Artificial intelligence-based lead ranking
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. This helps to focus on the most promising leads to maximize sales. 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. 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: The following predictors are provided in the Pega Sales Automation adaptive
model: 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. Lead ranking architecture is based on the following concepts: To view details for each data page, perform the following steps: To view the data flow details, perform the following steps: The following are the lead ranking strategies: To view details for each strategy, perform the following steps: 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:Lead score overview
Historical data and adaptive learning
Historical data
B2C model predictors B2B model predictors AvgTimeToConvertLeads AvgTimeToConvertLeads ExistingCustomer ContactAge LeadsAge DownloadCount LeadsContactActivities ExistingCustomer LeadRating Industry LeadSource LeadAge NumberOfActivities LeadCompany NumberOfActivitiesByEmail LeadsContactActivities NumberOfActivitiesByEmail LeadsContactInEmailCount NumberOfActivitiesByPhone LeadsContactOutEmailCount NumberOfWonOpportunities LeadSource ValidEmail LeadRating ValidPhone NumberOfActivities NumberOfActivitiesByEmail NumberOfActivitiesByMeeting NumberOfActivitiesByPhone NumberOfWonOpportunities OrgSubscribeCount ValidEmail ValidPhone WebsiteVisitCount Adaptive learning
Lead ranking architecture
Data pages
Data flows
Strategies
Adaptive models
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