We looked at how the aggregation of customer data
Posted: Mon Dec 23, 2024 4:57 am
Further Examples
In the two examples given above, the aggregations were based upon the full set of transactional records. However, each aggregation type allows a selection of transactional records to be specified. An example of how this could be used is that you may create an expression to return the difference between the average order value in the last 12 months and the previous 12 months.
In this initial version we have supported almost all of the aggregations that could have been created using the existing RFV wizards. Furthermore, we have supported the Maximum cyprus mobile numbers Distinct Count and the Rank Coefficient aggregations that are available in the Data Grid aggregation functionality. Finally we have also supported a Select Nth Distinct extension to the Recency aggregation.
We hope the developments detailed in this blog post will empower you to simplify your processes of creating expressions, allowing you to reference aggregation results swiftly and easily.
In our first post introducing the concept of ‘on-the-fly aggregation’, is fast becoming widespread practice for marketers and analysts alike.
Summarising transactional information for a client or customer is a useful tool for providing more simplified analysis. Most typical types of aggregation required to do this can currently be achieved in FastStats® using the RFV wizards. In previous releases, it would have been necessary to create an intermediate Virtual Variable to then use on a data grid or in a selection. The most recent Apteco blog discussed how to use aggregations in expressions, in a way that is both powerful and flexible.
In this piece, we will be looking at a new type of aggregation that will enable you to answer more complex questions, such as those that involve dependent events and conditional courses of customer activity.
In the two examples given above, the aggregations were based upon the full set of transactional records. However, each aggregation type allows a selection of transactional records to be specified. An example of how this could be used is that you may create an expression to return the difference between the average order value in the last 12 months and the previous 12 months.
In this initial version we have supported almost all of the aggregations that could have been created using the existing RFV wizards. Furthermore, we have supported the Maximum cyprus mobile numbers Distinct Count and the Rank Coefficient aggregations that are available in the Data Grid aggregation functionality. Finally we have also supported a Select Nth Distinct extension to the Recency aggregation.
We hope the developments detailed in this blog post will empower you to simplify your processes of creating expressions, allowing you to reference aggregation results swiftly and easily.
In our first post introducing the concept of ‘on-the-fly aggregation’, is fast becoming widespread practice for marketers and analysts alike.
Summarising transactional information for a client or customer is a useful tool for providing more simplified analysis. Most typical types of aggregation required to do this can currently be achieved in FastStats® using the RFV wizards. In previous releases, it would have been necessary to create an intermediate Virtual Variable to then use on a data grid or in a selection. The most recent Apteco blog discussed how to use aggregations in expressions, in a way that is both powerful and flexible.
In this piece, we will be looking at a new type of aggregation that will enable you to answer more complex questions, such as those that involve dependent events and conditional courses of customer activity.