# Predictive churn model drives over £1m in EBITDA

> Private equity-backed accounting and HR software provider · SaaS & Tech

A software provider wanted to focus retention effort on its highest-risk customers. QuantSpark's machine-learning model identified likely churners four times more accurately than random selection, supporting an estimated £1m-plus in EBITDA.

## At a glance

- **£1m+** estimated EBITDA benefit
- Engagement: 2 weeks

## What was the problem?

The provider wanted to reduce customer churn and drive more value from its existing base. It needed to identify high-risk customers so they could be prioritised for retention outreach.

## What did QuantSpark do?

Over a two-week period, QuantSpark analysed and parametrised 2.5 years of revenue data, including variance, gradient and averages alongside other customer attributes, and used the most influential parameters to tune a machine-learning model. The team also built a secure web portal to disseminate the model's outputs across the business and ensure easy access for all stakeholders.

## What changed?

The model was four times more likely to correctly identify churners than random selection. The business used the predicted churn scores to prioritise retention outreach, with an estimated £1m-plus in EBITDA benefit (a projection). Insight from the model's drivers also refined sales strategy, including a strengthened customer training programme that made customers stickier.

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Canonical page: https://quantspark.ai/case-studies/predictive-churn-model-accounting-hr-saas
More about QuantSpark: https://quantspark.ai/llms.txt