I'm trying to do some analysis on customers behavior. Basically, I have information on customer's loyalty points activities data (e.g. how many points they have earned, how many points they have used, how recent they have used/earned points etc).
I'm just wondering how should I go about clustering customers based on the above information? I'm trying to apply the RFM concept then use K-means to segment my customers (although it is a little bit more complicated as I have a few more variables than just R,F,M , for instance, I have recency, frequency and monetary on both points earn and use, as well as other ratios and metrics). Is RFM a good way to do this?
Essentially I have two objectives:
To segment customers
Via segmenting customers, identify changes in customers behavior (e.g.customers who spent all of their points before churning, or in other words, are customers who all of the sudden spent all of their points have a higher propensity to leave the program and churn?), provided that segmentation is the right method for such task?
Clustering <- kmeans(RFM_Values4, centers = 10)
Please enlighten me, need some guidance on the best methods to tackle such problems.