Introduction

The IBM Behavior Based Customer Insight for Insurance solution gives you the information and insight that you need to provide proactive service to your customers.

Audience

This guide is intended for administrators and users of the Behavior Based Customer Insight for Insurance solution. It provides information on installation, configuration, and use of the Behavior Based Customer Insight for Insurance solution.

Finding information

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

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 Behavior Based Customer Insight for Insurance have accessibility features. For more information, see Accessibility features.

Behavior Based Customer Insight for Insurance 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.

Overview

The IBM Behavior Based Customer Insight for Insurance solution provides analytics that help insurers to improve customer experience and retention.

The solution includes IBM Predictive Customer Intelligence, reporting and dashboard templates, predictive models, and application interfaces for integration with operational systems. It uses enterprise and third-party data that is related to transactions, customer information, and location to divide customers into behavior-based segments, and it predicts the probably of various life events. The solution enables insurance companies to take targeted actions with their policyholders and to have effective customer interactions across any interaction point. It delivers insights and predictions to users within various divisions of the insurance company including marketing, territory management, distribution, and customer service.

The solution enables insurers to deliver personalized, timely, and relevant advice and offerings and helps customers to make informed decisions related to their insurance needs.

What need does the solution address for insurance companies?

Insurance companies have a large amount of data related to their clients. However, they lack the capacity to use the data to quickly gain insight into the needs of their clients. With greater insights provided by the solution, they are able to anticipate customer needs and deliver timely, relevant offers. This results in greater convenience for clients, increased client loyalty, and higher revenues.

Features of the solution

The solution provides the following features:

Analytics features that deliver insights from customer behavior

Analytics that provide predictions related to customer life events, retention, risk, and lifetime value

Predefined data models to streamline collection and preparation of data, easing the reliance on technical resources.

The ability to integrate insights programmatically into operational processes and other applications through APIs

Benefits for insurers

The solution provides customer segmentation and customer insights that use customer, product, and transaction data to provide insurers with the following benefits:

The ability to provide customers with targeted offers, interactions, and engagement

Improved conversion, cross-sell, upsell, and retention rates

Reduced lapse rates

Improved customer loyalty

Increased insurance company profitability

Installation and configuration

The IBM Behavior Based Customer Insight for Insurance solution is for use with IBM Predictive Customer Intelligence.

The installation and configuration of the solution involves going to Passport Advantage, downloading the solution, and then configuring the various parts of it within your environment.

Prerequisites
Before you install the Behavior Based Customer Insight for Insurance solution, there are prerequisites that must be met.

Contents of the download package
The Behavior Based Customer Insight for Insurance package, which you download from Passport Advantage, contains several parts.

Overview of the installation and configuration process
This is an overview of the general tasks that you must complete to install and configure the Behavior Based Customer Insight for Insurance solution.

Planning the installation and configuration of the solution
Before you install the solution, you must decide which computers you want the various parts of the solution to reside on.

Downloading the solution
You must download the Behavior Based Customer Insight for Insurance solution from IBM Passport Advantage.

Modifying the sudoers file for the user who runs the installation
To run the Behavior Based Customer Insight for Insurance installation, you must be either root user or have sudo permission on each node computer, and you must disable requiretty during the installation.

Computer firewall settings required for the installation
The solutions installer requires bidirectional communication with each node when you install a server component or server artifact component, and your firewall can interfere with this communication.

The solution installer
The solution installer provides automation that will help you to install the solution.

Creating the database
Before you can use the IBM Behavior Based Customer Insight for Insurance solution, you must create a database.

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 Behavior Based Customer Insight for Insurance solution. 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 Behavior Based Customer Insight for Insurance database through an ODBC data source connection.

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

Deploying 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 Behavior Based Customer Insight for Insurance solution.

Installing the EAR file
The EAR file contains the REST APIs that with which you can embed the IBM Behavior Based Customer Insight for Insurance solution in your existing system.

Prerequisites

Before you install the Behavior Based Customer Insight for Insurance solution, there are prerequisites that must be met.

The following conditions must be met before the application is installed:

IBM Predictive Customer Intelligence is installed and configured.

You have a Linux system that you can install to. The application installer works only on Linux.

You understand your existing environment so that you can determine where you want the various components of the application to reside.

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

Parent topic: Installation and configuration

Contents of the download package

The Behavior Based Customer Insight for Insurance package, which you download from Passport Advantage, contains several parts.

The following parts are included in the package:

Solution installer

DB2 databases

SPSS project streams, models, and rules

Cognos BI reports, Framework Manager models, and Dynamic cube models

EAR file that contains the REST APIs

Parent topic: Installation and configuration

Overview of the installation and configuration process

This is an overview of the general tasks that you must complete to install and configure the Behavior Based Customer Insight for Insurance solution.

The following diagram provides a high-level overview of the installation and configuration process.

Diagram that provides an overview of the installation process rect 116,28,231,53 Downloading the solution rect 120,57,226,84 Computer firewall settings required for the installation rect 38,108,169,145 The solution installer rect 242,348,298,382 Creating the database rect 313,350,366,391 Deploying the IBM Cognos content rect 373,351,410,399 Importing IBM SPSS project streams, models, and rules rect 423,350,471,391 Configuring ODBC for IBM SPSS Modeler Server rect 478,356,526,411 Installing the EAR file

You need to complete the following tasks:

On the server from which you want to install the solution, download the tar.gz file from IBM Passport Advantage.

Plan where you want the various parts of the solution to reside.

Open the ports that are required for the installation. You can do this by running the firewall.sh script, manually opening the ports on the installation node, or disabling the firewall. You also must open ports on the other node computers that are used for the solution.

On the installation computer, run the setup.sh file to install the solution installer, and then run the solution installer to deploy files to the locations that you specify.

Create the DB2 database on the data node computer.

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

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

Install the Cognos content on the BI node computer.

Copy the Framework Manager model to a Microsoft Windows computer.

Install the EAR file.

Parent topic: Installation and configuration

Planning the installation and configuration of the solution

Before you install the solution, you must decide which computers you want the various parts of the solution to reside on.

You must ensure that no application is listening on the ports that are used during the installation of the solution.

Parent topic: Installation and configuration

Downloading the solution

You must download the Behavior Based Customer Insight for Insurance solution from IBM Passport Advantage.

Before you begin

You must have a Passport Advantage account and be entitled to the package.

About this task

Passport Advantage contains package repositories. Instead of downloading the package from Passport Advantage and creating a repository locally, you access the repository on Passport Advantage.

Procedure

Set the Passport Advantage preference to connect to Passport Advantage.

Download the tar.gz file.

Move the tar file to the Linux node from which you want to run the installation.

Decompress the tar file.

Parent topic: Installation and configuration

Modifying the sudoers file for the user who runs the installation

To run the Behavior Based Customer Insight for Insurance installation, you must be either root user or have sudo permission on each node computer, and you must disable requiretty during the installation.

To install with sudo user permissions, you must also add the user to the sudoers file.

Procedure

Log in as root user.

Enter the following command to open the sudoers file for editing:

visudo -f /etc/sudoers

Locate the following line:

                        Defaults requiretty

Press the I key to insert text.

Type a number sign ( #) in front of Defaults requiretty to comment out the line. For example, the line will appear as

                        #Defaults requiretty

If you run the installer as a user with sudo user permissions, go to the end of the file, and add the following line for your user:

                           username ALL=(ALL) NOPASSWD: ALL

Press the Esc key, and enter :wq to save and close the file.

Repeat these steps on each computer on which you install a Behavior Based Customer Insight for Insurance node.

Parent topic: Installation and configuration

Computer firewall settings required for the installation

The solutions installer requires bidirectional communication with each node when you install a server component or server artifact component, and your firewall can interfere with this communication.

To ensure this bidirectional communication, you must configure your firewall settings for the installation. Alternatively, you can temporarily disable your firewall during the installation.

A script is provided to open all necessary ports on the installation node. You can either use the firewall.sh script, open the nodes manually, or disable the firewall settings.

In addition to opening ports on the installation node computer, you must open some ports on each computer to which content from the solution will be copied.

Disabling firewall settings during installation (optional)
If you choose to temporarily disable your computer's firewall during the installation of the IBM Behavior Based Customer Insight for Insurance application, ensure that you enable the firewall on each node after the installation is complete

Configuring your firewall settings on the installation node computer by using the Firewall script
You can run the firewall.sh script to open the ports on the installation node.

Manually configuring firewall settings on the installation node
If you prefer not to use the firewall.sh script, you can manually configure your firewall settings on the installation node.

Configuring firewall settings on other computers that will contain content from the solution
On all node computers to which content from the application will be copied, the ports 443, 9683, and 8080 must be opened for the installation process.

Parent topic: Installation and configuration

Disabling firewall settings during installation (optional)

If you choose to temporarily disable your computer's firewall during the installation of the IBM Behavior Based Customer Insight for Insurance application, ensure that you enable the firewall on each node after the installation is complete

About this task

Important: If you disable your computer's firewall during the installation, you must still ensure that the required ports are opened on each node computer when you restart your firewall.

Procedure

Log on to the computer as the root user or as a user with sudo permissions.

Type the following command:

/etc/init.d/iptables stop

Repeat these steps on each computer on which you install a node and on the server from which you are running the installation.

What to do next

After the installation is complete, on each node, type /etc/init.d/iptables start to restart your firewall.

Parent topic: Computer firewall settings required for the installation

Configuring your firewall settings on the installation node computer by using the Firewall script

You can run the firewall.sh script to open the ports on the installation node.

About this task

The firewall.sh script opens the following ports on the solution installer node:

8080 incoming

443 incoming

9683 incoming

22 outgoing

Procedure

Log on to the computer that contains the solution installer node as the root user or as a user with sudo permissions.

Back up your existing firewall settings by typing the following command: /etc/init.d/iptables save.

Run the firewall.sh script.

Parent topic: Computer firewall settings required for the installation

Manually configuring firewall settings on the installation node

If you prefer not to use the firewall.sh script, you can manually configure your firewall settings on the installation node.

Procedure

As the root user or as a user with sudo permissions, log on to the computer that contains the solution installer node .

Back up your existing firewall settings by typing the following command: . /etc/init.d/iptables save

On the computer that contains the solution installer node, add rules for the ports that are required by the installer by typing the following commands:

                           iptables -I INPUT -p tcp --dport 8080 -j ACCEPT
                              iptables -I OUTPUT -p tcp --sport 8080 -m state --state
                              RELATED,ESTABLISHED -j ACCEPT

                           iptables -I INPUT -p tcp --dport 443 -j ACCEPT
                              iptables -I OUTPUT -p tcp --sport 443 -m state --state
                              RELATED,ESTABLISHED -j ACCEPT

                           iptables -I INPUT -p tcp --dport 9683 -j ACCEPT
                              iptables -I OUTPUT -p tcp --sport 9683 -m state --state
                              RELATED,ESTABLISHED -j ACCEPT

                           iptables -I OUTPUT -p tcp --dport 22 -j ACCEPT
                              iptables -I INPUT -p tcp --sport 22 -m state --state
                              RELATED,ESTABLISHED -j ACCEPT

Parent topic: Computer firewall settings required for the installation

Configuring firewall settings on other computers that will contain content from the solution

On all node computers to which content from the application will be copied, the ports 443, 9683, and 8080 must be opened for the installation process.

Procedure

On each of the computers to which content for the solution will be copied, add rules for the ports that are required by the installer by typing the following commands:

                           iptables -I OUTPUT -p tcp --dport 443 -j ACCEPT
                              iptables -I INPUT -p tcp --sport 443 -m state --state RELATED,ESTABLISHED -j ACCEPT

                           iptables -I OUTPUT -p tcp --dport 9683 -j ACCEPT
                              iptables -I INPUT -p tcp --sport 9683 -m state --state RELATED,ESTABLISHED -j ACCEPT

                           iptables -I OUTPUT -p tcp --dport 8080 -j ACCEPT
                              iptables -I INPUT -p tcp --sport 8080 -m state --state RELATED,ESTABLISHED -j ACCEPT

Type the following command to save your firewall settings:

/etc/init.d/iptables save

Type the following command to restart your firewall:

/etc/init.d/iptables restart

Verify that your changes are applied by using the following command:

iptables -L

The added port appears in the list.

Repeat these steps on each computer on which you will install an IBM Behavior Based Customer Insight for Insurance node.

Parent topic: Computer firewall settings required for the installation

The solution installer

The solution installer provides automation that will help you to install the solution.

The Industry Solution Installer runs on the latest versions of Chrome and Firefox. It is not tested on Safari and does not run on Internet Explorer.

The Industry Solution provides the following functionality:

Deploys the required files to defined nodes.

You can create a new configuration or start from a configuration that you previously saved.

Automated validation.

Electronic license acceptance.

Automated clean-up of installed files if the license is declined.

The setup.sh script installs nodejs, chef client, and chef server. It extracts and uploads the cookbooks and sets up everything so that chef can be used to install the software.

The setup.sh script takes the following five arguments (the fifth argument is a password, and if omitted, it will be prompted for):

sh setup.sh <username> <first name> <last name> <e-mail> <password>

Where

username: The user name to create in chef server (can be used to log on to the chef server) Must be all lowercase characters.

first name: The first name of the user that is created in chef (for display purposes only)

last name: The last name of the user that is created in chef (for display purposes only)

e-mail: A valid email address for notifications from chef server

password: A password for the user that is created in chef (this is optional: a password will be prompted for if it is not included on the command line).

Running the industry solution installer
You can now run the Industry Solution Installer to deploy files to the correct nodes. Nodes are the target computers on which you deploy the software.

Parent topic: Installation and configuration

Running the industry solution installer

You can now run the Industry Solution Installer to deploy files to the correct nodes. Nodes are the target computers on which you deploy the software.

Before you begin

Ensure that you know the following information for each of the nodes that are used in your solution and that you open the necessary ports.

Host name or IP address

Password of the root user ID or a user ID and password that has sudo permissions

About this task

In this task, you create an installation configuration for your solution by dragging nodes onto a canvas and then dragging components into the nodes.

Important: The Industry Solution Installer runs on the latest versions of Chrome and Firefox. It does not run on Internet Explorer and it is untested on Safari.

Procedure

Run the setup shell script setup.sh.

After the solution installer finishes, a browser window opens. If a browser is not installed, a URL is displayed from which you can connect to the UI. The default URL is https:// solution installer node/UI/Index.html .

Click New Configuration

If you have a configuration that was previously saved, you can start from that saved configuration.

From the Mandatory Content List pane, select Node.

A visual representation of an empty node is displayed on the canvas.

In the Property Editor, type a name for the node, and in the Host Name box, specify the server on which the node will be placed.

For example, you might create a node that is named BI Node.

Enter a user name and password for a user that has sudo access.

If you click Validate before all the mandatory content is added, a message indicates that the node is not valid. For example, if you validate the BI Node before adding Cognos BI Content, then a message indicates that the node is not yet valid.

From the Optional Content list, drag any content that you want to add into the appropriate node. The reason that this content is optional is because you might already have some of the necessary components installed in your environment.

For every node that you want to add content to, define the node and then drag content objects onto it.

Click Validate.

After all nodes are defined, the contents are specified, and each node is validated, click Run.

Parent topic: The solution installer

Creating the database

Before you can use the IBM Behavior Based Customer Insight for Insurance solution, you must create a database.

About this task

You run one script to create the database, and then run another script to populate the database. The installer will have copied the compressed file to your data node computer.

Procedure

Log on to the data node computer as root user.

On the data node computer, if the compressed file is not already decompressed, then decompress it.

Open a terminal window, and go to the directory where the content files were decompressed.

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 decompressed folder, run the ./Install_DB.sh script.

Do one of the following actions:

If you want to load the sample data that is provided with the solution, then run the ./Load_Data.sh script.

If you do not want to load the sample data, then run the following SQL commands:

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, which is in the db2inst1 home directory. In the log file, search for rows were rejected. The value should be zero. If it is not, there are data load issues.

Parent topic: Installation and configuration

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 Behavior Based Customer Insight for Insurance solution. 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

Copy the .pes file to the computer where IBM SPSS Collaboration and Deployment Services Deployment Manager is installed.

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.

Under Resource definitions / Credentials, create a new credential named INSURNC .

Under Resource definitions / Data Sources, create a new data source named IBMPCIBNK.

After the .pes file is imported to the CnDS, open the job and click the step listed in the job.

Make sure that Modeler Server and Modeler Login fields are set to the correct definitions.

Click the ODBC Data Sources tab, and make sure that ODBC DSN and credentials are set to the newly created data source and credential.

Results

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

Parent topic: Installation and configuration

Configuring the data view for IBM SPSS models

To configure the data view, IBM® SPSS® Modeler must be connected to the IBM Behavior Based Customer Insight for Insurance 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, complete 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, complete the steps on the client computer where IBM SPSS Modeler client is installed.

The Behavior Based Customer Insight for Insurance solution uses the Analytic Data View and JDBC source connections for accessing IBM Analytical Decision Manager. When the Behavior Based Customer Insight for Insurance files are imported through IBM SPSS Collaboration and Deployment Services Deployment Manager, a new version of the Enterprise View is created that contains the tables for the application.

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
                                 INSURANCE 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 Behavior Based Customer Insight for Insurance database. The database account that is provided in the ODBC connection must be the same user that was used for creating the tables.

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

Parent topic: Installation and configuration

Configuring ODBC for IBM SPSS Modeler Server

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 Behavior Based Customer Insight for Insurance Server deployment.

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

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

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

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

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

For example, add the data source as:

                        [ODBC Data Sources]

                              INSURNC
                           =IBM DB2 ODBC Driver

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

For example, include the following content:

                        [

                              INSURNC
                           ]
                           Driver=/opt/ibm/db2/V10.1/lib64/libdb2o.so
                           DriverUnicodeType=1
                           Description=IBM DB2 ODBC Driver
                           ApplicationUsingThreads=1
                           AuthenticationMethod=0
                           BulkBinaryThreshold=32
                           BulkCharacterThreshold=-1
                           BulkLoadBatchSize=1024
                           CharsetFor65535=0
                           #Database applies to DB2 UDB only
                           Database=

                              INSURNC

                           DefaultIsolationLevel=1
                           DynamicSections=200
                           EnableBulkLoad=0
                           EncryptionMethod=0
                           FailoverGranularity=0
                           FailoverMode=0
                           FailoverPreconnect=0
                           GrantAuthid=PUBLIC
                           GrantExecute=1
                           GSSClient=native
                           HostNameInCertificate=
                           IpAddress=
                           IP_Address_of_DB_server
                           KeyPassword=
                           KeyStore=
                           KeyStorePassword=
                           LoadBalanceTimeout=0
                           LoadBalancing=0
                           LogonID=db2inst1
                           MaxPoolSize=100
                           MinPoolSize=0
                           Password=
                           password
                           PackageCollection=NULLID
                           PackageNamePrefix=DD
                           PackageOwner=
                           Pooling=0
                           ProgramID=
                           QueryTimeout=0
                           ReportCodePageConversionErrors=0
                           TcpPort=50000
                           TrustStore=
                           TrustStorePassword=
                           UseCurrentSchema=0
                           ValidateServerCertificate=1
                           WithHold=1
                           XMLDescribeType=-10

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

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

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

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

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

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

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

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

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

                        . <
                           odbc.sh_path>

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

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

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

Save modelersrv.sh.

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

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

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

                              rm –fr libspssodbc.so

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

                              ln –s libspssodbc_datadirect_utf16.so libspssodbc.so

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

                        [

                              INSURNC
                           ]
                           Database=

                              INSURNC

                           Protocol=TCPIP
                           DriverUnicodeType=1
                           Port=50000
                           Hostname=
                           ip or
                           hostname
                           UID=
                           username
                           PWD=
                           password

Save the odbc.ini file.

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 more 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: Installation and configuration

Deploying 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 Behavior Based Customer Insight for Insurance solution.

Preparing the IBM Cognos content
You must move the IBM® Cognos® Framework Manager content for the IBM Behavior Based Customer Insight for Insurance solution to the Cognos Deployment folder.

Creating a data source connection to the database
You must create a data source connection to the Behavior Based Customer Insight for Insurance database.

Deploying dynamic cubes for the sample data
If you choose to use the sample data for the Behavior Based Customer Insight for Insurance solution, you must deploy the dynamic cubes.

Deploying the Framework Manager Model
The Behavior Based Customer Insight for Insurance solution uses a Framework Manager model, which must be deployed..

Deploying the IBM Cognos reports
You must deploy the Behavior Based Customer Insight for Insurance reports by using IBM Cognos Administration.

Parent topic: Installation and configuration

Preparing the IBM Cognos content

You must move the IBM® Cognos® Framework Manager content for the IBM Behavior Based Customer Insight for Insurance solution to the Cognos Deployment folder.

The Solution installer supports copying content only to Linux nodes. If you are modifying the Framework Manager model, it must be copied to a Windows computer. Instructions for doing this by using the browser’s download capabilities are included in the SolutionInstaller > NodeRoot > Downloads > IS_<Solution_Name>_1.0 folder, in a file that is called IS_<SolutionName>_1.0_ClientDeployment Files.txt.

Procedure

Copy the IBM Cognos Framework Manager model file from the computer where you deployed the application to the computer where you installed IBM Cognos Framework Manager, and decompress the file.

The Framework Manager file is named Insurance_CustomerInsight_CD.fmd .

If you want to edit the Framework Manager models, you must catalog the Behavior Based Customer Insight for Insurance database on the computer where Framework Manager is installed.

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

Enter the following command to catalog the database node: db2 catalog tcpip node NODE_NAME remote data_node_name server PORT_NUMBER NODE_NAME can be any value. PORT_NUMBER is 50000 by default.

Enter the following command to catalog the PCI database:

db2 catalog database INSURNC at node NODE_NAME authentication server.

You must use the same node_name that you used in the DB2 catalog database command.

Download IBM Cognos Business Intelligence Cube Designer software from Passport Advantage. Cube Designer requires Framework manager to be installed on the same node as well.

Go to the location where the installation files were downloaded and extracted and then double-click the issetup.exe file.

Select the language to use for the installation. The language that you select determines the language of the user interface. All supported languages are installed. You can change the user interface to any of the installed languages after installation.

On the Installation Location panel, select the location where Framework Manager is installed.

Accept the defaults on the Component Selection page.

Follow the directions in the installation wizard to copy the required files to your computer.

If you are installing in a directory that contains other IBM Cognos Business Intelligence components, you are prompted to create backup copies of the files that will be replaced.

In the Finish page of the installation wizard, complete the following steps:

If you want to see the log files, click View for the appropriate log file.

If you want to see late-breaking information about the product, select the check box for IBM Cognos Release Notes.

Click Finish.

Parent topic: Deploying the IBM Cognos content

Creating a data source connection to the database

You must create a data source connection to the Behavior Based Customer Insight for Insurance 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 New Data Source.

In the Name box, type , 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 .

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 .

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: Deploying the IBM Cognos content

Deploying dynamic cubes for the sample data

If you choose to use the sample data for the Behavior Based Customer Insight for Insurance solution, you must deploy the dynamic cubes.

Before you begin

You must create a data source connection to the database that contains the data by completing the steps in the following link:

Creating a data source in the Cognos Administration portal (http://www.ibm.com/support/knowledgecenter/SSEPGG_10.5.0/com.ibm.dwe.migrate.doc/data_source_cs_cognos.html)

Procedure

Add the dispatcher and gateway information of the computer in which the data source is created to the Cognos configuration of the local system from which cubes are published. This is the computer on which the cube designer is installed.

The dispatcher and gateway URI are shown below:

Gateway URI: http://localhost:80/ibmcognos/cgi-bin/cognos.cgi

Dispatcher URI: http://localhost:9080/p2pd/servlet/dispatch

Open IBM Cognos Cube Designer and click Open Existing to load the .fmd file.

In Cognos Cube Designer, click the data source to display the Properties window.

Change the Content Manager data source to the name of the data source connection that is created in Cognos Server, and change the schema name to the name under which data objects are created.

Right-click the data source and refresh the metadata.

To publish the cubes, right-click a package and click Publish. The following message appears.

Click OK.

Cubes are published to the Cognos server. A message appears to indicate that the cubes are published successfully.

Check the status of the cube in Cognos Administration under Data Sources . Initially all cubes will have a status of Unknown.

Select all the cubes that you published, right-click the drop-down menu next to the cube, select Add data store to server group, and click OK. Now the cubes move to an Unavailable status.

From the same drop-down menu, and click Start. If an error message is encountered when you publish the cube, check the log file in C:\Program Files\IBM\cognos\c10_64\logs\XQE on the computer in which IBM Cognos BI is installed, and open the latest log file with a file name that starts with xqelog followed by a date and time stamp. The error message in the log will look similar to the following message:

Parent topic: Deploying the IBM Cognos content

Deploying the Framework Manager Model

The Behavior Based Customer Insight for Insurance solution uses a Framework Manager model, which must be deployed..

Before you begin

Ensure that a data source connection to the database that contains the data has been established. This will have been completed in the previous procedure, Creating a data source connection to the database

Procedure

Open the Insurance_CustomerInsight_FM.zip file, and extract the contents.

Open IBM Cognos Framework Manager and select Open a Project.

Select the PCI_INSURANCE.cpf file from the extracted folder.

After the file is loaded, the Project Viewer window looks like the following image.

Rename the data source to the name of the data source connection created in Cognos, and then right-click the package, and select Publish Packages. The window, shown below, is displayed.

Click Next to publish the package.

Parent topic: Deploying the IBM Cognos content

Deploying the IBM Cognos reports

You must deploy the Behavior Based Customer Insight for Insurance reports by using IBM® Cognos® Administration.

Procedure

Open a web browser.

Go to the IBM Cognos BI portal URL.

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 the deployment archive, and click Next.

The deployment archive is named Insurance_CustomerInsight_BIContent.zip .

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 content 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 Behavior Based Customer Insight for Insurance content is available in Public Folders in the Insurance_CustomerInsight_BIContent.zip file.

Rave libraries are also available as part of the solution. They are packaged in the Insurance_CustomerInsight_Rave.zip file.

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 libraries tab, click the Import button.

Select the Insurance_CustomerInsight_Rave.zip file, and click Import.

Parent topic: Deploying the IBM Cognos content

Installing the EAR file

The EAR file contains the REST APIs that with which you can embed the IBM Behavior Based Customer Insight for Insurance solution in your existing system.

Procedure

Log in to the WebSphere Application Server Network Deployment administrative console.

Load the IS_Insurance_Solution_EAR.ear file into one of the WebSphere Application Server nodes, either the BI node or the Analytics node.

Install the application through WebSphere Application Server.

Click the Applications link, and then click Install New Application .

From the Specify the EAR, WAR, JAR, or SAR module to upload and install page, select Local file system, and then browse to the location on your system of the EAR file.

Start the installed application, IS_Insurance .

Parent topic: Installation and configuration

Reports

The Behavior Based Customer Insight for Insurance solution provides life events patterns and customer segments to help insurers to improve customer engagement and interaction with the goal of increasing customer loyalty and company profitability. It helps insurers to develop effective retention programs or replacement offers for customer at risk of churn.

The following reports are available:

Executive Dashboard

Segment Details

Customer Summary

Customer Details

Life Event Analysis

Life Event Summary

Executive dashboard
The Executive Dashboard, which is displayed when you sign in to the solution, shows analysis for the different sales categories. It also indicates the segments with customers that have the highest churn propensity based on behavior-based segmentation and other policy and customer characteristics.

Segment Details report
The Segment Details report provides information and analysis for a selected segment.

Customer Summary report
The Customer Summary report provides a list of the customers for a selected segment and category.

Customer Details report
The report shows details for a selected customer, including renewal risk, lifetime value, and customer risk.

Life Event Analysis report
The report shows the distribution by behavioral segment of customers for the territory and product line that you select. It displays the predicted likelihood of life events by age for the segment that you select.

Life Event Summary report
The report shows a list of customers that have the selected current life event and belong to a certain age group.

Executive dashboard

The Executive Dashboard, which is displayed when you sign in to the solution, shows analysis for the different sales categories. It also indicates the segments with customers that have the highest churn propensity based on behavior-based segmentation and other policy and customer characteristics.

After you select a territory, product line, and period, you can view statistics and the predictions for target completion for the various categories of business. For the period that you select, you can see your sales performance relative to your sales targets. The At risk distribution by segments chart shows the segments with customers that are at risk of leaving the company. The bars in the chart indicate the churn rate and are plotted on a relative basis. That is, the bar for the segment with the highest churn propensity extends for the full length and the other bars are relative to that bar.

Parent topic: Reports

Segment Details report

The Segment Details report provides information and analysis for a selected segment.

To access this report, on the Executive Dashboard, click the customer segment that you want get further information on, and then click Segment Details. Details are shown in charts, and you can view specific information by hovering your mouse pointer over a chart. You can use filters to change the territory, product line, and period.

Parent topic: Reports

Customer Summary report

The Customer Summary report provides a list of the customers for a selected segment and category.

To access this report, from the Executive Dashboard, click the customer segment that you want to get further information on, and then click Customers. Use the filters at the top of the report to change the criteria that determines the customers who are displayed on the list.

Parent topic: Reports

Customer Details report

The report shows details for a selected customer, including renewal risk, lifetime value, and customer risk.

There are two ways to access this report. From the Customer Summary report, you can click the name of the customer who you want to get further information on. The details shown on this page is for the policy listed for this customer on the Customer Summary report, which means it is based on the period filter that is applied. The scores are tied to the policy.

The second way to access this report is from the Life Event Summary report. When you click the name of the customer who you want to get information on, the details shown on the report are for the policy that is closest to its renewal date. The details are based on the current period.

Parent topic: Reports

Life Event Analysis report

The report shows the distribution by behavioral segment of customers for the territory and product line that you select. It displays the predicted likelihood of life events by age for the segment that you select.

You can use the Event box to view the life event likelihood chart for a different life event. For example, if you select Financial Windfall as a life event, then each bubble in the chart shows the likelihood of undergoing a financial windfall at each age.

Parent topic: Reports

Life Event Summary report

The report shows a list of customers that have the selected current life event and belong to a certain age group.

To access this report, from the Life Event Analysis report, click a bubble from the Life Event Likelihood chart.

The age group of the customers shown in the list is plus or minus two years relative to the location of the bubble on the Life Event Analysis report that you click. For example, if you click the bubble corresponding to age 40 then this list will show all the customers between age 38 and 42. The report also shows past life events. Each check mark indicates that the corresponding life event has occurred.

Parent topic: Reports

Using the solution

The solution provides reporting and dashboard templates and sample predictive models.

The following reports are available:

Executive Dashboard

Segment Details

Customer Summary

Customer Details

Life Event Analysis

Life Event Summary

Example of using the solution
This example, which is fictitious, shows how the solution can be used to assess retention performance.

Example of using the solution

This example, which is fictitious, shows how the solution can be used to assess retention performance.

Christina Sizemore is a territory manager at an insurance company. Her goal is to determine the key actions to take to improve retention performance in her territories. She sets up a new Executive Dashboard report by choosing the territory as Georgia, the product line as Auto, and the period as June.

She checks the completion status of each target for the period. She also finds that there are some segments that have high churn propensity based on the behavior-based segmentation models. She selects a customer segment that appears to need attention. She wants to take a closer look at those customers that have a high churn rate. Christina chooses the Support Seeking Skeptic segment, which has the highest churn propensity, and then clicks Segment Details.

She views the Segmentation Details report for the Support Seeking Skeptic segment.

She is able to view key metrics about the customer segment in a single view and can filter on product line, period, and territory. The Segmentation Details report provides Christina with information including customer risk distribution, CLTV, Churn, Tenure, and Retention. She then selects View Customers from the Churn Propensity chart to get further information about customers. Christina can now see the full list of customers within the selected customer segment that have a high churn propensity and the insights about each customer. She can select an individual customer for more details.

She clicks the name of Amy R Metz to access the Customer Details report.

The Customer Details report provides Christina with a view of Amy’s relationship with the company. It provides customer scores along with key insights into her preferences and tenure with the company. With this information, Christina can develop a tailored plan for Amy, which will ensure the retention of the customer.

Christina can view the Life Event Analysis report to determine the right actions to target the market. Analyzing historical data, based on peer information, she can use peer group analysis to determine the likelihood of common life events by segment. Information is shown by the bubbles in the Life Event Likelihood chart, which can help her to create targeted offers for her customers.

Christina is interested in researching the Price Oriented Minimalist segment, so she selects Price Oriented Minimalist as her customer group. She selects Financial Windfall as the Current Life Event. Then a list of customers who are at the age most likely to have a financial windfall is shown when she clicks the bubble that has the highest likelihood.

Christina then uses the detailed view to identify life events related to retention actions that can help her to improve products, pricing, engagement models, and targeted offers and messaging.

Parent topic: Using the solution

Accessibility features

Accessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products.

For information about the commitment that IBM has to accessibility, see the IBM Accessibility Center (www.ibm.com/able).

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

Report output

In IBM Cognos Administration, you can enable system-wide settings to create accessible report output. For more information, see the IBM Cognos Business Intelligence Administration and Security Guide . In IBM Cognos Report Studio, you can enable settings to create accessible output for individual reports. For more information, see the IBM Cognos Report Studio User Guide . You can access the previously mentioned documents at IBM Knowledge Center (http://www.ibm.com/support/knowledgecenter).

Troubleshooting

This section contains troubleshooting information for the Behavior Based Customer Insight for Insurance installation.

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.

After running the solution installer, Chef processes are still running after executing the cleanup.sh script
After running the cleanup.sh script on the solution installer node, all chef processes should be terminated. However, in some cases chef processes are still running.

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 (http://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.

Parent topic: Troubleshooting

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 (http://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 (http://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 (http://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 (http://www.ibm.com/developerworks).

IBM Redbooks

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

IBM Redbooks (http://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

After running the solution installer, Chef processes are still running after executing the cleanup.sh script

After running the cleanup.sh script on the solution installer node, all chef processes should be terminated. However, in some cases chef processes are still running.

If you try to run the solution installer setup.sh file again, you may receive a message indicating that required ports are in use, or creation of the chef user might fail.

The solution to this issue is to reboot the installation node after having run "cleanup.sh".

Note: This only applies to the solution installer node and not the client/target nodes.

Parent topic: Troubleshooting

JDBC connection to WebSphere Application Server

The IBM® Behavior Based Customer Insight for Insurance solution provides a set of RESTful APIs to allow you to create applications to programmatically execute operations. If you're using Websphere Application Server as your database, you must set up a JDBC connection to the server. The provided instructions and procedures are based on WAS v8.5.5.

To set up your JDBC connection, you must complete the following tasks, in order:

Configure the JDBC provider

Create an authentication alias

Configure the JDBC data source

Configure the JDBC provider

In this section, you'll set up the DB2 using IBM J2CC Driver (XA) for your JDBC connection.

Log in to the WebSphere Application Server console.

Navigate to the JDBC providers section.

If you already have the DB2 Using IBM J2CC Driver (XA) setup, proceed to Create an authentication alias. Otherwise, create a new JDBC provider by clicking New..., as highlighted in the image.

Enter the JDBC provider details as per the following image, then click Next.

Use the default Class path settings, as shown in the following image, and click Apply.

Specify the directory location where the db2jcc4.jar and db2jcc_license_cisuz.jar files are present on the server. The jar files are included in the WebSphere installation package.

Click Next.

Review the summary page and click Finish.

Create an authentication alias

In this section, you'll specify the authorized users (IDs and passwords) who have access to the database.

From the WAS console, go to the Global Security section and click J2C authentication data.

Click New...

In the next page, enter an arbitrary Alias name. Enter the User ID and Password that you use to connect to your database. The Description field is optional.

Click Apply and then OK.

Configure the JDBC data source

In this section, you'll configure your JDBC data source and test the connection.

From the WAS console, go to the Data Sources section and click New...

In the next page, enter an arbitrary Data source name.

Click Next.

Choose Select an existing JDBC provider. Select the JDBC provider that you created in the first task. Click Next.

Provide the Database name that you wish to access. Provide the Server name that you wish to connect to and the Port number . The remaining details can retain their default settings. Click Next.

Set the authentication alias that you created earlier, then click Next.

Verify the summary information and click Finish.

When you're prompted to save the server configuration changes, click Save.

Select the JDBC data source that you have created and click Test connection . If successful, a message will appear indicating that a successful connection was established.

REST API reference

The IBM® Behavior Based Customer Insight for Insurance solution provides a set of RESTful APIs to allow you to create applications to programmatically invoke operations and extend industry solutions capabilities.

The representational state transfer (REST) API provides a lightweight interface that uses HTTP requests to communicate to the server. The APIs can be used to extract and aggregate data for further processing in other systems. All supported resources are listed in the following sections and are actionable using a GET operation.

If a request returns an empty JSON response, this indicates that there were no records in the database that matched the parameters specified in the request. If an error occurred, one of the following error codes may be returned:

IS_INSURANCE_ERR00

Unknown error

IS_INSURANCE_ERR01

A specified churn parameter is invalid. Accepted values: true or false.

IS_INSURANCE_ERR02

The specified personid is invalid.

IS_INSURANCE_ERR03

The specified pagestart is invalid.

IS_INSURANCE_ERR04

The specified pagesize is invalid.

IS_INSURANCE_ERR05

The specified month is invalid.

IS_INSURANCE_ERR06

Database error

IS_INSURANCE_ERR07

The specified sortorder is invalid.

IS_INSURANCE_ERR08

The specified startdate is invalid.

IS_INSURANCE_ERR09

The specified enddate is invalid.

IS_INSURANCE_ERR10

Both month and startdate parameters cannot be used in combination.

IS_INSURANCE_ERR11

Both month and enddate parameters cannot be used in combination.

IS_INSURANCE_ERR12

Both startdate and enddate parameters must be specified.

IS_INSURANCE_ERR13

The enddate parameter cannot be later than the last day of the next month.

IS_INSURANCE_ERR14

The startdate parameter cannot be later the specified enddate.

IS_INSURANCE_ERR15

No records found.

IS_INSURANCE_ERR16

A month parameter is required.

IS_INSURANCE_ERR17

An lob parameter is required.

IS_INSURANCE_ERR18

A territory parameter is required.

IS_INSURANCE_ERR19

A segment parameter is required.

IS_INSURANCE_ERR20

A personid parameter is required.

IS_INSURANCE_ERR21

The specified churnrisk parameter is invalid.

IS_INSURANCE_ERR22

The specified cltvrisk parameter is invalid.

IS_INSURANCE_ERR23

A lifeevent parameter is required.

The Behavioral Segmentation REST API allows you to create your own segments and associated segment definitions. This is a generic API that applies to Behavior Based Customer Insight for Banking, Behavior Based Client Insight for Wealth Management, and Behavior Based Customer Insight for Insurance. The following error codes apply to the Behavioral Segmentation REST API.

IS_SEGMENTATION_ERR00

Unknown error

IS_SEGMENTATION_ERR01

Could not parse the JSON input. Please check the JSON input and try again.

IS_SEGMENTATION_ERR02

Failed to insert the segment into the database.

IS_SEGMENTATION_ERR03

Category does not exist in the data dictionary.

IS_SEGMENTATION_ERR06

Database error.

IS_SEGMENTATION_ERR15

No records found.

IS_SEGMENTATION_ERR16

Failed to create batch statements for insert.

IS_SEGMENTATION_ERR17

Failed to insert the segment definitions into the database.

IS_SEGMENTATION_ERR18

The industry parameter is required.

IS_SEGMENTATION_ERR19

The industry parameter is incorrect. Supported values are banking, wealth, or insurance.

IS_SEGMENTATION_ERR20

The segmented parameter is required.

Errors will include a "flag: false" indicator in the response body.

The URLs used to issue REST commands must adhere to the US-ASCII character set, including reserved and restricted characters. For example, a space must be URL-encoded as "%20".

Note: For additional information on the API, please reference the provided Javadocs that are included in the installation package. Information on the data model is posted at the following location:

Industry Solutions techdoc http://www.ibm.com/support/docview.wss?uid=swg27046047

Customizing segments
The Behavioral Segmentation REST API allows you to create and delete custom segments and their associated segment definitions.

/isinsurance/bi/churn
This resource provides the churn propensity information for various customers.

/isinsurance/bi/churn/percentage
This resource provides the percentage and number of customers that are likely to churn.

/isinsurance/bi/segments/customers/percentage
This resource returns the segments, number of customers, and percentage of customers for a particular line-of-business and territory.

/isinsurance/bi/segments/customer/{personid}
This resource returns the segment that a particular customer belongs to.

/isinsurance/bi/customerrisk/categories
This resource returns customer risk categories with respective policy counts.

/isinsurance/bi/cltvrisk/categories
This resource returns all CLTV (customer lifetime value) risk categories with respective policy counts.

/isinsurance/bi/lifeevent/history
This resource returns the life event history of all customers or a particular customer.

/isinsurance/bi/lifeevent/likelihood
This resource returns the likelihood score and likelihood category (high, medium, or low) of a life event for various age groups.

/isinsurance/bi/products
This resource returns all available products and the number of customers for each product.

/isinsurance/bi/retention
This resource returns the retention rate and average retention rate for all months or a particular month.

/isinsurance/bi/tenure
This resource returns all tenure categories along with the number of customers falling under each category.

/issegmentation/bs/{industry}/segment/create
This resource allows you to create a new segment with an associated segment definition; defined in JSON format. If the specified segment name already exists, then the segment definition will be updated by the provided JSON.

/issegmentation/bs/{industry}/segment/delete/{segment_id}
This resource allows you to create a new segment with an associated segment definition; defined in JSON format. If the specified segment name already exists, then the segment definition will be updated by the provided JSON.

Customizing segments

The Behavioral Segmentation REST API allows you to create and delete custom segments and their associated segment definitions.

Segment data dictionary

Segment definitions must conform to the behavioral categories defined in the segment data dictionary. All categories in the data dictionary are top-level and have a corresponding list of sub-categories. When creating a segment, you must provide conditions that define a segment at the sub-category level. The segment data dictionary for your industry solution is specified in the following table.

Table 2. Insurance data dictionary

Categories | Sub-categories
TEXT ANALYTICS SCORE | Text Analytics Score
POLICY SCORE | Policy Score
CAMPAIGN SCORE | Campaign Score
PAYMENT TYPE | Payment Type

Note: In this release of Behavior Based Customer Insight for Insurance, there are no defined sub-categories. To maintain a common template, the category names have been used as sub-categories.

The above categories are related to the following scores.

TEXT ANALYTICS SCORE

The score that is associated with the data (extracted through text analytics) that tells us about the customer interaction.

POLICY SCORE

The score that is associated with the insurance policy. It is the average of the policy product score, coverage score, and the deductible score.

CAMPAIGN SCORE

The score that is associated with marketing data. This data tells you whether a customer responds more to price-oriented campaigns or service-oriented campaigns.

PAYMENT TYPE

The score that is associated with the payment data. This data tells you the payment frequency for the policies.

Segment definition

A segment definition is created by using any combination of the above categories and the relevant sub-categories. As mentioned, conditions that define a segment are specified at the sub-category level. Some predefined segments are included in your industry solution. The REST API accepts the segment definition in the form of a JSON input string and persists it to the database. The JSON format for the segment definition is as follows:

                     {
                        "segment": "segment_name",
                        "categories": [
                        {
                        "name": "category_name",
                        "subcategories": [
                        {"name": "subcat_name", "operator": "value", "deviation": value}, ...
                        ]
                        }, ...
                        ]
                        }

segment

This attribute is a string that holds the segment name.

categories

This is an array of categories.

Within the categories array for each category, you have:

name

This attribute is a string that holds the category name.

subcategories

This is an array of subcategories.

Within the subcategories array for each subcategory, you have:

name

This attribute is a string that holds the subcategory name.

operator

This attribute is a string that holds the operator value. Possible values are listed below.

deviation

This is a float value that is used to specify "the number of times the standard deviation" .

The rules for operator values are determined by the following conditions.

greater, if condition used is > (M + deviation*SD).

upper, if condition used is > (M - deviation*SD).

lower, if condition used is < (M + deviation*SD).

less, if condition used is < (M - deviation*SD).

equal, if condition used is = value.

greater num, if condition used is > value.

less num, if condition used is < value.

range, if condition used is > (M - deviation*SD) AND (M + deviation*SD).

Note: Mean (M) and Standard Deviation (SD) values are calculated at the category level, for each category. These values are calculated by the Segmentation SPSS model using the customer transactions data. As a user, you supply the deviation value (i.e., the number of times the standard deviation).

The following example illustrates the segment definition structure.

Insurance segment name: Security Oriented Individualist

The following sample JSON input defines these conditions:

TEXT ANALYTICS SCORE > (M – 1.0*SD) AND < (M + 1.0*SD). The operator value is range.

POLICY SCORE > (M – 1.0*SD) AND < (M + 1.0*SD). The operator value is range.

CAMPAIGN SCORE > (M – 1.0*SD) AND < (M + 1.0*SD). The operator value is range.

PAYMENT TYPE is bundled. Payment Type could be either bundled (deviation=1) or unbundled (deviation=0*SD). The operator value is equal.

                        {
                           "segment": "Security Oriented Individualist",
                           "categories": [
                           {
                           "name": "TEXT ANALYTICS SCORE",
                           "subcategories": [
                           {"name": "TEXT ANALYTICS SCORE", "operator": "range", "deviation": 1}
                           ]
                           },
                           {
                           "name": "POLICY SCORE",
                           "subcategories": [
                           	         {"name": "POLICY SCORE", "operator": "range", "deviation": 1}
                           ]
                           },
                           {
                           "name": "CAMPAIGN SCORE",
                           "subcategories": [
                           { "name": "CAMPAIGN SCORE", "operator": "range", "deviation": 1}
                           ]
                           },
                           {
                           "name": "PAYMENT TYPE",
                           "subcategories": [
                           {"name": "PAYMENT TYPE","operator": "equal", "deviation": 1}
                           ]
                           }
                           ]
                           }

Parent topic: REST API reference

/isinsurance/bi/churn

This resource provides the churn propensity information for various customers.

/isinsurance/bi/churn

The response data can be filtered using a combination of required and optional parameters:

churnrisk

(Optional) The risk category value for Churn Risk. Supported values: HIGH, MEDIUM, LOW, and ON TRACK. Default: Data related to all risk categories is returned.

cltvrisk

(Optional) The risk category value for CLTV Risk. Supported values: HIGH, MEDIUM, LOW, and ON TRACK. Default: Data related to all risk categories is returned.

lob

(Optional) Specified line of business, such as Home, Auto, etc.

month

(Optional) Specified month as a string value, for example "August". Default value is the next upcoming month.

pagesize

(Optional) Specifies the number of records to return. May be used in combination with the pagestart parameter to implement pagination on websites. If neither parameter is defined, the top 100 customers are returned. Results are sorted by renewal date, premium amount, and personid; in that order.

Default: 100

pagestart

(Optional) Specifies the starting record to return. Used in combination with the pagesize parameter to implement pagination on websites. If neither parameter is defined, the top 100 customers are returned.

Default: 1

personid

(Optional) The record for a particular person is returned. If no personid parameter is specified, records for the top 100 persons of the upcoming month are returned. Persons are sorted based on renewal date, premium amount, and personid; in that order.

segment

(Optional) The customer's associated segment, such as Support Seeking Skeptic.

sortorder

(Optional) The order in which records are sorted, by renewal date. Support values: ASC or DESC. Default is to sort in ascending (ASC) order.

territory

(Optional) The customer's region, such as Ohio, Georgia, etc.

Sample JSON response:

                     {
                        "month":"august",
                        "customers":
                        [
                        {
                        "premium_amount": 900,
                        "churn_risk": "Medium",
                        "cltv_risk": "Low",
                        "channel": "Affinity",
                        "segment": "Informed Optimizer",
                        "product_line": "AUTO",
                        "renewal_date": "01-Aug-2015",
                        "person_id": 1008938,
                        "policy_id": 1008938,
                        "territory": "OHIO",
                        "full_name": "Julio M Hernandez",
                        "product": "Auto Basic"
                        },
                        {
                        "premium_amount": 900,
                        "churn_risk": "Medium",
                        "cltv_risk": "Low",
                        "channel": "Bank",
                        "segment": "Support Seeking Skeptic",
                        "product_line": "AUTO",
                        "renewal_date": "01-Aug-2015",
                        "person_id": 1012595,
                        "policy_id": 1012595,
                        "territory": "OHIO",
                        "full_name": "Delois T Burns",
                        "product": "Auto Basic Plus"
                        }
                        ],
                        "flag": true
                        }

Example:

<baseurl>/isinsurance/bi/churn?month=August&churnrisk=low&cltvrisk=low&territory=kentucky

This example returns the churn propensity for the month of August and returns a list of customers with low risk and residing in Kentucky.

Parent topic: REST API reference

/isinsurance/bi/churn/percentage

This resource provides the percentage and number of customers that are likely to churn.

/isinsurance/bi/churn/percentage

The response data can be filtered using a combination of required and optional parameters:

lob

(Required) Specified line of business, such as Home, Auto, etc.

month

(Required) Specified month as a string value, for example "August".

segment

(Required) The customer's associated segment, such as Support Seeking Skeptic.

territory

(Required) The customer's region, such as Ohio, Georgia, etc.

Sample JSON response:

                     {
                        "month": "AUGUST",
                        "churn_customers": 393,
                        "segment": "Support Seeking Skeptic",
                        "product_line": "AUTO",
                        "percentage": "49.68",
                        "total_customers": 791,
                        "territory": "OHIO",
                        "flag": true
                        }

Example:

<baseurl>/isinsurance/bi/churn/percentage?month=August&lob=auto&territory=ohio&segment=Support Seeking Skeptic

This example returns the percentage and number of customers that are likely to churn. Results are for the month of August, the automotive line-of-business, the Support Seeking Skeptic segment, in the area of Ohio.

Parent topic: REST API reference

/isinsurance/bi/segments/customers/percentage

This resource returns the segments, number of customers, and percentage of customers for a particular line-of-business and territory.

/isinsurance/bi/segments/customers/percentage

The response data can be filtered using a combination of required and optional parameters:

lob

(Required) Specified line of business, such as Home, Auto, etc.

territory

(Required) The customer's region, such as Ohio, Georgia, etc.

Sample JSON response:

                     {
                        "segments":
                        [
                        {
                        "segment": "Security Oriented Individualist",
                        "percentage": "25.15",
                        "num_customers": 3100
                        },
                        {
                        "segment": "Demanding Support Seeker",
                        "percentage": "8.39",
                        "num_customers": 1034
                        },
                        {
                        "segment": "Loyal Quality Seeker",
                        "percentage": "8.07",
                        "num_customers": 994
                        },
                        {
                        "segment": "Support Seeking Skeptic",
                        "percentage": "33.37",
                        "num_customers": 4112
                        },
                        {
                        "segment": "Informed Optimizer",
                        "percentage": "8.66",
                        "num_customers": 1067
                        },
                        {
                        "segment": "Price Oriented Minimalist",
                        "percentage": "16.37",
                        "num_customers": 2017
                        }
                        ],
                        "product_line": "AUTO",
                        "territory": "OHIO",
                        "flag": true
                        }

Example:

<baseurl>/isinsurance/bi/segments/customers/percentage?lob=auto&territory=ohio

This example provides a list of segments for the automotive line-of-business in the Ohio area. For each segment, the percentage of customers and number of customers is returned.

Parent topic: REST API reference

/isinsurance/bi/segments/customer/{personid}

This resource returns the segment that a particular customer belongs to.

/isinsurance/bi/segments/customer/{personid}

The response data can be filtered using a combination of required and optional parameters:

personid

(Required) The ID for a specific customer.

Sample JSON response:

                     {
                        "segment": "Price Oriented Minimalist",
                        "person_id": 1001830,
                        "full_name": "Virgilio G Southern",
                        "flag": true
                        }

Example:

<baseurl>/isinsurance/bi/segment/customer/1001830

This example returns the segment that customer_id (1001830) is associated with.

Parent topic: REST API reference

/isinsurance/bi/customerrisk/categories

This resource returns customer risk categories with respective policy counts.

/isinsurance/bi/customerrisk/categories

The response data can be filtered using a combination of required and optional parameters:

lob

(Required) Specified line of business, such as Home, Auto, etc.

month

(Required) Specified month as a string value, for example "August".

segment

(Required) The customer's associated segment, such as Support Seeking Skeptic.

territory

(Required) The customer's region, such as Ohio, Georgia, etc.

Sample JSON response:

                     {
                        "month": "AUGUST",
                        "segment": "price oriented minimalist",
                        "product_line": "AUTO",
                        "territory": "OHIO",
                        "flag": true,
                        "risk_categories":
                        [
                        {
                        "policy_count": 1,
                        "risk_category": "High"
                        },
                        {
                        "policy_count": 2267,
                        "risk_category": "Low"
                        },
                        {
                        "policy_count": 80,
                        "risk_category": "Medium"
                        }
                        ]
                        }

Example:

<baseurl>/isinsurance/bi/customerrisk/categories?month=august&lob=auto&territory=ohio&segment=price oriented minimalist

This example returns the risk categories for the "price oriented minimalist" segment, in the automotive line-of-business, in the Ohio area, for the month of August.

Parent topic: REST API reference

/isinsurance/bi/cltvrisk/categories

This resource returns all CLTV (customer lifetime value) risk categories with respective policy counts.

/isinsurance/bi/cltvrisk/categories

The response data can be filtered using a combination of required and optional parameters:

lob

(Required) Specified line of business, such as Home, Auto, etc.

month

(Required) Specified month as a string value, for example "August".

segment

(Required) The customer's associated segment, such as Support Seeking Skeptic.

territory

(Required) The customer's region, such as Ohio, Georgia, etc.

Sample JSON response:

                     {
                        "month": "AUGUST",
                        "segment": "Informed Optimizer",
                        "product_line": "AUTO",
                        "territory": "OHIO",
                        "flag": true,
                        "risk_categories":
                        [
                        {
                        "policy_count": 1200,
                        "risk_category": "Low"
                        },
                        {
                        "policy_count": 40,
                        "risk_category": "Medium"
                        }
                        ]
                        }

Example:

<baseurl>/isinsurance/bi/cltvrisk/categories?month=august&lob=auto&territory=ohio&segment=Informed Optimizer

This example returns the CLTV risk categories for the informed optimizer segment, the automotive line-of-business, in the Ohio area, for the month of August.

Parent topic: REST API reference

/isinsurance/bi/lifeevent/history

This resource returns the life event history of all customers or a particular customer.

/isinsurance/bi/lifeevent/history

The response data can be filtered using a combination of required and optional parameters:

lob

(Required) Specified line of business, such as Home, Auto, etc.

pagesize

(Optional) Specifies the number of records to return. May be used in combination with the pagestart parameter to implement pagination on websites. If neither parameter is defined, the top 100 customers are returned. Results are sorted by renewal date, premium amount, and personid; in that order.

Default: 100

pagestart

(Optional) Specifies the starting record to return. Used in combination with the pagesize parameter to implement pagination on websites. If neither parameter is defined, the top 100 customers are returned.

Default: 1

personid

(Optional) The record for a particular person is returned. If no personid parameter is specified, records for the top 100 persons of the upcoming month are returned. Persons are sorted based on renewal date, premium amount, and personid; in that order.

segment

(Required) The customer's associated segment, such as Support Seeking Skeptic.

territory

(Required) The customer's region, such as Ohio, Georgia, etc.

Sample JSON response:

                     {
                        "segment": "support seeking skeptic",
                        "product_line": "AUTO",
                        "customers":
                        [
                        {
                        "person_id": 1062209,
                        "full_name": "Adam K Jahnke",
                        "life_events":
                        [
                        {
                        "likelihood_level": "Medium",
                        "life_event": "Graduation",
                        "age": 21
                        },
                        {
                        "likelihood_level": "Medium",
                        "life_event": "Buying a Home",
                        "age": 26
                        },
                        {
                        "likelihood_level": "Medium",
                        "life_event": "New Child",
                        "age": 29
                        },
                        {
                        "likelihood_level": "Medium",
                        "life_event": "Marriage",
                        "age": 30
                        },
                        {
                        "likelihood_level": "Medium",
                        "life_event": "Divorce",
                        "age": 33
                        },
                        {
                        "likelihood_level": "Medium",
                        "life_event": "New Job",
                        "age": 37
                        }
                        ],
                        "current_life_event": "New Job"
                        }
                        ],
                        "territory": "OHIO",
                        "flag": true
                        }

Example:

<baseurl>/isinsurance/bi/lifeevent/history?lob=auto&segment=support seeking skeptic&territory=ohio&personid=1062209

This example returns the life event history for a particular customer.

Parent topic: REST API reference

/isinsurance/bi/lifeevent/likelihood

This resource returns the likelihood score and likelihood category (high, medium, or low) of a life event for various age groups.

/isinsurance/bi/lifeevent/likelihood

The response data can be filtered using a combination of required and optional parameters:

lifeevent

(Required) Refers to specific events in a customer's life, such as marriage.

level

(Optional) Indicates the likelihood that a particular life event will occur. Supported values: HIGH, MEDIUM, LOW, and ON TRACK. Default: Data related to all levels is returned.

lob

(Required) Specified line of business, such as Home, Auto, etc.

segment

(Required) The customer's associated segment, such as Support Seeking Skeptic.

territory

(Required) The customer's region, such as Ohio, Georgia, etc.

Sample JSON response:

                     {
                        "segment": "support seeking skeptic",
                        "product_line": "AUTO",
                        "customer_ages":
                        [
                        {
                        "likelihood_score": 0.62,
                        "likelihood_level": "Medium",
                        "age": 26
                        },
                        {
                        "likelihood_score": 0.5,
                        "likelihood_level": "Medium",
                        "age": 27
                        },
                        {
                        "likelihood_score": 0.44,
                        "likelihood_level": "Medium",
                        "age": 28
                        },
                        {
                        "likelihood_score": 0.34,
                        "likelihood_level": "Medium",
                        "age": 31
                        },
                        {
                        "likelihood_score": 0.52,
                        "likelihood_level": "Medium",
                        "age": 32
                        },
                        {
                        "likelihood_score": 0.38,
                        "likelihood_level": "Medium",
                        "age": 35
                        }
                        ],
                        "life_event": "MARRIAGE",
                        "territory": "OHIO",
                        "flag": true
                        }

Example:

<baseurl>/isinsurance/bi/lifeevent/likelihood?lob=auto&segment=support seeking skeptic&territory=ohio&lifeevent=marriage&level=medium

This example returns likelihood scores for the specified lob, segment, territory, life event, and level.

Parent topic: REST API reference

/isinsurance/bi/products

This resource returns all available products and the number of customers for each product.

/isinsurance/bi/products

The response data can be filtered using a combination of required and optional parameters:

lob

(Optional) Specified line of business, such as Home, Auto, etc.

month

(Optional) Specified month as a string value, for example "August". Default value is the next upcoming month.

pagesize

(Optional) Specifies the number of records to return. May be used in combination with the pagestart parameter to implement pagination on websites. If neither parameter is defined, the top 100 customers are returned. Results are sorted by renewal date, premium amount, and personid; in that order.

Default: 100

pagestart

(Optional) Specifies the starting record to return. Used in combination with the pagesize parameter to implement pagination on websites. If neither parameter is defined, the top 100 customers are returned.

Default: 1

segment

(Optional) The customer's associated segment, such as Support Seeking Skeptic.

sortorder

(Optional) The order in which records are sorted, by renewal date. Support values: ASC or DESC. Default is to sort in ascending (ASC) order.

territory

(Optional) The customer's region, such as Ohio, Georgia, etc.

Sample JSON response:

                     {
                        "month": "August",
                        "products":
                        [
                        {
                        "segment": "Support Seeking Skeptic",
                        "product_line": "AUTO",
                        "territory": "OHIO",
                        "num_customers": 1956,
                        "product": "Auto Basic Plus"
                        },
                        {
                        "segment": "Support Seeking Skeptic",
                        "product_line": "AUTO",
                        "territory": "OHIO",
                        "num_customers": 1894,
                        "product": "Auto Basic"
                        },
                        {
                        "segment": "Security Oriented Individualist",
                        "product_line": "AUTO",
                        "territory": "OHIO",
                        "num_customers": 1495,
                        "product": "Auto Basic Plus"
                        },
                        {
                        "segment": "Security Oriented Individualist",
                        "product_line": "AUTO",
                        "territory": "OHIO",
                        "num_customers": 1378,
                        "product": "Auto Basic"
                        }
                        ],
                        "flag": true
                        }

Example:

<baseurl>/isinsurance/bi/products?month=August&lob=auto&territory=ohio

This example returns all the products and number of customers; based on the parameters in the request.

Parent topic: REST API reference

/isinsurance/bi/retention

This resource returns the retention rate and average retention rate for all months or a particular month.

/isinsurance/bi/retention

The response data can be filtered using a combination of required and optional parameters:

lob

(Optional) Specified line of business, such as Home, Auto, etc.

month

(Optional) Specified month as a string value, for example "August". Default value is the next upcoming month.

pagesize

(Optional) Specifies the number of records to return. May be used in combination with the pagestart parameter to implement pagination on websites. If neither parameter is defined, the top 100 customers are returned. Results are sorted by renewal date, premium amount, and personid; in that order.

Default: 100

pagestart

(Optional) Specifies the starting record to return. Used in combination with the pagesize parameter to implement pagination on websites. If neither parameter is defined, the top 100 customers are returned.

Default: 1

segment

(Optional) The customer's associated segment, such as Support Seeking Skeptic.

sortorder

(Optional) The order in which records are sorted, by renewal date. Support values: ASC or DESC. Default is to sort in ascending (ASC) order.

territory

(Optional) The customer's region, such as Ohio, Georgia, etc.

Sample JSON response:

                     {
                        "retention_data":
                        [
                        {
                        "month": "July",
                        "retention_rate": 16,
                        "segment": "Support Seeking Skeptic",
                        "product_line": "AUTO",
                        "territory": "OHIO",
                        "avg_retention_rate": 23.28
                        },
                        {
                        "month": "September",
                        "retention_rate": 16,
                        "segment": "Support Seeking Skeptic",
                        "product_line": "AUTO",
                        "territory": "OHIO",
                        "avg_retention_rate": 23.28
                        },
                        {
                        "month": "August",
                        "retention_rate": 18,
                        "segment": "Support Seeking Skeptic",
                        "product_line": "AUTO",
                        "territory": "OHIO",
                        "avg_retention_rate": 34.71
                        },
                        {
                        "month": "June",
                        "retention_rate": 60,
                        "segment": "Support Seeking Skeptic",
                        "product_line": "AUTO",
                        "territory": "OHIO",
                        "avg_retention_rate": 49.42
                        }
                        ],
                        "flag": true
                        }

Example:

<baseurl>/isinsurance/bi/retention?lob=auto&territory=ohio&segment=Support Seeking Skeptic

This example returns the retention rate and average retention rates for the specified lob, segment, and territory.

Parent topic: REST API reference

/isinsurance/bi/tenure

This resource returns all tenure categories along with the number of customers falling under each category.

/isinsurance/bi/tenure

The response data can be filtered using a combination of required and optional parameters:

lob

(Required) Specified line of business, such as Home, Auto, etc.

month

(Required) Specified month as a string value, for example "August".

segment

(Required) The customer's associated segment, such as Support Seeking Skeptic.

territory

(Required) The customer's region, such as Ohio, Georgia, etc.

Sample JSON response:

                     {
                        "tenure_categories":
                        [
                        {
                        "tenure_category": "6 mos",
                        "percentage": "4.01",
                        "num_customers": 1345
                        },
                        {
                        "tenure_category": "12 mos",
                        "percentage": "11.62",
                        "num_customers": 3902
                        },
                        {
                        "tenure_category": "18 mos",
                        "percentage": "71.08",
                        "num_customers": 23867
                        },
                        {
                        "tenure_category": "24 mos",
                        "percentage": "9.95",
                        "num_customers": 3342
                        },
                        {
                        "tenure_category": ">24 mos",
                        "percentage": "3.34",
                        "num_customers": 1122
                        }
                        ],
                        "month": "AUGUST",
                        "segment": "Support Seeking Skeptic",
                        "product_line": "AUTO",
                        "territory": "OHIO",
                        "flag": true
                        }

Example:

<baseurl>/isinsurance/bi/tenure?month=August&lob=auto&territory=ohio&segment=Support Seeking Skeptic

This example returns all tenure categories and the number of customers associated with each category. Results are filtered by the specified lob, month, segment, and territory.

Parent topic: REST API reference

/issegmentation/bs/{industry}/segment/create

This resource allows you to create a new segment with an associated segment definition; defined in JSON format. If the specified segment name already exists, then the segment definition will be updated by the provided JSON.

/issegmentation/bs/{industry}/segment/create

This resource is issued as a POST request. The "industry" parameter must be specified as one of the following values:

banking

wealth

insurance

Sample JSON response:

                     Content-Type: application/json
                        HTTP status code: 200
                        Body: {
                        	"segment_name":"name",
                        	"segment_id":id,
                        	"flag":true
                        }

Example:

<baseurl>/issegmentation/bs/banking/segment/create

This example creates a new segment in the Behavior Based Customer Insight for Banking database. The segment name and definition are defined in the JSON body of the request. For information on the proper JSON structure, see Customizing segments.

Parent topic: REST API reference

/issegmentation/bs/{industry}/segment/delete/{segment_id}

This resource allows you to create a new segment with an associated segment definition; defined in JSON format. If the specified segment name already exists, then the segment definition will be updated by the provided JSON.

/issegmentation/bs/{industry}/segment/delete/{segment_id}

This resource is issued as a PUT request. The "industry" parameter must be specified as one of the following values:

banking

wealth

insurance

The "segment_id" represents the ID of the segment to be deleted from the database.

Sample JSON response:

                     Content-Type: application/json
                        HTTP status code: 200
                        Body: {
                        	"flag":true
                        }

Example:

<baseurl>/issegmentation/bs/banking/segment/create/1010

This example deletes a segment, with ID 1010, from the Behavior Based Customer Insight for Banking database. For information on the proper JSON structure, see Customizing segments.

Parent topic: REST API reference

BI model

Multiple cubes were built to support the reports. This document gives a brief description of each cube along with the metadata model ( the facts and dimensions involved in each cube).

CROSS_SELL_RATE_FACT cube

This cube contains the cross sell rate targets achieved along with the revenue across different territories, Line of Business, and Time period.

CONVERSION_RATE_FACT cube

This cube contains the conversion rate targets achieved along with the revenue across different territories, Line of Business, and Time period..

NEW_BUSINESS_FACT cube

This cube contains the new business targets achieved along with the revenue across different territories, Line of Business, and Time period.

RENEWAL_FACT cube

This cube contains the policy renewals target achieved along with the revenue across different territories, Line of Business, and Time period..

CHANNEL_FACT cube

This cube contains the new business target achieved for each channel along with the revenue across different territories, Line of Business, and Time period.

CUSTOMER_CHURN_FACT cube

This cube contains details about the number of persons and the number of policies that are more likely to churn along with the premium at risk across different territories, Line of Business, Market Segment, and Time Period.

PRODUCTS_FACT cube

This cube contains product distribution across different territories, Line of Business, Market Segment, and Time Period.

RETENTION_RATE_FACT cube

This cube contains information about the retention rate across different territories, Line of Business, Market Segment, and Time Period.

TENURE_FACT cube

This cube contains the tenure of customers associated with the company divided into multiple buckets across different territories, Line of Business, Market Segment, and Time Period.

CHURN_PROPENSITY_FACT cube

This cube gives the percentage of customers that are going to churn across a given Territory, Line of Business, Market Segment, and Time Period.

CUSTOMER_DETAILS_FACT cube

This cube contains all the customer information such as Churn Risk, Customer Risk, CLTV score, Renewal Date, type of Product, Channel through which the product was bought, Market Segment, and customer address information.

INSURANCE_CUSTOMER_SEGMENT_FACT cube

This cube contains the customer distribution of segments across different territories and Line of Business.

INSURANCE_LIFE_EVENT_FACT cube

This cube contains the probability of life events happening at different ages in a customer's lifetime. The probabilities are divided into high, medium, and low based on user configured thresholds.

INSURANCE_CLTV_RISK_FACT cube

This cube contains the CLTV score of customers bucketed into high, medium and low based on user configured thresholds across different Territories, Line of Business, Market Segment and Time period.

INSURANCE_CUSTOMER_RISK_FACT cube

This cube contains the Risk score of customers divided into high, medium, and low based on user configured thresholds across different Territories, Line of Business, Market Segment, and Time period.

INSURANCE_CUSTOMER_DETAILS_FACT cube

This cube contains customer details such as Insurance Policy ID, Renewal Date, Number of Claims filed, Age, and preferred contact method.

INSURANCE_CUSTOMER_RISK_DETAILS_FACT cube

This cube contains the risk associated with a policy for a customer.

INSURANCE_CLTV_DETAILS_FACT cube

This cube contains the CLTV score associated with a policy for a customer.

INSURANCE_CHURN_DETAILS_FACT cube

This cube contains the Churn score associated with a policy for a customer.

Client data ingestion: SPSS streams

The IBM solution works with IBM Predictive Customer Insight.

Two SPSS streams are provided that enable the client to ingest data into the solution database. The client data in a predefined Comma Separated Value (CSV) file format is provided to the stream as input, and the stream extracts the data from the CSV file and inserts the records into relevant tables in the solution database. The client data can be of the following types:

Customer demographic details (including contact information)

Customer transactions data

There are two separate streams for each of the above types of data.

Purpose of the SPSS streams
The SPSS streams act as an important ETL process between the client data and the solution database.

CSV formats
This section provides the CSV formats for both customers and transactions.

Prerequisites for the ETL streams
There are no prerequisites for the Insurance Customer ETL stream. However, there are prerequisites for the Insurance Transaction ETL stream.

End result of using the ETL streams
This page illustrates the results of the ETL streams.

Purpose of the SPSS streams

The SPSS streams act as an important ETL process between the client data and the solution database.

They perform the following operations:

Extract raw client data from the CSV files

Transform data according to the solution needs (such as eliminating the unwanted spaces)

Filter the transformed data and load it into relevant tables in the solution database

By using the streams, clients can easily ingest large sets of data that is related to customers and their transactions.

The following two streams are provided:

Insurance Customer ETL – Customer demographic details (including contact information)

Insurance Transaction ETL - Customer transactions data

Parent topic: Client data ingestion: SPSS streams

CSV formats

This section provides the CSV formats for both customers and transactions.

Important: In the following CSV files, you must replace the NULL values by default values for the streams to work correctly.

Format for the CSV file input to the Insurance Customer ETL

                     "CITY","COUNTRY","STREET","POSTAL_CODE","REGION","CONTACT_INFORMATION","BIRTH_DATE","FULL_NAME"

                     CITY > city where the customer lives.
                        COUNTRY > country where the customer lives.
                        STREET > street address where the customer lives.
                        POSTAL_CODE > postal code of the area where the customer lives.
                        REGION >  region where the customer lives.
                        CONTACT_INFORMATION> customer's telephone number.
                        BIRTH_DATE > customer's date of birth.
                        FULL_NAME > customer's full name.

Sample CSV file for customers

                     "CITY","COUNTRY","STREET","POSTAL_CODE","REGION","CONTACT_INFORMATION","BIRTH_DATE","FULL_NAME"

                     "Cape Fear","US","393 Green Park","21244-9792","Kentucky",
                        "(443) 199-2637","1980-08-27","Virgilio G Southern"

Format for the CSV file input to the Insurance Transaction ETL

                     "BIRTH_DATE","FULL_NAME","REGION","NAME","CALENDAR_MONTH_NAME",
                        "DESCRIPTION","NAME","CLAIM_ID","COMMUNICATION_MEDIUM","DESCRIPTION",
                        "CODE_LABEL","CITY","COUNTRY","STREET","POSTAL_CODE","REGION",
                        "CONTACT_INFORMATION","DESCRIPTION","DESCRIPTION","INSURANCE_POLICY_ID",
                        "NUMBER_OF_CLAIMS_DENIED","CLAIMS_AMOUNT","CLAIMS_COUNT","GROSS_PREMIUM",
                        "NET_PREMIUM","POLICY_TENURE","SCORE_VALUE","SCORE_VALUE","SCORE",
                        "GROSS_INCOME","NUMBER_OF_DEPENDENT_CHILDREN","WRITTEN_PREMIUM","AGE_OF_PARTY",
                        "CLAIMS_AMOUNT","CLAIMS_COUNT","NUMBER_OF_CANCELLED_POLICIES",
                        "NUMBER_OF_CLAIMS_DENIED","YEARS_SINCE_DRIVERS_LICENSE_OBTAINED",
                        "MONTHS_SINCE_LAST_ADDRESS_CHANGE","NUMBER_OF_NEW_CUSTOMERS_ACQUIRED",
                        "NUMBER_OF_RENEWALS","PERCENTAGE_OF_CONVERSION_RATE_OF_LEADS_TO_ACCOUNTS_OPENED",
                        "CROSS_SELL_RATE","REVENUE_GENERATED_PER_RENEWAL",
                        "REVENUE_GENERATED_FOR_NEW_BUSINESS","REVENUE_GENERATED_FOR_CROSS_SELL",
                        "REVENUE_GENERATED_FO_CONVERSION","RETENTION_RATE","CUSTOMER_AGE","DESCRIPTION",
                        "CAMPAIGN_ID","TASK_ID","HOUSEHOLD_ID","LINE_OF_BUSINESS_NAME","PAYMENT_ID"

Notice the following multiple instances in the above format:

two instances of “NAME”

five instances of “DESCRIPTION”

two instances of “REGION”

two instances of “NUMBER_OF_CLAIMS_DENIED”

two instances of “CLAIMS_AMOUNT”

two instances of “CLAIMS_COUNT”

two instances of “SCORE_VALUE”

The first instance of “NAME” relates to the Financial Services Product name.

The second instance of “NAME” relates to the Market Segment name. In the stream this is referred as NAME(1).

The first instance of “DESCRIPTION” relates to channel role description.

The second instance of “DESCRIPTION” relates to score description. In the stream this is referred as DESCRIPTION(1).

The third instance of “DESCRIPTION” relates to communication content description. In the stream this is referred as DESCRIPTION(2).

The fourth instance of “DESCRIPTION” relates to life event description. In the stream this is referred as DESCRIPTION(3).

The fifth instance of “DESCRIPTION” relates to life event category description. In the stream this is referred as DESCRIPTION(4).

The first instance of “REGION” relates to the geographic area region.

The second instance of “REGION” relates to the region where the customer lives. In the stream this is referred as REGION(1).

The first instance of “NUMBER_OF_CLAIMS_DENIED” relates to the number of claims denied at the claim level.

The second instance of “NUMBER_OF_CLAIMS_DENIED” relates to the number of claims denied at the person level. In the stream this is referred as NUMBER_OF_CLAIMS_DENIED(1).

The first instance of “CLAIMS_AMOUNT” relates to the number of claims amount at the policy level.

The second instance of “CLAIMS_AMOUNT” relates to the number of claims amount at the person level. In the stream this is referred as CLAIMS_AMOUNT(1).

The first instance of “CLAIMS_COUNT” relates to the number of claims count at the insurance policy level.

The second instance of “CLAIMS_COUNT” relates to the number of claims count at the person level. In the stream this is referred as CLAIMS_COUNT(1).

The first instance of “SCORE_VALUE” relates to the number of score value at the person level.

The second instance of “SCORE_VALUE” relates to the number of score value at the insurance policy level. In the stream this is referred as SCORE_VALUE(1).

BIRTH_DATE > customer's date of birth.

FULL_NAME > customer's full name.

CALENDAR_MONTH_NAME > month of the transaction

CLAIM_ID> Claim number associated

COMMUNICATION_MEDIUM > Medium used for communication.

CODE_LABEL > Response code label.

CITY > city where the customer lives.

COUNTRY > country where the customer lives.

STREET > street address where the customer lives.

POSTAL_CODE > postal code of the area where the customer lives.

CONTACT_INFORMATION> customer's telephone number.

INSURANCE_POLICY_ID> Insurance policy number.

GROSS_PREMIUM> Insurance policy's gross premium amount.

NET_PREMIUM> Insurance policy's net premium amount.

POLICY_TENURE> Insurance policy's tenure.

SCORE> Insurance policy's score for the analytic.

GROSS_INCOME> customer's gross income.

NUMBER_OF_DEPENDENT_CHILDREN> number of dependent children a customer has

WRITTEN_PREMIUM> Customer's premium amount

AGE_OF_PARTY> customer's age

NUMBER_OF_CANCELLED_POLICIES> number of canceled policy associated with a customer

YEARS_SINCE_DRIVERS_LICENSE_OBTAINED> year's since a customer obtained his drivers license

MONTHS_SINCE_LAST_ADDRESS_CHANGE> month's since customer last changed his address

NUMBER_OF_NEW_CUSTOMERS_ACQUIRED> number of new customer's acquired

NUMBER_OF_RENEWALS> number of renewals a customer has had

PERCENTAGE_OF_CONVERSION_RATE_OF_LEADS_TO_ACCOUNTS_OPENED> conversion rate of a customer

CROSS_SELL_RATE> cross sell rate of a sale

REVENUE_GENERATED_PER_RENEWAL> revenue generated per policy renewal

REVENUE_GENERATED_FOR_NEW_BUSINESS> revenue generated for new business

REVENUE_GENERATED_FOR_CROSS_SELL> revenue generated for cross sell

REVENUE_GENERATED_FO_CONVERSION> revenue generated for conversion

RETENTION_RATE> retention rate of a customer

CUSTOMER_AGE> age of customer when a particular life event occurs

CAMPAIGN_ID> campaign associated with a particular insurance policy

TASK_ID> task associated with the communication

HOUSEHOLD_ID> household id associated with a customer

LINE_OF_BUSINESS_NAME> product group associated with a business line

PAYMENT_ID> mode of payment

Sample CSV file (transactions)

In the following sample there are three transactions related to the customer provided in the customer's .csv file above.

                     "BIRTH_DATE","FULL_NAME","REGION","NAME","CALENDAR_MONTH_NAME",
                        "DESCRIPTION","NAME","CLAIM_ID","COMMUNICATION_MEDIUM","DESCRIPTION",
                        "CODE_LABEL","CITY","COUNTRY","STREET","POSTAL_CODE","REGION","CONTACT_INFORMATION",
                        "DESCRIPTION","DESCRIPTION","INSURANCE_POLICY_ID","NUMBER_OF_CLAIMS_DENIED",
                        "CLAIMS_AMOUNT","CLAIMS_COUNT","GROSS_PREMIUM","NET_PREMIUM","POLICY_TENURE",
                        "SCORE_VALUE","SCORE_VALUE","SCORE","GROSS_INCOME","NUMBER_OF_DEPENDENT_CHILDREN",
                        "WRITTEN_PREMIUM","AGE_OF_PARTY","CLAIMS_AMOUNT","CLAIMS_COUNT",
                        "NUMBER_OF_CANCELLED_POLICIES","NUMBER_OF_CLAIMS_DENIED",
                        "YEARS_SINCE_DRIVERS_LICENSE_OBTAINED","MONTHS_SINCE_LAST_ADDRESS_CHANGE",
                        "NUMBER_OF_NEW_CUSTOMERS_ACQUIRED","NUMBER_OF_RENEWALS",
                        "PERCENTAGE_OF_CONVERSION_RATE_OF_LEADS_TO_ACCOUNTS_OPENED","CROSS_SELL_RATE",
                        "REVENUE_GENERATED_PER_RENEWAL","REVENUE_GENERATED_FOR_NEW_BUSINESS",
                        "REVENUE_GENERATED_FOR_CROSS_SELL","REVENUE_GENERATED_FO_CONVERSION","RETENTION_RATE",
                        "CUSTOMER_AGE","DESCRIPTION","CAMPAIGN_ID","TASK_ID","HOUSEHOLD_ID",
                        "LINE_OF_BUSINESS_NAME","PAYMENT_ID"

                     "1980-08-27","Virgilio G Southern","GEORGIA","Auto Premium","June",
                        "Agency","Price Oriented Minimalist","1","E-mail","Policy Score","Yes","Cape Fear",
                        "US","393 Green Park","21244-9792","Kentucky","(443) 199-2637",
                        "Delgado price price paid is big and cumbersome","Graduation",
                        "1001830","0","609.00","1", "609.00","609.00","29","1","0.670","1","50140.00","0",
                        "609.00","609.00","35","408.00","3","1","0","17","51","NULL","NULL","NULL","NULL",
                        "NULL","NULL","NULL","NULL","NULL","22","Life Event","2","1","100110","AUTO","1"

Parent topic: Client data ingestion: SPSS streams

Prerequisites for the ETL streams

There are no prerequisites for the Insurance Customer ETL stream. However, there are prerequisites for the Insurance Transaction ETL stream.

The following perquisites are required for the Insurance Transaction ETL to work:

The 'Insurance Customer ETL' stream must be run first to load the customer details into the PERSON table. This is because the 'insurance Transaction ETL' stream uses this table to establish relationships between the customer and his transactions.

Each person is identified by a distinct key: (FULL_NAME, BIRTH_DATE). A combination of these values would uniquely identify a person. Using this key as a reference, only transactions related to the customers loaded through the 'insurance Customer ETL' stream must be provided in the CSV input file for the 'insurance Transaction ETL' stream.

Each person's contact information is identified by a distinct key: (CITY, COUNTRY,STREET,POSTAL_CODE,REGION(1),CONTACT_INFORMATION) . A combination of all these values uniquely identifies a person's contact point. Using this key as a reference, only transactions related to the customers loaded through the 'insurance Customer ETL' stream must be provided in the CSV input file for the 'insurance Transaction ETL' stream.

The CLAIM_ID provided in the CSV input file must be present in the CLAIM table of the insurance solution database.

The INSURANCE_POLICY_ID provided in the CSV input file must be present in the INSURANCE_POLICY table of the insurance solution database.

The CALENDAR_MONTH_NAME provided in the CSV input file must be present in the CALENDAR_DATE table of the insurance solution database.

The COMMUNICATION_MEDIUM provided in the CSV input file must be present in the COMMUNICATION_PROFILE table of the insurance solution database.

The CODE_LABEL provided in the CSV input file must be present in the CODE table of the insurance solution database.

The Financial Services Product name (NAME) provided in the CSV input file must be present in the FINANCIAL_SERVICES_PRODUCT of the insurance solution database.

The Market Segment name (NAME(1)) provided in the CSV input file must be present in the MARKET_SEGMENT table of the insurance solution database.

The channel description (DESCRIPTION) provided in the CSV input file must be present in the CHANNEL_ROLE table of the insurance solution database.

The score description (DESCRIPTION(1)) provided in the CSV input file must be present in the SCORE table of the insurance solution database.

The communication content description (DESCRIPTION(2)) provided in the CSV input file must be present in the COMMUNICATION_CONTENT table of the insurance solution database.

The life event description (DESCRIPTION(3)) provided in the CSV input file must be present in the EVENT table of the insurance solution database.

The life event description (DESCRIPTION(4)) provided in the CSV input file must be present in the EVENT CATEGORY table of the insurance solution database.

The CAMPAIGN_ID provided in the CSV input file must be present in the CAMPAIGN table of the insurance solution database.

The TASK_ID provided in the CSV input file must be present in the TASK table of the insurance solution database.

The HOUSEHOLD_ID provided in the CSV input file must be present in the HOUSEHOLD table of the insurance solution database.

The LINE_OF_BUSINESS_NAME provided in the CSV input file must be present in the PRODUCT_GROUP table of the insurance solution database.

The PAYMENT_ID provided in the CSV input file must be present in the PAYMENT table of the insurance solution database.

The geographic area region (REGION) provided in the CSV input file must be present in the GEOGRAPHIC_AREA of the insurance solution database.

Parent topic: Client data ingestion: SPSS streams

End result of using the ETL streams

This page illustrates the results of the ETL streams.

Insurance Customer ETL

The stream, shown in the following image, filters and loads the input data from the CSV file.

The stream in the image above filters and loads the input data from the CSV file in the following way:

"BIRTH_DATE","FULL_NAME" are inserted into the PERSON table. The combination of these columns forms a distinct key which identifies a particular customer.

"CITY","COUNTRY","STREET","POSTAL_CODE","REGION","CONTACT_INFORMATION" are inserted into the CONTACT_POINT table. The combination of these columns forms a distinct key which identifies a particular contact. After that, the stream establishes relationship between a customer and his or her contacts. This relationship information is stored in the PERSON_CONTACT_POINT_BRIDGE table. In total, three tables (PERSON, CONTACT_POINT and PERSON_CONTACT_POINT_BRIDGE) are populated as a result of running this stream.

Insurance Transaction ETL

The stream, shown in the following image, filters and loads the input data from the CSV file.

The stream performs the following steps:

Merges the data from the transactions csv file with the CALENDAR_DATE_DIMENSION table using the CALENDAR_DATE field and picks up the corresponding CALENDAR_DATE_IDs.

Merges the data from the transactions csv file with the FINANCIAL_SERVICES_PRODUCT table using the NAME field and picks up the corresponding FINANCIAL_SERVICES_PRODUCT_IDs.

Merges the data from the transactions csv file with the GEOGRAPHIC_AREA table using the REGION field and picks up the corresponding GEOGRAPHIC_AREA_IDs.

Merges the data from the transactions csv file with the MARKET_SEGMENT table using the NAME field and picks up the corresponding MARKET_SEGMENT_IDs.

Merges the data from the transactions csv file with the CHANNEL_ROLE table using the DESCRIPTION field and picks up the corresponding CHANNEL_ROLE_IDs.

Merges the data from the transactions csv file with the COMMUNICATION_PROFILE table using the COMMUNICATION_MEDIUM field and picks up the corresponding COMMUNICATION_PROFILE_IDs.

Merges the data from the transactions csv file with the SCORE table using the DESCRIPTION field and picks up the corresponding SCORE_IDs.

Merges the data from the transactions csv file with the CODE table using the CODE_LABEL field and picks up the corresponding CODE_IDs.

Merges the data from the transactions csv file with the EVENT table using the DESCRIPTION field and picks up the corresponding EVENT_IDs.

Merges the data from the transactions csv file with the EVENT_CATEGORY table using the DESCRIPTION field and picks up the corresponding EVENT_CATEGORY_IDs.

Merges the data from the transactions csv file with the COMMUNICATION_CONTENT table using the DESCRIPTION field and picks up the corresponding COMMUNICATION_CONTENT_IDs.

Merges the data from the transactions csv file with the PERSON table using the key: (FULL_NAME, BIRTH_DATE) and picks up the corresponding PERSON_IDs.

Merges the data from the transactions csv file with the CLAIM table using the key: (CLAIM_ID) and picks up the corresponding CLAIM_IDs associated with a customer.

Merges the data from the transactions csv file with the INSURANCE_POLICY table using the key: (INSURANCE_POLICY_ID) and picks up the corresponding INSURANCE_POLICY_IDs associated with a customer.

Merges the data from the transactions csv file with the CONTACT_POINT table using the key: (CITY, COUNTRY,STREET,POSTAL_CODE,REGION,CONTACT_INFORMATION) and picks up the corresponding CONTACT_POINT_IDs.

Merges the data from the transactions csv file with the CAMPAIGN table using the key: (CAMPAIGN_ID) and picks up the corresponding CAMPAIGN_IDs.

Merges the data from the transactions csv file with the TASK table using the key: (TASK_ID) and picks up the corresponding TASK_IDs.

Merges the data from the transactions csv file with the HOUSEHOLD table using the key: (HOUSEHOLD_ID) and picks up the corresponding HOUSEHOLD_IDs.

Merges the data from the transactions csv file with the PAYMENT table using the key: (PAYMENT_ID) and picks up the corresponding PAYMENT_IDs.

Merges the data from the transactions csv file with the PRODUCT_GROUP table using the LINE_OF_BUSINESS_NAME field and picks up the corresponding LINE_OF_BUSINESS_IDs.

Inserts the primary keys along with the corresponding NUMBER_OF_CLAIMS_DENIED fields in to the CLAIM_OFFER_SNAPSHOT_FACT table as a result of running this stream.

Inserts the primary keys along with the corresponding CLAIMS_AMOUNT and CLAIMS_COUNT fields in to the POLICY_SNAPSHOT_FACT table as a result of running this stream.

Inserts the primary keys along with the corresponding GROSS_PREMIUM, NET_PREMIUM and POLICY_TENURE fields in to the POLICY_PAYMENTS_FACT table as a result of running this stream.

Inserts the primary keys into the CAMPAIGN_SALES_FACT table as a result of running this stream.

Inserts the primary keys along with the corresponding SCORE_VALUE fields into the PERSON_SCORE_FACT table as a result of running this stream.

Inserts the primary keys along with the corresponding SCORE_VALUE and SCORE fields into the POLICY_SCORE_FACT table as a result of running this stream.

Inserts the primary keys along with the corresponding GROSS_INCOME, NUMBER_OF_DEPENDENT_CHILDREN, WRITTEN_PREMIUM, AGE_OF_PARTY, CLAIMS_AMOUNT, CLAIMS_COUNT, NUMBER_OF_CANCELLED_POLICIES, NUMBER_OF_CLAIMS_DENIED, YEARS_SINCE_DRIVERS_LICENSE_OBTAINED, and MONTHS_SINCE_LAST_ADDRESS_CHANGE fields into the PERSON_VALUE_FACT table as a result of running this stream.

Inserts the primary keys along with the corresponding NUMBER_OF_NEW_CUSTOMERS_ACQUIRED, NUMBER_OF_RENEWALS, PERCENTAGE_OF_CONVERSION_RATE_OF_LEADS_TO_ACCOUNTS_OPENED, CROSS_SELL_RATE, REVENUE_GENERATED_PER_RENEWAL, REVENUE_GENERATED_FOR_NEW_BUSINESS, REVENUE_GENERATED_FOR_CROSS_SELL, REVENUE_GENERATED_FO_CONVERSION and RETENTION_RATE fields in to the SALES_AND_ADMINISTRATION_PERFORMANCE_FACT table as a result of running this stream.

Inserts the primary keys into the CUSTOMER_CAMPAIGN_RESPONSE_FACT table as a result of running this stream.

Inserts the primary keys in to the COMMUNICATIONS_FACT table as a result of running this stream.

Inserts the primary keys along with the corresponding NUMBER_OF_EVENTS and CUSTOMER_AGE into the CUSTOMER_LIFE_EVENTS_FACT table as a result of running this stream.

Parent topic: Client data ingestion: SPSS streams

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Parent topic: Notices