Customer Success teams talk about churn all the time, but far fewer know how to turn churn data into something actionable. In this episode of The Customer Success Pro Podcast, Anika Zubair sits down with Julie Fox, Director of Digital and Scaled Customer Success at Hyland, to unpack how modern CS teams can move beyond reactive churn management and into proactive, data driven revenue forecasting.

Julie brings deep experience across enterprise, mid market, and digital first models, with leadership experience spanning portfolios from $20 million to more than $1 billion in revenue. In this conversation, she shares how scaled Customer Success teams can use customer behavior, usage patterns, support signals, and business context to spot risk early, drive stronger adoption, and build more accurate forecasts.

If you are a Customer Success Manager, CS leader, or post sale operator trying to scale retention and growth, this episode offers a practical look at what modern digital customer success should actually look like.

Why Reactive Churn Management Is Not Enough

One of the biggest themes in this episode is that most CS teams are reacting to symptoms, not signals. By the time a customer says they are unhappy, goes silent, or suddenly turns red in the health score, Julie explains that the decision may already be made. That means teams are often scrambling too late in the cycle, usually close to renewal, when the real issues started much earlier.

Instead of swarming the most visibly at risk accounts at the last minute, Julie argues that Customer Success teams need to identify the patterns that happen before churn becomes obvious. That includes things like declining usage depth, single threaded adoption, feature abandonment, weak workflow expansion, and long gaps between meaningful activity.

This shift matters because true churn prevention is not about saving accounts at the eleventh hour. It is about building an operating model that catches risk sooner and supports customers before they drift too far from value. For scaled and digital CS teams, this approach is even more important because there are simply too many accounts to manage through a one to one high touch strategy.

The Signals That Matter in Scaled Customer Success

Julie breaks churn analysis into three layers: quantitative patterns, customer context, and outcome alignment. Together, these create a more complete picture of account health and future renewal risk.

The first layer, quantitative patterns, includes the signals most teams already have access to, such as product usage trends, support ticket activity, academy or community engagement, and overall behavioral data. But Julie makes an important point here. High support activity is not always a negative signal. In many cases, it shows that a customer is still engaged and trying to make the product work.

The second layer is customer context. This includes lifecycle stage, maturity of use case, stakeholder changes, industry complexity, and how the customer actually operates. A finance or healthcare customer may use a product very differently than a customer in another industry, so context matters when interpreting behavior.

The third layer is outcome alignment, which may be the most important of all. Are customers actually achieving the outcomes they bought the product for? If the answer is no, then renewal risk exists, even if the account still looks healthy on the surface. That is why Julie emphasizes value realization as a core part of any retention and forecasting strategy.

This framework is especially powerful for digital customer success because it helps teams move past vanity metrics like simple logins and instead focus on whether customers are building real, durable value.

How to Turn Customer Data Into Proactive Plays

A standout part of this conversation is Julie’s explanation of how scaled CS teams should operationalize insight. In a digital model, success does not come from reviewing every account individually. It comes from grouping customers into patterns, then building plays, campaigns, and interventions around those patterns.

That means identifying buckets of customers who behave similarly and creating the right support for them at the right time. For example, if a group of customers has not adopted a key feature connected to ROI, that may trigger an in app guide, a use case based webinar, an educational email sequence, or a digital business review. If customers tend to hit a specific support issue at a predictable point in their lifecycle, the answer is not just to respond faster. It is to anticipate the question and provide the answer before they even ask.

Julie also highlights the importance of partnering closely with product and support teams. These teams often hold the richest customer insight, especially in scaled environments. Support can reveal the most common friction points. Product can show exactly where users get stuck or fall off. Together, those insights can help Customer Success build a more frictionless experience that nudges customers toward value instead of waiting for them to raise a hand.

This is where proactive customer success becomes a true growth lever. It is no longer just about retention. It is about guiding customers to deeper adoption, stronger outcomes, and more expansion opportunities.

Better Forecasting Starts With Better Signals

One of the most valuable takeaways from this episode is Julie’s view that digital Customer Success can dramatically improve forecasting accuracy. When teams define risk more consistently, track it earlier, and layer different kinds of data together, forecasting becomes more predictive and more credible.

Julie is refreshingly honest that forecasting often starts as educated guesswork. Many teams begin with simple overlays such as renewal date plus health score. But over time, better forecasting comes from analyzing historical churn patterns, testing assumptions, and improving the model as you learn. If green accounts are still churning, then the health model is wrong. If certain behavior patterns consistently lead to downsell or churn, those patterns need more weight in the forecast.

The lesson here is not that you need perfect data before you begin. It is that you need to start with what you have, test what works, and keep refining. As Julie explains, the more layers you add thoughtfully, the more accurate your forecasts become.

That makes this episode especially useful for CS leaders building or rebuilding a scaled motion. You do not need a perfect system on day one. You need a willingness to map the customer journey, identify what success should look like, templatize what can be made consistent, and adapt quickly as you learn.

Key Takeaways

Julie Fox makes a strong case for why the future of Customer Success is more predictive, more behavioral, and more tightly tied to revenue. Her perspective is especially relevant for teams managing large customer bases where one to one support is no longer enough.

Here are the biggest lessons from this conversation:

• Customer Success teams must stop reacting to churn symptoms and start looking for earlier risk signals.
• Strong churn analysis should include quantitative patterns, customer context, and outcome alignment.
• Digital CS works best when it delivers a frictionless, proactive experience across multiple channels, not just more meetings.
• Support and product teams are essential partners in identifying patterns and building smarter plays.
• Revenue forecasting improves when teams consistently track risk, learn from historical behavior, and refine their models over time.

For any CS professional trying to build scalable retention and growth, this episode is a reminder that better data means nothing without better action.

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