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Activating and training Pega Sales Automation for Insurance adaptive models for artificial intelligence

Updated on December 22, 2021

Artificial intelligence in Pega Sales Automation for Insurance helps you to proactively assess risks on deals in the pipeline, coach newly recruited sales representatives, and identify leads that have a high probability of being convertible to opportunities.

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Before you begin: If you installed the Pega Sales Automation for Insurance sample application, reset the artificial intelligence sample data to its original form by using the Tools menu in the Sales Ops portal.

Before using artificial intelligence insights with Pega Sales Automation for Insurance, you must activate the feature for your implementation and then train Pega’s adaptive models for artificial intelligence.

To configure your application for artificial intelligence, log in to Pega Sales Automation for Insurance and complete the following procedures:

  1. Activate artificial intelligence.
  2. Verify Decision Strategy Manager (DSM) nodes.
  3. Import historical data.
  4. Truncate data sets.
  5. Delete existing models.
  6. Run data flows for opportunity insights.
  7. Run data flows for sales coach.
  8. Run data flows for lead ranking (B2B selling mode).
  9. Run data flows for lead ranking (B2C selling mode).
  10. Enable preloaded NBAs.

Activating artificial intelligence

  1. In the User portal, from the Explorer panel, select Administration.
  2. Go to AI Settings and enable any of the AI capabilities by selecting their individual checkboxes.
  3. Click Save.
  4. In the navigation pane of Dev Studio, click RecordsSysAdminJob Scheduler, override the job schedulers listed above, and then set the Enable job scheduler toggle to true for each of the following rules:
    • CRMIOpportunityInsights
    • DailySnapshotsForAE
    • DailySnapshotsForLead
    • RecommendationsForRep

Verifying Decision Strategy Manager (DSM) nodes

Verify DSM nodes.

  1. In the navigation pane of Dev Studio, click ConfigureDecisioningInfrastructureServices.
  2. Verify that each of the following services contains a node with a status of Normal:
    • Decision Data Store
    • Adaptive Decision Manager
    • Data Flow
    • Real-time Data Grid

Importing historical data

Pega-provided historical data consists of a snapshot of data from a production environment for various models.

  1. In the navigation pane of Dev Studio, click ConfigureApplicationDistributionImport.
  2. Click Choose File, browse for and select the HistoricalData file from your distribution media, and then follow the instructions in the import wizard.
  3. Repeat these steps for the SAIOpportunityInsightTrainingData and SAISalesRepEffectivenessData files.

Truncating data sets

Truncating data sets makes them easier to maintain.

  1. In the header of Dev Studio, in the search field, search for and select the pxDecisionResults data set of the Data-Decision-Results class.
  2. Click ActionsRun.
  3. In the Operations field, select Truncate.
  4. Click Execute.
  5. Repeat steps 1 to 4 for the PreviousStages data set of the SA-SR class.

Deleting existing models

You can delete existing models for example to free up space and keep your system tidy.

  1. In the header of Dev Studio, click ConfigureDecisioningModels Management.
  2. Select all of the existing models:
    • PredictWin
    • PredictMoveNextStage
    • PredictCloseDate
    • BaseWinModel
    • LeadRanking
    • PredictEffectiveness
  3. Click Delete Models.

Running the data flows for opportunity insights

Run the data flows for opportunity insights in order to pass the incoming data to the adaptive models, so that the system calculates opportunity insights.

Before you begin: Before performing the steps below, ensure that you have the EnableOpportunityInsights dynamic system setting set to true.
  1. In the header of Dev Studio, in the search field, search for and select the StoreBusinessOpportunitySnapshots data flow.
  2. Click ActionsRun.
  3. On the Data flow test run form, click Start.
  4. Repeat steps 1 to 3 for each of the following data flows:
    • TrainBusinessOppFromHistory
    • StoreIndividualOpportunitySnapshots
    • TrainIndvOppFromHistory

Running the data flows for sales coach

Run the data flows for the sales coach in order to pass the incoming data to the adaptive models, so that the system calculates sales coach suggestions.

Before you begin: Ensure that you have the EnableSalesCoach dynamic system setting to true. In the navigation pane of Dev Studio, click RecordsSysAdminDynamic System Settings, and then search for and enable the EnableSalesCoach dynamic system setting.
  1. In the header of Dev Studio, in the search field, search for and select the StoreSalesRepSnapshots data flow of the PegaInsCRM-Data-SFA-SalesRepPredictors class.
  2. Click ActionsRun.
  3. On the Data flow test run form, click Start.
  4. After receiving a confirmation message with a successful result, repeat steps 1 to 3 for the CaptureEffectivenessOutcomes and StorePropensitySnapshots data flows of the PegaInsCRM-Data-SFA-SalesRepPredictors class.

Running the data flows for lead ranking (B2B selling mode)

Run the data flows for lead ranking in order to pass the incoming data to the adaptive models, so that the system calculates the B2B lead scores.

Before you begin: Before performing the steps below, ensure that you have the EnableLeadRanking dynamic system setting set to true. In the Dev Studio navigation pane, click RecordsSysAdminDynamic System Settings, and then search for and enable the EnableLeadRanking dynamic system setting.
  1. In the header of Dev Studio, in the search field, search for and select the StoreLeadSnapshots data flow.
  2. Click ActionsRun.
  3. On the Data flow test run form, click Start.
  4. Repeat steps 1 to 3 for the CaptureLeadOutcomes data flow.
  5. To set up predictors and calculate lead score for existing leads and store them in the lead predictor table, run the InitialiseLeadPredictorTable activity.

Running the data flows for lead ranking (B2C selling mode)

Run the data flows for lead ranking in order to pass the incoming data to the adaptive models, so that the system calculates the B2C lead scores.

  1. In the header of Dev Studio, in the search field, search for and select the StoreIndividualLeadSnapshots data flow.
  2. Click ActionsRun.
  3. On the Data flow test run form, click Start.
  4. Repeat steps 1 to 3 for the CaptureInvidualLeadOutcomes data flow.
  5. To set up predictors and calculate lead score for existing leads and store them in the lead predictor table, run the InitialiseLeadPredictorTable activity.

Enabling preloaded NBAs

You have preloaded NBAs in your system. To use them, enable them in Dev Studio.

  1. In the navigation pane of Dev Studio, click RecordsSysAdminDynamic System Setting, and then search for the UsePreloadedNBA dynamic system setting.
  2. Set the UsePreloadedNBA dynamic system setting to true.
  3. Optional: To see changes to the job scheduler instantly, run the LoadNBAForALLOpps data flow.
  • Previous topic Artificial intelligence features in Pega Sales Automation for Insurance
  • Next topic Pega Sales Automation for Insurance artificial intelligence-based opportunity insights

Tags

Pega Sales Automation 8.7

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