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JSON file structure for historical data

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To perform better offline analysis of adaptive model historical data, learn more about the parameters that Pega Platform uses to describe the data that you extract.

When you extract historical customer responses from adaptive models, Pega Platform saves the historical data in JSON format. Consult the following property descriptions and sample output for a better understanding of the JSON file structure.

Properties for model context and predictors

When you define an adaptive model, you specify the names of the model context and predictors. To save the historical data in JSON format upon extraction, Pega Platform transforms the names that you specify to make them compliant with the JSON format.

See the following table for examples of property names before and after data conversion to a JSON file.

Property name in Pega Platform Property name in the historical data file Description
pyName Context_Name Model context
Age Age Model predictor
Devices(1).DeviceType Devices_1_DeviceType A model predictor that is embedded in a page list or group
Param.Rank Param_Rank Model parameter predictor
IH.Web.Outbound.Reject.pxLastGroupID IH_Web_Outbound_Reject_pxLastGroupID Interaction History (IH) predictor that is added to the model when you enable IH summaries for the model

For more information, see Adding adaptive model predictors.

Properties for decision strategy results

Consult the following table to learn more about how Pega Platform transforms the names of properties that describe decision strategy results, such as the strategy outcome. The names of these properties remain the same for all adaptive models in Pega Platform.

Property name in Pega Platform Property name in the historical data file
pyOutcome Decision_Outcome
pySubjectID Decision_SubjectID
pxInteractionID Decision_InteractionID
pxRank Decision_Rank
pxDecisionTime Decision_DecisionTime

For more information, see About Strategy rules.

Meta properties

Consult the following list to learn more about the properties that Pega Platform uses to describe the model itself.

id
The unique ID of a customer response.

You can use the response ID to identify potential duplicate records in the historical data file.

positiveSampling
Percentage of all positive responses to the model that Pega Platform uses to create the historical data file.

For more information, see Extracting historical responses from adaptive models.

negativeSampling
Percentage of all negative responses to the model that Pega Platform uses to create the historical data file.

For more information, see Extracting historical responses from adaptive models.

dataCenter
Name of the Cassandra data center from which Pega Platform captured the response upon historical data extraction.

You can use the data center name to identify the data center that wrote the record in an active-active multi-data center setup. For more information about Cassandra data centers, see Configuring multiple data centers.

rulesetName
Name of model ruleset.
rulesetVersion
Version of model ruleset.

Sample output

Pega Platform saves historical data in JSON format, as in the following sample output:{ "Param_International":"false", "Context_Direction":"Inbound", "Param_UnlimitedSMS":"false", "Context_Channel":"Call Center", "positiveSampling":"100.0", "Decision_SubjectID":"CE-967", "Decision_Rank":"3896.0", "rulesetVersion":"08-04-03", "Context_Name":"Apple iPhone 8 32GB", "IH_Web_Outbound_Reject_pxLastGroupID":"Phones", "Param_CLVSegment":"Lapsed", "Context_Group":"Phones", "id":"d747ba0d-e065-55a2-816d-1167632be149", "negativeSampling":"100.0", "Context_Issue":"Sales", "Decision_InteractionID":"-6604045570247117991", "dataCenter":"datacenter1", "Decision_OutcomeTime":"20160228T000000.000 GMT", "Param_FourG":"false", "Param_SubscriptionCount":"1.0", "Param_OverallUsage":"0.54", "Decision_Outcome":"Reject", "Param_ChurnSegment":"Low", "Decision_DecisionTime":"20191008T101224.796 GMT", "Param_Sentiment":"Negative", "rulesetName":"DMSample" }

  • Extracting historical responses from adaptive models

    Extract historical customer responses from adaptive models in your application for offline analysis. You can also build a model in a machine learning service of your choice, based on the historical responses that you extract.

  • Defining an adaptive model

    Predict customer behavior and adjust your marketing strategy by configuring an adaptive model.

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