Predictors

Probability to move to next stage

Adaptive models predict the likelihood that an opportunity will move from the current stage to the next stage. Pega Sales Automation uses the average number of all opportunities that have moved from the current stage to the next stage as the base propensity. The difference between the likelihood that an opportunity will move and the base propensity is an indicator of how you are progressing with the opportunity.

Win probability

Adaptive models predict the likelihood of winning an opportunity. Pega Sales Automation uses the average number of all won opportunities for a given stage as the base propensity. The difference between the likelihood that you will win an opportunity and the base propensity is an indicator of how you are progressing with the opportunity.

Close date

Adaptive models predict the quarter (for B2B) or the days (0-60, for B2C) when an opportunity is most likely to close. Pega Sales Automation compares the target date range that is set by the sales representative and the predicted closing quarter/days to indicate whether the opportunity close date is earlier than expected, on time, or delayed.

Strategy models

Opportunity win model

Prediction for this model is: will this opportunity ever be won? The input predictor range should be as complete as possible. B2B and B2C opportunities have different predictors.

Opportunity win base propensity model

This model does not use predictors. Instead, the model uses an opportunity stage as the context to provide the base propensity of winning an opportunity in the current stage.

Move to next stage model

Prediction for this model is: will this opportunity ever move to a higher stage? When an opportunity moves from the current stage to the next stage, the application sends a positive, one-response to all opportunities in the Adaptive Decision Manager (ADM) data cache. Pega Sales Automation also takes a snapshot of the previous stage. The response is filtered by using an additional Decision Data Store (DDS) data set. The response strategy for the move to the next stage model filters out the decision results for stages that have already been marked as positive. It then determines whether the current stage is higher than the stage in the snapshot and returns only the decision results for which this is true.

B2B and B2C opportunity have different predictors.

Move to next stage base propensity model

This model does not use predictors. Instead, the model uses an opportunity stage as the context to provide the base propensity of moving from the current stage to the next stage.

Win date model

This data model supports separate models for the following outcomes:

Win date model outcomes

B2B opportunity	B2C opportunity
Will this opportunity be won in 0-90 days from now?	Will this opportunity be won in 0-5 days from now?
Will this opportunity be won in 90-180 days from now?	Will this opportunity be won in 5-10 days from now?
Will this opportunity be won in 180-270 days from now?	Will this opportunity be won in 10-15 days from now?
Will this opportunity be won in 270-360 days from now?	Will this opportunity be won in 15-20 days from now?
	Will this opportunity be won in 20-25 days from now?
	Will this opportunity be won in 25-30 days from now?
	Will this opportunity be won in 30-35 days from now?
	Will this opportunity be won in 35-40 days from now?
	Will this opportunity be won in 40-45 days from now?
	Will this opportunity be won in 45-50 days from now?
	Will this opportunity be won in 50-55 days from now?
	Will this opportunity be won in 55-60 days from now?

Pega Sales Automation uses propositions (different for B2B and B2C opportunities) in a Decision Data shape to model the date ranges. The propositions have both a minimum and maximum days attribute. If you require more granularity, you can edit the propositions to meet your needs and to create a smoother display for the dashboard widget.

Win date base propensity model

This model does not use predictors. Instead, the model uses an opportunity stage as the context to provide the base propensity of winning an opportunity in a specified time frame.

Data pages

Opportunity insights use the D_PredictSAOpportunity data page.

• For Pega Sales Automation, review the D_PredictSAOpportunity data page.

  The Analytics widgets in the opportunity run in the context of the D_PredictSAOpportunity data page. This data page has the SAPredictOpportunity activity as a data source, which runs the SAPredictOpportunity data flow. The data flow fetches the precalculated predictors using a data set. The data flow sets the predictors on opportunity case and uses the EvaluateOpportunity strategy of B2B/B2C opportunity to predict the propensity to move to the next stage, to win an opportunity, and to predict the close date quarter.

• For Pega Sales Automation for Financial Services, review the D_PredictSAOpportunity data page.

  This data page populates the propensity for an Opportunity case. Analytics widgets of the opportunity insights run in the context of the D_PredictSAOpportunity data page. This data page has the SAPredictOpportunity activity as a data source, which runs the PopulatePredictors activity. The PopulatePredictors activity replaces the following activities: GetContactGrothRatio, GetEmailActivityRatio, GetTrendsDataRatio, and GetCIRatio, which are available in the Pega Sales Automation application. When there are multiple products specified, the PopulatePredictors activity iterates all of the products propensity, but displays the least propensity model. This workflow is applicable only for the credit and debit product types.

Data flows

StoreOpportunitySnapshots

The solution uses the StoreOpportunitySnapshots data flow during the initial training of the models by using the business opportunity data that is fetched from the internal production data. This data flow converts each record from the PegaCRM-Data-SFA-Predictors class into opportunity objects and then routes them to the EvaluateOpportunity strategy. In the data flow, the mode for the EvaluateOpportunity strategy is set to Make decision and store data for later response capture.

TrainFromHistory

The input for TrainFromHistory comes from the RD OpportunityStages, which fetches one record per business opportunity from the predictors’ data table, sorted by maximum stage. This data flow calls the OpportunityClosed and MovedToNextStage data flows.

StoreIndvOpportunitySnapshots

The solution uses the StoreOpportunitySnapshots data flow during the initial training of the models by using the individual opportunity data that is fetched from the internal production data. This data flow converts each record from the PegaCRM-Data-SFA-Predictors class into opportunity objects and then routes them to the EvaluateOpportunity strategy. In the data flow, the mode for the EvaluateOpportunity strategy is set to Make decision and store data for later response capture.

TrainIndvOppFromHistory

The input for TrainFromHistory comes from the RD OpportunityStages, which fetches one record per individual opportunity from the predictors data table, sorted by maximum stage. This data flow calls the OpportunityClosed and MovedToNextStage data flows.

OpportunityClosed

The OpportunityClosed data flow calls the CloseOpportunity strategy, which captures responses for decisions in the past period.

MovedToNextStage

The MoveToNextStage data flow calls the HandleNextStageResponses strategy, which is the core strategy for training the PredictNextStageModels adaptive model. The available responses depend on whether an opportunity moves up or down from the current stage.

SnapshotOneOpportunity

The SnapshotOneOpportunity data flow reuses the EvaluateOpportunity strategy, which is used to train the model during bulk processing. This data flow runs on a daily basis and captures all predictors for the opportunity each time it runs.

SAPredictOpportunity

The SAPredictOpportunity data flow reuses the EvaluateOpportunity strategy to get the analytic results for the opportunity.

To view the details for each data flow, perform the following steps:

1. In the navigation pane of Dev Studio, click RecordsData ModelData Flow.
2. To open the data flow record, click a data flow name.

Strategies

The following are the opportunity insights strategies:

• EvaluateOpportunity
• HandleNextStageResponses

To view the details of each strategy, perform the following steps:

1. In the navigation pane of Dev Studio, click RecordsDecisionStrategy.
2. To open a strategy record, click a strategy name.

Adaptive models

Each adaptive model has predictors, context, and outcomes that you must configure before you can train the models to predict outcome propensities based on your use cases. Base propensity models do not have any predictors, but they have the OpportunityStage property in their context so you can train different models for each stage.

Outcome propensities are configured for the following models:

• PredictiveMoveNextStage
• BaseWinModel
• PredictWin
• PredictCloseDate

To view the details of each model, perform the following steps:

1. In the navigation pane of Dev Studio, click RecordsDecisionAdaptive Model.
2. To open a model record, click a model name.