A predictive churn analysis and behavioral intervention strategy for a wealth management firm hemorrhaging high-value clients to digital-first competitors.
Traditional CRM data showed 94% client satisfaction. Actual attrition: $72M AUM per year. Satisfaction surveys measure what clients say. Our behavioral analysis measures what they do.
Our predictive model identified three behavioral patterns that precede 67% of all client departures. These aren't complaints. They're silences.
Clients who skip two consecutive quarterly reviews have a 4.2x higher probability of departing within 6 months. Not because the review matters — because skipping signals they've already started evaluating alternatives. Your CRM isn't flagging this. Ours does.
A withdrawal of 5-15% of assets — not enough to trigger alerts — preceded 61% of full departures within 120 days. It's a test. The client is moving money to a competitor to "try it out" before committing fully. By the time you notice, they're gone.
Response time to advisor communications increased by 3x+ in the 90 days before departure. They're not angry. They're disengaged. They've emotionally left — the paperwork just hasn't caught up. An automated engagement score catches this in real-time.
Four intervention strategies triggered automatically when our model identifies at-risk clients. Not generic "check-in calls" — behaviorally designed touchpoints that address the real reason they're leaving.
When engagement drops, send a personalized email showing the client's portfolio performance against 3 benchmarks — market average, peer group, and their own stated goals. The behavioral insight: clients don't leave because of poor returns. They leave because they THINK their returns are poor. Context changes perception.
The advisor calls — but NOT about the portfolio. "I noticed we haven't connected in a while. How's the family? Any changes I should know about?" The behavioral insight: clients feel like a number when every interaction is about money. One human call reactivates the relationship bond.
If the client has made a small test withdrawal, send a transparent analysis of what switching actually costs — tax implications, timing costs, relationship rebuilding, learning curve. Not fear-mongering. Facts. The behavioral insight: people underestimate switching costs by 60-70%.
Measured against the 12 months prior to engagement. Every metric moved because the underlying behavioral detection and intervention architecture changed.
| Metric | Before | After | Change |
|---|---|---|---|
| Annual AUM Attrition | $72M | $31M | ▼ 57% reduction |
| Client Retention Rate | 91% | 96.8% | ▲ 5.8 percentage points |
| Avg. At-Risk Detection Lead Time | 14 days | 94 days | ▲ 6.7x earlier |
| Intervention Success Rate | N/A | 57% | New capability |
| ROI on Program Investment | — | 14:1 | $41M retained on $2.9M investment |