Customer Retention

Churn
Prevention

Stop churn before it becomes a cancellation notice.

The Challenge

By the time churn is visible, it is often too late

Customers rarely leave without warning — but the warnings are scattered across systems that no one is watching together. Declining usage, rising support tickets, slower payments, fewer logins: individually unremarkable, collectively a clear exit signal.

Teams that respond to cancellations are playing defence at the wrong moment. The window for effective retention intervention closes weeks before the customer calls.

Signals we monitor

Usage frequency & recency decay · Support ticket volume & sentiment · Engagement with communications · Payment pattern changes · Net promoter & satisfaction indicators · Contract milestone proximity · Cross-product adoption breadth

Our Approach

How we build it

1

Churn Definition & Data Mapping

We work with you to define churn precisely for your business — lapsed contracts, cancelled subscriptions, inactivity thresholds — then map every available signal across your data estate.

2

Behavioural Feature Engineering

We engineer features that capture decay over time: engagement momentum scores, support escalation patterns, payment behaviour shifts — the signals that precede churn by weeks or months.

3

Risk Scoring & Tier Assignment

Each customer receives a continuously updated churn risk score, segmented into actionable tiers — so teams focus intensive effort on high-risk accounts and automated nudges on medium-risk ones.

4

Retention Playbooks & AI Assist

We embed risk scores and guided playbooks into your CRM, Teams, or service platform — with optional AI-assisted retention agents that surface account context and recommended outreach in real time.

Typical Outcomes

What to expect

$10M+
Recurring revenue protected in first year of deployment
Weeks
Earlier risk detection compared to calendar-based approaches
3 tiers
Risk segmentation enabling proportionate, prioritised response
Live
Continuously updated scores as customer behaviour evolves
Related Case Study

Protecting $10M across 250,000 contracts

A global B2B services company transformed its retention programme with predictive churn models, Azure OpenAI-powered retention assistants, and risk dashboards — protecting $10M in recurring revenue in the first year.

Read the case study →

Hybrid model approach

When stakeholders were uncertain about methodology, we implemented both interpretable and ensemble approaches — validating through cross-validation and business workshops to build the organisational trust that enabled a global rollout.

Find out who is at risk in your customer base today

We can analyse your existing data and produce a risk distribution map before any engagement begins.

Request a risk assessment