AI for Customer Advisory Board Candidate Selection
Use behavioral, engagement, and influence signals to recommend ideal advisory board candidates. Reduce selection time by up to 82% while improving board effectiveness by 43%.
Executive Summary
Within Customer Marketing → Loyalty & Retention Programs, AI streamlines the end-to-end process of recommending customer advisory board (CAB) candidates. By unifying product usage, NPS/CSAT, community engagement, deal influence, and advocacy signals, AI produces a ranked, explainable shortlist in hours—not days—improving board quality and diversity of perspective.
Why AI for Advisory Board Candidate Selection?
AI evaluates customer influence scores, product adoption depth, peer references, and feedback quality to predict the likelihood of strategic contribution. It then explains why each candidate is recommended so your team can act with confidence.
Process Transformation
🔴 Manual Process (14–28 hours, 12 steps)
- Customer influence assessment (2–3h)
- Advisory criteria development (1–2h)
- Candidate identification (1–2h)
- Evaluation framework (1–2h)
- Selection process (2h)
- Outreach strategy (1–2h)
- Engagement planning (1–2h)
- Board formation (2h)
- Effectiveness monitoring (1h)
- Feedback quality assessment (1h)
- Optimization (1h)
- Succession planning (1–2h)
🟢 AI-Enhanced Process (3–5 hours, 82% time savings)
- Ingest product, advocacy, and commercial signals
- AI scoring: influence, engagement, strategic fit, diversity
- Explainable shortlist with outreach recommendations
- Automated monitoring and succession suggestions
TPG standard practice: require data lineage for every score, threshold low-confidence cases for human review, and track post-meeting feedback quality to reinforce the model.
Key Metrics to Track
Operational definitions: Effectiveness is measured via meeting usefulness ratings and implemented recommendations; influence score blends deal impact, peer visibility, and product depth; time to fill is from requisition to confirmed acceptance.
Ecosystem & Enablers
AI models analyze behavior patterns to personalize loyalty programs and optimize rewards and pricing, increasing retention by 25–95%. Applied to CAB selection, the same signals reveal high-leverage candidates who contribute meaningful strategic feedback.
Implementation Timeline
| Phase | Duration | Key Activities | Deliverables |
|---|---|---|---|
| Discovery | Week 1 | Define CAB criteria & scoring schema; identify data sources | Scoring rubric & data map |
| Integration | Week 2–3 | Connect product usage, CRM, advocacy, loyalty platforms | Unified candidate feature store |
| Modeling | Week 4–5 | Train ranking model; calibrate thresholds & explainability | Ranked shortlist with reasons |
| Pilot | Week 6 | Run one CAB cycle; collect meeting/feedback quality | Pilot results & adjustments |
| Scale | Week 7–8 | Roll out to all regions; automate succession planning | Production workflow & dashboards |