Churn Prediction
SaaSy's churn prediction engine runs independently from the health score. Where the health score is a snapshot, churn prediction is forward-looking: it weighs behavioral trajectories to estimate the probability that a customer will not renew.
How probability is calculated
The engine adds risk contributions from several signal groups, then normalises to a 0.0–1.0 probability. The probability maps to a risk level:
| Probability | Risk level |
|---|---|
| < 0.30 | Low |
| 0.30–0.49 | Medium |
| 0.50–0.69 | High |
| ≥ 0.70 | Critical |
Engagement signals
| Signal | Contribution |
|---|---|
| Days since last login > 30 | +0.25 |
| Days since last login 15–30 | +0.15 |
| Active user percentage < 30% | +0.20 |
| Feature adoption rate < 40% | +0.15 |
Behavioural trends
| Signal | Contribution |
|---|---|
| Login frequency trend: declining | +0.20 |
| Feature usage trend: declining | +0.20 |
| Payment issues (per incident, max 3) | +0.15 each |
NPS
| NPS score | Contribution |
|---|---|
| 0–6 (Detractor) | +0.25 |
| 7–8 (Passive) | +0.10 |
| 9–10 (Promoter) | -0.00 (protective — see below) |
Contract and account factors
| Signal | Contribution |
|---|---|
| Auto-renew off AND renewal in < 60 days | +0.15 |
| More than 2 support escalations in 30 days | +0.20 |
| Any downgrades in account history | +0.15 |
Protective factors
These factors don't reduce the raw score directly, but they are surfaced in the prediction output and used to qualify the confidence level:
- Auto-renewal enabled — reduces urgency of intervention.
- Feature adoption rate > 60% — indicates product stickiness.
- NPS ≥ 9 (Promoter) — strong satisfaction and loyalty signal.
- Contract value > $100k — enterprise contracts often have structural retention.
Confidence score
The prediction comes with a confidence level (0.0–1.0) based on how many signals were available:
| Signals available | Confidence |
|---|---|
| 5 or more | 0.85–0.90 |
| 3–4 | 0.75–0.82 |
| 1–2 | 0.65–0.72 |
When SaaSy has access to your live database (all connected integrations), confidence increases because it can pull real activity data rather than relying on interpolated inputs.
Risk factors and interventions
For each prediction, SaaSy surfaces the top contributing risk factors and recommends interventions based on the risk level:
| Risk level | Recommended intervention | Timeline |
|---|---|---|
| Critical | Executive escalation + retention package | Within 48 hours |
| High | Success plan reset | Within 1 week |
| Medium | Health check call | Within 2 weeks |
| Low | Regular touchpoint | Monthly cadence |
Factor-specific interventions are also generated when identifiable triggers exist:
- Low User Adoption → Targeted activation and training campaign.
- Payment Issues → Billing review and payment terms conversation.
Trend analysis
The prediction output includes a trajectory based on the previous prediction:
| Change in probability | Trajectory |
|---|---|
| +15% or more | Rapidly Deteriorating |
| +1% to +14% | Worsening |
| -1% to -14% | Improving |
| -15% or more | Improving Significantly |
| Within ±1% | Stable |
Momentum is also tracked:
- Negative — both login frequency and feature usage trending down.
- Positive — both trending up.
- Mixed — trends diverging.
How alerts fire
A RenewalRisk alert fires when churn probability crosses the threshold configured in
Settings > Alert Rules. By default this is set to fire at High risk level (probability ≥ 0.50).
You can lower this to Medium (0.30) to get earlier warnings at the cost of more noise.
See Alerts & Rules for how to configure thresholds and notification channels.
Viewing predictions
Open any customer from the Customers dashboard and click the Churn Risk badge. The detail panel shows:
- Current probability and risk level.
- Top risk factors with severity and evidence.
- Protective factors.
- Recommended interventions with assigned owner and timeline.
- Trend trajectory vs. previous prediction.