EDA in Public (Part 3): RFM Analysis for Customer Segment...

What’s Happening

Real talk: How to build, score, and interpret RFM segments step by step The post EDA in Public (Part 3): RFM Analysis for Customer Segmentation in Pandas appeared first on Towards Data Science.

If you’ve been following along, we’ve come a long way. In Part 1 , we did the “dirty work” of cleaning and prepping. (yes, really)

In Part 2 , we zoomed out to a high-altitude view of NovaShop’s world — spotting the big storms (high-revenue countries) and the seasonal patterns (the massive Q4 rush).

The Details

But here’s the thing: a business doesn’t actually sell to “months” or “countries. If you treat every customer exactly the same, you’re making two expensive mistakes: Over-discounting: Giving a “20% off” coupon to someone who was already reaching for their wallet.

Ignoring the “Quiet” Ones: Failing to notice when a formerly loyal customer stops visiting, until they’ve been gone for six months and it’s too late to win them back. Instead of guessing, we’re going to use the data to let the users tell us who they are.

Why This Matters

We do this using the gold standard of retail analytics: RFM Analysis . Recency (R): How just did they buy? (Are they still engaged with us?

The AI space continues to evolve at a wild pace, with developments like this becoming more common.

Key Takeaways

• ) Frequency (F): How often do they buy?
• (Are they loyal, or was it a one-off?
• ) Monetary (M): How much do they spend?
• (What is their total business impact?

The Bottom Line

You cannot track behavior without a consistent identity. We can’t know how “frequent” a customer is if we don’t know who they are!

What do you think about all this?