RFV stands for Recency, Frequency, Value. It’s a technique database analysts use to segment their customer data by plotting customers into a three dimensional space using three metrics:
- Recency – a measure of how recently a customer last purchased.
- Frequency – how often they purchase within a given time period.
- Value – the amount they spend within a given time period.
Because we are plotting across three axes, we can think of the subject database as a cube. Assuming a customer has a Recency measure if of X, and Frequency measure of Y and a Value measure of Z we can use these values to plot each customer within a three dimensional space.
Now, let’s imagine each axis in your cube is subdivided like a Rubik’s cube – with each axis divided into three – let’s call them high, medium and low. Within the cube these 3x3x3 axes would give you 27 segments.
Imagine a block in one corner of the Rubik’s cube. This could contain those customers who:
- Bought within the last month (R)
- Buy at least 12 times per year (F)
- Spend more than £100 per year (V)
Now imagine a segment in the opposite corner of the block. This could contain customers who have:
- Not bought for 11 months
- Only bought once in the last year
- Only spent £5
Compared to the first set, these are very low value customers.
When you know the value of all customers, in all of the segments, it is possible to calculate the value of changing their distribution within the base by moving them from one RFV segment to another.
So, for example, if we moved a portion of the customers in the lowest value RFV segments to one of the neighbouring higher value segments we could increase our annual revenue by £x.
We could estimate that if we shifted 5,000 customers from one segment to another by increasing purchase frequency from 1 per year to 2 and increased their value from £5 to £10 we would increase the revenues from those customers from £25,000 to £50,000. If we were able to produce other RFV shifts in other customers in the base we might increase revenues from £1m to £1.5m etc.
So, RFV enables data-driven marketers to understand the structure of the customer database and develop strategies to target known potential revenue gains within a customer base. With the revenue gains estimated it is possible to cost out the activities and incentives required to stimulate the target change in behaviour and estimate a future ROI of that activity.
This article assumes that any activities related to customer data collection and profiling are performed with customer consent and compliant with GDPR.