Win-back analytics and lifecycle automation
I led the analytics that found churned, high-value customers worth winning back. The business acted on it and recovered $5M+ in transaction volume within four months. Alongside it, I built the Snowflake to Customer.io pipeline behind lifecycle campaigns.
- Transaction volume recovered
- $5M+Transaction volume recovered
- From analysis to recovered volume
- 4 monthsFrom analysis to recovered volume
- Activity summaries sent to B2B customers
- WeeklyActivity summaries sent to B2B customers
The win-back
I led the analytics that identified loyal customers who had churned and sized the opportunity. The business implemented the intervention and recovered $5M+ in transaction volume within four months. The figure is transaction volume, not revenue.
I turned the approach into a repeatable retention workflow, and the findings informed wider efforts to identify, serve and keep this segment. I was subsequently invited to leadership meetings.
| What I owned | What the business owned |
|---|---|
| The analytics: finding the customers, sizing the opportunity, and making the approach repeatable | The intervention itself, run across teams |
The lifecycle pipeline
- A Snowflake to Customer.io reverse ETL pipeline that syncs customer attributes and events into the engagement platform.
- Feature-adoption campaigns that use that data to reach the right customers.
- Weekly and monthly transaction and activity summaries for B2B customers, with analytics embedded in the email.
Customer-level data and campaign internals belong to Afriex, so this page stays at the level of method and results.