These personas are created by giving each customer three scores. The scores are based on the recency (R), frequency (F), and monetary value (M) of purchases made. Each is scored between 1 (worst) and 5 (best).
The RFM scores are relative and appropriate to your customers: a customer scoring 5 recency for you may not score 5 for another retailer.
How scores are created
All scores are created by the same ranking method. As an example, let us look at how the Frequency (F) score is calculated:
- For each customer, get the total number of orders and rank them (high to low).
- Split them into 5 equal groups (quintiles). In fact, we do this a little differently — I’ll explain below.
- Give each customer a score based on which quintile they belong to: 5 for the top quintile, 1 for the bottom quintile.
This ranking and scoring process is repeated for Recency (R) and Monetary (M) values.
Finally, the three RFM scores are joined together (not added) to create a 3 digit persona.
- A customer identified as a “244” is a frequent high-spender, but they have not made an order for quite some time.
- A customer identified as a “511” is a new customer.
A better RFM model
The RFM model we have created in Engagement Cloud provides eight standard personas to help target customers.
Whilst you can also create your own granular personas, those provided by the standard model do not overlap.
Non-overlapping personas are important and helpful. If you choose to coupon a particular persona, you want to be sure you don’t simultaneously coupon the same people differently as part of another persona.
Another way Engagement Cloud’s model is different is how we treat quintiles. We use a dynamic range rather than a fixed range.
Quintiles in RFM are commonly made up of an equal number of customers (a fixed range). They are straight 20% slices of R, F, and M dimensions.
Fixed-sized quintiles are potentially flawed if you consider that a customer may be scored as a 5 or a 4 based on small variances in their data. For example, someone who spent $673.10 should have the same Monetary score as someone who spent $673.11. With a fixed range model they may become different personas.
To create dynamic range quintiles we normalize the data. “Normalize” here just means we make the data easier to compare.
- Recency is grouped by unique days since last purchase
- Frequencies are grouped by unique values
- Monetary amounts may be subject to rounding and grouping
This approach leads to quintiles that are not evenly sized. This is a good thing. Customers are scored accurately by appropriately dealing with small data variance that could make them jump a whole persona.
Combining F and M keeps it simple and accurate
The final part of the model is to add up the F and M dimensions. This gives us an R dimension with a potential score of 1 – 5 and an FM dimension with a potential score of 2 – 10.
F and M are added together to help ensure segments do not overlap and to allow for easy visualization of your customers by RFM persona. This is done with a treemap.
Other RFM models treat this problem differently (such as throwing out a dimension). We think a combined FM score is a good compromise between accuracy and simplicity.
RFM is an easy way for retailers to extend their current behavioral targeting and reporting.
- Data-driven personas for no effort.
- Easy segmentation and context for your marketing activities.
- At-a-glance overviews of your entire customer base. Risks and opportunities are visual and actionable.
- When combined with retailer reporting KPIs, RFM gives you new and interesting ways to slice your data and find out where the money is.
Keep your eye on Engagement Cloud’s upcoming release for a lot more on RFM and retailer reporting.