Predictive Issue Detection
To proactively detect issues using predictive issue detection, I’ll apply AI and data monitoring across key signals in real time.
1. Key Signals to Monitor
- A. Data Monitored
- Redemption delays.
- Surge in merchant-specific complaints.
- Cancellation spikes.
- Low review velocity (silence = signal).
- B. Redemption Patterns
- Spike in redemption failures at specific merchants or for specific deals.
- Unusual redemption delay (e.g. vouchers not being used after purchase).
- Patterns of failed booking attempts.
- C. Customer Support Queries
- Volume of inbound complaints tied to specific deals or merchants.
- Use of trigger phrases: “merchant unresponsive,” “can’t book,” “closed location”.
- Escalation frequency or repeat contacts for the same issue.
- D. Review & Sentiment Trends
- Decline in customer star ratings within a short time frame.
- Drop in review volume (could signal avoidance).
- Natural Language Processing (NLP) sentiment analysis to flag negative emotion shifts.
2. AI Models & Rules
- Anomaly Detection Models: Anomaly detection on merchant behavior. Flag sudden changes in voucher usage, support volume, or reviews.
- Sentiment Scoring Engines: Real-time review and query text analysis from reviews and support chat.
- Clustering Algorithms: Group issues by topic/merchant to identify systemic issues early.
- Threshold Triggers: If a merchant crosses a 3-day threshold of >5% voucher complaints → auto-flag.
- Booking drop-off after purchase: merchant availability issue.
3. Automated Actions Based on Risk Scores
- Action Triggers:
- Flag merchant for Trust Score review.
- Remove or pause deal.
- Auto-alerts to Merchant Success team.
| Risk Level | Trigger | Action |
|---|---|---|
| Low | Slight uptick in support volume | Monitor, add to watchlist |
| Medium | Negative reviews + rise in failed redemptions | Internal alert, queue for manual review |
| High | Spikes in complaints, failed redemptions, sentiment ↓ | Auto-pause deal, notify merchant ops, escalate |
4. Merchant Feedback Loop
- Notify the merchant early if patterns suggest issues.
- Provide analytics dashboards showing performance and feedback.
- Offer support (e.g., coaching or operational guidance) before deactivation.
5. Platform-Wide Prevention
- Smart Content Tagging: Mark deals with known fulfillment friction.
- Proactive Customer Messaging: “Heads up” alerts or instructions when known issues arise.
- Automated QA Checks: Validate new deal listings based on merchant risk profiles.
Why This Works
- Catches emerging problems before they escalate into widespread CX failures.
- Enables faster intervention by identifying patterns across reviews, redemptions, and queries.
- Protects brand trust by pausing risky deals and alerting merchant ops in real time.