There are so many possibilities!
Posted: Mon Dec 23, 2024 4:44 am
There are a number of different ways of ordering the resulting groups. We’re then able to choose which function to apply to the resulting best set in order to return a value. Some of them are numeric (such as working out the amount I’ve spent on my highest value destination). The one in the example above is categorical – it will return the name of the destination.
We can check the results of this aggregation by adding the expression to a data grid. In the example of the top person, the sum of their one transaction to Australia cyprus mobile number example is greater than the sum of their two to the USA.
On-the-fly Aggregations part 3
We’ll be looking to extend this to allow a wider range of functions and the ability to pick not just the top group – although this is the one that will have the most common use case in marketing analytics. The above aggregation can be used on its own within an expression or combined in an expression in any way you wish. For example, you may want to find out whether the highest value destination this year is the same as last year for each customer.
Multi-level aggregations
How many transactions did a customer make in the year after their first transaction? This can be a useful attribute to analyse or use in a predictive model. Prior to the on-the-fly aggregation functionality this would have been a more long-winded process.
We can check the results of this aggregation by adding the expression to a data grid. In the example of the top person, the sum of their one transaction to Australia cyprus mobile number example is greater than the sum of their two to the USA.
On-the-fly Aggregations part 3
We’ll be looking to extend this to allow a wider range of functions and the ability to pick not just the top group – although this is the one that will have the most common use case in marketing analytics. The above aggregation can be used on its own within an expression or combined in an expression in any way you wish. For example, you may want to find out whether the highest value destination this year is the same as last year for each customer.
Multi-level aggregations
How many transactions did a customer make in the year after their first transaction? This can be a useful attribute to analyse or use in a predictive model. Prior to the on-the-fly aggregation functionality this would have been a more long-winded process.