How Predictive Analytics Helped Us Reduce Client Churn by 45%

When a B2B SaaS company with 3,000 enterprise accounts came to Diztaly, they had an acute churn problem: 8.5% quarterly churn among accounts in their $10K–$50K ARR tier — nearly double their target rate. Customer success managers were working reactively — only finding out an account was at risk when a cancellation email arrived. Our intervention: a predictive analytics programme that gave the CS team 90-day advance warning for every at-risk account. The result: churn in that tier reduced to 3.8% within six months, recovering an estimated $4.2M in ARR.

The Diagnostic

We began with a data audit — identifying every data signal available across the client's systems: product usage data (login frequency, feature activation depth, time-in-product per week), customer engagement data (email open rates, support ticket volume, NPS scores, QBR attendance), account health data (licence utilisation, invoice payment timeliness, expansion or contraction history), and external signals (company news, headcount changes, funding events). The audit identified 47 potentially predictive variables across six data sources.

Building the Churn Prediction Model

We trained a gradient boosting model on 18 months of historical account data, with churn outcome (churned or retained within 90 days) as the target variable. The model's feature importance analysis revealed the five most predictive churn signals: declining weekly active users (strongest predictor), support ticket volume increasing without resolution (second strongest), low QBR attendance rate, negative trend in NPS over three consecutive quarters, and reduced licence utilisation. The model achieved 84% accuracy on holdout data — meaning it correctly identified 84% of accounts that subsequently churned while maintaining a manageable false positive rate.

The Intervention Programme

The model's predictions were integrated into the CS team's workflow through a Salesforce dashboard that displayed each account's churn risk score, the top three risk factors contributing to that score, and AI-recommended intervention actions. CS managers received weekly alerts for accounts where the risk score had increased by 15+ points since the previous week. A structured intervention playbook was developed for each risk factor — so that when "declining feature adoption" was flagged, the CS manager knew exactly which resources to share, which product specialist to loop in, and which executive conversation to request.

Results

This programme demonstrates the direct financial impact of predictive analytics in marketing and customer success. The same methodology applies across churn prediction, lead scoring, and next-best-offer modelling — wherever historical outcome data exists to train a predictive model.