Aggregation enhances customer contact policies and limits the number of strategy components by fetching, grouping, and filtering information. For example, you can create an aggregated data set that groups all the offers that a customer accepted within the last 30 days and use that data set as a shape in a strategy to avoid creating duplicate offers.

You can create the data sets from the Create menu, from the landing page, or directly from the strategy components where they are used.

You can manage existing data sets on the landing page. For example, you can precompute the aggregations and store the data in Cassandra-based tables to speed up processing. This process is called materialization and it is togglable. You can also recreate aggregations by discarding all previously computed data and recalculating the aggregation.

You can use the Advanced option to change the access group and facilitate debugging by toggling the display of metrics in the data flow run.

From the Interaction history summaries tab, you can navigate to the data flow run that initiates aggregations. The data flow run displays statistics for aggregations, such as the number of preaggregated records.

Interaction history aggregations are node-dependent which means that they require a decision data store node and a real-time node.