Artificial intelligence-based opportunity insights
Pega Sales Automation uses decisioning capabilities to provide predictive
insights for opportunities. Opportunity insights are displayed in the opportunity record for each opportunity. The
Opportunity insights widget is dynamic and responds to changes, such as stage changes
and changes in customer activity or contact growth. Pega Sales Automation uses a single strategy to drive the insights that are
displayed on the dashboard widget and to take periodic predictor snapshots. The strategy runs all three model types (move to next stage, opportunity win, and win
date), distinguishing them by using labels that are mapped to the issue and group
hierarchy. For the opportunity win and move to next stage models, the strategy also
evaluates a model without predictors (opportunity win base propensities) to establish a
base propensity for benchmarking. Pega Sales Automation uses self-learning adaptive models to generate
opportunity predictions. This approach is based on core decision management capabilities and
provides the flexibility to add and remove predictors as your needs change. The following predictors are provided in the adaptive
model: Review the following adaptive models architecture concepts: 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. 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. Adaptive models predict the quarter 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 to indicate whether the opportunity close date is
earlier than expected, on time, or delayed. Prediction for this model is: will this opportunity ever be won? The
input predictor range should be as complete as possible. All models
share the same predictors, although they might treat them
differently. 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. 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) dataset. 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. 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. This data model supports separate models for the following outcomes: Pega Sales Automation uses propositions 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. 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. Opportunity insights use 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 GetContactGrowthRatio,
GetEmailActivityRatio,
GetTrendsDataRatio, and
GetCIRatio activities before calling the
SAPredictOpportunity data flow. The data flow
uses the EvaluateOpportunity strategy to predict
the propensity to move to the next stage, to win an opportunity, and to
predict the close date quarter. 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. The solution uses the StoreOpportunitySnapshots data
flow during the initial training of the models by using the 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. The input for TrainFromHistory comes from the
RD OpportunityStages, which fetches one record
per opportunity from the predictors’ data table, sorted by maximum
stage. This data flow calls the OpportunityClosed
and MovedToNextStage data flows. The OpportunityClosed data flow calls the
CloseOpportunity strategy, which captures
responses for decisions in the past period. 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. 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. The SAPredictOpportunity data flow reuses the
EvaluateOpportunity strategy to get the
analytic results for the opportunity. To view details for each data flow, perform the following steps: The following are the opportunity insights strategies: To view details for each strategy, perform the following steps: 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: To view details for each model, perform the following steps:Opportunity insights workflow overview
Historical data and adaptive learning
Pega Sales Automation Pega Sales Automation for
Financial Services B2B and B2C opportunities B2B opportunity B2C opportunity .ActiveDays .ActiveDays .ActiveDays .AllTotal30Vs90 .AllTotal30Vs90 .Age (months or years in business) .AllUnique30Vs90 .AllUnique30Vs90 .AllTotal30Vs90 .ContactGrowthPast30Vs90Days .AnnualRevenue .AllUnique30Vs90 .CountOfClsPast30Vs90Days .ContactGrowthPast30Vs90Days .AnnualIncome .OpportunitySource .CountOfClsPast30Vs90Days .ContactGrowthPast30Vs90Days .OrganizationIndustry .CurrentAssets .CountOfClsPast30Vs90Days .OrganizationRevenue .EstimatedCreditScore .CurrentAssets .OrgDownloadActivity30Vs90Days .LoanAmount .EstimatedCreditScore .OrgSubscrbeActivity30Vs90Days .MonthlyDebtToIncomeRatio .LoanAmount .OrgWebsiteActivity30Vs90Days .MonthsInBusiness .MonthlyDebtToIncomeRatio .ReceivedTotal30Vs90 .NumberOfEmployees .NoOfAccounts .ReceivedUnique30Vs90 .OpportunitySource .NoOfHouseholdMembers .SentTotal30Vs90 .OrganizationIndustry .OpportunitySource .SentUnique30Vs90 .OrgDownloadActivity30Vs90Days .pyPostalCode .StageDuration .OrgSubscribeActivity30Vs90Days .ReceivedTotal30Vs90 .StageSequence .OrgWebsiteActivity30Vs90Days .ReceivedUnique30Vs90 .PreviousYearGrowth .SentTotal30Vs90 .pyPostalCode .SentUnique30Vs90 .ReceivedTotal30Vs90 .StageDuration .ReceivedUnique30Vs90 .StageSequence .SentTotal30Vs90 .TotalOutstandingDebt .SentUnique30Vs90 .ValueOfCollateral .StageDuration .StageSequence .TotalOutstandingDebt .ValueOfCollateral Opportunity insights architecture
Predictors
Strategy models
Data pages
Data flows
Strategies
Adaptive models
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