CRM Analytics, RFM Analysis
After learning and completing a project about rule based classification, this week’s subject is CRM analytics.
CRM means Customer Relationship Management. There are 3 types of CRM: collaborative, analytical, and operational. As a data scientist, the most important approach is -as you can guess- the analytical approach.
Most companies have large and valuable customer databases and it would be a shame to not use the data in them. To make better business decisions, companies can use RFM analysis and segmentize their customers. It would be too hard/impossible to provide different approaches to each customer. By segmentizing, companies can approach customer group(s) more strategically.
RFM Analysis
RFM stands for Recency, Frequency, Monetary.
Recency: Time between the last transaction made and the analysis date.
Frequency: Number of transactions.
Monetary: Total price of the transactions.
RFM is a data driven, rule based segmentation technique.
In table-1, we can observe customers’ RFM information and try to compare them but values need to be standardized to evaluate and segmentize each customer. Without transforming these values it is not possible to decide whether a customer’s R, F and M values are high or not. For example, C3’s recency is 46 days, this looks bad compared to C1 or C2 but it is possible that there are some customers with 300+ days recency. This standardization can be done by scoring each metric in a 1–5 rating system.
Now we can clearly see, C3’s recency score is bad as 1 is the lowest score possible. After creating the score table, Each customer’s RFM score can be determined in a couple ways like calculating the mean of the RFM values or labeling with the RFM values. Let’s use labeling.
Still it can be seen that there are too many RFM scores. (111,112,…,555) 5³ = 125 values to be exact. For this we can use the 2-axis RF Grid to segmentize.
Notice that M score is not used. This is because our goal is to reduce the number of groups in a logical way and monetary is not the most important metric. As, a high monetary score can be gained with one big purchase or a lot of small purchases.
Lots of the times keeping existing customers is far more efficient than gaining new customers. So companies might focus on the groups with high frequency and low recency scores like “Can’t loose them”, “at risk”. This customers were “champions” or “loyal customers” at some point and it would be beneficial to win them back. Note that different approaches should be taken in different fields and industries.
This process should be done periodically to update customers’ scores and to evaluate the performance of the strategies applied to the customers since the last segmentation process.
Thank you for reading.
Barış Cengiz
