Customer Churn Risk Report

90-Day Cancellation Exposure — Predictive Model + Full Account Analysis
Analysis as of April 28, 2026  |  300 customers across Starter, Pro, and Enterprise plans  |  Predictive model trained and validated
Revenue Alert: 28 customers are at risk of cancelling in the next 90 days, putting $41,239 in monthly recurring revenue at risk — equivalent to nearly $495,000 annualized. The predictive model has now identified the exact drivers: customers with very low product activity churn at 10x the rate of engaged users.
$41,239
Monthly Revenue at Risk
~$495K annualized
28
At-Risk Customers
9.3% of customer base
6.9%
MRR at Risk
of $601,689 total MRR
300
Total Customers
$601.7K total MRR

What the Model Says Is Driving Cancellations

Predictive Model — Trained & Validated
A model was trained on the full history of 300 customers — their billing records, usage behaviour, plan type, and company profile — to identify which factors most reliably predict who cancels. Here are the three drivers it ranked most highly, each quantified from actual customer data.
Top Driver — Strongest Signal
Overall Product Activity
44%
churn rate for customers with 0–3 product events in the past year
Customers in the lowest activity band churn at 44% — compared to just 4% for those with 9 or more events. That is a 40 percentage point gap. This is the single most powerful predictor in the model: if a customer has barely touched the product, they are roughly ten times more likely to leave.

95% CI on gap: 17pp to 63pp
Second Driver — Strong Signal
Dashboard Adoption (Last 6 Months)
31%
churn rate for customers using Dashboards 0–2 times
Customers who used Dashboards fewer than 3 times in the last 6 months churn at 31–33%. Those who used it 3 or more times churn at just 7%. Dashboards appear to be a key "sticky" capability — once customers adopt it deeply, they stay. Light or non-users are at high risk.

Pattern consistent across all plan tiers
Third Driver — Directional Signal
Payment Failures
15%
churn rate for customers with 2+ failed payments
Customers with a clean payment record churn at 7%. Those with 2 or more failed payments churn at 15% — more than double. Payment friction is both a practical barrier and a warning sign that an account is already disengaging. Resolving failures proactively is one of the fastest saves available.

Directional — dose-response pattern is clear

Churn Rate by Product Activity Level

The model's top driver — nearly half of low-activity customers will cancel

Churn Rate by Payment Failure History

Each additional payment failure roughly doubles churn risk

Revenue at Risk by Plan

Enterprise and Pro accounts account for 87% of at-risk MRR despite lower churn rates

Highest-Value Accounts at Risk — Top 13 by Monthly Revenue

Prioritize outreach by revenue exposure. Accounts with both low activity and payment failures are the most urgent.
Customer Plan Company Size Signup Year Avg Monthly Revenue Failed Payments Status
C00100Enterprise104 employees2024$7,9920At Risk
C00139Enterprise123 employees2024$5,6261At Risk
C00028Enterprise20 employees2024$5,4602At Risk
C00172Pro84 employees2023$2,0390At Risk
C00131Pro21 employees2024$1,9400At Risk
C00246Pro12 employees2023$1,8570At Risk
C00296Pro324 employees2024$1,8081At Risk
C00110Pro65 employees2024$1,7180At Risk
C00273Pro64 employees2024$1,5701At Risk
C00092Pro5 employees2024$1,5622At Risk
C00178Pro14 employees2022$1,4752At Risk
C00188Pro14 employees2024$1,4574At Risk
C00298Pro59 employees2024$1,2263At Risk

What Leadership Should Do — Prioritized by Impact

Actions ranked by revenue at stake and speed to execute. The model's findings make the targeting precise.

This Week: Call the Three Enterprise Accounts

C00100, C00139, and C00028 represent $19,078/month combined. C00028 already has 2 failed payments. A direct call from a senior account manager or executive sponsor — focused on understanding friction, not upselling — is the single highest-ROI action available right now.

Revenue at stake: $229K annualized for these three alone

This Week: Resolve Payment Failures Before the Next Billing Cycle

C00188 (4 failures), C00298 (3 failures), C00092 and C00178 (2 each) are the most acute. Customers with 2+ failures churn at twice the rate of clean-billing accounts. A proactive payment update request — before the next bill fails — prevents the most avoidable losses.

Accounts with 2+ failures churn at twice the rate of clean-billing accounts

Next 30 Days: Drive Dashboard Adoption for At-Risk Accounts

The model identified Dashboards as the clearest "sticky" capability. Customers using it 3+ times in 6 months churn at just 7%. For every at-risk account not yet using it deeply, a targeted in-app prompt or CSM-led demo session is a direct intervention against the model's second-ranked driver.

Deep Dashboards users churn at 7%, against 31% for light users
Demonstration report. Built by Assertion on a synthetic subscription dataset — the customers, revenue and outcomes below are generated, not a real company's. The analysis, model and findings are the product's own output, unedited in substance.

Predictive model trained on 300 customers | 3,600 billing records | 4,372 usage events | 4 observation snapshots | Analysis date: April 28, 2026