In the Segments category you will find RFM related charts; some display aggregated data for the whole selected period, while others display how the data varied in time (“timeline”).
Which is the difference between these two types of charts and why are the numbers so different in some cases?
Surely you have already noticed the date picker in the top of most Reveal pages. This is the place where you select the period to display the charts’ data for.
For the aggregated charts, we take into account the state at the end date to calculate the RFM for each of the customers. In other words, the customer will be included in the RFM Group in which he was part of at the date equal to the end date.
For the timeline chart, each point within the chart is computed based on a short period for which the calculations are made (eg: that week/month/year). And for each period, the same applies: the end date for that short period is the date for which the RFM Group is computed.
Let’s take an example: the order count by RFM group charts.
In the aggregated chart, the count of each RFM group is displayed, where the RFM groups are calculated at the moment of the end date from the date picker:
In the timeline chart, the order count is also displayed, but grouped by month/week/year depending on the time interval you selected: https://prnt.sc/uabgiz
You will see the order count of RFM Group “Lover”, for example, on each month of the selected period. But keep in mind that a customer who was a Lover in June 2019, might no longer be part of the same group in June 2020. This is expected since we compute the RFM values for each period separately.
What this means is that if viewing metrics that have a “unique” condition applied, such as “Customer count”, then the sum of all points from the timeline chart will probably be larger than the sum of all bars within the aggregated chart: if a customer moved between 3 groups, he/she will be counted 3 times in this summing process. However, the sum on a single period (eg: a given month), should be equal with the sum of aggregated values for that month.
Still fuzzy about this? Drop us a line. 🙂 We’d be more than happy to help!
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