Creating an interaction history summary data set
Simplify decision strategies by creating data set records for interaction history summaries. These data sets aggregate interaction history to limit and refine the data that strategies process.
Use interaction history summaries to filter customer data and integrate multiple arbitration and aggregation components into a single import component. For example, you can create a data set that groups all offers that a customer accepted within the last 30 days and use that data set in your strategy to avoid creating duplicate offers.
- In the header of Dev Studio, click .
- Click Create.
- In the Data Set Record Configuration section, define the data
- In the Label field, enter the data set label.The identifier is automatically created based on the data set label.
- Optional: To change the automatically created identifier, click Edit, enter an identifier name, and then click OK.
- In the Type list, select Summaries.
- In the Label field, enter the data set label.
- In the Context section, specify the ruleset, applicable Strategy Result class, and ruleset version of the data set.
- Click Create and open.
- In the Data source list, select one of the following
- Data set
- Data flow
- Choose the data source from the list, depending on the data type that you selected in
the previous step.
Result: The system displays the Group by section. The appearance of the section varies depending on the Data source selection. Note: If the source is not configured (the Aggregation record is not created), then the system displays the Configure source button. Click the button to configure the data source by choosing the Access group and Event timestamp. Keys will also be displayed, based on the source definition.
You do not need to configure the data source for Customer Profile Designer, as the system generates the Aggregation record in the background.
- In the Group by section, select the properties by which you want to group the data.
- In the Aggregates section, add aggregates, and then specify when
conditions for the aggregates, if applicable:
- Click Add aggregate.
- In the Name field, enter the existing property (for the aggregate output).
- In the Function field, specify the aggregate function.
- In the Time window list, specify the time span for which you
want to aggregate data:
- Select All time option, to aggregate data from the entire interaction history.
- Select Last, and then specify the time window, to aggregate data from a specific time period.
Note: Starting from Pega Platform version 8.6, you can have more then one time window for the summary data set as a result of the separate time periods of the aggregate. As a best practice, logically sort those aggregates into smaller number of data sets if possible.
- Optional: To add when conditions for the aggregates, in the Condition section, click an empty field, and then specify the when condition.
For example: To ensure that the customer does not receive duplicate offers, define the aggregate and when conditions, and then use the data set in the strategy of your application to prevent offers for which the value of the CountPositives property is greater than 0 for a specific customer. Use the following settings:
- Name: .CountPositives
- Function: Count
- Conditions: pyOutcome = Accepted
- Optional: To specify the aggregation start time, in the Start aggregating as
- Click The first record in the source radio button to start aggregating from the first record.
- Click A defined date radio button to start aggregating from a date that you specify.
- Optional: To further limit the data that the data set aggregates, in the Conditions section, define the filter conditions.
- Click Save.
- Optional: To save processing time, turn on preaggregation for the new data set:
- In the header of Dev Studio, click .
- Next to the data set for which you want to turn on preaggregation, click . Preaggregated data sets save processing time because they include the latest interactions. Data sets that are not preaggregated do not include the latest interactions and therefore they query the database.
- Applying sample scripts for archiving and purging
The interaction history tables contain transactional data which may grow fast. By using the sample scripts, the users can archive the data in the archiving database and delete (purge) the records from the source database. The scripts allow you to move the FACT table records, merge the Dimension records, and delete the records from the FACT table. Before you use any of the scripts, back up the source and target interaction history tables and create indexes on the columns. Indexes improve performance when you read data from the archived tables.
- Interaction History methods
You can use a rule-based API to associate known customer IDs with IDs that are generated by external interactions through different channels and devices or to separate them.
- Monitoring interaction results
Ensure that you stay up to date with recent interaction results by filtering and analyzing the interaction history records.
- Extending Interaction History
Learn about the underlying configuration of Interaction History and how to extend it to match your business objectives.
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