Ever enhanced the value of your email data with RFM scoring?
Recency, Frequency and Monetary value...
As you may know, the RFM scoring model is traditionally based upon transactional customer data. Using Recency, Frequency and Monetary value allows you to group customers, based on their purchase behavior and potential value.
The model is successfully used in mailorder and e-business to bring structure into the huge amount of data. It also helps to decide on campaign investment efficiently and effectively.
The proven technique shows that customers who bought recently are more likely to purchase again, in comparison with customers who haven’t. This also applies to customers who bought frequently. Simply put: the most valuable customers have the tendency to become even more valuable along the way.
In fact, the same model can be used in email marketing. Although e-commerce business has the advantage that you can track visitors right up to the shopping basket, other email marketers can use the large amount of email and website interaction data they collected over time. You can use RFM metrics on these data to measure the value of your subscribers and/or the engagement levels of each individual subscriber.
- Recency – When measuring engagement, the metric monitors the date the subscriber opened or clicked on a message for the last time over a defined period of time (depending on the lifecycle of the products/services)
- Frequency – When measuring engagement, the metric monitors the number of times a subscriber opened or clicked over a defined period set amount of time.
- Monetary Value – When measuring engagement, this can be applied to a number of value metrics – total spent per subscriber over a set amount of time, estimated value per subscriber, or another engagement score based upon open or click metrics.
The scoring uses a scale from 3 to 1 (or possibly 5 to 1, depending on the business). 3 (or 5) is attributed to the customers and segments with the highest Recency, Frequency and Value. Combining these 3 metrics creates segments, where a segment 333 groups most active/most valuable contacts: new interactors will have a RF score 31, the most engaged subscribers have score 33. Adding Monetary Value as a third metric refines the scoring.
Use this RFM scoring matrix as a helpful reference:
This simple scoring and sorting provides a clear view on the engagement levels and values of your subscribers, and provides you with the information you need to target each group effectively. And it also allows you to test different offers and messages to each group and track the impact on behavior and migration between segments.
Conclusion: Although the RFM scoring model is traditionally based upon transactional customer data, this model can be used in email marketing too. It brings structure into the huge amount of data and helps you decide on campaign investment efficiently.