Product · Revenue Retention

Churn scored.
Revenue recovered.

14 behavioral signals feed a continuous churn model that scores every active subscriber daily. When a score crosses the threshold, the system selects the right intervention for the specific signal — not a generic discount, a targeted response.

14Behavioral Signals Monitored
24hScoring Cycle · Every Active Subscriber
0.80Default Threshold → Human Escalation
01 — The Signals01

Not one metric.
Fourteen.

Single-metric churn prediction is brittle. A customer who hasn't logged in may be on vacation. A customer who has contacted support 3 times may be deeply engaged. The model uses a composite of 14 signals weighted by their predictive value for each customer profile.

🔑
Login Frequency Trend

Not just whether they logged in — the trend over 30, 60, and 90 days. Decay in login frequency is one of the strongest leading indicators of churn.

⚙️
Feature Utilization Depth

Which features the customer uses and how deeply. A customer using 2% of their plan's capabilities is more at risk than usage suggests.

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Payment Failure History

Number of failed payments in the last 90 days, whether dunning has been triggered, and current payment health status.

💬
Support Sentiment

Tone and frequency of support interactions. Escalating frustration is a churn signal even when the issue is resolved.

📅
Account Age

Newer accounts churn at higher rates. Age is a stabilizing factor that adjusts risk scores for early-tenure customers.

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Usage Trajectory

Whether usage is growing, stable, or contracting over the last billing period. Contraction is a more reliable signal than absolute usage level.

The model also incorporates population intelligence — cross-customer cohort data that identifies which churn patterns are most common for customers with similar profiles. A customer in the 78th percentile for churn risk within their cohort gets earlier intervention than a score of 0.62 alone would suggest.
02 — The Interventions02

The right response
for the right signal.

The system identifies not just that a customer is at risk, but why. Price churn gets a different response than engagement churn. High-risk customers above the confidence threshold get escalated to a human with a full briefing — not an automated message.

Every intervention offer is logged with outcome tracking. The self-improvement engine analyzes which offers worked for which customer profiles and proposes refinements to the intervention logic over time.

Price friction signal
BNPL offer — full revenue over installments
Engagement decay signal
Targeted re-engagement offer
Feature underuse signal
Plan downgrade suggestion
Billing failure signal
Rail fallback + retry sequence
Score above 0.80
Human escalation with full briefing
Score 0.50–0.80
Autonomous intervention
Score below 0.50
Monitoring only · no action
All interventions
Audit log entry + outcome tracking
03 — BNPL as a Retention Tool03

Recover full revenue.
Not a discount.

When the churn signal is price friction, the conventional response is a discount. ZenoPay's BNPL engine offers a different approach: split the next billing cycle into 2–12 installments, charge each installment on schedule across the optimal rail, and recover the full plan value.

The customer gets breathing room. You keep the full MRR. The installment schedule is stored, monitored, and retried with the same dunning logic as subscription renewals.

Installment range
2 – 12 installments
Minimum BNPL amount
$50
Maximum BNPL amount
$10,000
Processing
Daily cron · all 7 rails eligible
Failed installment
Rail fallback + dunning retry
Trigger condition
Price churn signal only
Revenue impact
Full plan value recovered
04 — Other Capabilities04

Part of a complete
intelligence platform.

Churn intelligence is one of six integrated systems. Scoring data feeds into the brain's upgrade detection and intervention offer selection.

Stop watching
customers leave.

Early detection with the right intervention recovers revenue that discounts cannot. Reach out for early access.

Get Early Access ← Back to ZenoPay