The Pega Sales Automation application uses decisioning capabilities to provide predictive lead scores for lead ranking. The lead score predicts how likely it is that a lead will turn into an opportunity. This helps to focus on the most promising leads to maximize sales.

For information about this process, review the following sections:

• Lead score overview
• Historical data and adaptive learning
• Lead ranking architecture

The lead score is displayed on the Leads list page. The lead score is dynamic and responds to changes in the lead properties. For example, the lead score changes when you change the number of activities that are associated with the lead. The lead score can also differ when you change the lead source.

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The Pega Sales Automation application uses self-learning, adaptive models to generate the lead score. This approach is based on core decision management capabilities. If needed, add or remove predictors. For more information, see Artificial intelligence-based opportunity insights.

The adaptive model provides a set of key predictors that are based on the analysis of data from real production environments. For example, the system uses:

• Production data to create weekly snapshots for each closed lead over one-year period
• Records to define core predictors
• Records to train the adaptive models

The following predictors are provided in the Pega Sales Automation adaptive model:

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An agent runs daily and executes a data flow that calls a decision strategy. A decision strategy contains the Lead Ranking adaptive model, which represents properties to capture the data that is required by the models. A decision strategy uses the standard Delayed Learning cache.

When a lead is converted to an opportunity or closed without being converted, the system triggers a response strategy from a data flow to retrieve the data for relevant decisions and train the applicable models.

• Increase sales with artificial intelligence capabilities

• Increase sales with artificial intelligence capabilities

Review the following lead ranking architecture sections:

• Data pages
• Data flows
• Strategies
• Adaptive models

D_crmLeadsList

The lead score in the leads list runs in the context of the D_crmLeadsList data page. The D_crmLeadList data page has the crmLeadsListWithScore report definition data source, which retrieves all lead information from the database. The system uses the data and the crmLeadsListPostLoad rule in post-load processing.

CalculateLeadScore

crmLeadsListPostLoad is a post-processing activity that combines the data for each lead that is available in the PegaCRM-Work-SFA-Lead lead table with the PegaCRM-Data-SFA-LeadPredictors table. The system runs the RankedLead data flow to predict the propensity for each lead. The calculated propensity represents the likelihood for that particular lead to be converted to an opportunity.

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

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

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StoreLeadSnapshots (B2B)

The StoreLeadSnapshots data flow uses the lead data from the production environment. The StoreLeadSnapshots data flow converts each record from the PegaCRM-Data-SFA-LeadTrainingPredictors class into lead objects. After that, the system routes converted records to the EvaluateLeadRanking B2B strategy. Use the Make decision and store data for later response capture mode for the EvaluateLeadRanking strategy.

StoreIndividualLeadSnapshots (B2C)

The StoreIndividualLeadSnapshots data flow uses the lead data from the production environment. The StoreIndividualLeadSnapshots data flow converts each record from the PegaCRM-Data-SFA-LeadTrainingPredictors class into lead objects. After that, the system routes converted records to the EvaluateLeadRanking B2C strategy. Use the Make decision and store data for later response capture mode for the EvaluateLeadRanking strategy.

CaptureLeadOutcomes (B2B)

The LeadRankingOutcomes report definition is the input for the CaptureLeadOutcomes data flow. The LeadRankingOutcomes report definition retrieves one record per lead from the historical predictors data table, sorted by the maximum snapshot date. The system then retrieves the outcome for each lead. The CaptureLeadOutcomes data flow calls the LeadRankingCloseProbation data flow.

CaptureIndividualLeadOutcomes (B2C)

The LeadRankingOutcomes report definition is the input for the CaptureIndividualLeadOutcomes data flow. The LeadRankingOutcomes report definition retrieves one record per lead from the historical predictors data table, sorted by the maximum snapshot date. The system then retrieves the outcome for each lead. The CaptureIndividualLeadOutcomes data flow calls the LeadRankingCloseProbation data flow.

RankLead

The RankLead data flow runs on a demand basis. The system invokes the RankLead data flow by using the CalculateLeadScore activity for each lead to calculate the lead score. The RankLead data flow collects predictor properties for each lead and the lead static data. After the system collects lead static and predictor properties data, it routes the data to the EvaluateLeadRanking strategy to calculate the lead score. Use the Make decision mode for the EvaluateLeadRanking strategy.

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

1. In the navigation pane of Dev Studio, click RecordsData ModelData Flow.
2. Select a data flow to open it and view the data flow record.

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The following are the lead ranking strategies:

• EvaluateLeadRanking
• CloseLead

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

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

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The lead architecture includes the LeadRanking adaptive model that provides predictors based on the historical data. The input predictor range should be as complete as possible before training the adaptive models to predict outcome propensities based on your use cases, configure predictors, context, and outcomes for the adaptive model.

To view the adaptive models details, perform the following steps:

1. In the navigation pane of Dev Studio, click RecordsDecisionAdaptive Model.
2. Select an adaptive model to open it and view the adaptive model record.
   1. To set lead ranking outcomes, define positive or negative outcome values on the Outcomes tab.
   2. To view the advanced settings, such as performance monitoring or data analysis binning, open the Settings tab.

• Increase sales with artificial intelligence capabilities

• Increase sales with artificial intelligence capabilities

• Increase sales with artificial intelligence capabilities