I'm trying to study 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/earn points etc). I'm using R to conduct this analysis
I'm just wondering how should I go about segmenting customers based on the above information? I'm trying to apply the RFM concept then use K-means to segment my customers(although I have a few more variables than just R,F,M , as i have recency,frequency and monetary on both points earn and use, as well as other ratios and metrics) . Is this a good way to do this?
Essentially I have two objectives: 1. To segment customers 2. Via segmenting customers, identify customers behavior(e.g.customers who spend all of their points before churning), 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.