# A repeatable data-science playbook across a private equity portfolio

> A leading European software-focused private equity investor · Private Equity

QuantSpark built a repeatable data-science playbook for a leading European software-focused private equity investor, deploying cloud data platforms and machine learning across its portfolio to reduce churn, sharpen renewals and evidence growth.

## At a glance

- **>£2m** incremental EBITDA at one portfolio company
- Engagement: 18+ months across 7+ portfolio companies

## What was the problem?

The investor's portfolio businesses were data rich but data siloed. Product usage, service-call, billing and marketing streams sat in disconnected legacy systems, leaving no end-to-end customer view. Within a typical three-to-five-year hold, management teams needed to unlock value quickly, reducing churn, sharpening renewals and evidencing growth for future investors, which had previously been technically unrealistic for mid-sized businesses.

## What did QuantSpark do?

Engaged at firm level by the sponsor's value-creation team, QuantSpark deployed blended teams of consultants, data scientists and developers working in daily iterations and weekly sprints. It applied a repeatable two-step method: first build a cloud data platform that cleaned and connected sources into a single dataset with automated KPI dashboards; then layer machine learning on top through two or three projects per company, including churn early-warning models, lead-prioritisation scoring and renewal call-timing optimisation. Reusable tooling, including a churn-analytics dashboard and a revenue-metrics tool, was productised and handed to the sponsor's own analysts.

## What changed?

The partnership spanned more than 18 months and seven-plus portfolio companies, with each project delivering or on track for EBITDA growth in the multiple millions of pounds. At one portfolio company, processing more than 25 million call logs to optimise the call centre drove more than £2m of incremental EBITDA. At another, a churn early-warning model enabling proactive retention delivered an estimated £1m-plus of EBITDA saved. At a third, an interactive BI dashboard shaped growth strategy and investor discussions.

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