AI customer segmentation & hyper-personalization
Machine Learning · Data Science · Marketing Automation
The client
A retail company looking to improve customer segmentation and make marketing campaigns more personalized and data-driven.
Challenge
The client relied on basic RFM segmentation, which limited visibility into deeper customer behavior. Audience targeting was largely manual, making personalization hard to scale and leaving revenue opportunities untapped.
Scope & features
- Advanced behavioral customer segmentation
- ML-based clustering and pattern detection
- Automated customer lifestyle profiles
- Basket and churn analysis
- Segment-level marketing recommendations
- Hyper-personalized campaign targeting
- Automated ad delivery
Solution
Built an ML-driven segmentation and personalization engine that identified customer groups missed by traditional methods and automatically generated actionable marketing insights.
Outcome
Manual segmentation work dropped by ~70%, and campaigns became more relevant and personalized, with improved conversion performance.
Tech stack
- Python
- scikit-learn
- HDBSCAN
- UMAP
- OpenAI API
- Node.js
- React
- PostgreSQL
~70%
less manual segmentation work
Start with an AI audit
A focused review of your operations, data and automation — and a prioritized roadmap with ROI estimates. The same entry point our long-term partners started from.
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