Decision Strategy Manager

Transcription

1 Decision Strategy Manager DMSample - Retention Predictive Model Version 7.1.8

2 2015 by Pegasystems Inc. All rights reserved This document describes products and services of Pegasystems Inc. It may contain trade secrets and proprietary information. The document and product are protected by copyright and distributed under licenses restricting their use, copying distribution, or transmittal in any form without prior written authorization of Pegasystems Inc. This document is current as of the date of publication only. Changes in the document may be made from time to time at the discretion of Pegasystems. This document remains the property of Pegasystems and must be returned to it upon request. This document does not imply any commitment to offer or deliver the products or services described. This document may include references to Pegasystems product features that have not been licensed by your company. If you have questions about whether a particular capability is included in your installation, please consult your Pegasystems service consultant. For Pegasystems trademarks and registered trademarks, all rights reserved. Other brand or product names are trademarks of their respective holders. Although Pegasystems Inc. strives for accuracy in its publications, any publication may contain inaccuracies or typographical errors. This document or Help System could contain technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Pegasystems Inc. may make improvements and/or changes in the information described herein at any time. This document is the property of: Pegasystems Inc. 1 Rogers Street Cambridge, MA Phone: (617) Fax: (617) Publishing date: May 22, 2015 Contact Product Support Visit the Pega Discovery Network 2015 by Pegasystems Inc. All rights reserved

3 Use Case Overview 2 Configure Application 3 Application Dependencies 3 Predictive Analytics Director Portal 3 Data Set 3 Reference Application 3 Create Project 5 Prepare Data 6 Source Selection 6 Sample Construction 6 Outcome Definition 6 Analyze Data 7 Develop Model 8 Predictor Grouping 8 Hall of Fame 8 Analyze Model 10 Export Model by Pegasystems Inc. All rights reserved 1

4 Use Case Overview Decision Management enabled applications can use predictive models to drive processes and influence decisions. Predictive models calculate behavior based on customer data. The use of predictive models requires two stages: the modeling process in Predictive Analytics Director (PAD), or another modeling tool if using PMML, and the definition of predictive model instances in Designer Studio. The modeling process in PAD consists of developing a predictive model based on a project template that allows you to prepare the data for sampling purposes, going through the modeling development stages to generate predictive models that can be compared and analyzed and, finally, selecting the most suitable model. Predictive model instances are defined in Designer Studio and can be used in strategies and process flows. This documentation describes the steps taken to develop and generate the scoring model used in the Top Offers and Credit Risk Recommendation DMSample case types. The purpose of this model is to address retention based on the probability of churn behavior. 1. Configure application (page 3) 2. Create project (page 5) 3. Prepare data (page 6) 4. Analyze data (page 7) 5. Develop model (page 8) 6. Analyze model (page 10) 7. Export model (page 12) 2015 by Pegasystems Inc. All rights reserved 2

5 Configure Application Application dependencies (page 3) Predictive Analytics Director portal (page 3) Data set (page 3) Reference application (page 3) Application Dependencies Your application needs to have the necessary dependencies to use Decision Management functionality. The easiest way to achieve this is by creating an application that is build on PegaDM. The example we are about to illustrate is available in the DMSample reference application (page 3). Predictive Analytics Director Portal To develop and export predictive models, your access group needs to be set up to launch a specific portal. Among the portals available to users in an access group, you need to include Padportal. Once you have configured the access group, you can access the Predictive Analytics Director portal through the corresponding entry in the Launch menu. Data Set For modeling purposes, you require a suitable data set. Follow these steps to locate and download the CSV file that you can use it in the data preparation stage (page 6): 1. Access the DMSample application by adding DMSample:Administrators to your access group list or logging in as administrator@pega.com. 2. Switch to the DMSample application. 3. In the records explorer, expand the Technical category. 4. Click Binary File and locate the CSV: App name (directory): webwb File name: churndataset File type (extension): csv 5. Download the file to a location of your choice. Reference Application Process Commander includes DMSample, a Decision Management sample application that illustrates typical use cases of Decision Management functionality without the external Visual Business Director and Adaptive Decision Manager services. DMSample is designed to show the principles of using predictive analytics, building strategies, configuring interactions and wiring processes to use the power of Decision Management functionality. Although not using the external Adaptive Decision Manager service, DMSample demonstrates adaptive learning by using the corresponding embedded process. You have two ways to access this application: add the DMSample:Administrators access group to your operator record, or login as administrator@pega.com and switch to DMSample. In the DMSample application, you can also launch the Predictive Analytics Director portal to access the project used to generate the churn scoring model used in DMSample strategy design. In a new Pega 7.1.8, this project is part of the default Predictive Analytics Director examples. In an environment that has been upgraded from a release previous to Pega 7.1.8, you have to perform the following steps to add this project to the default Predictive Analytics Director examples: 1. In Designer Studio, search for the DMSampleScoringModel binary file by Pegasystems Inc. All rights reserved 3

6 2. Use the download file button to save this archive onto disk as DMSampleScoringModel.zip. 3. Launch the Predictive Analytics Director portal. 4. In the Predictive Analytics Director portal's navigation, go to Manage Resources. 5. In the row containing Examples, use the import action to import the ZIP file you saved in the second step by Pegasystems Inc. All rights reserved 4

7 Create Project Follow these steps to create the PAD project: 1. Access the Predictive Analytics Director portal by using the corresponding entry in the Designer Studio's Launch menu. 2. In the Predictive Analytics Director portal's Home page, click the Create link under the Retention category. 3. Define the project details by providing the project name, password and description. In this tutorial, the name of the project is DMSample Scoring Model and the supplied password pega, the same password you use to open the other PAD example projects. 4. In the template selection settings, select the Retention business issue and the Churn Modeling template. 5. Click Create Project. 6. Proceed with the data preparation stage (page 6). By default, the next step after creating a new project is the data preparation project stage. If you decide to close the project, you can go back to the project by using the corresponding entry in your recent items list. Another way to access the project is through the Manage Projects page as long at it is in closed state. Since closing a project at any stage implies that it stays in open state in the Manage Projects page, you have to use the reset project action so that you can go back into the project by Pegasystems Inc. All rights reserved 5

8 Prepare Data The data preparation stage is done in three steps: 1. Source selection (page 6) 2. Sample construction (page 6) 3. Outcome definition (page 6) Source Selection The source selection step consists of selecting the CSV file containing the data set. 1. Click Browse to select the CSV file. The data set you use in this step is based on your local copy of the churn data set CSV file. 2. Click Preview to display the first 100 records. 3. Click Load Data to incorporate the data set into the project. 4. Click Next to go to the sample construction step. Sample Construction The sample construction step applies sampling criteria to the fields included in the data set. 1. Leave the weight field undefined so that sampling is done with the assumption that each case counts as 1. The weight field is typically available when sampling has been applied in the process of creating the data set, which is not the case with our simple (and slightly artificial) data set. 2. The list of fields allows you to filter out columns in the data set that are not useful for sampling purposes. Again, the data set we are using is purposefully built for the predictive model we want to generate, so we use all fields. 3. Use uniform sampling as the sampling method. Because our data set was created on the basis of one uniform population, we do not have any field that can be used as the stratum field to identify subpopulations. 4. Use the following sample size settings: Use the entire population for sampling (100%). For validation purposes, retain 10% of the population. For testing purposes, retain 5% of the population. 5. Click Create Sample. 6. Click Next to go to the outcome definition step. Outcome Definition The outcome definition step consists of selecting the model type and how to interpret the outcome. 1. Select the scoring model type. 2. Use CHURN as the outcome field. 3. Assign the symbols to categories by adding Missing to the loyal category and YES to the churned category. 4. Click Next to go to the data analysis stage (page 7) by Pegasystems Inc. All rights reserved 6

9 Analyze Data In the data analysis stage, define the role of each predictor. So that you can analyze the predictive performance of each field, select all fields and change their role to predictor. Follow these steps to complete the data analysis step: 1. Start by checking the CUSTOMER field and changing the role to ignored since there is no meaningful insight to be taken for modeling by using this data. 2. The general rule is not to use predictors with performance below 52% as predictors. Select the START_DATE field and change the role to value, a role that is more suitable for fields that are not known at the time of decision but can be associated with subsequent behavior. 3. Use the remaining fields (AGE, USAGE_DATA, CONTRACT_END_MONTHS and GENDER) as predictors. 4. Click Next to go to the model development stage (page 8) by Pegasystems Inc. All rights reserved 7

10 Develop Model The model development stage consists of two steps: 1. Predictor grouping (page 8) 2. Hall of Fame (page 8) Predictor Grouping Predictive Analytics Director automatically groups predictors that offer the same information. In the case of our data set, each group corresponds to one predictor. 1. Leave the grouping level at 0.45 (default). 2. Click Use all predictors. 3. Leave the Use Group and Use Predictor check boxes enabled for all groups. 4. Click Next to go to the Hall of Fame. Hall of Fame The Hall of Fame contains the models you want to consider for model analysis. By default, the Hall of Fame contains one default regression model, one default decision tree model, plus any optional benchmark models. During modeling, you can create more models and save them to the Hall of Fame. In this step, we create a model to compare the performance between two regression models: one using all predictors as created in the predictor grouping step, and another using the best of each group. 1. Click New Regression Model. 2. In the Regression Model dialog, click Use best of each group and then Create Model. 3. The dialog displays details about the generated model. This information allows you to analyze the model by looking at the formula, score card and sensitivity information by Pegasystems Inc. All rights reserved 8

11 4. Click Save to Hall of Fame. 5. Provide a name and description for this model and click Submit. In this example, the new model is named Regression-Best of each group. 6. Click OK to complete this process. 7. The Hall of Fame displays the initially generated models, plus the model we just generated: 8. Click Next to go to the model analysis stage (page 10) by Pegasystems Inc. All rights reserved 9

12 Analyze Model The model analysis stage provides the graphical visualization supporting score comparison, score distribution and class comparison. After comparing models, click Next to go through the model analysis steps until you reach class comparison, at which step you click Next to go to the model export stage (page 12). Score comparison is visualized in the form of comparison curves. The behavior graph displays the cumulative behavior, the lift graph displays the improvement in cumulative behavior over the average behavior, the discrimination graph displays the discrimination and the gains graph displays the cumulative percentage of positive cases. Each model has an individual visualization of the score distribution. When visualizing models, select cases based on the type of outcome you want to predict. For example, churned: 2015 by Pegasystems Inc. All rights reserved 10

13 Class comparison is also visualized in the form of comparison curves, allowing you to inspect the indicators for the generated classes by Pegasystems Inc. All rights reserved 11

14 Export Model The model export stage allows you to create an OXL file or generate a predictive model rule directly. Which method you take depends on the workflow you are going to follow; for example, export the model if the predictive model is to be used in another application or system, create a predictive model if you are the rule is to be generated in a ruleset that can be accessed by the application you used to launch the Predictive Analytics Director portal. In the following steps, we export the model an OXL to later create a predictive model in the DMSample application. 1. Select the Regression model. 2. Click Save to file. 3. Save the OXL file by Pegasystems Inc. All rights reserved 12