# Real-time dashboards to evaluate a churn prediction model

> Private equity-backed cyber security software provider · SaaS & Tech

A cyber security software provider needed to know whether its churn and downsell prediction model was working in production. QuantSpark built a dashboard suite that replaced slow, ad-hoc analysis with real-time visibility for the board.

## What was the problem?

The business had a churn and downsell prediction model in production but limited visibility into how it performed across business units and customer segments. Answering board and C-suite questions on the model's effectiveness required repeated, time-consuming ad-hoc analysis.

## What did QuantSpark do?

QuantSpark mapped the core business questions and user journeys the model needed to answer, working with the data science team and senior management to define the metrics that mattered. Using DBT and Snowflake, the team engineered a set of well-structured data tables, then designed and built a suite of real-time dashboards tracking precision and recall over time, performance by region and customer segment, the signals driving risk scores, and whether customer-success engagement was concentrated on high-risk accounts.

## What changed?

The dashboard suite gave the board and senior management real-time answers on model performance, replacing the ad-hoc analysis that had previously consumed analyst time. It surfaced where the model succeeded and where it could improve, and paired each view with a recommended call to action to strengthen both the model and the customer-success processes around it.

---

Canonical page: https://quantspark.ai/case-studies/churn-model-performance-dashboards
More about QuantSpark: https://quantspark.ai/llms.txt