# Predicting churn to protect a compliance SaaS business

> A health and safety compliance SaaS and accreditation business, owned by a UK private equity house · Private Equity

A health and safety compliance SaaS and accreditation business wanted to move from BI reporting to predictive retention. QuantSpark built a proof-of-concept churn model that identified the customers most likely to leave at renewal, enabling prioritised outreach.

## At a glance

- **76%** of churn instances predicted in test data

## What was the problem?

The client offers accreditation, certification and SaaS products supporting SME health and safety compliance, and collects significant customer data through its subscription model. It wanted to move beyond BI and KPI tracking towards predictive value, but had no data-driven way to prioritise renewals outreach or identify at-risk customers. Strategically, it needed to lift retention on its flagship accreditation product above a 90% target.

## What did QuantSpark do?

QuantSpark delivered in two workstreams. First, a two-week Data Diagnostic: 13 workshops with 24 stakeholders across the C-suite, sales, marketing, finance and management information, reviewing eight documents and six datasets to produce a capabilities and process audit and a prioritised, scoped Opportunity Menu. Second, a proof-of-concept predictive churn model for the flagship product: we hypothesised churn drivers, matched them to internal datasets, and built a Random Forest classifier scoring churn risk at the renewal level.

## What changed?

The churn model identified 76% of churn instances in test data, and contacting the top 50% of highest-risk customers was projected to capture 87% of all churners, supporting prioritised, targeted renewals outreach. Between 17% and 33% of churn was flagged as unavoidable, so the model targets the roughly 83% of avoidable volume. These are model-performance and projected figures from a proof of concept; no realised retention uplift is evidenced in the delivery record.

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