Churn Prediction Software | CRM-Integrated
RaftLabs builds customer churn prediction models trained on your customer behaviour, usage, and engagement data. Risk scores are delivered directly to your CRM so your retention team can act on the customers most likely to leave, before they cancel, not after.
We start by assessing your existing customer data: what behavioural signals you are capturing, how far back the history runs, and what your current churn rate is. A model is only worth building if the signal is there. If it is, we define the accuracy target, build the model, and wire the scores into the tools your team already uses.
Churn risk scores delivered to your CRM so your team acts without a separate tool
Trained on your customer behaviour, usage, and engagement data, not generic benchmarks
Early warning signals identified before the customer signals obvious intent to leave
Model monitoring and automated retraining as customer behaviour patterns shift
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
The problem
You only find out a customer is churning when they cancel or stop responding, by then it is too late to save them?
Your retention team knows some customers are at risk but has no systematic way to identify which ones to prioritise?
Short answer
RaftLabs builds churn prediction models trained on your customer behaviour and usage data, delivering risk scores directly to your CRM so retention teams act before customers cancel. Monitoring and automated retraining keep scores accurate as behaviour shifts. A first model wired into one CRM starts near $20,000 and grows toward $50,000 as you add integrations and monitoring.
Key takeaways
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Customer churn has a lead time. Most customers who cancel have been showing warning signs for weeks or months before they act, declining usage, fewer logins, support tickets that went unresolved, a billing issue that was never followed up. The problem is that those signals are distributed across your product analytics, your CRM, your billing system, and your support tool, and no one is looking at all of them together, in time to do something about it.
A churn prediction model connects those signals, learns which combinations are predictive for your specific customer base, and surfaces a ranked list of at-risk customers so your retention team can prioritise outreach. The model does not replace the retention conversation, but it tells your team who to have it with before the customer has already decided to leave. RaftLabs builds and integrates these models end to end, from data assessment through CRM delivery and ongoing monitoring.
Retention is where the economics sit. A saved customer keeps paying without the cost of winning a new one, and small gains compound across a book of business. That is why the accounts worth saving, caught while there is still time to act, are the ones a churn model exists to find.
Capabilities
A machine learning classifier trained on your labelled customer data, churned and retained, using the algorithm your data volume and interpretability needs call for. Production B2B SaaS models regularly reach AUC 0.80 to 0.90, and score calibration makes a 0.75 score mean a true 75% churn probability.
The quality of a churn model depends almost entirely on the features derived from your customer data: raw event logs do not predict churn, but the right summary statistics over the right time windows do. We engineer usage frequency and trend features across 7, 30, and 90 day windows, adoption depth, session trends, and support and billing health signals, with SHAP values making each customer's score explainable.
Feature importance analysis that surfaces which specific behaviours are the strongest leading indicators of churn in your customer base, from your historical data, not generic SaaS benchmarks. SHAP plots rank the top signals and quantify lead time per signal, so a 50% decline in weekly logins that precedes churn by 45 days gives your team a 45-day intervention window.
Automated delivery of updated churn scores to your CRM on a configurable weekly or daily schedule, so the score your rep sees reflects last week's behaviour, not last month's model run. Salesforce writes score, risk tier, and timestamp to custom fields and can trigger an intervention task when an account moves to High risk; HubSpot properties feed retention Workflows, with webhook delivery for custom CRMs.
A reporting layer for customer success team leads that shows risk distribution, model accuracy against actual outcomes, and whether retention interventions convert at-risk customers to retained. It answers "what percentage of our ARR is at high churn risk right now?" with an exportable at-risk list sorted by ARR, calibration charts that validate predictions against real churn rates, and intervention tracking that proves the retention programme works.
Automated accuracy tracking that compares predicted churn scores against actual renewal and cancellation outcomes, catching model drift before it sends your retention team after the wrong customers. Population stability index flags feature distribution shifts monthly, monthly retraining promotes a new model only if it beats the current one on a held-out set, and the MLflow registry keeps full version lineage for audit.
Churn is rare. In most subscription businesses, the customers who leave in any given month are a small fraction of the base, so a model that predicts nobody churns can still look accurate. We train against that class imbalance directly, weighting the rare churn cases and judging the model on precision and recall at your operating threshold, never on raw accuracy.
A false positive is not free. Every account you flag is a call your retention team makes instead of another, and flag too many and the list turns into noise your reps stop trusting. We tune the threshold to your team's real capacity, so the accounts surfaced each week are the ones worth the outreach.
Ranking risk is not the same as knowing who to call. A pure risk score points your team at accounts that would have renewed anyway, and at a few where a clumsy save call pushes the customer toward the door. Uplift modelling targets the persuadable accounts instead, the ones where an intervention actually changes the outcome. When your history supports it, we model who to save, not just who is leaving.
Timing matters too. A churned-or-not label hides when a customer is likely to go. Survival analysis models time to churn, so your team sees roughly how long the intervention window runs.
Tell us what customer data you are capturing and what your current churn rate is. We will assess whether a prediction model is worth building and what accuracy you can realistically expect from your data.
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Read moreA churn model learns from the behavioural patterns that precede a customer leaving. The most predictive signals are typically: product or service usage frequency and depth, support contact history, billing events (late payments, plan downgrades, failed charges), engagement with communications (email opens, login frequency), and any customer satisfaction scores you collect. You need a minimum of 12 to 18 months of customer history to distinguish genuine churn predictors from seasonal behaviour, and enough historical churn events to train on, as a rough guide, at least a few hundred confirmed churn examples in your training data. We assess your data coverage in the first engagement phase and tell you whether what you have is sufficient.
Churn model accuracy varies significantly based on your industry, your customer data richness, and the nature of your churn. In B2B SaaS with detailed product usage data, well-built models typically achieve AUC scores of 0.80 to 0.90, meaning they correctly rank customers by churn risk the large majority of the time. In businesses with sparse behavioural data, accuracy will be lower. What matters practically is the precision-recall trade-off at your operating threshold: how many at-risk customers does the model surface, and what fraction of those flagged are genuinely at risk? We optimise for the threshold that matches your retention team's capacity, surfacing more at-risk customers than your team can contact is not useful.
A churn score sitting in a data warehouse your team never opens is not useful. We integrate scores into the tools your retention team already works in, typically your CRM. We push updated churn scores to a custom field in Salesforce, HubSpot, or your specific CRM on a configurable schedule, so account managers and customer success reps see the risk score alongside the customer record without switching tools. We also build a simple dashboard for team leads who want to see the full risk distribution, configure alert thresholds, and track whether retention outreach is working.
We start small on purpose. A first model, scored against one CRM, covering data assessment, feature engineering, training and validation, and score delivery, starts near $20,000. That first model is a v1 you validate against real renewal outcomes before expanding. As you add a monitoring dashboard, automated retraining, and integrations with more tools, the engagement grows toward $50,000. Data that needs significant cleaning pushes the number up. We provide a fixed-cost quote after a data assessment call where we review your customer history and define the accuracy target.
Work with us
We scope Churn Prediction Software in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.