In SaaS, churn doesn’t happen all at once. It builds up silently. A customer stops using one feature, then logs in less frequently, then goes quiet in support. And before you know it, they’re gone. At that point, it's too late to fix things—and too costly to ignore. That’s why a churn prediction model is one of the most important tools for any B2B SaaS company trying to build sustainable growth. A churn prediction model is a way to forecast which customers are likely to cancel their subscription, based on how their behavior compares to patterns observed in past churned accounts. It gives customer success teams the ability to act before a customer disengages entirely. But building a model that works—one that flags risk early enough to do something about it—depends on the quality of the customer data you have, and how well you're able to connect it across your product and your CRM. What we are going to cover today: A churn prediction model helps B2B SaaS companies identify at-risk customers before they leave, using patterns in product usage and engagement to surface early warning signs. Customer success teams become strategic when equipped with the right metrics, allowing them to anticipate churn, personalize outreach, and prioritize efforts based on real behavior—not guesswork. June connects product analytics with CRM data, making it possible to build accurate health scores, track engagement trends, and create churn prediction models grounded in both user behavior and account context. With features like embedded dashboards, June also helps companies share usage insights directly with their customers—turning data into a tool for re-engagement, accountability, and retention.
Customer success is where churn becomes visible. It's where usage is monitored, value is delivered, and relationships are maintained. But the best customer success teams aren’t just solving problems after they happen—they're anticipating them. And that shift from reactive to strategic customer success starts with the ability to read the signals.
Not all churn is the same. Some customers churn early because onboarding failed. Others churn after years because usage started to decay. And some don’t churn at all—they just stop growing. The role of customer success is to understand these patterns, recognize the early indicators, and intervene with the right approach. That might mean re-engaging a dormant user, unblocking a stuck team, or simply surfacing the value they’ve already received. But you can’t do any of that if you don’t know what’s happening in the first place.
A churn prediction model gives customer success managers a layer of visibility that transforms how they prioritize their work. Instead of guessing which accounts might be at risk, they have a clear signal. Instead of waiting for a red flag in QBR prep, they see the trend in real time. And instead of managing every account the same way, they tailor their actions based on behavior, engagement, and timing.
Most churn doesn't come as a surprise to the customer—it only comes as a surprise to the company. A churn prediction model helps close that gap. It tracks the subtle behavioral shifts that often lead to cancellation. The team that stops exploring new features. The account that plateaus in usage. The user who logs in but doesn’t actually get anything done.
These signals can vary depending on the product, the use case, or the customer segment, but they always exist. What’s needed is a system that helps surface them before they grow into full-blown risks, a system that ties together what customers are doing, what they’re not doing, and what that means in the broader context of their relationship with your company.
When done right, this doesn’t just help reduce churn—it helps drive better outcomes. Customers get more attention when they need it. Teams work more efficiently. And the entire experience becomes more proactive.
This is where June comes in. June connects product analytics with CRM data to give SaaS companies a full picture of their customers—how they behave, how engaged they are, and how that aligns with their business context. It doesn’t just show you what users are doing inside the product; it shows you how that activity relates to deal size, lifecycle stage, user segments, and more.
This integration is what makes churn prediction models powerful. Product data tells you what’s happening in real time. CRM data gives you the context to interpret it correctly. When those two sources are aligned, you get a much sharper view—not just of who is using the product, but why it matters, and what’s likely to happen next.
June lets you define your own engagement signals—whether that’s feature usage, onboarding completion, or frequency of activity. You can build customer health scores based on these inputs, and track how those scores evolve over time. The CRM connection adds crucial detail, like renewal dates, company size, or team structure, giving you the insight needed to act precisely.
What makes June especially effective is that it’s not just a tool for internal analysis. With embedded dashboards, you can surface usage data directly to your customers. That means your users can see their own engagement—how active their team is, which features are being used, and whether they’re getting full value from the product. This visibility increases accountability and makes churn prevention a shared responsibility. Instead of telling your customers they’re at risk, you can show them—and help them course-correct before it’s too late.
Churn is part of any SaaS business. But surprise churn doesn’t have to be. A well-designed churn prediction model—built on connected product and CRM data—gives you the power to act before the damage is done. And it gives your customer success team the tools to stop managing symptoms and start addressing the root causes.
June helps you build that model, monitor it in real time, and act on it in meaningful ways. Whether it’s surfacing usage insights through embedded dashboards or flagging early signs of disengagement, June turns churn prediction into a practical, proactive process.
It’s not about eliminating churn altogether. It’s about making sure that when it happens, it never comes as a surprise.
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