What churn data is really saying

Hey CS Pro,

Most teams do not have a churn problem. They have a timing problem. They are waiting until renewal risk is loud, obvious, and painful, when the truth is the customer started drifting way earlier.

In this week’s podcast episode, I sat down with Julie Fox, Director of Digital and Scaled Customer Success at Hyland, to talk about how modern CS teams can stop reacting so late and start using customer data to forecast revenue more proactively.

(youtube) https://youtu.be/BAeZgZQuYEM

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Symptoms, not signals

That line from Julie really stuck with me.

Because when a customer stops replying, starts sounding vague, or suddenly shows up red in your health score, most teams treat that as the moment to act. But by then, the damage has usually already been done.

The customer has already felt friction. They have already questioned the value. They have already started deciding whether your product is worth renewing.

And that is exactly why this conversation matters so much.

Julie shared that in scaled and digital customer success, the goal is not just to track activity. It is to understand behavior patterns over time. Not just who logged in, but how they are using the product. Not just whether they submitted support tickets, but what those tickets are telling you. Not just whether they attended a webinar, but whether their behavior moved closer to meaningful adoption after that.

That is the difference between reactive churn management and proactive revenue forecasting.

Too many teams are still spending all their energy swarming red accounts right before renewal. They roll out the red carpet, throw in extra meetings, and hope that more attention will somehow save the account.

But that is exhausting for your team, overwhelming for your customer, and honestly, not very effective.

Julie’s approach is much smarter.

Instead of obsessing over who is already on fire, she looks at earlier indicators like declining usage depth, feature abandonment, lack of adoption across multiple users, weak expansion into core workflows, and long gaps between meaningful actions.

That is where the real story lives.

Because churn is rarely caused by one big dramatic moment. It is usually a series of small missed moments that pile up over time.

The best scaled CS teams think in journeys, not just accounts

One of the things I loved most about Julie’s perspective is that she does not define digital customer success as just sending emails at scale.

She defines it as designing the full customer experience.

That means looking beyond just CS touchpoints and thinking about the entire journey. Product. Support. Marketing. Education. Community. In app guidance. Human support when it is needed.

And honestly, that is the mindset more teams need.

If you are managing thousands of customers, you cannot just copy and paste your enterprise model and hope it works. You also cannot solve scale by throwing more bodies at the problem

You need to understand what customers actually need, when they need it, and how they want to receive support.

Some customers want human interaction. Others do not want a meeting unless it is absolutely necessary. Some want answers directly inside the product. Others want a webinar, a guide, or a quick piece of educational content they can use on their own time.

That means scaled CS is not about removing the human touch. It is about being far more intentional about when and where human effort adds the most value.

Julie talked about starting with customer journey mapping, and I could not agree more.

If you want to get smarter about churn and forecasting, you need to map two things.

First, what you expect customers to do throughout their journey.

Second, what success actually looks like from their point of view.

Those are not always the same thing.

And when you can line those two things up, you start spotting the gaps earlier. You can see when a customer is technically active but not actually progressing. You can see when someone bought a product but has not integrated it into their workflow. You can see when there is usage, but no depth. Motion, but no value.

That is the kind of insight that gives you leverage.

Revenue forecasting gets better when your signals get smarter

Julie made a point that I think every CS leader needs to hear. One of the hidden benefits of digital CS is better forecasting.

Not because the data is magically perfect. It is not.

But because when you build consistent ways of identifying risk early, understanding customer behavior, and running targeted interventions, your forecasting becomes far more credible.

Instead of saying, “These accounts are up for renewal and some of them feel risky,” you can say, “Here is what is happening, here is why it matters, and here is what we are doing about it.”

That is a very different conversation.

Julie broke this down into three layers she looks at when analyzing churn risk.

Quantitative patterns, things like product usage, support activity, and engagement data.

Customer context, things like lifecycle stage, stakeholder changes, use case maturity, and industry complexity.

Outcome alignment, which is the big one. Are customers actually achieving what they bought the product for?

That last one matters most.

Because a customer can be busy, engaged, and still not be getting meaningful business value.

And if you cannot clearly tie their current experience back to revenue, cost savings, or risk reduction, then your renewal story is weak no matter how many check in calls your team had.

I also appreciated how honest Julie was about forecasting. She said what a lot of people are afraid to admit, when you first start, some of it IS guesswork.

And that is okay.

The goal is not perfection on day one. The goal is to get better over time.

Try a model. Learn from the outcomes. See where your health scores were wrong. Look at which green accounts actually churned. Adjust. Improve. Keep layering better signals into your process.

That is how you move from reactive forecasting to predictive forecasting.

Not by pretending your data is perfect, but by getting more disciplined about how you use the data you do have.

I hope you enjoyed this week’s newsletter.

If you have any questions or suggestions, please feel free to contact us.

Cheers to your CS success,

Anika