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?
Churn Prediction Software
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
RaftLabs builds churn prediction models trained on your customer behaviour and usage data, delivering risk scores directly to your CRM so retention teams can prioritise outreach before customers cancel. We include model monitoring and automated retraining so scores stay accurate as behaviour patterns shift.
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.
What we build
Churn risk scoring model
A machine learning classifier trained on your labelled customer data -- customers who churned and customers who did not -- using gradient boosting, logistic regression, or neural network architectures depending on what your data volume and feature set support. The model outputs a calibrated probability score for each customer so risk levels are comparable across different customer segments and time periods.
Customer behaviour feature engineering
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 identify and engineer the behavioural features that carry the most predictive signal in your data: usage frequency trends, feature adoption depth, engagement recency, support interaction patterns, and billing health indicators.
Early warning signal identification
Feature importance analysis that identifies which specific behaviours are the strongest leading indicators of churn in your customer base -- not what the literature says in general, but what your data shows for your customers. These signals become operational insights your success team can monitor and act on even before a model score is triggered, and they inform the product decisions that reduce churn structurally.
CRM and sales tool integration
Automated delivery of updated churn scores to your CRM on a configurable schedule. Salesforce, HubSpot, Pipedrive, and custom CRM integration via API or database connector. Scores are written to a custom field, visible in the customer record, and available for filtering and workflow automation so your retention team can build sequences triggered by risk threshold crossings without leaving their CRM.
Churn prediction dashboard
A reporting layer for team leads and customer success managers that shows the full risk distribution across your customer base, segment-level churn trends, and the accuracy of recent model predictions against actual outcomes. Configurable alert thresholds, exportable at-risk customer lists, and intervention tracking so you can measure whether outreach triggered by the model is actually reducing churn.
Model monitoring and retraining pipeline
Automated tracking of model accuracy over time, comparing predicted churn probabilities against actual outcomes as customers renew or cancel. Drift detection that flags when the model's predictive accuracy is degrading -- typically a sign that customer behaviour patterns have shifted. Scheduled retraining pipelines that incorporate new data and update scores on a defined cadence so the model stays calibrated to your current customer base.
You already have the data. You just are not using it.
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.
Related predictive analytics services
Predictive Analytics -- overview of our full predictive analytics practice
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Fraud Detection -- real-time and batch fraud scoring for transactions and claims
Predictive Maintenance -- equipment failure prediction from sensor data
Related services
AI Development -- custom ML model development for churn prediction use cases
Custom CRM Development -- CRM built to surface churn scores and support retention workflows
Frequently asked questions
A 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.
A churn prediction model with CRM integration -- including data assessment, feature engineering, model training and validation, score delivery to your CRM, and a monitoring dashboard -- typically runs $20,000 to $50,000. If your data requires significant cleaning and engineering, or if you need integration with multiple tools, the cost is at the upper end of that range. We provide a fixed-cost quote after a data assessment call where we review your customer history and define the accuracy target.