•1 min read•from Data Science
Rfm clustering problem
Our take
When tackling the RFM clustering problem in your furniture and decor enterprise, it's essential to address the low silhouette score of 0.3, indicating weak cluster separation. Simplifying to just frequency and monetary value has led to concentration around specific values, which compromises the effectiveness of your analysis. Consider refining your feature selection by experimenting with additional variables, such as customer demographics or preferences, alongside tenure and interpurchase metrics.
I work at a furniture/decor entreprise. I try to do rfm with kmeans. but the silhouette is low 0.3.., I removed r and just kept fm. but it all concentrate in f=2, or distinct f. when i keep only f》2 , it concentrate in f=3 and other distinct f also. I tried adding other variables : tenure, interpurchase time, coefficient variation of interpurchase time to get better clustering. What should I do?
I took two periods only 2025, then 2025 and 2024.
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#real-time data collaboration#real-time collaboration#rows.com#financial modeling with spreadsheets#RFM#clustering#kmeans#silhouette#furniture#decor#tenure#interpurchase time#coefficient variation#clustering variables#distinct f#f=2#f=3#customer segmentation#data analysis#feature selection