First step to understand various customer characteristics is to build a segment. Many methodology has been created and the most simple yet very useful segmentation type is RFM segmentation.
R stand for Recency, meaning the number of time units that have passed since the customer last purchase. F stand for Frequency, the number of purchase per time unit and M stand for Monetary, the total amount spent per time unit. The order of RFM is also NOT without meaning. The most recent (R) customer who made transaction is more valuable compared to customer who transacted few unit time ago. From this three metrics, a cut off value based on value is determined to decide which cluster does a customer belongs to.
This segmentation is very simple, but is there any catch regarding the methodology? Supposed we already decide a unit time cut off as the metrics of R, and observed the relationship between frequency and monetary. In a case when the price and quantity variance of product are low, we expect that a high correlation between frequency and monetary. See chart below for reference (data is simulated and normalized), where variance of price unit and quantity are low, hence the correlation between frequency and monetary is high, 0.92
By using the median of frequency and monetary as the cut off and put the customers accordingly based on their value, we get the customer segmentation like below matrix, a good segment but not optimal because the clusters are concentrated is two clusters, Low Frequency – Low Monetary and High Frequency – High Monetary
The final segment is okay, but the strategy that can be derived from such as cluster will be limited (not granular). How to improve it? One way to improve those segment is to take another metrics to related monetary value but has low correlation between frequency and the new metrics.
In the final case, I take the average spend per transaction by each customer and got very low correlation (-0.09). The customers in final segment is spread evenly in each of the cluster.
Do you think that the approach is good? Comment and feedback are welcome.
#muse #3 #customer #marketing #analytics #RFM
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