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.

Proactive Customer Relationship Management for Energy and UtilitiesEnergy and utilities organizations have the challenge of generating, transmitting, and distributing energy and other natural resources efficiently in a highly regulated and often competitive market.IBM Predictive Customer Intelligence can be used by energy and utility organizations to transform your data into useful insights for more strategic decisions and a competitive advantage. Specifically, you can increase customer retention and customer satisfaction by proactively resolving customer issues. IBM Predictive Customer Intelligence can also help you to identify areas for cross-sell and up-sell across all lines of business.Increased customer retentionA call center agent uses the data in the call center application to do the following activities:See the customer's energy usage to verify whether the customer is on the most appropriate contract for their needs.See the customer's level of social network influence.See what actions and offers the customer is eligible for.Determine the best offers for customers by creating business rulesUsing IBM Analytical Decision Management, the business analyst creates business rules to determine which actions are valid for a customer. For example, you might create a rule that targets retention actions to customers who are receptive home owners, who have a home over a specific size, and where energy usage exceeds a baseline.Customer satisfaction, predicting churn, and propensity to respond to offers by creating predictive modelsUsing IBM SPSS Modeler, a data modeler creates predictive models to predict the following factors:Customer satisfaction.The probability that customers will choose another provider.The probability that customers will adhere to a demand response program. A demand response program is where customers reduce their energy use at times of peak demand to save money.The most profitable Rate Plan.The results from these models are used in IBM Analytical Decision Management to create business rules.Industry accelerator artifactsThe IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities 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.

Proactive Customer Relationship Management for Energy and Utilities

Energy and utilities organizations have the challenge of generating, transmitting, and distributing energy and other natural resources efficiently in a highly regulated and often competitive market.

IBM® Predictive Customer Intelligence can be used by energy and utility organizations to transform your data into useful insights for more strategic decisions and a competitive advantage. Specifically, you can increase customer retention and customer satisfaction by proactively resolving customer issues. IBM Predictive Customer Intelligence can also help you to identify areas for cross-sell and up-sell across all lines of business.

Increased customer retention

A call center agent uses the data in the call center application to do the following activities:

See the customer's energy usage to verify whether the customer is on the most appropriate contract for their needs.

See the customer's level of social network influence.

See what actions and offers the customer is eligible for.

Determine the best offers for customers by creating business rules

Using IBM Analytical Decision Management, the business analyst creates business rules to determine which actions are valid for a customer. For example, you might create a rule that targets retention actions to customers who are receptive home owners, who have a home over a specific size, and where energy usage exceeds a baseline.

Customer satisfaction, predicting churn, and propensity to respond to offers by creating predictive models

Using IBM SPSS® Modeler, a data modeler creates predictive models to predict the following factors:

Customer satisfaction.

The probability that customers will choose another provider.

The probability that customers will adhere to a demand response program. A demand response program is where customers reduce their energy use at times of peak demand to save money.

The most profitable Rate Plan.

The results from these models are used in IBM Analytical Decision Management to create business rules.

Industry accelerator artifacts
The IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities 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.

Industry accelerator artifactsThe IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator includes the following artifacts.IBM Cognos Business Intelligence reportsPCI_EU_CRM_CognosContent.zip PCI_EU_CRM_FMProject.zip PCI_Images.zip The following reports are included: Billing reportsCase Detail reportsProfile reports: Dials report (Key performance indicators for a customer)Usage reportsIBM PCI Energy and Utilities DashboardPCI Energy and Utilities WorkspaceChurn Analysis DashboardChurn Analysis report 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_EU_CRM_App.zipPredictive modelsPCI_EU_CRM_CDS_Archive.pes The individual stream files that are contained in the pes file are available in the Streams folder. The following predictive models are included: ChurnPrediction.strCredit Rating Model.strDemand Response Program Acceptance.strEnergy_Efficiency.strOffer Acceptance Propensity.strRecommended Rate Plan.strSatisfaction.strSatisfaction_EU.strSavings_Potential.strSegmentation_EU.strSentiment.strSustainability.str The Predictive Models are described in Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry acceleratorA number of predictive models are provided in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator..IBM DB2 databasePCI_EU_CRM_Data.zipProactive Customer Relationship Management for Energy and UtilitiesEnergy and utilities organizations have the challenge of generating, transmitting, and distributing energy and other natural resources efficiently in a highly regulated and often competitive market.

Industry accelerator artifacts

The IBM® Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator includes the following artifacts.

IBM Cognos® Business Intelligence reports

PCI_EU_CRM_CognosContent.zip

PCI_EU_CRM_FMProject.zip

PCI_Images.zip

The following reports are included:

Billing reports

Case Detail reports

Profile reports:

Dials report (Key performance indicators for a customer)

Usage reports

IBM PCI Energy and Utilities Dashboard

PCI Energy and Utilities Workspace

Churn Analysis Dashboard

Churn Analysis report

The reports are described in Industry accelerator reports.

IBM Analytical Decision Management applications and templates

PCI_EU_CRM_App.zip

Predictive models

PCI_EU_CRM_CDS_Archive.pes

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

The following predictive models are included:

ChurnPrediction.str

Credit Rating Model.str

Demand Response Program Acceptance.str

Energy_Efficiency.str

Offer Acceptance Propensity.str

Recommended Rate Plan.str

Satisfaction.str

Satisfaction_EU.str

Savings_Potential.str

Segmentation_EU.str

Sentiment.str

Sustainability.str

The Predictive Models are described in Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry accelerator.

IBM DB2® database

PCI_EU_CRM_Data.zip

Parent topic: Proactive Customer Relationship Management for Energy and Utilities

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).Proactive Customer Relationship Management for Energy and UtilitiesEnergy and utilities organizations have the challenge of generating, transmitting, and distributing energy and other natural resources efficiently in a highly regulated and often competitive market.

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: Proactive Customer Relationship Management for Energy and Utilities

Industry accelerator installationThe Proactive Customer Relationship Management for Energy and Utilities 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.Images for IBM Cognos Business Intelligence reports.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 Proactive Customer Relationship Management for Energy and Utilities 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.

Images for IBM Cognos Business Intelligence reports.

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 Proactive Customer Relationship Management for Energy and Utilities 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 Proactive Customer Relationship Management for Energy and Utilities 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 Proactive Customer Relationship Management for Energy and Utilities industry accelerator database file is PCI_1.0_EU_CRM\Database\PCI_EU_CRM_Data.zip. 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 Proactive Customer Relationship Management for Energy and Utilities 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 Proactive Customer Relationship Management for Energy and Utilities industry accelerator database file is PCI_1.0_EU_CRM\Database\PCI_EU_CRM_Data.zip.

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_EU_CRM\Analytics\Applications\PCI_EU_CRM_App.zipTemplate: PCI_1.0_EU_CRM\Analytics\Templates\EnergyandUtilities.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 Energy and Utilities.Click Save.Industry accelerator installationThe Proactive Customer Relationship Management for Energy and Utilities 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_EU_CRM\Analytics\Applications\PCI_EU_CRM_App.zip

Template: PCI_1.0_EU_CRM\Analytics\Templates\EnergyandUtilities.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 Energy and Utilities.

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 Proactive Customer Relationship Management for Energy and Utilities industry accelerator file is PCI_1.0_EU_CRM\Analytics\PCI_EU_CRM_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 Proactive Customer Relationship Management for Energy and Utilities 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 Proactive Customer Relationship Management for Energy and Utilities industry accelerator file is PCI_1.0_EU_CRM\Analytics\PCI_EU_CRM_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. 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 EUTIL 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.Industry accelerator installationThe Proactive Customer Relationship Management for Energy and Utilities 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.

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 EUTIL 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.

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] EUTIL=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: [EUTIL] 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=EUTIL 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. [EUTIL] Database=EUTIL 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]
EUTIL=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:

[EUTIL]
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=EUTIL
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.

[EUTIL]
Database=EUTIL
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 Proactive Customer Relationship Management for Energy and Utilities 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 Proactive Customer Relationship Management for Energy and Utilities industry accelerator IBM Cognos content file is PCI_1.0_EU_CRM\BI\PCI_EU_CRM_CognosContent.zip. Decompress the IBM Cognos report image file where you downloaded the industry accelerator. The report images file is PCI_1.0_EU_CRM\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 Energy and Utilities industry accelerator Framework Manager file is compressed in PCI_1.0_EU_CRM\BI\PCI_EU_CRM_FMProject.zip. The usage report functional accelerator Framework Manager file is compressed in PCI_1.0_PCIReports_Usage\BI\PCI_PCIReports_Usage_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 EUTIL 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 Proactive Customer Relationship Management for Energy and Utilities industry accelerator IBM Cognos content file is PCI_1.0_EU_CRM\BI\PCI_EU_CRM_CognosContent.zip.

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

The report images file is PCI_1.0_EU_CRM\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 Energy and Utilities industry accelerator Framework Manager file is compressed in PCI_1.0_EU_CRM\BI\PCI_EU_CRM_FMProject.zip.

The usage report functional accelerator Framework Manager file is compressed in PCI_1.0_PCIReports_Usage\BI\PCI_PCIReports_Usage_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 EUTIL 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 EUTIL, 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 EUTIL.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 EUTIL.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 EUTIL, 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 EUTIL.

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 EUTIL.

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_EU_CRM_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_EU_CRM_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 Proactive Customer Relationship Management for Energy and Utilities 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 Proactive Customer Relationship Management for Energy and Utilities industry acceleratorA number of predictive models are provided 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 Proactive Customer Relationship Management for Energy and Utilities industry accelerator
A number of predictive models are provided 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 Proactive Customer Relationship Management for Energy and Utilities industry acceleratorA number of predictive models are provided in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.The following models form the basis of the predictive models in the industry accelerator:Churn PredictionChurnPrediction.str predicts a customer's propensity to seek another provider.Credit Rating Model Credit Rating Model.str predicts the probability that customers will miss payments.Demand Response Program AcceptanceDemand Response Program Acceptance.str predicts the probability that customers will be receptive to, and whose load shed would contribute to, reduction goals.Offer Acceptance PropensityOffer Acceptance Propensity.str predicts the likelihood of each customer accepting various offers.Recommended Rate PlanRecommended Rate Plan.str recommends the most profitable Rate Plan.Savings PotentialSavings_Potential.str identifies a customer’s energy savings potential.SustainabilitySustainability.str identifies a customer’s awareness of sustainable energy options. SatisfactionSatisfaction.str determines the satisfaction score for customers based on customer call center records and sentiment score.Satisfaction_EU.str enters data for a customer's SATISFACTION_SCORE if it is empty.SegmentationSegmentation_EU.str classifies customers into different segments.SentimentSentiment.str determines the sentiment score for customers based on social media information, such as Twitter.Energy efficiencyEnergy_Efficiency.str enters data for a customer's CUSTOMER_LIFE_TIME_VALUE_RATIO if it is empty.Predictive modelsUse IBM Predictive Customer Intelligence models to predict what is likely to happen in the future, based on patterns from past data. Predict churnChurn is defined as a customer's propensity to leave their current energy and utilities provider. The Churn prediction model uses information such as customer demographic data, consumption behavior data, complaints, standard yearly energy usage for the past seven years, property characteristics, and the customer's sentiment score.Predict credit ratingThe Credit Rating model predicts the credit rating and the probability that customers will miss payments.Predict the need for demand response program assistanceThe Demand Response Program Acceptance model predicts the probability that customers will be receptive to, and whose load shed would contribute to, reduction goals.Predict offer acceptance propensityThe Offer Acceptance Propensity model predicts the likelihood that a customer will accept various offers based on the historical response to offers by customers with similar characteristics.Recommend the right rate planThe Recommended Rate Plan model in the IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator recommends the most profitable rate plan.Understand customer energy and utilities savings potentialThe savings potential model is used to categorize customers by their energy savings potential into high, medium, and low potential.Understand customer energy and utilities sustainabilityThis customer segmentation model predicts the customer’s level of awareness of sustainable energy options. Understand customer sentimentThe Sentiment model in the IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator determines customer sentiment.

Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry accelerator

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

The following models form the basis of the predictive models in the industry accelerator:

Churn Prediction

ChurnPrediction.str predicts a customer's propensity to seek another provider.

Credit Rating Model

Credit Rating Model.str predicts the probability that customers will miss payments.

Demand Response Program Acceptance

Demand Response Program Acceptance.str predicts the probability that customers will be receptive to, and whose load shed would contribute to, reduction goals.

Offer Acceptance Propensity

Offer Acceptance Propensity.str predicts the likelihood of each customer accepting various offers.

Recommended Rate Plan

Recommended Rate Plan.str recommends the most profitable Rate Plan.

Savings Potential

Savings_Potential.str identifies a customer’s energy savings potential.

Sustainability

Sustainability.str identifies a customer’s awareness of sustainable energy options.

Satisfaction

Satisfaction.str determines the satisfaction score for customers based on customer call center records and sentiment score.

Satisfaction_EU.str enters data for a customer's SATISFACTION_SCORE if it is empty.

Segmentation

Segmentation_EU.str classifies customers into different segments.

Sentiment

Sentiment.str determines the sentiment score for customers based on social media information, such as Twitter.

Energy efficiency

Energy_Efficiency.str enters data for a customer's CUSTOMER_LIFE_TIME_VALUE_RATIO if it is empty.

Predict churn
Churn is defined as a customer's propensity to leave their current energy and utilities provider. The Churn prediction model uses information such as customer demographic data, consumption behavior data, complaints, standard yearly energy usage for the past seven years, property characteristics, and the customer's sentiment score.

Predict credit rating
The Credit Rating model predicts the credit rating and the probability that customers will miss payments.

Predict the need for demand response program assistance
The Demand Response Program Acceptance model predicts the probability that customers will be receptive to, and whose load shed would contribute to, reduction goals.

Predict offer acceptance propensity
The Offer Acceptance Propensity model predicts the likelihood that a customer will accept various offers based on the historical response to offers by customers with similar characteristics.

Recommend the right rate plan
The Recommended Rate Plan model in the IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator recommends the most profitable rate plan.

Understand customer energy and utilities savings potential
The savings potential model is used to categorize customers by their energy savings potential into high, medium, and low potential.

Understand customer energy and utilities sustainability
This customer segmentation model predicts the customer’s level of awareness of sustainable energy options.

Understand customer sentiment
The Sentiment model in the IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator determines customer sentiment.

Parent topic: Predictive models

Predict churnChurn is defined as a customer's propensity to leave their current energy and utilities provider. The Churn prediction model uses information such as customer demographic data, consumption behavior data, complaints, standard yearly energy usage for the past seven years, property characteristics, and the customer's sentiment score.The example stream for predicting churn in the IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator is named ChurnPrediction.str.Data preparation for churn prediction starts with aggregating all available information about the customer. The data is classified in the following categories:Demographic data, such as age, gender, education, marital status, employment status, income, and home ownership status.Customer energy and utilities consumption behavior data, such as if a customer owns an electric vehicle and if a customer uses photovoltaics.Complaints, such as the number of open and closed complaints.Property characteristics, such as building type, the year it was built, building surface area, and building volume.Standard yearly energy usage for the past seven years.A Neural Networks algorithm produces the churn propensity score. The algorithm predicts a target based on one or more inputs by finding unknown and possibly complex patterns in the data. The algorithm learns by examining individual records, generating a prediction for each record, and adjusting the weights whenever it makes an incorrect prediction. This process is repeated many times, and the algorithm continues to improve its predictions until one or more of the stopping criteria is met. The model also shows which predictors are most important to predict churn; for example, the most important predictors might be Sentiment Score, Customer Lifetime Value (CLTV), and the Number of Open Complaints.Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry acceleratorA number of predictive models are provided in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Predict churn

Churn is defined as a customer's propensity to leave their current energy and utilities provider. The Churn prediction model uses information such as customer demographic data, consumption behavior data, complaints, standard yearly energy usage for the past seven years, property characteristics, and the customer's sentiment score.

The example stream for predicting churn in the IBM® Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator is named ChurnPrediction.str.

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

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

Customer energy and utilities consumption behavior data, such as if a customer owns an electric vehicle and if a customer uses photovoltaics.

Complaints, such as the number of open and closed complaints.

Property characteristics, such as building type, the year it was built, building surface area, and building volume.

Standard yearly energy usage for the past seven years.

A Neural Networks algorithm produces the churn propensity score. The algorithm predicts a target based on one or more inputs by finding unknown and possibly complex patterns in the data. The algorithm learns by examining individual records, generating a prediction for each record, and adjusting the weights whenever it makes an incorrect prediction. This process is repeated many times, and the algorithm continues to improve its predictions until one or more of the stopping criteria is met. The model also shows which predictors are most important to predict churn; for example, the most important predictors might be Sentiment Score, Customer Lifetime Value (CLTV), and the Number of Open Complaints.

Parent topic: Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry accelerator

Predict credit ratingThe Credit Rating model predicts the credit rating and the probability that customers will miss payments.The example stream in the IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator is named Credit Rating Model.str.The Credit Rating model uses a TwoStep model to classify customers based on credit history, annual income, ownership, family members, and so on. Then, a logistic regression algorithm is applied to read payment history and customer classification to evaluate the customers' ability to pay their bills. Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry acceleratorA number of predictive models are provided in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Predict credit rating

The Credit Rating model predicts the credit rating and the probability that customers will miss payments.

The example stream in the IBM® Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator is named Credit Rating Model.str.

The Credit Rating model uses a TwoStep model to classify customers based on credit history, annual income, ownership, family members, and so on. Then, a logistic regression algorithm is applied to read payment history and customer classification to evaluate the customers' ability to pay their bills.

Parent topic: Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry accelerator

Predict the need for demand response program assistanceThe Demand Response Program Acceptance model predicts the probability that customers will be receptive to, and whose load shed would contribute to, reduction goals.The example stream in the IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator is named Demand Response Program Acceptance.str.A C5.0 algorithm reads the history of demand response programs and customer demographic information. The algorithm uses this information to predict whether customers will be receptive to, and whose load shed would contribute to, reduction goals.A demand response program encourages customers to reduce their energy use at times of peak demand to save money or gain incentives.Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry acceleratorA number of predictive models are provided in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Predict the need for demand response program assistance

The Demand Response Program Acceptance model predicts the probability that customers will be receptive to, and whose load shed would contribute to, reduction goals.

The example stream in the IBM® Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator is named Demand Response Program Acceptance.str.

A C5.0 algorithm reads the history of demand response programs and customer demographic information. The algorithm uses this information to predict whether customers will be receptive to, and whose load shed would contribute to, reduction goals.

A demand response program encourages customers to reduce their energy use at times of peak demand to save money or gain incentives.

Parent topic: Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry accelerator

Predict offer acceptance propensityThe Offer Acceptance Propensity model predicts the likelihood that a customer will accept various offers based on the historical response to offers by customers with similar characteristics.The example stream in the IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator is named Offer Acceptance Propensity.str.The data that is used for predicting offer acceptance propensity is classified in the following categories:Demographic data, such as education, social class, and income level.Property characteristics, such as building type, year it was built, building surface area, and building volume.Standard yearly energy usage for the past seven years.Offer profit for different offers.An auto classifier is used to predict whether a customer is likely to accept an offer. The algorithm with the highest rank on Lift is chosen. The target is either a 1, indicating the customer is likely to accept the offer, or 0, meaning they will not. The outputs of the predictive model, including the 1 or 0 indicator, and the corresponding level of confidence are used to calculate the offer acceptance propensity.Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry acceleratorA number of predictive models are provided in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Predict offer acceptance propensity

The Offer Acceptance Propensity model predicts the likelihood that a customer will accept various offers based on the historical response to offers by customers with similar characteristics.

The example stream in the IBM® Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator is named Offer Acceptance Propensity.str.

The data that is used for predicting offer acceptance propensity is classified in the following categories:

Demographic data, such as education, social class, and income level.

Property characteristics, such as building type, year it was built, building surface area, and building volume.

Standard yearly energy usage for the past seven years.

Offer profit for different offers.

An auto classifier is used to predict whether a customer is likely to accept an offer. The algorithm with the highest rank on Lift is chosen. The target is either a 1, indicating the customer is likely to accept the offer, or 0, meaning they will not. The outputs of the predictive model, including the 1 or 0 indicator, and the corresponding level of confidence are used to calculate the offer acceptance propensity.

Parent topic: Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry accelerator

Recommend the right rate planThe Recommended Rate Plan model in the IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator recommends the most profitable rate plan.The example stream is named Recommended Rate Plan.str. Average usage is considered by customer, and the bill amount is calculated for all available rate plans. The current rate plan bill totals are compared with all other rate plan bill totals, and from this comparison, the most profitable rate plan is selected.Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry acceleratorA number of predictive models are provided in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Recommend the right rate plan

The Recommended Rate Plan model in the IBM® Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator recommends the most profitable rate plan.

The example stream is named Recommended Rate Plan.str.

Average usage is considered by customer, and the bill amount is calculated for all available rate plans. The current rate plan bill totals are compared with all other rate plan bill totals, and from this comparison, the most profitable rate plan is selected.

Parent topic: Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry accelerator

Understand customer energy and utilities savings potentialThe savings potential model is used to categorize customers by their energy savings potential into high, medium, and low potential.The example stream in the IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator is named Savings_Potential.str.The data is classified in the following categories:Demographic data, such as education, social class, and income level.Property characteristics, such as building type, year it was built, building surface area, and building volume.Standard yearly energy usage for the past seven years.An auto cluster model runs a K-Means algorithm and a Two Step Cluster algorithm. The model chooses the better quality clustering method to slot customers into one of three groups. By analyzing the savings potential attributes’ distribution for each cluster, each customer group is assigned a high, medium, or low savings potential. A Logistic Regression algorithm reads the savings potential attributes to predict which savings potential level a customer will be in the future.Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry acceleratorA number of predictive models are provided in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Understand customer energy and utilities savings potential

The savings potential model is used to categorize customers by their energy savings potential into high, medium, and low potential.

The example stream in the IBM® Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator is named Savings_Potential.str.

The data is classified in the following categories:

Demographic data, such as education, social class, and income level.

Property characteristics, such as building type, year it was built, building surface area, and building volume.

Standard yearly energy usage for the past seven years.

An auto cluster model runs a K-Means algorithm and a Two Step Cluster algorithm. The model chooses the better quality clustering method to slot customers into one of three groups. By analyzing the savings potential attributes’ distribution for each cluster, each customer group is assigned a high, medium, or low savings potential. A Logistic Regression algorithm reads the savings potential attributes to predict which savings potential level a customer will be in the future.

Parent topic: Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry accelerator

Understand customer energy and utilities sustainabilityThis customer segmentation model predicts the customer’s level of awareness of sustainable energy options. The example stream in the IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator is named Sustainability.str.The data is classified in the following categories:Demographic data, such as education, social class, and income level.Life style survey data, such as if a customer is using green energy.Charitable donation information.An auto cluster model runs a K-Means algorithm and a Two Step Cluster algorithm. The model chooses the better quality clustering method to slot customers into one of three groups. By analyzing the input attributes’ distribution for each cluster, each customer group is assigned a high, medium, or low awareness. A Logistic Regression algorithm reads the sustainability attributes to predict which level of awareness a customer will be in the future.Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry acceleratorA number of predictive models are provided in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Understand customer energy and utilities sustainability

This customer segmentation model predicts the customer’s level of awareness of sustainable energy options.

The example stream in the IBM® Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator is named Sustainability.str.

The data is classified in the following categories:

Demographic data, such as education, social class, and income level.

Life style survey data, such as if a customer is using green energy.

Charitable donation information.

An auto cluster model runs a K-Means algorithm and a Two Step Cluster algorithm. The model chooses the better quality clustering method to slot customers into one of three groups. By analyzing the input attributes’ distribution for each cluster, each customer group is assigned a high, medium, or low awareness. A Logistic Regression algorithm reads the sustainability attributes to predict which level of awareness a customer will be in the future.

Parent topic: Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry accelerator

Understand customer sentimentThe Sentiment model in the IBM Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator determines customer sentiment.The example stream in the industry accelerator is named Sentiment.str.An IBM SPSS Text Analytics model mines unstructured data to establish the customer sentiment. The data comes from social media sources such as Twitter. The data is categorized into areas such as outages, power line issues, customer service, and bill issues. A sentiment score is determined, based on the set of categories that is defined in the Text Analytics node.Predictive models in the Proactive Customer Relationship Management for Energy and Utilities industry acceleratorA number of predictive models are provided in the IBM Predictive Customer Intelligence Inject Next Best Action Recommendations into every Insurance Call Center Interaction industry accelerator.

Understand customer sentiment

The Sentiment model in the IBM® Predictive Customer Intelligence Proactive Customer Relationship Management for Energy and Utilities industry accelerator determines customer sentiment.

The example stream in the industry accelerator is named Sentiment.str.

An IBM SPSS® Text Analytics model mines unstructured data to establish the customer sentiment. The data comes from social media sources such as Twitter. The data is categorized into areas such as outages, power line issues, customer service, and bill issues. A sentiment score is determined, based on the set of categories that is defined in the Text Analytics node.

Parent topic: Predictive models in the Proactive Customer Relationship Management for Energy and Utilities 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. BillingReportsThe Billing report displays the customer billing history, calculated bill amount, and potential savings.CaseDetailReportsThe CaseReport shows the open and closed case history of the customer: a bar chart that plots the case status per month, a list with detail case information and a list that shows social media posts.ProfileReportsThe Dials report shows the analytics key performance indicators (KPIs) for a customer.For the Telecommunications call center sample only, the SocialNetworkChart report shows the social network chart for a customer ID.UsageReportsThe Usage report shows the voice and data usage of the customer.Dashboards and reports for energy sustainability and savings potential analysis The dashboards show two key customer segments that are derived from the predictive models: Energy sustainability and savings potential. Energy sustainability is a rating of a customer's awareness of green energy options. Savings potential identifies customers who might be able to reduce their energy consumption. The dashboard can be used to help energy and utility companies run effective marketing campaigns for these key customer segments by identifying where the campaign profit is. There are two dashboard reports and four list reports. IBM PCI Energy and Utilities DashboardThis is an Active Report, a self-contained IBM Cognos Business Intelligence application, that lets you browse and explore data offline on a desktop or mobile device. The Overview tab shows the charts and key metrics that provide a high-level overview of the customer base, including the number of customers, energy consumption, and profit margin. You can view the charts by different categories, such as Age Group, Income Level, Education, Employment, or Lifestyle using a slider control. The tab includes the following information: The Key Metrics table displays the customer count, energy usage (in kW/h), total billed amount, and average profit margin for the reporting month. A graphic indicates the direction in which the measure is trending.A row of line charts shows the progression of some of the key metrics: Usage (kWh), Billed Amount and Profit Margin over six months by the selected category.Two treemap charts display the distribution of customers in the key customer segments by the selected category. The Usage tab compares the consumption behavior in the key customer segments. The tab includes the following information: Vertical bar charts compare the peak and off-peak usage in the last year between the segment groups.Stacked charts compare the average, minimum, and maximum daily usage averaged by month over the last 12 months. The chart can be analyzed by the segment groups using a drop-down filter. The Campaign tab shows past campaign receptiveness in the key customer segments. The tab includes the following information: Vertical bar charts compare the Actual Campaign Profit and the Predicted Campaign Profit between the segment.Vertical bar charts compare the Average Actual Campaign Profit and the Average Predicted Campaign Profit between the segment.Horizontal bar charts shows the Actual Campaign Profit and Profit Margin by segment. The Offer tab shows the most profitable campaign offers and how well they have been received in the past. The tab includes the following information: A Bubble chart shows the relationships between an Offer's Profit Margin, Actual Campaign Profit, and Offer Accepted Rate. The chart can be analyzed by the segment groups using a drop-down filter.Vertical bar charts compare the offer's Profit Margin by segment. A global Age Group filter is available to restrict customers to the selected age groups in all of the tabs. An Offer filter is available to restrict the offers in the Offer tab. Use the filter icon at the top of the dashboard to show and hide the filter area.PCI Energy and Utilities WorkspaceThe IBM Predictive Customer Intelligence Energy and Utilities Workspace is an interactive dashboard that is intended to be used while connected to live data. It provides more flexibility in the information that is displayed on the reports than the offline dashboard. The workspace consists of the same tabs and charts as the IBM Predictive Customer Intelligence Energy and Utilities Dashboard. There are more global filters, such as Income Level, Education, Employment, and Lifestyle Category, to help you analyze the results. Three detailed list reports can be drilled to from the treemap charts in the Overview tab: Top Payment Customer List shows the top N list of customers with the highest billed amount.Top Profit Margin Customer List shows the top N list of customers with the highest profit margin.Top Usage Customer List shows the top N list of customers with the highest energy usage. You can drill-through to the Customer Offers detail list report from these reports to show the offers that a selected customer has already received. The four list reports can also be run as stand-alone.Dashboards and reports to analyze customers at risk of churn The analysis of customers at risk of churn helps energy and utility companies identify individuals from a large pool of customers who are at risk of leaving the utility company for another provider. The analysis also helps you identify characteristics that can influence churn. Churn Analysis DashboardThis is an Active Report, a self-contained IBM Cognos Business Intelligence application, that lets you browse and explore data offline on a desktop or mobile device. This report has a static bar chart and a bubble chart. The bar chart shows the top 10 most important predictors as determined by the Churn Prediction model. The bubble chart shows the relationship of the churn score and the predictors. You can choose different pre-defined predictor dimensions and measures. And you can restrict the customers with a specific churn score percentage using a slider control.Churn Analysis reportThe Churn Analysis report is intended to be used while connected to the data directly. This report provides more flexibility in the information that is displayed on the report, and can drill-though to a detailed list report. The report consists of the same charts and features as the Churn Analysis Dashboard. You can drill-through to the Top Customers at Risk of Churn List report from the bubble chart to show the top N customers who are likely to churn. This report can also be run as a stand-alone report.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.

BillingReports

The Billing report displays the customer billing history, calculated bill amount, and potential savings.

CaseDetailReports

The CaseReport shows the open and closed case history of the customer: a bar chart that plots the case status per month, a list with detail case information and a list that shows social media posts.

ProfileReports

The Dials report shows the analytics key performance indicators (KPIs) for a customer.

For the Telecommunications call center sample only, the SocialNetworkChart report shows the social network chart for a customer ID.

UsageReports

The Usage report shows the voice and data usage of the customer.

Dashboards and reports for energy sustainability and savings potential analysis

The dashboards show two key customer segments that are derived from the predictive models: Energy sustainability and savings potential. Energy sustainability is a rating of a customer's awareness of green energy options. Savings potential identifies customers who might be able to reduce their energy consumption. The dashboard can be used to help energy and utility companies run effective marketing campaigns for these key customer segments by identifying where the campaign profit is. There are two dashboard reports and four list reports.

IBM PCI Energy and Utilities Dashboard

This is an Active Report, a self-contained IBM Cognos Business Intelligence application, that lets you browse and explore data offline on a desktop or mobile device.

The Overview tab shows the charts and key metrics that provide a high-level overview of the customer base, including the number of customers, energy consumption, and profit margin. You can view the charts by different categories, such as Age Group, Income Level, Education, Employment, or Lifestyle using a slider control. The tab includes the following information:

The Key Metrics table displays the customer count, energy usage (in kW/h), total billed amount, and average profit margin for the reporting month. A graphic indicates the direction in which the measure is trending.

A row of line charts shows the progression of some of the key metrics: Usage (kWh), Billed Amount and Profit Margin over six months by the selected category.

Two treemap charts display the distribution of customers in the key customer segments by the selected category.

The Usage tab compares the consumption behavior in the key customer segments. The tab includes the following information:

Vertical bar charts compare the peak and off-peak usage in the last year between the segment groups.

Stacked charts compare the average, minimum, and maximum daily usage averaged by month over the last 12 months. The chart can be analyzed by the segment groups using a drop-down filter.

The Campaign tab shows past campaign receptiveness in the key customer segments. The tab includes the following information:

Vertical bar charts compare the Actual Campaign Profit and the Predicted Campaign Profit between the segment.

Vertical bar charts compare the Average Actual Campaign Profit and the Average Predicted Campaign Profit between the segment.

Horizontal bar charts shows the Actual Campaign Profit and Profit Margin by segment.

The Offer tab shows the most profitable campaign offers and how well they have been received in the past. The tab includes the following information:

A Bubble chart shows the relationships between an Offer's Profit Margin, Actual Campaign Profit, and Offer Accepted Rate. The chart can be analyzed by the segment groups using a drop-down filter.

Vertical bar charts compare the offer's Profit Margin by segment.

A global Age Group filter is available to restrict customers to the selected age groups in all of the tabs. An Offer filter is available to restrict the offers in the Offer tab. Use the filter icon at the top of the dashboard to show and hide the filter area.

PCI Energy and Utilities Workspace

The IBM Predictive Customer Intelligence Energy and Utilities Workspace is an interactive dashboard that is intended to be used while connected to live data. It provides more flexibility in the information that is displayed on the reports than the offline dashboard. The workspace consists of the same tabs and charts as the IBM Predictive Customer Intelligence Energy and Utilities Dashboard. There are more global filters, such as Income Level, Education, Employment, and Lifestyle Category, to help you analyze the results.

Three detailed list reports can be drilled to from the treemap charts in the Overview tab:

Top Payment Customer List shows the top N list of customers with the highest billed amount.

Top Profit Margin Customer List shows the top N list of customers with the highest profit margin.

Top Usage Customer List shows the top N list of customers with the highest energy usage.

You can drill-through to the Customer Offers detail list report from these reports to show the offers that a selected customer has already received.

The four list reports can also be run as stand-alone.

Dashboards and reports to analyze customers at risk of churn

The analysis of customers at risk of churn helps energy and utility companies identify individuals from a large pool of customers who are at risk of leaving the utility company for another provider. The analysis also helps you identify characteristics that can influence churn.

Churn Analysis Dashboard

This is an Active Report, a self-contained IBM Cognos Business Intelligence application, that lets you browse and explore data offline on a desktop or mobile device.

This report has a static bar chart and a bubble chart. The bar chart shows the top 10 most important predictors as determined by the Churn Prediction model. The bubble chart shows the relationship of the churn score and the predictors. You can choose different pre-defined predictor dimensions and measures. And you can restrict the customers with a specific churn score percentage using a slider control.

Churn Analysis report

The Churn Analysis report is intended to be used while connected to the data directly.

This report provides more flexibility in the information that is displayed on the report, and can drill-though to a detailed list report. The report consists of the same charts and features as the Churn Analysis Dashboard.

You can drill-through to the Top Customers at Risk of Churn List report from the bubble chart to show the top N customers who are likely to churn. This report can also be run as a stand-alone report.

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.When you run some reports, you are prompted for a customer ID. Enter 21 as the customer ID, and click Submit. Industry 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.

When you run some reports, you are prompted for a customer ID. Enter 21 as the customer ID, and click Submit.

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 Energy and Utilities industry accelerator IBM Cognos Framework Manager file is PCI_EU_CRM_FMProject\IBM PCI Energy and Utilities Model.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 Energy and Utilities industry accelerator IBM Cognos Framework Manager file is PCI_EU_CRM_FMProject\IBM PCI Energy and Utilities Model.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 1. 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. 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. Any references in this information to non-IBM Web sites are provided for convenience only and do not in any manner serve as an endorsement of those Web sites. The materials at those Web sites are not part of the materials for this IBM product and use of those Web sites is at your own risk. IBM may use or distribute any of the information you supply in any way it believes appropriate without incurring any obligation to you.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: IBM Software Group Attention: Licensing 3755 Riverside Dr. Ottawa, ON K1V 1B7 Canada Such information may be available, subject to appropriate terms and conditions, including in some cases, payment of a fee. The licensed program described in this document and all licensed material available for it are provided by IBM under terms of the IBM Customer Agreement, IBM International Program License Agreement or any equivalent agreement between us.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. If you are viewing this information softcopy, the photographs and color illustrations may not appear.This Software Offering does not use cookies or other technologies to collect personally identifiable information.©

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.

Any references in this information to non-IBM Web sites are provided for convenience only and do not in any manner serve as an endorsement of those Web sites. The materials at those Web sites are not part of the materials for this IBM product and use of those Web sites is at your own risk.

IBM may use or distribute any of the information you supply in any way it believes appropriate without incurring any obligation to you.

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:

IBM Software Group

Attention: Licensing

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Such information may be available, subject to appropriate terms and conditions, including in some cases, payment of a fee.

The licensed program described in this document and all licensed material available for it are provided by IBM under terms of the IBM Customer Agreement, IBM International Program License Agreement or any equivalent agreement between us.

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.

If you are viewing this information softcopy, the photographs and color illustrations may not appear.

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© Product Information This document applies to IBM Predictive Customer Intelligence Version 1.1.1 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

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Product Information

This document applies to IBM Predictive Customer Intelligence Version 1.1.1 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