Beauty brand lifts AOV with AI product recommendations
The problem
A beauty retailer used AI to personalize product recommendations, improving conversion rates and average order value.
The AI solution
An SMB beauty retailer needed to increase conversion and AOV without increasing ad spend. Manual merchandising could not scale across SKUs and seasons.
They implemented AI‑driven recommendations—factoring in browsing behavior, purchase history, and product attributes—to personalize category and PDP modules. The stack combined a recommendation service with A/B testing and analytics.
Results: higher AOV and conversion through better cross‑sell and up‑sell experiences. For context on similar results, see the original coverage.
For SMBs: start with a small catalog segment. Measure lift in add‑to‑cart, AOV, and PDP click‑through. Use lightweight tools first, then consider custom models once you outgrow off‑the‑shelf options.
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Results & metrics
- 10–15 hours saved per week
- 20–30% faster cycle times
- Lower error rates and fewer escalations
Lessons for SMBs
- Start small: pilot one workflow and measure it rigorously
- Blend heuristics with ML for quicker wins and safer rollout
- Train staff and bake new steps into SOPs to lock in gains
Learn more
Read the full case study: Raconteur: FC Beauty case
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