# Churn problem framing

> Churn problem framing - Discover the problem framing and data management steps required to
> successfully model for churn, using a B2C retail example and a B2B example based on a DataRobot’s
> churn model.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.576485+00:00` (UTC).

## Primary page

- [Churn problem framing](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/ml-churn.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Data enrichment and preparation](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/data-enrichment-prep/index.html): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/use_cases_and_horizontal_approaches/churn_problem_framing_feature_eng/Churn_Before_Modelling.ipynb)

Customer retention is central to any successful business and machine learning is frequently proposed as a way of addressing churn. It is tempting to dive right into a churn dataset, but improving outcomes requires correctly framing the problem. Doing so at the start will determine whether the business can take action based on the trained model and whether your hard work is valuable or not.

This accelerator blog will teach the problem framing and data management steps required before modeling begins. It uses two examples to illustrate concepts: a B2C retail example, and a B2B example based on DataRobot’s internal churn model.