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Use Case
Generic churn prediction tools train on industry benchmarks. Your churn drivers are specific to your product: login frequency, feature adoption depth, support ticket volume, billing history, NPS trend. The signals that predict churn for a project management tool are different from the signals for a financial platform.
A custom model learns the signals that actually predict churn in your customer base, trained on your historical churned accounts, not a vendor's generalised dataset. The output is a daily churn risk score per account, pushed to your CRM, triggering the right CS workflow before the customer cancels.
We build the feature pipeline, the model, the CRM integration, and the cohort retention dashboard. Your CS team acts on predictions, not spreadsheets.
Tell us about your churn problem.
Six components that turn your product and CRM data into daily churn risk scores with automated intervention workflows.
The model is trained on signals that actually predict churn in your product: login frequency, feature adoption depth, support ticket volume, billing history, NPS trend, and usage trajectory. These signals are extracted from your product analytics and CRM and engineered into model features.
A classification model outputs a churn probability score per account, updated on a daily or weekly cadence. The model learns which combinations of signals correlate with eventual cancellation in your customer base, not industry benchmarks.
Risk scores are segmented into tiers (high, medium, low) with thresholds you define based on your CS team's capacity to intervene. High-risk accounts trigger immediate CSM outreach; medium-risk accounts enter a nurture sequence; low-risk accounts are monitored.
Daily churn scores are pushed to Salesforce or HubSpot. Account risk tier changes (e.g., an account moving from low to high risk) trigger automated tasks for the assigned CSM, a call reminder, a health check email, or an escalation to the account executive.
A dashboard showing retention curves by cohort, acquisition channel, plan tier, and industry segment. The dashboard makes it easy to see which customer segments churn fastest and which interventions are correlated with improved retention in historical data.
Model accuracy is monitored over time. As your product evolves and churn drivers shift, the model is retrained on a scheduled basis to stay current. Significant accuracy degradation triggers an alert for a manual model review.
From data audit to live CRM integration, the build sequence.
Data audit and feature identification
We review your available data sources: product analytics events, CRM account fields, billing history, support tickets, NPS responses. We identify which signals have predictive power and which are missing, and whether gaps need to be filled before modelling.
Feature engineering pipeline
Raw event data is transformed into model features: rolling averages, rate-of-change metrics, time-since-last-activity signals, and engagement depth scores. Features are designed to capture behaviour trends, not just point-in-time values.
Model training and evaluation
The churn model is trained on historical accounts with known outcomes (churned vs retained). We evaluate precision, recall, and AUC, and tune the model to your preferred operating point. A model that catches 80% of churns at the cost of some false positives may be better for your CS team than one with perfect precision but low recall.
Risk tier calibration
Risk thresholds are calibrated so the number of high-risk accounts per week matches your CS team's intervention capacity. There is no value in flagging 200 high-risk accounts per week if your team can only action 30.
CRM integration and workflow setup
Churn scores are pushed to Salesforce or HubSpot on a daily schedule. CRM automations are configured to trigger the right workflow for each risk tier change: task creation, email enrollment, Slack alerts to the account team.
Monitoring dashboard and model refresh
The cohort dashboard is deployed for your CS leadership. Model performance is monitored monthly. Retraining is scheduled quarterly or triggered by a detected accuracy drop.
Businesses with recurring revenue where customer retention is a measurable operational priority.
Monthly churn of even 2% compounds to a significant annual revenue impact. Identifying which accounts are trending toward cancellation 30 to 60 days before they churn gives the CS team time to intervene, rather than processing a cancellation that could have been prevented.
Annual subscribers are quiet until they cancel at renewal. A churn model that scores accounts on engagement metrics in the 90 days before renewal identifies at-risk subscribers early enough to run a win-back campaign or offer retention pricing.
Clients who are disengaging (fewer logins, lower balance trends, no recent activity) are at risk of moving assets. A model that surfaces disengagement signals to advisors gives them the prompt to reach out before the client transfers their account.
We’d rather decline than take a project that won’t deliver value.
Companies with fewer than 500 historical churned accounts
Churn prediction models learn from historical examples of churned accounts. With very few churned examples, the model cannot learn reliable patterns, and you risk overfitting to noise. If you have fewer than 500 historical churn events, a rules-based health scoring system is more appropriate than a predictive model. We can build either.
Products with primarily involuntary churn
If most of your churn is involuntary (failed payments, expired cards), behavioural churn prediction adds limited value, the signal is a billing event, not a product engagement pattern. The right solution for involuntary churn is a dunning and payment recovery system. We can build that too, but it is a different engagement.
Tell us your monthly churn rate, which data sources you have (product analytics, CRM, billing), and which CRM you use. We'll reply within one business day.