AI CHURN PREDICTION
Customer churn prediction and analysis
We help retention teams identify customers at risk and prioritise useful interventions, measuring prediction quality alongside business outcomes.
- Scored on precision@k and lift alongside retention outcomes
- SHAP reasons on every account, so the save is specific
- Ranked, calibrated risk, drawn where your retention budget sits
Measure churn prediction by useful interventions.
The whole value is in finding the few customers about to leave, and being right often enough that your retention budget is not wasted.
The useful question is how many customers in the reachable group are correctly identified as at risk. Customer value can then help prioritise effort where the commercial exposure is material.
A ranked list with no reasons is a dead end. The retention team cannot tailor an offer to a number.
We set expectations on business value, not the vanity number.
Because churn data is imbalanced, we agree the measure before we build.
Accuracy appears nowhere on that list, deliberately. It is dominated by the customers who were never going to leave, so it hides the errors that cost money.
A tabular problem, solved with the strongest tool for it.
Gradient-boosted ensembles
XGBoost and tree ensembles engineered over usage, support, billing, tenure, and engagement decay. State of the art on the tabular signals that precede churn.
Explicit imbalance handling
Class weighting and threshold tuning, so the rare churners are not drowned out by the majority. The model is optimised for the few that matter, not the many that do not.
Calibrated risk score
A ranked, calibrated output the business can threshold where its retention capacity sits. Draw the line at the list length your team can actually work.
SHAP explainability
Every score decomposed into its drivers: usage down this quarter, two open tickets, a recent price rise. A prediction becomes a playbook, per account, not a global ranking.
Production monitoring
Performance tracked in production on our MLOps foundation, retrained as behaviour shifts. The model stays honest as your customers change.
Workflow integration
Ranked lists and drivers landed into the CRM and tools your team already works in. The reasons travel with the score, into the point of action.
From metric agreement to a model your team can act on.
Baseline and metric agreement
We define churn, the horizon, and the metric, precision@k or lift, that reflects your economics before building.
Feature engineering and model build
Over usage, billing, support, and engagement signals, tuned for imbalance.
Per-customer SHAP explanations
Reasons delivered alongside every risk score, into your CRM.
Retention workflow integration
Ranked lists and drivers landing where your team already works.
Monitoring and retraining
Performance tracked in production and retrained as behaviour shifts.
The model ranks. People decide.
High-confidence, high-value risks route to a human-led retention play, with the SHAP reasons attached so the outreach is specific. Marginal or low-value cases can trigger automated, low-cost nudges.
The loop closes on outcomes: whether a saved customer stayed feeds back as signal, so the model and the playbook both sharpen over time. Automation prioritises and explains; your team keeps control of the save.
Part of our applied AI solutions portfolio.
Applied AI Solutions
The broader portfolio of generative, predictive and vision systems this model belongs to.
Demand Forecasting
Complements churn for a fuller customer-and-demand picture.
Responsible AI & Governance
Explanations align to the governance and review your risk team requires.
What retention leaders ask us first.
Because churn is rare, so accuracy is dominated by the customers who were never going to leave. We quote AUC, precision@k, and lift, the measures that tell you whether the customers you flag are genuinely at risk and worth contacting.
Every score comes with SHAP-derived reasons, the specific factors driving that customer's risk, so retention can match the intervention to the cause instead of guessing.
As far as your data supports and your interventions need. We set the horizon with you so the window is long enough to act but short enough to stay accurate.
Let us define the metric that reflects your economics and show what a fit-for-purpose churn model would flag.
A ranked, explained list of who to save and why, drawn where your retention budget sits.