligence component is installed. For example, copy the folder and contents so that you have a C:\IBM\PCI_IndustryAccelerators\1.0\license folder on Microsoft Windows operating systems or an /opt/IBM/PCI_IndustryAccelerators/1.0/license folder on Linux operating systems on each node computer.

The folder contains the license files. The folder should exist on each server and client node computer.

Parent topic: Industry accelerator installation

Predictive modelsUse IBM Predictive Customer Intelligence models to predict what is likely to happen in the future, based on patterns from past data. For example, models can predict the following situations:How likely it is that a customer will churn in the next quarter.Whether a customer will be a promoter of a service, or a detractorHow valuable the customer is in terms of future revenueModels can be used in the same way as business rules. However, while rules might be based on corporate policies, business logic, or other assumptions, models are built on actual observations of past results, and can discover patterns that might not otherwise be apparent. While business rules bring common business logic to applications, models lend insight and predictive power. The ability to combine models and rules is a powerful feature.Model data sourcesYou or the administrator need to specify the data sources to use in the IBM Predictive Customer Intelligence solution for modeling, analysis, simulation and testing, and scoring.Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator. Training predictive modelsPredictive models must be trained to determine which data is useful and which data is not needed. When a model gives you accurate predictions, you are ready to use the predictive model for real-time scoring.Scoring a modelTo score a model means to apply it to some data in order to obtain a result or prediction that can be used as input to decisions.Create business rulesFor IBM Predictive Customer Intelligence, you use IBM Analytical Decision Management to bring together your company's business rules, predictive models, and optimizations.Deploy an applicationYou can deploy the IBM Predictive Customer Intelligence application to a testing environment or to a production environment, such as a call center or a website. You can also deploy it to contribute to batch processing.

Predictive models

Use IBM® Predictive Customer Intelligence models to predict what is likely to happen in the future, based on patterns from past data.

For example, models can predict the following situations:

How likely it is that a customer will churn in the next quarter.

Whether a customer will be a promoter of a service, or a detractor

How valuable the customer is in terms of future revenue

Models can be used in the same way as business rules. However, while rules might be based on corporate policies, business logic, or other assumptions, models are built on actual observations of past results, and can discover patterns that might not otherwise be apparent. While business rules bring common business logic to applications, models lend insight and predictive power. The ability to combine models and rules is a powerful feature.

Model data sources
You or the administrator need to specify the data sources to use in the IBM Predictive Customer Intelligence solution for modeling, analysis, simulation and testing, and scoring.

Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator
A number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Training predictive models
Predictive models must be trained to determine which data is useful and which data is not needed. When a model gives you accurate predictions, you are ready to use the predictive model for real-time scoring.

Scoring a model
To score a model means to apply it to some data in order to obtain a result or prediction that can be used as input to decisions.

Create business rules
For IBM Predictive Customer Intelligence, you use IBM Analytical Decision Management to bring together your company's business rules, predictive models, and optimizations.

Deploy an application
You can deploy the IBM Predictive Customer Intelligence application to a testing environment or to a production environment, such as a call center or a website. You can also deploy it to contribute to batch processing.

Model data sourcesYou or the administrator need to specify the data sources to use in the IBM Predictive Customer Intelligence solution for modeling, analysis, simulation and testing, and scoring.Use IBM Analytical Decision Management to plan the model and to decide which data sources to use. You need the following types of data in the modeling process:Historical or analytical dataTo build the model, you need information about what to predict. For example, if you want to predict churn, you need information about customers such as their complaints history, number of months since they upgraded their plan, sentiment score, demographic history, and estimated income. This is often referred to as historical data or analytical data, and it must contain some or all of the fields in the project data model, plus an additional field that records the outcome or result that you want to predict. This extra field is used as the target for modeling.Operational or scoring dataTo use the model to predict future results, you need data about the group or population in which you are interested. This is often referred to as operational data or scoring data. The project data model is typically based on this data.You can use the following types of data sources:A database that supports ODBC, such as IBM DB2.An Enterprise View that is defined in IBM SPSS Collaboration and Deployment Services.A file that is used by IBM SPSS Statistics, such as a text file (txt), or a comma separated file (csv).When you add a new data source, map all of its fields to ensure compatibility with the project data model. For example, if the project data model requires a field named purchase with values Yes and No for the measurement level flag, then any data source that you use must have a compatible field. If the field names are not identical, they can be mapped accordingly. Note that the input and associated mapped field must have the same data type.You can characterize the information that each data field represents. Define a measurement level to determine how a given field is used in business rules, models, or other applications.You can derive additional fields or attributes for the application by using the expression manager. For example, if you use banking data, you may want to create an expression that shows the ratio between a customer's income and the number of loan accounts that the customer has. Expressions are always numeric with a measurement of Continuous; this cannot be changed.To enforce corporate-wide policies, use global selections to choose the records to include or exclude from processing by the application. Global selections can be particularly effective when used in combination with shared rules. Shared rules are saved as separate objects that can be used by multiple applications. If the shared rule changes, all applications that use the rule can then be updated.Data mining that uses IBM SPSS Modeler focuses on the process of running data through a series of nodes. This is referred to as a stream. This series of nodes represents operations to be performed on the data, while links between the nodes indicate the direction of data flow. Typically, you use a data stream to read data into IBM SPSS Modeler, run it through a series of manipulations, and then send it to a destination, such as a table or a viewer. For example, to open a data source, you add a new field, select records based on values in the new field, and then display the results in a table. In this example, your data stream would consist of the following nodes: Variable File node, which reads the data from the data sourceDerive node, which adds the new calculated field to the data setSelect node, which uses the selection criteria to exclude records from the data streamTable node, which displays the results of your manipulations on screenFor more information about these features, see the IBM SPSS Modeler Help (www.ibm.com/support/knowledgecenter/SS3RA7_16.0.0/com.ibm.spss.modeler.help/clementine/entities/clem_family_overview.htm).Predictive modelsUse IBM Predictive Customer Intelligence models to predict what is likely to happen in the future, based on patterns from past data.

Model data sources

You or the administrator need to specify the data sources to use in the IBM® Predictive Customer Intelligence solution for modeling, analysis, simulation and testing, and scoring.

Use IBM Analytical Decision Management to plan the model and to decide which data sources to use. You need the following types of data in the modeling process:

Historical or analytical data

To build the model, you need information about what to predict. For example, if you want to predict churn, you need information about customers such as their complaints history, number of months since they upgraded their plan, sentiment score, demographic history, and estimated income. This is often referred to as historical data or analytical data, and it must contain some or all of the fields in the project data model, plus an additional field that records the outcome or result that you want to predict. This extra field is used as the target for modeling.

Operational or scoring data

To use the model to predict future results, you need data about the group or population in which you are interested. This is often referred to as operational data or scoring data. The project data model is typically based on this data.

You can use the following types of data sources:

A database that supports ODBC, such as IBM DB2®.

An Enterprise View that is defined in IBM SPSS® Collaboration and Deployment Services.

A file that is used by IBM SPSS Statistics, such as a text file (txt), or a comma separated file (csv).

When you add a new data source, map all of its fields to ensure compatibility with the project data model. For example, if the project data model requires a field named purchase with values Yes and No for the measurement level flag, then any data source that you use must have a compatible field. If the field names are not identical, they can be mapped accordingly. Note that the input and associated mapped field must have the same data type.

You can characterize the information that each data field represents. Define a measurement level to determine how a given field is used in business rules, models, or other applications.

You can derive additional fields or attributes for the application by using the expression manager. For example, if you use banking data, you may want to create an expression that shows the ratio between a customer's income and the number of loan accounts that the customer has. Expressions are always numeric with a measurement of Continuous; this cannot be changed.

To enforce corporate-wide policies, use global selections to choose the records to include or exclude from processing by the application. Global selections can be particularly effective when used in combination with shared rules. Shared rules are saved as separate objects that can be used by multiple applications. If the shared rule changes, all applications that use the rule can then be updated.

Data mining that uses IBM SPSS Modeler focuses on the process of running data through a series of nodes. This is referred to as a stream. This series of nodes represents operations to be performed on the data, while links between the nodes indicate the direction of data flow. Typically, you use a data stream to read data into IBM SPSS Modeler, run it through a series of manipulations, and then send it to a destination, such as a table or a viewer.

For example, to open a data source, you add a new field, select records based on values in the new field, and then display the results in a table. In this example, your data stream would consist of the following nodes:

Variable File node, which reads the data from the data source

Derive node, which adds the new calculated field to the data set

Select node, which uses the selection criteria to exclude records from the data stream

Table node, which displays the results of your manipulations on screen

For more information about these features, see the IBM SPSS Modeler Help (www.ibm.com/support/knowledgecenter/SS3RA7_16.0.0/com.ibm.spss.modeler.help/clementine/entities/clem_family_overview.htm).

Parent topic: Predictive models

Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator. The following models are provided:Segmentation modelIn the Segmentation model (Segmentation.str) customers are segmented based on their financial sophistication. This model enables the insurer to sell insurance policies that are appropriate to the customer.Churn Prediction modelChurn.str predicts a customer's propensity to churn by using information about the customer such as household and financial data, transactional data, and behavioral data.Customer Lifetime Value model (CLTV)CLTV.str predicts customer lifetime value. CLTV is based on the revenue that the customer brings in to the company, the cost of maintaining the policies, the cost of retention, and the likelihood of the customer surrendering the policies soon.Campaign Response modelCampaign Response Model.str predicts the probability that customers will respond to targeted offers so that you send out offers only to the customers whose propensity to respond is above a particular threshold.Auto Churn modelAuto Churn.str predicts the customers likely to churn from the current list of active customers. This model considers the Auto policy customers only.Life Stage Segment modelLifestage Current Segment.str groups customers based on their current life stage, which would help in recommending the right insurance policy to the customer based on their current life stage segment.Buying Propensity modelBuying_Propensity_Model.str identifies the life policies that are mostly bought by customers belonging to each life stage segment as defined in the life stage segment model. Data Processing StreamDataProcessing Stream.str transforms and aggregates data that is obtained from the customer's visit to the insurer's website so that it can be used to define rules for recommending the right insurance policy to each customer. Insurance Policy Recommendation modelInsurance_Policy_Recommendation.str recommends the correct insurance policy by considering information such as the customer's web activity data and also the life insurance policy buying propensity for the given customer's life stage segment, based on historical data.Social Media Analytics (SMA) modelSMA_Text_Analytics.str extracts customer life stage event information from customers social media posts.Sentiment Scoring modelSentiment.str extracts the sentiment score from customer comments that are captured while recording customer complaints.External Learning modelThe External Learning Model (ExternalLearning.str) predicts the likelihood that a customer will accept a particular offer. It bases that likelihood on the past responses for customers with similar characteristics.Predictive modelsUse IBM Predictive Customer Intelligence models to predict what is likely to happen in the future, based on patterns from past data. Data used in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorIn the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator, the Insurance company runs a multi-line business.Define customer segmentsWhen you define customer segments, you profile the customers into groups that have similar demand characteristics. In the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator, customers are profiled based on their financial sophistication.Predict churn in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorThe Churn prediction model predicts a customer's propensity to churn by using information about the customer such as household and financial data, transactional data, and behavioral data.Understand Customer Lifetime Value (CLTV)The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses Customer Lifetime Value (CLTV) to understand customer profitability.Predict customer response to a campaignTargeting offers to the correct customers is an important part of promotion planning and campaign design. The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Campaign Response model to predict the probability that the customer will respond to targeted offers. Predict churn for auto policy holdersThe IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Auto Churn prediction model to predict the customers that are likely to churn from the current list of active customers that hold auto policies.Group customers based on their current life stageThe Insurance sample uses the Lifestage Segment model to group customers based on their current life stage. Identify the life policies bought by life stage segmentsThe Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator categorizes customer buying propensity for life policies into life stage segments using the Apriori model.Recommend the correct insurance policyThe IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Insurance Policy Recommendation model to recommend the correct insurance policy for customers.Transform and aggregate customer dataThe IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the DataProcessing Stream.str to transform and aggregate data about customer activity on the insurer's web site.Extract life stage event information from social media postsYou can use social media to get valuable insights about customers. The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the SMA_Text_Analytics.str to extract information from social media posts.Extract sentiment scores from customer complaintsCustomer interactions with call center agents can be a source of valuable data to determine customer satisfaction levels. The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Sentiment Scoring model to extract the sentiment score from customer comments that are captured while recording customer complaints.Data modelThe historic data that is used for predictive modeling in IBM Predictive Customer Intelligence is stored in an IBM DB2 database.

Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

A number of predictive models are used in the IBM® Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

The following models are provided:

Segmentation model

In the Segmentation model (Segmentation.str) customers are segmented based on their financial sophistication. This model enables the insurer to sell insurance policies that are appropriate to the customer.

Churn Prediction model

Churn.str predicts a customer's propensity to churn by using information about the customer such as household and financial data, transactional data, and behavioral data.

Customer Lifetime Value model (CLTV)

CLTV.str predicts customer lifetime value. CLTV is based on the revenue that the customer brings in to the company, the cost of maintaining the policies, the cost of retention, and the likelihood of the customer surrendering the policies soon.

Campaign Response model

Campaign Response Model.str predicts the probability that customers will respond to targeted offers so that you send out offers only to the customers whose propensity to respond is above a particular threshold.

Auto Churn model

Auto Churn.str predicts the customers likely to churn from the current list of active customers. This model considers the Auto policy customers only.

Life Stage Segment model

Lifestage Current Segment.str groups customers based on their current life stage, which would help in recommending the right insurance policy to the customer based on their current life stage segment.

Buying Propensity model

Buying_Propensity_Model.str identifies the life policies that are mostly bought by customers belonging to each life stage segment as defined in the life stage segment model.

Data Processing Stream

DataProcessing Stream.str transforms and aggregates data that is obtained from the customer's visit to the insurer's website so that it can be used to define rules for recommending the right insurance policy to each customer.

Insurance Policy Recommendation model

Insurance_Policy_Recommendation.str recommends the correct insurance policy by considering information such as the customer's web activity data and also the life insurance policy buying propensity for the given customer's life stage segment, based on historical data.

Social Media Analytics (SMA) model

SMA_Text_Analytics.str extracts customer life stage event information from customers social media posts.

Sentiment Scoring model

Sentiment.str extracts the sentiment score from customer comments that are captured while recording customer complaints.

External Learning model

The External Learning Model (ExternalLearning.str) predicts the likelihood that a customer will accept a particular offer. It bases that likelihood on the past responses for customers with similar characteristics.

Data used in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator
In the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator, the Insurance company runs a multi-line business.

Define customer segments
When you define customer segments, you profile the customers into groups that have similar demand characteristics. In the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator, customers are profiled based on their financial sophistication.

Predict churn in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator
The Churn prediction model predicts a customer's propensity to churn by using information about the customer such as household and financial data, transactional data, and behavioral data.

Understand Customer Lifetime Value (CLTV)
The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses Customer Lifetime Value (CLTV) to understand customer profitability.

Predict customer response to a campaign
Targeting offers to the correct customers is an important part of promotion planning and campaign design. The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Campaign Response model to predict the probability that the customer will respond to targeted offers.

Predict churn for auto policy holders
The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Auto Churn prediction model to predict the customers that are likely to churn from the current list of active customers that hold auto policies.

Group customers based on their current life stage
The Insurance sample uses the Lifestage Segment model to group customers based on their current life stage.

Identify the life policies bought by life stage segments
The Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator categorizes customer buying propensity for life policies into life stage segments using the Apriori model.

Recommend the correct insurance policy
The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Insurance Policy Recommendation model to recommend the correct insurance policy for customers.

Transform and aggregate customer data
The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the DataProcessing Stream.str to transform and aggregate data about customer activity on the insurer's web site.

Extract life stage event information from social media posts
You can use social media to get valuable insights about customers. The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the SMA_Text_Analytics.str to extract information from social media posts.

Extract sentiment scores from customer complaints
Customer interactions with call center agents can be a source of valuable data to determine customer satisfaction levels. The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Sentiment Scoring model to extract the sentiment score from customer comments that are captured while recording customer complaints.

Data model
The historic data that is used for predictive modeling in IBM Predictive Customer Intelligence is stored in an IBM DB2® database.

Parent topic: Predictive models

Data used in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorIn the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator, the Insurance company runs a multi-line business.The following types of data are used.Customer master dataThis includes customer's demographic data, employment and income data, and information about the household as well. POLICYHOLDER and HOUSEHOLD tables capture most of this data. Typically, Master Data Management systems are the source of customer master data.Customer policy dataThis includes aggregated customer information, such as the number and types of policies that are owned by the customer, total premium that is paid by the customer, average claim amount, tenure of the customer, number of complaints, number of claims, and customer sentiment data. POLICYHOLDER_FACT and POLICY_FACT tables capture most of this data.Customer transaction dataThis includes data about all the customer transactions such as the policies purchased, their inception and maturity/renewal date, data related to all of the complaints made by the customer in the past, and also data that is related to all of the claims that have been made by the customer. POLICIES, CLAIMS, COMPLAINTS, COMPLAINT_DETAILS tables contain this data.Customer Social Media DataApart from the customer data that is available within the enterprise, insurance organizations might also want to get insights from external sources of data. For example, the social media channels where customers post comments about their experiences with their insurers, as well as about their needs and life-events that can potentially lead to an opportunity to sell appropriate insurance products. SMA_DATA and SMA_DATA_ANALYSIS tables captures such external data, as well as the summarized analysis of this social media data. The fetching of social media data is outside the scope of this solution.Customer web browsing dataMany insurance organizations today allow their customers to buy or explore their insurance products online through their websites. Technology makes it possible to track customers' activities on their websites, giving them vital insights about the customers' current interest in specific insurance products. Web Analytics tools can be used to analyze customers' website activities and use this information along with other customer data to make the right recommendations to the customers at the right time. ACTIVITY_FEED_DATA, ONLINE_BROWSING_HISTORY, and ONLINE_BROWSING_SUMMARY tables contain customers web activity data.Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Data used in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

In the IBM® Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator, the Insurance company runs a multi-line business.

The following types of data are used.

Customer master data

This includes customer's demographic data, employment and income data, and information about the household as well. POLICYHOLDER and HOUSEHOLD tables capture most of this data. Typically, Master Data Management systems are the source of customer master data.

Customer policy data

This includes aggregated customer information, such as the number and types of policies that are owned by the customer, total premium that is paid by the customer, average claim amount, tenure of the customer, number of complaints, number of claims, and customer sentiment data. POLICYHOLDER_FACT and POLICY_FACT tables capture most of this data.

Customer transaction data

This includes data about all the customer transactions such as the policies purchased, their inception and maturity/renewal date, data related to all of the complaints made by the customer in the past, and also data that is related to all of the claims that have been made by the customer. POLICIES, CLAIMS, COMPLAINTS, COMPLAINT_DETAILS tables contain this data.

Customer Social Media Data

Apart from the customer data that is available within the enterprise, insurance organizations might also want to get insights from external sources of data. For example, the social media channels where customers post comments about their experiences with their insurers, as well as about their needs and life-events that can potentially lead to an opportunity to sell appropriate insurance products. SMA_DATA and SMA_DATA_ANALYSIS tables captures such external data, as well as the summarized analysis of this social media data.

Note: The fetching of social media data is outside the scope of this solution.

Customer web browsing data

Many insurance organizations today allow their customers to buy or explore their insurance products online through their websites. Technology makes it possible to track customers' activities on their websites, giving them vital insights about the customers' current interest in specific insurance products. Web Analytics tools can be used to analyze customers' website activities and use this information along with other customer data to make the right recommendations to the customers at the right time. ACTIVITY_FEED_DATA, ONLINE_BROWSING_HISTORY, and ONLINE_BROWSING_SUMMARY tables contain customers web activity data.

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Define customer segmentsWhen you define customer segments, you profile the customers into groups that have similar demand characteristics. In the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator, customers are profiled based on their financial sophistication.Customers are segmented into financially sophisticated, and novice categories. This means that insurers can target each segment with cross-selling insurance policies that are appropriate to increase the effectiveness of cross-sell campaigns.The example stream for defining customer segments is named Segmentation.str.The model that is used is a TwoStep cluster.The inputs for the example Segmentation model are customer master data and customer policy data, specifically:Demographic data: age, gender, marital status, and employment status.Insurance policy-related data: insurance lines, policies, premiums, tenure, and insurance score.Financial data: income, retirement plans, home ownership status, vehicle ownership.These inputs are aggregated. As each record is read, based on a distance criterion, the TwoStep cluster algorithm decides whether it should be merged with an existing cluster, or used to start a new cluster. If the number of clusters becomes too large (you can set the maximum number), then the distance criterion is increased, and clusters whose distance is now less than the modified distance criterion are merged. In this way, the records are clustered into a preliminary set of clusters in a single pass of the data.Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Define customer segments

When you define customer segments, you profile the customers into groups that have similar demand characteristics. In the IBM® Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator, customers are profiled based on their financial sophistication.

Customers are segmented into financially sophisticated, and novice categories. This means that insurers can target each segment with cross-selling insurance policies that are appropriate to increase the effectiveness of cross-sell campaigns.

The example stream for defining customer segments is named Segmentation.str.

The model that is used is a TwoStep cluster.

The inputs for the example Segmentation model are customer master data and customer policy data, specifically:

Demographic data: age, gender, marital status, and employment status.

Insurance policy-related data: insurance lines, policies, premiums, tenure, and insurance score.

Financial data: income, retirement plans, home ownership status, vehicle ownership.

These inputs are aggregated. As each record is read, based on a distance criterion, the TwoStep cluster algorithm decides whether it should be merged with an existing cluster, or used to start a new cluster. If the number of clusters becomes too large (you can set the maximum number), then the distance criterion is increased, and clusters whose distance is now less than the modified distance criterion are merged. In this way, the records are clustered into a preliminary set of clusters in a single pass of the data.

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Predict churn in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorThe Churn prediction model predicts a customer's propensity to churn by using information about the customer such as household and financial data, transactional data, and behavioral data.The inputs for the Churn prediction model are customer demographic data, insurance policies, premiums, tenure, claims, complaints, and the sentiment score from past surveys.The example stream for predicting churn is named Churn.str.Data preparation for churn prediction starts with aggregating all available information about the customer. The data that is obtained for predicting the churn is classified in the following categories:Demographic data, such as age, gender, education, marital status, employment status, income, home ownership status, and retirement plan.Policy-related data, such as insurance lines, number of policies in the household, household tenure, premium, disposable income, and insured cars.Claims, such as claim settlement duration, number of claims that are filed and denied.Complaints, such as number of open and closed complaints.Survey sentiment data. Sentiment scores from past surveys are captured in the latest, and average note attitude score fields. The note attitude score is derived from customer negative feedback only. If the note attitude is zero, the customer is more satisfied while as the number increases, satisfaction level decreases.A CHAID algorithm is used to predict churn. The CHAID (Chi-squared Automatic Interaction Detection) is a classification method that builds decision trees by using chi-square statistics to work out the best places to make the splits in the decision tree. The CHAID model output is decision rules/tree and a predictor importance plot. This output shows you which the most important predictors are for whether a customer is likely to churn or not. For example, the most important predictors might be HOUSEHOLD_TENURE, LATEST_NOTE_ATTITUDE, and NUMBER_OF_POLICIES_IN_HOUSEHOLD.Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Predict churn in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

The Churn prediction model predicts a customer's propensity to churn by using information about the customer such as household and financial data, transactional data, and behavioral data.

The inputs for the Churn prediction model are customer demographic data, insurance policies, premiums, tenure, claims, complaints, and the sentiment score from past surveys.

The example stream for predicting churn is named Churn.str.

Data preparation for churn prediction starts with aggregating all available information about the customer. The data that is obtained for predicting the churn is classified in the following categories:

Demographic data, such as age, gender, education, marital status, employment status, income, home ownership status, and retirement plan.

Policy-related data, such as insurance lines, number of policies in the household, household tenure, premium, disposable income, and insured cars.

Claims, such as claim settlement duration, number of claims that are filed and denied.

Complaints, such as number of open and closed complaints.

Survey sentiment data. Sentiment scores from past surveys are captured in the latest, and average note attitude score fields. The note attitude score is derived from customer negative feedback only. If the note attitude is zero, the customer is more satisfied while as the number increases, satisfaction level decreases.

A CHAID algorithm is used to predict churn. The CHAID (Chi-squared Automatic Interaction Detection) is a classification method that builds decision trees by using chi-square statistics to work out the best places to make the splits in the decision tree. The CHAID model output is decision rules/tree and a predictor importance plot. This output shows you which the most important predictors are for whether a customer is likely to churn or not. For example, the most important predictors might be HOUSEHOLD_TENURE, LATEST_NOTE_ATTITUDE, and NUMBER_OF_POLICIES_IN_HOUSEHOLD.

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Understand Customer Lifetime Value (CLTV)The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses Customer Lifetime Value (CLTV) to understand customer profitability.CLTV is a commonly used approach to determine how much each customer is worth in monetary terms. It helps insurers to determine how much money must be spent to acquire or retain a customer. CLTV is defined as the expected net profit a customer will contribute to the business over time. Advanced analytics provide new insights on how customer lifetime value can be calculated.The example stream is named CLTV.str.The inputs for this model are customer demographic data, policies, premiums, tenure, policy maintenance cost, complaints, and customer survey sentiment.CLTV is determined by the margin amount that the customer contributes each month and the probability that the customer might churn in any month. Customers with higher margin amount and a lower probability to churn have a high CLTV. CLTV is derived by the following formula:EquationCi= probability for customer i to generate revenue in time tN = total number of periodsd = monthly discount ratet = time of cash flowThe probability Ci can be estimated by using Cox regression. Cox regression is a method for investigating the effect of several variables upon the time a specified event takes to happen. In the context of an outcome such as churn, this is known as Cox regression for survival analysis.CLTV is calculated by considering the following:When the Cox model is scored, the customer's past survival time is considered, and the churn probability is predicted for one to five years.The NetProfit value is derived by using following expression.NET_PROFIT =(TOTAL_PREMIUM - MAINTENANCE_COST)*12The customer lifetime value is derived as follows:CLTV =(NET_PROFIT * C1 / (1 + 0.12)) + (NET_PROFIT * C2 / (1 + 0.11) ** 2) + (NET_PROFIT * C3 / (1 + 0.1) ** 3) + (NET_PROFIT * C4 / (1 + 0.09) ** 4) + (NET_PROFIT * C5 / (1 + 0.08) ** 5) + (NET_PROFIT * POLICYHOLDER_TENURE)Where Ci=(1,2,3,4,5) is the renewal probability, which is the future value that a customer can bring. The last item is the historical/current value of one customer.The CLTV values are further classified into Low, Medium, and High categories by using the following calculations:CLTV_CAT = if CLTV <=30083.625 then 'LOW' elseif CLTV > 30083.625 and CLTV <= 46488.000000000007 then 'MEDIUM' elseif CLTV > 46488.000000000007 then 'HIGH' else 'LOW' endifPredictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Understand Customer Lifetime Value (CLTV)

The IBM® Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses Customer Lifetime Value (CLTV) to understand customer profitability.

CLTV is a commonly used approach to determine how much each customer is worth in monetary terms. It helps insurers to determine how much money must be spent to acquire or retain a customer. CLTV is defined as the expected net profit a customer will contribute to the business over time. Advanced analytics provide new insights on how customer lifetime value can be calculated.

The example stream is named CLTV.str.

The inputs for this model are customer demographic data, policies, premiums, tenure, policy maintenance cost, complaints, and customer survey sentiment.

CLTV is determined by the margin amount that the customer contributes each month and the probability that the customer might churn in any month. Customers with higher margin amount and a lower probability to churn have a high CLTV.

CLTV is derived by the following formula:

Ci= probability for customer i to generate revenue in time t

N = total number of periods

d = monthly discount rate

t = time of cash flow

The probability Ci can be estimated by using Cox regression. Cox regression is a method for investigating the effect of several variables upon the time a specified event takes to happen. In the context of an outcome such as churn, this is known as Cox regression for survival analysis.

CLTV is calculated by considering the following:

When the Cox model is scored, the customer's past survival time is considered, and the churn probability is predicted for one to five years.

The NetProfit value is derived by using following expression.

NET_PROFIT =(TOTAL_PREMIUM - MAINTENANCE_COST)*12

The customer lifetime value is derived as follows:

CLTV =(NET_PROFIT * C₁ /  (1 + 0.12))
		+ (NET_PROFIT * C₂ /  (1 + 0.11) ** 2)
		+  (NET_PROFIT * C₃ /  (1 + 0.1) ** 3)
		+  (NET_PROFIT * C₄ /  (1 + 0.09) ** 4)
		+  (NET_PROFIT * C₅ /  (1 + 0.08) ** 5)
		+ (NET_PROFIT * POLICYHOLDER_TENURE)

Where Ci=(1,2,3,4,5) is the renewal probability, which is the future value that a customer can bring. The last item is the historical/current value of one customer.

The CLTV values are further classified into Low, Medium, and High categories by using the following calculations:

CLTV_CAT =
		if CLTV <=30083.625  then 'LOW'
		elseif CLTV > 30083.625 and CLTV <= 46488.000000000007 then 'MEDIUM'
		elseif CLTV > 46488.000000000007 then 'HIGH'
		else 'LOW'
		endif

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Predict customer response to a campaignTargeting offers to the correct customers is an important part of promotion planning and campaign design. The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Campaign Response model to predict the probability that the customer will respond to targeted offers. The Campaign Response model helps in sending out offers only to the customers whose propensity to respond is above a particular threshold.The example stream for predicting customer response to a campaign is named Campaign Response Model.str.The customer’s previous offer response data is the input for the model, and the model is trained based on that data.The Decision List algorithm is used to identify the characteristics of customers who are most likely to respond favorably based on previous campaigns. The model generates rules that indicate a higher or lower likelihood of a binary (1 or 0) outcome. The Campaign Response model considers only those customers who are currently with the insurance company, and not those customers who churned.Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Predict customer response to a campaign

Targeting offers to the correct customers is an important part of promotion planning and campaign design. The IBM® Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Campaign Response model to predict the probability that the customer will respond to targeted offers.

The Campaign Response model helps in sending out offers only to the customers whose propensity to respond is above a particular threshold.

The example stream for predicting customer response to a campaign is named Campaign Response Model.str.

The customer’s previous offer response data is the input for the model, and the model is trained based on that data.

The Decision List algorithm is used to identify the characteristics of customers who are most likely to respond favorably based on previous campaigns. The model generates rules that indicate a higher or lower likelihood of a binary (1 or 0) outcome. The Campaign Response model considers only those customers who are currently with the insurance company, and not those customers who churned.

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Predict churn for auto policy holdersThe IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Auto Churn prediction model to predict the customers that are likely to churn from the current list of active customers that hold auto policies.The example stream for predicting the customers likely to churn is named Auto Churn.str.The CHAID decision tree algorithm is used to predict churn. This model is similar to the Churn model, except that this model considers only auto policy data for predicting churn. For more information, see Predict churn in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorThe Churn prediction model predicts a customer's propensity to churn by using information about the customer such as household and financial data, transactional data, and behavioral data..In this industry accelerator, the most important predictors of churn are the last survey sentiment score (LATEST_NOTE_ATTITUDE) and CLAIM_SETTLEMENT_DURATION.Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Predict churn for auto policy holders

The IBM® Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Auto Churn prediction model to predict the customers that are likely to churn from the current list of active customers that hold auto policies.

The example stream for predicting the customers likely to churn is named Auto Churn.str.

The CHAID decision tree algorithm is used to predict churn. This model is similar to the Churn model, except that this model considers only auto policy data for predicting churn. For more information, see Predict churn in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

In this industry accelerator, the most important predictors of churn are the last survey sentiment score (LATEST_NOTE_ATTITUDE) and CLAIM_SETTLEMENT_DURATION.

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Group customers based on their current life stageThe Insurance sample uses the Lifestage Segment model to group customers based on their current life stage. The example stream for grouping customers into their current life stage is named Lifestage Current Segment.str.The model uses simple rules to get the current life stage segment of customer. Some examples of defined segments are:Newly marriedYoung familyYoung and affluentSingleDivorcedPredictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Group customers based on their current life stage

The Insurance sample uses the Lifestage Segment model to group customers based on their current life stage.

The example stream for grouping customers into their current life stage is named Lifestage Current Segment.str.

The model uses simple rules to get the current life stage segment of customer. Some examples of defined segments are:

Newly married

Young family

Young and affluent

Single

Divorced

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Identify the life policies bought by life stage segmentsThe Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator categorizes customer buying propensity for life policies into life stage segments using the Apriori model.The example stream is named Buying_Propensity_Model.str.The Apriori model is an association algorithm that extracts association rules from data. The algorithm uses the past insurance policy purchase data and provides the buying propensity score for each policy at customer life stage segment level. The output is processed further into a summary format, which is then used by IBM Analytical Decision Management to deliver appropriate offers to customers.Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Identify the life policies bought by life stage segments

The Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator categorizes customer buying propensity for life policies into life stage segments using the Apriori model.

The example stream is named Buying_Propensity_Model.str.

The Apriori model is an association algorithm that extracts association rules from data. The algorithm uses the past insurance policy purchase data and provides the buying propensity score for each policy at customer life stage segment level. The output is processed further into a summary format, which is then used by IBM® Analytical Decision Management to deliver appropriate offers to customers.

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Recommend the correct insurance policyThe IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Insurance Policy Recommendation model to recommend the correct insurance policy for customers.The example stream is Insurance_Policy_Recommendation.str.The Insurance Policy Recommendation model compares insurance policies that are browsed by the customer with the list of insurance policies that have a higher buying propensity for the customer's life stage segment. The model recommends the correct insurance policy according to this data.Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Recommend the correct insurance policy

The IBM® Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Insurance Policy Recommendation model to recommend the correct insurance policy for customers.

The example stream is Insurance_Policy_Recommendation.str.

The Insurance Policy Recommendation model compares insurance policies that are browsed by the customer with the list of insurance policies that have a higher buying propensity for the customer's life stage segment. The model recommends the correct insurance policy according to this data.

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Transform and aggregate customer dataThe IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the DataProcessing Stream.str to transform and aggregate data about customer activity on the insurer's web site.To obtain the online browsing behavior data, deploy a web analytics solution. The stream pre-processes the activity feed data from a web analytics tool and loads it into tables in a format that makes it easy for analysis. The transformed data is stored in the ONLINE_BROWSING_HISTORY table.Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Transform and aggregate customer data

The IBM® Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the DataProcessing Stream.str to transform and aggregate data about customer activity on the insurer's web site.

To obtain the online browsing behavior data, deploy a web analytics solution.

The stream pre-processes the activity feed data from a web analytics tool and loads it into tables in a format that makes it easy for analysis. The transformed data is stored in the ONLINE_BROWSING_HISTORY table.

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Extract life stage event information from social media postsYou can use social media to get valuable insights about customers. The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the SMA_Text_Analytics.str to extract information from social media posts.IBM Predictive Customer Intelligence does not retrieve social media data; this solution assumes that social media data is available.The SMA Text Analytics model uses text analytics to read the social media data, and to extract life stage event information. Some examples of life stage events are events such as having a baby, getting a new job, buying a house, having a birthday, and getting married. This information is used to help recommend appropriate insurance polices to customers.Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Extract life stage event information from social media posts

You can use social media to get valuable insights about customers. The IBM® Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the SMA_Text_Analytics.str to extract information from social media posts.

Important: IBM Predictive Customer Intelligence does not retrieve social media data; this solution assumes that social media data is available.

The SMA Text Analytics model uses text analytics to read the social media data, and to extract life stage event information. Some examples of life stage events are events such as having a baby, getting a new job, buying a house, having a birthday, and getting married. This information is used to help recommend appropriate insurance polices to customers.

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Extract sentiment scores from customer complaintsCustomer interactions with call center agents can be a source of valuable data to determine customer satisfaction levels. The IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Sentiment Scoring model to extract the sentiment score from customer comments that are captured while recording customer complaints.The input is customer complaint details. The example stream for extracting the sentiment score is named Sentiment.str. The Text Analytics model reads the customer complaint details, and extracts meaningful words and concepts from the information. Negative concepts are used to derive the sentiment score. The sentiment score reflects the count of negative words that are used by customers while making the complaints. Example concepts are bad, not accessible, slow, and wrong.Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Extract sentiment scores from customer complaints

Customer interactions with call center agents can be a source of valuable data to determine customer satisfaction levels. The IBM® Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator uses the Sentiment Scoring model to extract the sentiment score from customer comments that are captured while recording customer complaints.

The input is customer complaint details. The example stream for extracting the sentiment score is named Sentiment.str. The Text Analytics model reads the customer complaint details, and extracts meaningful words and concepts from the information. Negative concepts are used to derive the sentiment score. The sentiment score reflects the count of negative words that are used by customers while making the complaints. Example concepts are bad, not accessible, slow, and wrong.

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Data modelThe historic data that is used for predictive modeling in IBM Predictive Customer Intelligence is stored in an IBM DB2 database. A Predictive Enterprise View (PEV) of the data is created which passes data to the real time scoring service. Database Views (DB Views) are also created to be used in IBM SPSS Modeler streams. The following table describes some of the data columns that are part of the Predictive Enterprise View and the Database Views.Key data columns in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorNAMEDESCRIPTIONAGEThe age of the policy holder.CLTVCustomer Lifetime Value.EDUCATIONThe education level of the policy holder.EMPLOYMENT_STATUSThe status of the person’s employment. GENDERThe person’s sex or gender. INCOMEThe policy holder’s annual income.MARITAL_STATUSThe marital status of the person.MAINTENANCE_COSTThe cost of maintaining this policy.MONTHS_SINCE_POLICY_INCEPTIONThe number of months since the policy holder started the policy.MONTHS_SINCE_LAST_CLAIMThe number of months since the policy holder filed the last claim.NUMBER_OF_CLAIMS_DENIEDThe number of claims that were denied.NUMBER_OF_CLAIMS_FILEDThe number of claims that are filed.CLAIM_SETTLEMENT_DURATIONThe time, in days between the date when the claim opened and the date when the claim was closed, and the customer satisfaction confirmed, based on the status of the claim.NUMBER_OF_COMPLAINTSThe number of complaints the policy holder has submitted.NO_OF_CLOSED_COMPLAINTSThe number of complaints that have been closed.NUMBER_OF_OPEN_COMPLAINTSThe number of complaints that are open.LATEST_NOTE_ATTITUDEThe noted attitude of the last communication.AVG_NOTE_ATTITUDEAverage communication note attitude.NUMBER_OF_POLICIESThe number of policies that the policy holder has.POLICYHOLDER_IDAny value without business meaning that uniquely distinguishes each occurrence of this entity.POLICY_IDAny value without business meaning that uniquely distinguishes each occurrence of this entity.POLICY_TYPEIndicates the policy type, for example, fixed term and flexible term.VEHICLE_OWNERSHIPIndicates whether the policy holder owns a vehicle or not.VEHICLE_TYPEThe vehicle type.VEHICLE_SIZEThe vehicle size.HOME_OWNERSHIP_STATUSThe tenancy status of residence.INSURANCE_LINESThe number of types of insurance products held by the policy holder.INSURANCE_SCOREThe insurance score, based on the credit score, as well as other factors such as claim filing history.LIFE_CUSTOMERIndicates whether a customer owns a life insurance policy.NON_LIFE_CUSTOMERIndicates whether a person owns a non-life insurance policy.NUMBER_OF_CHILDRENThe number of children of the policy holder.NUMBER_OF_INSURED_CARSThe number of cars insured with the insurance company.POLICYHOLDER_TENUREThe number of years the policy holder has been a customer with the insurance company.TOTAL_PREMIUMThe total premium on all the policies paid by the policyholder to the insurance company.RETIREMENT_PLANThe name of the retirement plan owned by the policyholderHOUSEHOLD_NUMBER_OF_CHILDRENThe number of children in householdHOUSEHOLD_NUMBER_OF_INSURED_ CARSThe number of insured cars in the household that are insured with the insurance company.NUMBER_OF_POLICIES_IN_HOUSEHOLDThe number of policies held by the household.HOUSEHOLD_TENUREThe number of years that a household has been classified within its customer status.HOUSEHOLD_PREMIUMThe total premium paid by the household to the insurance company.HOUSEHOLD_DISPOSABLE_INCOMEThe amount of money that the household can spend after having paid all the fixed expenses such as rent and mortgage repayment.Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry acceleratorA number of predictive models are used in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Data model

The historic data that is used for predictive modeling in IBM® Predictive Customer Intelligence is stored in an IBM DB2® database.

A Predictive Enterprise View (PEV) of the data is created which passes data to the real time scoring service. Database Views (DB Views) are also created to be used in IBM SPSS® Modeler streams. The following table describes some of the data columns that are part of the Predictive Enterprise View and the Database Views.

Table 1. Key data columns in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

NAME
DESCRIPTION

AGE
The age of the policy holder.

CLTV
Customer Lifetime Value.

EDUCATION
The education level of the policy holder.

EMPLOYMENT_STATUS
The status of the person’s employment.

GENDER
The person’s sex or gender.

INCOME
The policy holder’s annual income.

MARITAL_STATUS
The marital status of the person.

MAINTENANCE_COST
The cost of maintaining this policy.

MONTHS_SINCE_POLICY_INCEPTION
The number of months since the policy holder started the policy.

MONTHS_SINCE_LAST_CLAIM
The number of months since the policy holder filed the last claim.

NUMBER_OF_CLAIMS_DENIED
The number of claims that were denied.

NUMBER_OF_CLAIMS_FILED
The number of claims that are filed.

CLAIM_SETTLEMENT_DURATION
The time, in days between the date when the claim opened and the date when the claim was closed, and the customer satisfaction confirmed, based on the status of the claim.

NUMBER_OF_COMPLAINTS
The number of complaints the policy holder has submitted.

NO_OF_CLOSED_COMPLAINTS
The number of complaints that have been closed.

NUMBER_OF_OPEN_COMPLAINTS
The number of complaints that are open.

LATEST_NOTE_ATTITUDE
The noted attitude of the last communication.

AVG_NOTE_ATTITUDE
Average communication note attitude.

NUMBER_OF_POLICIES
The number of policies that the policy holder has.

POLICYHOLDER_ID
Any value without business meaning that uniquely distinguishes each occurrence of this entity.

POLICY_ID
Any value without business meaning that uniquely distinguishes each occurrence of this entity.

POLICY_TYPE
Indicates the policy type, for example, fixed term and flexible term.

VEHICLE_OWNERSHIP
Indicates whether the policy holder owns a vehicle or not.

VEHICLE_TYPE
The vehicle type.

VEHICLE_SIZE
The vehicle size.

HOME_OWNERSHIP_STATUS
The tenancy status of residence.

INSURANCE_LINES
The number of types of insurance products held by the policy holder.

INSURANCE_SCORE
The insurance score, based on the credit score, as well as other factors such as claim filing history.

LIFE_CUSTOMER
Indicates whether a customer owns a life insurance policy.

NON_LIFE_CUSTOMER
Indicates whether a person owns a non-life insurance policy.

NUMBER_OF_CHILDREN
The number of children of the policy holder.

NUMBER_OF_INSURED_CARS

The number of cars insured with the insurance company.

POLICYHOLDER_TENURE
The number of years the policy holder has been a customer with the insurance company.

TOTAL_PREMIUM
The total premium on all the policies paid by the policyholder to the insurance company.

RETIREMENT_PLAN
The name of the retirement plan owned by the policyholder

HOUSEHOLD_NUMBER_OF_CHILDREN

The number of children in household

HOUSEHOLD_NUMBER_OF_INSURED_

CARS

The number of insured cars in the household that are insured with the insurance company.

NUMBER_OF_POLICIES_IN_HOUSEHOLD

The number of policies held by the household.

HOUSEHOLD_TENURE
The number of years that a household has been classified within its customer status.

HOUSEHOLD_PREMIUM
The total premium paid by the household to the insurance company.

HOUSEHOLD_DISPOSABLE_INCOME
The amount of money that the household can spend after having paid all the fixed expenses such as rent and mortgage repayment.

Parent topic: Predictive models in the Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator

Training predictive modelsPredictive models must be trained to determine which data is useful and which data is not needed. When a model gives you accurate predictions, you are ready to use the predictive model for real-time scoring.You use a training data set to build the predictive model and a test set of data to validate the model that was created with the training set. Models must be retrained periodically with new data sets to adjust for changing behavior patterns. For information about using IBM SPSS Modeler, see IBM SPSS Modeler Help (www.ibm.com/support/knowledgecenter/SS3RA7_16.0.0/com.ibm.spss.modeler.help/clementine/entities/clem_family_overview.htm?lang=en).Predictive modelsUse IBM Predictive Customer Intelligence models to predict what is likely to happen in the future, based on patterns from past data.

Training predictive models

Predictive models must be trained to determine which data is useful and which data is not needed. When a model gives you accurate predictions, you are ready to use the predictive model for real-time scoring.

You use a training data set to build the predictive model and a test set of data to validate the model that was created with the training set.

Models must be retrained periodically with new data sets to adjust for changing behavior patterns. For information about using IBM® SPSS® Modeler, see IBM SPSS Modeler Help (www.ibm.com/support/knowledgecenter/SS3RA7_16.0.0/com.ibm.spss.modeler.help/clementine/entities/clem_family_overview.htm?lang=en).

Parent topic: Predictive models

Scoring a modelTo score a model means to apply it to some data in order to obtain a result or prediction that can be used as input to decisions.Depending on the application, the scoring results can be written to a database table or flat file, or used as inputs to the segment, selection, and allocation rules that drive decisions in an application.For more information, see IBM SPSS Collaboration and Deployment Services Deployment Manager User's Guide (www.ibm.com/support/knowledgecenter/SS69YH_6.0.0/com.spss.mgmt.content.help/model_management/thick/scoring_configuration_overview.html).Predictive modelsUse IBM Predictive Customer Intelligence models to predict what is likely to happen in the future, based on patterns from past data.

Scoring a model

To score a model means to apply it to some data in order to obtain a result or prediction that can be used as input to decisions.

Depending on the application, the scoring results can be written to a database table or flat file, or used as inputs to the segment, selection, and allocation rules that drive decisions in an application.

For more information, see IBM SPSS Collaboration and Deployment Services Deployment Manager User's Guide (www.ibm.com/support/knowledgecenter/SS69YH_6.0.0/com.spss.mgmt.content.help/model_management/thick/scoring_configuration_overview.html).

Parent topic: Predictive models

Create business rulesFor IBM Predictive Customer Intelligence, you use IBM Analytical Decision Management to bring together your company's business rules, predictive models, and optimizations.Using IBM Analytical Decision Management, the insights gained through predictive modeling can be translated to specific actions. You can combine predictive models with rules to allocate offers in accordance with business goals. This is done by using a combination of selection and allocation rules that are based on the output from predictive models.The steps that you take are:Define possible actionsIf a customer is not happy with a service, what should you do about it.Allocate offersWhich types of customers are the best candidates for which offers.Prioritize offersPrioritization determines which offers a customer receives.For more information, see the IBM Analytical Decision Management Application User's Guide (www.ibm.com/support/knowledgecenter/SS6A3P_8.0.0/com.ibm.spss.dm.userguide.doc/configurableapps/dms_define_rules.htm).Predictive modelsUse IBM Predictive Customer Intelligence models to predict what is likely to happen in the future, based on patterns from past data.

Create business rules

For IBM® Predictive Customer Intelligence, you use IBM Analytical Decision Management to bring together your company's business rules, predictive models, and optimizations.

Using IBM Analytical Decision Management, the insights gained through predictive modeling can be translated to specific actions.

You can combine predictive models with rules to allocate offers in accordance with business goals. This is done by using a combination of selection and allocation rules that are based on the output from predictive models.

The steps that you take are:

Define possible actions

If a customer is not happy with a service, what should you do about it.

Allocate offers

Which types of customers are the best candidates for which offers.

Prioritize offers

Prioritization determines which offers a customer receives.

For more information, see the IBM Analytical Decision Management Application User's Guide (www.ibm.com/support/knowledgecenter/SS6A3P_8.0.0/com.ibm.spss.dm.userguide.doc/configurableapps/dms_define_rules.htm).

Parent topic: Predictive models

Deploy an applicationYou can deploy the IBM Predictive Customer Intelligence application to a testing environment or to a production environment, such as a call center or a website. You can also deploy it to contribute to batch processing.You can deploy a stream in the IBM SPSS Modeler repository. A deployed stream can be accessed by multiple users throughout the enterprise and can be automatically scored and refreshed. For example, a model can be automatically updated at regularly scheduled intervals as new data becomes available.For more information, see IBM SPSS Collaboration and Deployment Services (www.ibm.com/support/knowledgecenter/SS69YH_6.0.0/com.spss.mgmt.content.help/model_management/_entities/whatsnew_overview_thick.html?cp=SS69YH_6.0.0%2F5).Predictive modelsUse IBM Predictive Customer Intelligence models to predict what is likely to happen in the future, based on patterns from past data.

Deploy an application

You can deploy the IBM® Predictive Customer Intelligence application to a testing environment or to a production environment, such as a call center or a website. You can also deploy it to contribute to batch processing.

You can deploy a stream in the IBM SPSS® Modeler repository. A deployed stream can be accessed by multiple users throughout the enterprise and can be automatically scored and refreshed. For example, a model can be automatically updated at regularly scheduled intervals as new data becomes available.

For more information, see IBM SPSS Collaboration and Deployment Services (www.ibm.com/support/knowledgecenter/SS69YH_6.0.0/com.spss.mgmt.content.help/model_management/_entities/whatsnew_overview_thick.html?cp=SS69YH_6.0.0%2F5).

Parent topic: Predictive models

Industry accelerator reportsAn IBM Cognos Business Intelligence report is provided with the IBM Predictive Customer Intelligence industry accelerator.You can customize the report by using IBM Cognos Report Studio. Report Studio is a report design and authoring tool. Report authors can use Report Studio to create, edit, and distribute a wide range of professional reports. For more information, see IBM Cognos Report Studio User Guide (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.1/com.ibm.swg.ba.cognos.ug_cr_rptstd.10.2.1.doc/c_rs_introduction.html). The metadata that the report displays comes from the package that is created in and published from IBM Cognos Framework Manager. The example Framework Manager project folder contains the compiled project file (.cpf). When you open the .cpf file, Framework Manager displays the modeled relationships of the data and the package definitions, which are made available to the reporting studios when published. You can modify the metadata for the report by using Framework Manager. For more information, see IBM Cognos Framework Manager User Guide (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.1/com.ibm.swg.ba.cognos.ug_fm.10.2.1.doc/c_ug_fm_introduction.html%23ug_fm_Introduction).Available reports The following IBM Predictive Customer Intelligence reports are available from the IBM Cognos Business Intelligence portal. CaseReportInsuranceShows the cases by month for the selected client.Churn and Sentiment ScoresThe Dial report shows churn propensity and sentiment score.Churn PropensityShows the churn propensity ratio (the willingness of the client to leave the provider) for the selected client.ClaimsReportThe list report shows customer claims information.ComplaintsThe bar chart and list report shows customer complaints information.PolicyDetailsThe list report shows customer policy information.SMA InsightShows the social media activity for the selected client.Social and Web AnalyticsThe pie chart and list report show customer social media post and insurance product browsing summary information.View IBM Predictive Customer Intelligence reportsYou can view the IBM Predictive Customer Intelligence industry accelerator report in IBM Cognos Business Intelligence.Modify the data model You use IBM Cognos Framework Manager to model the meta data for the IBM Predictive Customer Intelligence industry accelerator reports.

Industry accelerator reports

An IBM® Cognos® Business Intelligence report is provided with the IBM Predictive Customer Intelligence industry accelerator.

You can customize the report by using IBM Cognos Report Studio. Report Studio is a report design and authoring tool. Report authors can use Report Studio to create, edit, and distribute a wide range of professional reports. For more information, see IBM Cognos Report Studio User Guide (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.1/com.ibm.swg.ba.cognos.ug_cr_rptstd.10.2.1.doc/c_rs_introduction.html).

The metadata that the report displays comes from the package that is created in and published from IBM Cognos Framework Manager. The example Framework Manager project folder contains the compiled project file (.cpf). When you open the .cpf file, Framework Manager displays the modeled relationships of the data and the package definitions, which are made available to the reporting studios when published. You can modify the metadata for the report by using Framework Manager. For more information, see IBM Cognos Framework Manager User Guide (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.1/com.ibm.swg.ba.cognos.ug_fm.10.2.1.doc/c_ug_fm_introduction.html%23ug_fm_Introduction).

Available reports

The following IBM Predictive Customer Intelligence reports are available from the IBM Cognos Business Intelligence portal.

CaseReportInsurance

Shows the cases by month for the selected client.

Churn and Sentiment Scores

The Dial report shows churn propensity and sentiment score.

Churn Propensity

Shows the churn propensity ratio (the willingness of the client to leave the provider) for the selected client.

ClaimsReport

The list report shows customer claims information.

Complaints

The bar chart and list report shows customer complaints information.

PolicyDetails

The list report shows customer policy information.

SMA Insight

Shows the social media activity for the selected client.

Social and Web Analytics

The pie chart and list report show customer social media post and insurance product browsing summary information.

View IBM Predictive Customer Intelligence reports
You can view the IBM Predictive Customer Intelligence industry accelerator report in IBM Cognos Business Intelligence.

Modify the data model
You use IBM Cognos Framework Manager to model the meta data for the IBM Predictive Customer Intelligence industry accelerator reports.

View IBM Predictive Customer Intelligence reportsYou can view the IBM Predictive Customer Intelligence industry accelerator report in IBM Cognos Business Intelligence.Open a web browser.Go to the IBM Cognos BI portal URL.For example, go to http://bi_node_name/ibmcognos/.In the IBM Cognos Welcome page, click IBM Cognos content. In the Public Folders tab, click: PCI Industry Accelerators 1.0. Use the following inputs for the report:CaseReportInsurance, Churn and Sentiment Scores, Churn Propensity, ClaimsReport, and PolicyDetails: 1001920.SMA Insight: AaronBBailey@trashymail.comSocial and Web Analytics: 1006598Industry accelerator reportsAn IBM Cognos Business Intelligence report is provided with the IBM Predictive Customer Intelligence industry accelerator.

View IBM Predictive Customer Intelligence reports

You can view the IBM® Predictive Customer Intelligence industry accelerator report in IBM Cognos Business Intelligence.

Procedure

Open a web browser.

Go to the IBM Cognos BI portal URL. For example, go to http://bi_node_name/ibmcognos/.

In the IBM Cognos Welcome page, click IBM Cognos content.

In the Public Folders tab, click: PCI Industry Accelerators 1.0.

Use the following inputs for the report:

CaseReportInsurance, Churn and Sentiment Scores, Churn Propensity, ClaimsReport, and PolicyDetails: 1001920.

SMA Insight: AaronBBailey@trashymail.com

Social and Web Analytics: 1006598

Parent topic: Industry accelerator reports

Modify the data model You use IBM Cognos Framework Manager to model the meta data for the IBM Predictive Customer Intelligence industry accelerator reports. IBM Cognos Framework Manager is a meta data modeling tool that drives query generation for IBM Cognos software. A model is a collection of meta data that includes physical information and business information for one or more data sources. For information on modifying or creating Framework Manager models, see the IBM Cognos Framework Manager User Guide. This is available from IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.1/com.ibm.swg.ba.cognos.cbi.doc/welcome.html).Launch IBM Cognos Framework Manager.Click Open a Project and browse to the location where you copied and decompressed the IBM Cognos Framework Manager model file. The Insurance industry accelerator IBM Cognos Framework Manager file is PCI_Ins_CallCenter_FMProject\Insurance.cpf Industry accelerator reportsAn IBM Cognos Business Intelligence report is provided with the IBM Predictive Customer Intelligence industry accelerator.

Modify the data model

You use IBM® Cognos® Framework Manager to model the meta data for the IBM Predictive Customer Intelligence industry accelerator reports.

IBM Cognos Framework Manager is a meta data modeling tool that drives query generation for IBM Cognos software. A model is a collection of meta data that includes physical information and business information for one or more data sources.

For information on modifying or creating Framework Manager models, see the IBM Cognos Framework Manager User Guide. This is available from IBM Knowledge Center (www.ibm.com/support/knowledgecenter/SSEP7J_10.2.1/com.ibm.swg.ba.cognos.cbi.doc/welcome.html).

Procedure

Launch IBM Cognos Framework Manager.

Click Open a Project and browse to the location where you copied and decompressed the IBM Cognos Framework Manager model file.

The Insurance industry accelerator IBM Cognos Framework Manager file is PCI_Ins_CallCenter_FMProject\Insurance.cpf

Parent topic: Industry accelerator reports

Troubleshooting a problemIBM Predictive Customer Intelligence| Troubleshooting a problemTroubleshooting is a systematic approach to solving a problem. The goal of troubleshooting is to determine why something does not work as expected and how to resolve the problem.Review the following table to help you or customer support resolve a problem.Troubleshooting actions and descriptionsActionsDescriptionA product fix might be available to resolve your problem.Apply all known fix packs, or service levels, or program temporary fixes (PTF).Look up error messages by selecting the product from the IBM Support Portal, and then typing the error message code into the Search support box (www.ibm.com/ support/entry/portal/).Error messages give important information to help you identify the component that is causing the problem.Reproduce the problem to ensure that it is not just a simple error.If samples are available with the product, you might try to reproduce the problem by using the sample data.Ensure that the installation successfully finished.The installation location must contain the appropriate file structure and the file permissions. For example, if the product requires write access to log files, ensure that the directory has the correct permission.Review all relevant documentation, including release notes, technotes, and proven practices documentation.Search the IBM Knowledge Center to determine whether your problem is known, has a workaround, or if it is already resolved and documented.Review recent changes in your computing environment.Sometimes installing new software might cause compatibility issues.If the items in the table did not guide you to a resolution, you might need to collect diagnostic data. This data is necessary for an IBM technical-support representative to effectively troubleshoot and assist you in resolving the problem. You can also collect diagnostic data and analyze it yourself.Troubleshooting resourcesTroubleshooting resources are sources of information that can help you resolve a problem that you are having with an IBM product.

Troubleshooting a problem

Troubleshooting is a systematic approach to solving a problem. The goal of troubleshooting is to determine why something does not work as expected and how to resolve the problem.

Review the following table to help you or customer support resolve a problem.

Table 2. Troubleshooting actions and descriptions.

Actions | Description
A product fix might be available to resolve your problem. | Apply all known fix packs, or service levels, or program temporary fixes (PTF).
Look up error messages by selecting the product from the IBM Support Portal, and then typing the error message code into the Search support box (www.ibm.com/ support/entry/portal/). | Error messages give important information to help you identify the component that is causing the problem.
Reproduce the problem to ensure that it is not just a simple error. | If samples are available with the product, you might try to reproduce the problem by using the sample data.
Ensure that the installation successfully finished. | The installation location must contain the appropriate file structure and the file permissions. For example, if the product requires write access to log files, ensure that the directory has the correct permission.
Review all relevant documentation, including release notes, technotes, and proven practices documentation. | Search the IBM Knowledge Center to determine whether your problem is known, has a workaround, or if it is already resolved and documented.
Review recent changes in your computing environment. | Sometimes installing new software might cause compatibility issues.

If the items in the table did not guide you to a resolution, you might need to collect diagnostic data. This data is necessary for an IBM® technical-support representative to effectively troubleshoot and assist you in resolving the problem. You can also collect diagnostic data and analyze it yourself.

Troubleshooting resources
Troubleshooting resources are sources of information that can help you resolve a problem that you are having with an IBM product.

Troubleshooting resourcesIBM Predictive Customer Intelligence | Troubleshooting resourcesTroubleshooting resources are sources of information that can help you resolve a problem that you are having with an IBM product.Support PortalThe IBM Support Portal is a unified, centralized view of all technical support tools and information for all IBM systems, software, and services. The IBM Support Portal lets you access all the IBM support resources from one place. You can tailor the pages to focus on the information and resources that you need for problem prevention and faster problem resolution. Familiarize yourself with the IBM Support Portal by viewing the demo videos (https://www.ibm.com/blogs/SPNA/entry/the_ibm_support_portal_videos). Find the content that you need by selecting your products from the IBM Support Portal (www.ibm.com/support/entry/portal).Before contacting IBM Support, you will need to collect diagnostic data (system information, symptoms, log files, traces, and so on) that is required to resolve a problem. Gathering this information will help to familiarize you with the troubleshooting process and save you time.Service requestService requests are also known as Problem Management Reports (PMRs). Several methods exist to submit diagnostic information to IBM Software Technical Support.To open a PMR or to exchange information with technical support, view the IBM Software Support Exchanging information with Technical Support page (www.ibm.com/software/support/exchangeinfo.html).Fix CentralFix Central provides fixes and updates for your system's software, hardware, and operating system.Use the pull-down menu to navigate to your product fixes on Fix Central (www.ibm.com/systems/support/fixes/en/fixcentral/help/getstarted.html). You may also want to view Fix Central help.IBM developerWorksIBM developerWorks provides verified technical information in specific technology environments.As a troubleshooting resource, developerWorks provides easy access to the most popular practices, in addition to videos and other information: developerWorks (www.ibm.com/developerworks).IBM RedbooksIBM Redbooks are developed and published by the IBM International Technical Support Organization, the ITSO. IBM Redbooks (www.redbooks.ibm.com) provide in-depth guidance about such topics as installation and configuration and solution implementation.Software support and RSS feedsIBM Software Support RSS feeds are a quick, easy, and lightweight format for monitoring new content added to websites.After you download an RSS reader or browser plug-in, you can subscribe to IBM product feeds at IBM Software Support RSS feeds (https://www.ibm.com/software/support/rss).Log filesLog files can help you troubleshoot problems by recording the activities that take place when you work with a product.Error messagesThe first indication of a problem is often an error message. Error messages contain information that can be helpful in determining the cause of a problem.Troubleshooting a problemTroubleshooting is a systematic approach to solving a problem. The goal of troubleshooting is to determine why something does not work as expected and how to resolve the problem.

Troubleshooting resources

Troubleshooting resources are sources of information that can help you resolve a problem that you are having with an IBM® product.

Support Portal

The IBM Support Portal is a unified, centralized view of all technical support tools and information for all IBM systems, software, and services.

The IBM Support Portal lets you access all the IBM support resources from one place. You can tailor the pages to focus on the information and resources that you need for problem prevention and faster problem resolution. Familiarize yourself with the IBM Support Portal by viewing the demo videos (https://www.ibm.com/blogs/SPNA/entry/the_ibm_support_portal_videos).

Find the content that you need by selecting your products from the IBM Support Portal (www.ibm.com/support/entry/portal).

Before contacting IBM Support, you will need to collect diagnostic data (system information, symptoms, log files, traces, and so on) that is required to resolve a problem. Gathering this information will help to familiarize you with the troubleshooting process and save you time.

Service request

Service requests are also known as Problem Management Reports (PMRs). Several methods exist to submit diagnostic information to IBM Software Technical Support.

To open a PMR or to exchange information with technical support, view the IBM Software Support Exchanging information with Technical Support page (www.ibm.com/software/support/exchangeinfo.html).

Fix Central

Fix Central provides fixes and updates for your system's software, hardware, and operating system.

Use the pull-down menu to navigate to your product fixes on Fix Central (www.ibm.com/systems/support/fixes/en/fixcentral/help/getstarted.html). You may also want to view Fix Central help.

IBM developerWorks

IBM developerWorks® provides verified technical information in specific technology environments.

As a troubleshooting resource, developerWorks provides easy access to the most popular practices, in addition to videos and other information: developerWorks (www.ibm.com/developerworks).

IBM Redbooks

IBM Redbooks® are developed and published by the IBM International Technical Support Organization, the ITSO.

IBM Redbooks (www.redbooks.ibm.com) provide in-depth guidance about such topics as installation and configuration and solution implementation.

Software support and RSS feeds

IBM Software Support RSS feeds are a quick, easy, and lightweight format for monitoring new content added to websites.

After you download an RSS reader or browser plug-in, you can subscribe to IBM product feeds at IBM Software Support RSS feeds (https://www.ibm.com/software/support/rss).

Log files

Log files can help you troubleshoot problems by recording the activities that take place when you work with a product.

Error messages

The first indication of a problem is often an error message. Error messages contain information that can be helpful in determining the cause of a problem.

Parent topic: Troubleshooting a problem

NoticesThis information was developed for products and services offered worldwide.This material may be available from IBM in other languages. However, you may be required to own a copy of the product or product version in that language in order to access it. IBM may not offer the products, services, or features discussed in this document in other countries. Consult your local IBM representative for information on the products and services currently available in your area. Any reference to an IBM product, program, or service is not intended to state or imply that only that IBM product, program, or service may be used. Any functionally equivalent product, program, or service that does not infringe any IBM intellectual property right may be used instead. However, it is the user's responsibility to evaluate and verify the operation of any non-IBM product, program, or service. 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Therefore, the results obtained in other operating environments may vary significantly. Some measurements may have been made on development-level systems and there is no guarantee that these measurements will be the same on generally available systems. Furthermore, some measurements may have been estimated through extrapolation. Actual results may vary. Users of this document should verify the applicable data for their specific environment. Information concerning non-IBM products was obtained from the suppliers of those products, their published announcements or other publicly available sources. IBM has not tested those products and cannot confirm the accuracy of performance, compatibility or any other claims related to non-IBM products. Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products. 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Notices

This information was developed for products and services offered worldwide.

This material may be available from IBM in other languages. However, you may be required to own a copy of the product or product version in that language in order to access it.

IBM may not offer the products, services, or features discussed in this document in other countries. Consult your local IBM representative for information on the products and services currently available in your area. Any reference to an IBM product, program, or service is not intended to state or imply that only that IBM product, program, or service may be used. Any functionally equivalent product, program, or service that does not infringe any IBM intellectual property right may be used instead. However, it is the user's responsibility to evaluate and verify the operation of any non-IBM product, program, or service. This document may describe products, services, or features that are not included in the Program or license entitlement that you have purchased.

IBM may have patents or pending patent applications covering subject matter described in this document. The furnishing of this document does not grant you any license to these patents. You can send license inquiries, in writing, to:

IBM Director of Licensing

IBM Corporation

North Castle Drive

Armonk, NY 10504-1785

U.S.A.

For license inquiries regarding double-byte (DBCS) information, contact the IBM Intellectual Property Department in your country or send inquiries, in writing, to:

Intellectual Property Licensing

Legal and Intellectual Property Law

IBM Japan Ltd.

19-21, Nihonbashi-Hakozakicho, Chuo-ku

Tokyo 103-8510, Japan

The following paragraph does not apply to the United Kingdom or any other country where such provisions are inconsistent with local law: INTERNATIONAL BUSINESS MACHINES CORPORATION PROVIDES THIS PUBLICATION "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Some states do not allow disclaimer of express or implied warranties in certain transactions, therefore, this statement may not apply to you.

This information could include technical inaccuracies or typographical errors. Changes are periodically made to the information herein; these changes will be incorporated in new editions of the publication. IBM may make improvements and/or changes in the product(s) and/or the program(s) described in this publication at any time without notice.

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Licensees of this program who wish to have information about it for the purpose of enabling: (i) the exchange of information between independently created programs and other programs (including this one) and (ii) the mutual use of the information which has been exchanged, should contact:

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Any performance data contained herein was determined in a controlled environment. Therefore, the results obtained in other operating environments may vary significantly. Some measurements may have been made on development-level systems and there is no guarantee that these measurements will be the same on generally available systems. Furthermore, some measurements may have been estimated through extrapolation. Actual results may vary. Users of this document should verify the applicable data for their specific environment.

Information concerning non-IBM products was obtained from the suppliers of those products, their published announcements or other publicly available sources. IBM has not tested those products and cannot confirm the accuracy of performance, compatibility or any other claims related to non-IBM products. Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products.

All statements regarding IBM's future direction or intent are subject to change or withdrawal without notice, and represent goals and objectives only.

This information contains examples of data and reports used in daily business operations. To illustrate them as completely as possible, the examples include the names of individuals, companies, brands, and products. All of these names are fictitious and any similarity to the names and addresses used by an actual business enterprise is entirely coincidental.

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© Product Information This document applies to IBM Predictive Customer Intelligence Version 1.0.1 and may also apply to subsequent releases. Copyright Licensed Materials - Property of IBM © Copyright IBM Corp. 2015, 2016. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. IBM, the IBM logo and ibm.com are trademarks or registered trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the Web at Copyright and trademark information at www.ibm.com/legal/copytrade.shtml. Notices

©

Product Information

This document applies to IBM Predictive Customer Intelligence Version 1.0.1 and may also apply to subsequent releases.

Copyright

Licensed Materials - Property of IBM

© Copyright IBM Corp. 2015, 2016.

US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp.

IBM, the IBM logo and ibm.com are trademarks or registered trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the Web at " Copyright and trademark information " at www.ibm.com/legal/copytrade.shtml.

Parent topic: Notices