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hardData Analyst interview question

Marketing wants to segment customers for targeted campaigns. How would you build the segmentation, and how would you know it is useful?

What the interviewer wants to hear

  • Starts from the decision the segments will drive
  • Begins with simple rules such as RFM before clustering
  • Handles feature scaling and interpretability if clustering
  • Checks segments are stable, sizeable and different in behaviour
  • Validates usefulness through campaign tests

Model answer

I begin with the decision: are we choosing offers, channels or budget per segment? That decides which features matter. Often a simple RFM model, using recency, frequency and monetary value scored into bands, gives clear and explainable groups like "loyal high spenders" or "lapsing one-time buyers". If we need richer patterns, I try clustering such as k-means on scaled features like order frequency, basket size, categories and discount use, and I pick the number of clusters by both statistics and whether the groups make business sense. A useful segmentation has groups that are large enough to target, clearly different in behaviour, and stable when I rerun it on another month. The real test is impact: I would run campaigns where each segment gets a tailored message versus a generic one, and measure the lift per segment.

Can you answer it under time pressure?

Take a timed hard round and get scored on every answer.

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