Predictive Satisfaction
Satisfaction scores told the team how customers felt only after the survey closed, often too late to intervene. We built a model that forecasts satisfaction from behavioral signals, moving the warning forward by about six weeks.
At a glance
Client
Subscription services provider
Sector
Analytics
Engagement
Predictive modeling
Timeline
14 weeks
The challenge
Knowing too late to act
Satisfaction was measured by a quarterly relationship survey with a response rate under a third, so the picture was both late and partial. By the time a score dipped, the accounts behind it had often already cut usage or opened a cancellation conversation. Customer success was reacting to churn instead of getting ahead of it, with no way to prioritize which of hundreds of accounts to call first.
Our process
What we did
- 01
Outcome definition
Worked with success and finance to define what “at risk” means in revenue terms, not just a low score.
- 02
Signal discovery
Analyzed two years of product usage, support tickets, and billing events to find the behaviors that precede a satisfaction drop.
- 03
Model build
Trained and cross-validated a model that scores every account’s satisfaction risk continuously, not quarterly.
- 04
Early-warning feed
Pushed ranked at-risk accounts into the CSM workflow, each with the reasons behind its score.
- 05
Closed-loop validation
Fed intervention outcomes back into the model so its accuracy improves over time.
The results
0.89
model AUC on held-out accounts
82%
of churned accounts flagged before they left
6 wks
earlier warning than the quarterly survey
−31%
churn among flagged accounts after rollout
1,400+
accounts scored continuously, every day
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