Beauty retailer
Illustrative scenario: AI-driven churn prediction and automated win-backs for a beauty retailer.
Lower
90-day churn
Pre-lapse
Early warning
Cross-cat
Attach
Automated
Win-back journeys
Section 01
The challenge
Where this beauty & cosmetics scenario starts.
A beauty retailer with high 90-day churn — where many new members never return — often has no way to predict who is about to lapse, so every campaign is reactive and win-backs reach members who have already left.
Section 02
The approach
What we built together.
01
A churn-prediction model trained on loyalty + purchase history.
02
Automated win-back journey triggered before predicted lapse — not after.
03
Cross-category recommendations (skincare → makeup → fragrance) embedded in receipts and SMS.
04
A/B-tested offer strength, since high-CLV members often convert without deep discounts.
Section 03
Illustrative outcomes
The direction this scenario moves — illustrative, not a verified customer result.
Lower
90-day churn as win-back runs ahead of lapse (illustrative).
Attach
Cross-category attach (members buying 2+ categories) (illustrative).
ROI
Win-back campaign ROI vs a batch-blast approach (illustrative direction).
Share
Loyalty contribution to total revenue (illustrative direction).
“Predicting lapse before it happens lets win-back run ahead of churn, instead of chasing members who have already left.”
Illustrative scenario · Beauty & Cosmetics
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