How are you combating churn in your organisation?

Im doing some research around churn, retention and the ways CS teams tackle both.

How are you doing this today in your organization? Are you using any specific tools today for this purpose?

What is great about it and what’s annoying, or way more complicated than it needs to be?

If you could design the perfect CS tool, what would it do differently?

Would really appreciate real experiences from people who are in the CS trenches, not sales takes.

Thank you! :)

Find more posts tagged with

Technology

Data

Retention

CS Team

Accepted answers

All comments

Brandon Hemmelman

Tackling churn and retention issues are likely an evergreen item, so any solution that is built will need to be to pivot as changes occur.
That said, I have previously worked with Gainsight to help centralize customer insights and engagement to better inform likelihood to renew and better track churn reasons. Also stood up a basic DEAR model health score, which was somewhat helpful. It certainly is better than spreadsheets.
We also utilized homegrown solutions (i was at an analytics company) to understand customer usage/adoption.

An ideal CS tool to combat churn and improve retention should be able to do the following:

reliably track customer usage
reliably determine product fit to customer goals
maintain engagement metrics with key customer stakeholders
maintain a verifiable customer health score
bring in signals about a customer's business

There's certainly more than this for a perfect solution, but getting these right would be a solid start.

Curious to others thoughts too

Ed Powers

I suggest you focus on ways to detect an account's shared beliefs, and trust, in particular. How beliefs towards a vendor shift over time among decision makers is highly predictive of the choices they make. This can be done mining text across channels using AI, psychology, and linguistics, but sentiment analysis systems on the market today don't incorporate essential context. I just did a LinkedIn post about this.

yuliya67

I’ve worked on churn / expansion systems in both enterprise and scale-up environments, and a few patterns show up consistently:

1️⃣ Most CS teams are reactive. “Churn risk” often becomes visible only once commercial damage is already happening.

2️⃣ Tooling is fragmented — CRM, product analytics, support tickets, billing data — but no single prioritized “what to do next” view.

3️⃣ Health scores are usually static and rules-based. They rarely evolve based on real retention outcomes.

What works better in practice:

Behavior-based churn prediction (not just red/yellow/green health scoring)
Explicit expansion propensity scoring alongside churn
Clear next-best-action recommendations tied to commercial impact
Tight feedback loop: model → action → outcome → model refinement

If I could redesign a CS tool from scratch, it would do one thing well:

Turn messy customer signals into a ranked weekly action list tied directly to revenue at risk or expansion upside.

We’re currently running a small design research program with CS leaders to rethink this layer from first principles — always happy to exchange notes if useful.