Identify Dormant Customers & Trigger AI Re-Engagement
Pinpoint dormant users automatically and recommend the right win-back campaigns. AI analyzes usage, support, and engagement signals to accelerate re-activation and protect revenue.
Executive Summary
AI-driven re-engagement identifies dormant customers, ranks win-back potential, and recommends the best offer, message, and channel. Teams replace 12–20 hours of manual hunting, analysis, and campaign setup with 2–3 hours of automated targeting and activation—improving adoption and reducing churn risk.
How Does AI Find Dormant Customers and Recommend Win-Backs?
Recommendations flow into your lifecycle tools for quick activation: education sequences for under-adopters, usage nudge campaigns, support resolution follow-ups, or incentive-based win-backs for high-value accounts.
What Changes with AI-Led Re-Engagement?
🔴 Manual Process (11 steps, 12–20 hours)
- Dormancy criteria definition (1h)
- Customer identification (1–2h)
- Engagement history analysis (2–3h)
- Re-engagement strategy development (2–3h)
- Campaign creation (2h)
- Channel optimization (1h)
- Automation setup (1–2h)
- Testing (1h)
- Deployment (1h)
- Performance monitoring (1h)
- Optimization (1h)
🟢 AI-Enhanced Process (4 steps, 2–3 hours)
- AI portal/usage analysis; identify personalization opportunities (1h)
- Automated customization strategy and UX optimization (1h)
- Real-time implementation and testing (30m)
- Adoption monitoring and optimization (15–30m)
TPG standard practice: Define dormancy windows per tier, exclude accounts in active escalations, and establish a “two-touch or task” SLA to ensure timely follow-through on AI-recommended plays.
Key Metrics to Track
Interpreting the Metrics
- Re-Engagement Rate: % of dormant users showing renewed usage or response within 14–30 days.
- Win-Back Conversion: % of dormant accounts completing target action (return usage, renewal, or purchase).
- Dormancy Recovery Time: Median days from campaign start to sustained activity resumption.
- Time Saved: Analyst/marketer hours reduced via automated detection and pre-built plays.
Which AI Tools Power This?
These platforms plug into your marketing operations stack to automate detection, targeting, and outcome measurement.
Implementation Timeline
| Phase | Duration | Key Activities | Deliverables |
|---|---|---|---|
| Assessment | Week 1–2 | Define dormancy criteria by tier; audit data sources and comms channels | Dormancy framework |
| Integration | Week 3–4 | Connect product analytics, support, and messaging tools; enable connectors | Unified engagement dataset |
| Modeling | Week 5–6 | Train re-engagement propensity and win-back models; set thresholds | Recommendation engine v1 |
| Pilot | Week 7–8 | Activate on selected segments; validate response and recovery time | Pilot results & tuning |
| Scale | Week 9–10 | Roll out playbooks and SLAs; automate alerting and routing | Productionized workflows |
| Optimize | Ongoing | A/B test offers/channels; monitor drift; refresh models monthly | Continuous improvement |