E‑commerce cuts churn 15% with predictive analytics
The problem
A growing online retailer used predictive signals to flag at‑risk customers and trigger re‑engagement, reducing churn by ~15%.
The AI solution
Customer retention drives SMB profitability. One online retailer used predictive analytics to score churn risk based on recency, frequency, value, and support signals.
High‑risk cohorts received targeted offers and service outreach; loyal segments saw VIP perks. The team reviewed lift weekly and iterated creatives.
Outcome: ~15% churn reduction and higher repeat purchase rate.
Takeaway: start with simple RFM scoring and one re‑engagement play. Track cohort retention and margin.
Results & metrics
- Outcome: ~15% churn reduction and higher repeat purchase rate.
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: https://www.activdev.com/
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