Customers rarely announce their intention to break ties with a business or abandon a product. Companies must learn to spot and interpret both loyalty and churn signals. Subtle behavioral changes often show up weeks or months before a customer cancels, but only if teams know where to look and how to interpret what they’re seeing.
As services are increasingly digitized, the problem isn’t a lack of data. It’s knowing which data points actually reveal customer stickiness and which ones are just noise. AI-driven predictive models can help businesses pinpoint and better understand subtle churn signals. But even when leaders know what to look at and where, raw observations don’t move the needle on their own. Turning behavioral cues into measurable retention gains requires discipline, context and a willingness to design proactive interventions that center on real human emotions and reactions.
Here, members of the Senior Executive CMO Think Tank tap into their experience and expertise in customer engagement to explain how to separate meaningful loyalty and churn signals from metric clutter, as well as how to translate those insights into action. Their practical strategies can help leaders identify churn risk early, prioritize the right data sources, and build initiatives that don’t just gauge loyalty but actively create it.
“To turn signals into retention gains, identify the behaviors that correlate with long-term value, segment customers by where they are in that journey, and design interventions that remove friction or amplify momentum.”
Focus on Intent, Not Volume
Magda Paslaru, Founder and CEO of THE RAINBOWIDEA, stresses that the best way to predict loyalty or churn isn’t always looking at the data with the highest volume but at the data that reflects intent.
“We look at usage depth, friction points and ‘value moments’—the behaviors tied to meaningful outcomes,” she says. “These reveal whether customers are progressing or disengaging.”
The work doesn’t stop with identifying those signals. Paslaru says the ultimate goal isn’t to react to churn cues but to engineer the behaviors that produce loyalty.
“To turn signals into retention gains, identify the behaviors that correlate with long-term value, segment customers by where they are in that journey, and design interventions that remove friction or amplify momentum,” she says.
According to Paslaru, with the right strategies, retention doesn’t have to be a guessing game.
“When companies focus on the signals that reflect real progress, not just clicks, retention becomes both predictable and improvable.”
Look for Patterns, Not Single Signals
Paul L. Gunn Jr., Founder of KUOG Corporation, cautions against relying on isolated metrics to assess customer health.
“Behavioral cues often signal depth of loyalty and reveal emerging patterns that help us recognize churn risk,” he says. “When leaders rely on singular data points, they often fail to see these patterns.”
Gunn says some of the most telling signs of friction or hesitation can be found in everyday interactions.
“Solid predictive insights are often revealed by payment dynamics, support interactions and product usage behaviors,” he says. “Frustration is signaled by issues like delayed renewals, unresolved tickets, declining usage frequency or sudden changes in engagement.”
The key to boosting loyalty, Gunn asserts, is prioritizing vigilance and assuring customers you value them by responding quickly to signs of churn.
“Leaders who recognize churn signals in real time and respond with personalized outreach can translate behavioral observations into impactful, human-centered interventions.”
“The real leap happens when raw behavioral data is translated into human meaning. That means understanding context, emotion and motivation to uncover why a behavior is happening, not just that it happened.”
Find the Human Truth Within the Data
Daryl Travis, Founder and Chairman of Brandtrust, says that when it comes to assessing customer stickiness, certain behaviors should carry more weight.
“The strongest indicators of stickiness tend to come from behavior that involves effort, emotion or risk,” Travis says.
He believes retention strategies fall short when they focus on speed rather than meaning.
“Loyalty isn’t built by just reacting faster to data,” Travis says. “It’s built by recognizing the human truth behind the data—and designing for it.
“The real leap happens when raw behavioral data is translated into human meaning,” he continues. “That means understanding context, emotion and motivation to uncover why a behavior is happening, not just that it happened.”
Travis adds that once teams have pinpointed that “why,” the path to measurable retention gains is surprisingly short—because strategies will be centered on what customers truly need and value.
“The most effective initiatives target moments that matter—the handful of interactions that involve what are often “little things,” but are also moments when anxiety and effort are highest and trust is most important,” he says. “Improving those moments by even a little produces outsized returns.”
Track Usage and Support Statistics
Rachel Perkins is the Founder of Venturesome Strategies and serves as its Chief Strategist. She tailors her advice for software as a service companies, whose subscription-based business models make building a loyal customer base a critical, top-to-bottom initiative.
“In the SaaS sector, login frequency, depth of feature usage, and how embedded a product is within an organization are among the strongest indicators of future churn or long-term stickiness,” Perkins says.
She notes that support data adds essential additional context.
“Layering usage patterns with support signals—ticket volume, issue type and time to resolution—shows whether a customer is thriving or quietly struggling,” Perkins says.
She emphasizes the importance of tailored service. Even when customers are adopting a finished, proven product, it doesn’t mean a single onboarding call will be enough for a successful rollout.
“Most SaaS products are not ‘set it and forget it,’” Perkins stresses. “Teams need ongoing, strategic support—such as outreach to secure internal buy-in, tailored training by role, and guidance on best practices and use cases.”
If a SaaS team demonstrates that commitment to meeting each client’s unique needs, she says, they’ll achieve long-term loyalty and success.
“The product becomes part of clients’ daily workflows, not just another login they eventually abandon.”
Watch for Early Signs of Friction
Cody Gillund, Founder and Principal at Grounded Growth Studio and Founder of Pinyahta, encourages teams to pay attention to early shifts rather than dramatic drop-offs.
“The best signals of stickiness or churn aren’t the loudest; they’re the earliest and most consistent,” she says.
Those signals often show up as gradual drops in early enthusiasm and engagement.
“Companies should prioritize data sources that capture intent shifts: declining frequency, slower task completion, reduced depth of use, changes in feature mix, or stalled onboarding,” Gillund says.
She recommends three strategies for translating observations into action:
- Pattern mapping—that is, identifying the segments with shared behaviors.
- Root-cause interviews to understand the “why.”
- Closed-loop experiments that test targeted interventions.
“Retention gains come from addressing the friction customers feel before they voice it,” Gillund concludes.
“Customer success teams see these churn signals first, but too often they’re pushed into an upsell role instead of being treated as the signal hub they should be.”
Listen for Silence—and to Customer Success
Kurt Uhlir, Chief Marketing Officer at ez Home Search, says some of the most telling churn indicators show up as absence rather than activity.
“The strongest churn signals show up before metrics spike,” he says. “They show up as silence—fewer questions, narrower usage, slower replies.”
Uhlir points to customer success teams as an essential advance guard.
“Customer success teams see these churn signals first, but too often they’re pushed into an upsell role instead of being treated as the signal hub they should be,” Uhlir says. “When structured correctly, customer success strategies surface hesitation early and explain it in human terms.”
Using behavioral analysis tools can help clarify patterns present in feedback shared by customer-facing teams.
“Pair CS insights with tools like Amplitude or Fullstory to confirm where engagement thins and friction creeps in,” Uhlir suggests.
Ultimately, he says, customer loyalty increases when companies take a proactive approach to listening for and addressing frustration.
“Retention improves when teams act on early discomfort, address the moments when customers stumble, and stop waiting for cancellation notices to explain what went wrong.”
Retention Moves That Matter
- Prioritize intent-rich behaviors over high-volume metrics. Focus on usage depth, friction points and value moments that signal whether customers are truly progressing or disengaging.
- Analyze patterns across multiple data sources. Payment activity, support interactions and usage trends together reveal churn risk more reliably than any single data point.
- Translate data into human meaning. Give greater weight to behaviors involving effort, emotion or risk, and seek to understand why those behaviors occur, not just when they happen.
- Combine usage data with support insights. Login frequency and feature adoption gain clarity when layered with ticket volume, issue type and resolution speed.
- Act on early, quiet signals of friction. Small shifts in engagement often surface before customers complain, making them the best opportunity for timely intervention.
- Empower customer success as a listening engine. Treat customer success teams as a primary signal hub and pair their insights with behavioral analytics to spot churn risk sooner.
From Signals to Strategy
Customer loyalty is rarely the result of a single moment or metric. It’s built—or eroded—through a series of small interactions that signal progress, hesitation or frustration long before a customer decides to leave. The most effective retention strategies focus less on reacting to churn and more on intentionally shaping the behaviors and experiences that create stickiness in the first place.
AI and analytics can surface early warning signs, but lasting loyalty comes from acting on those signals with empathy, relevance and speed. When teams learn to identify what truly matters to customers and improve the moments where trust is tested, retention stops being a lagging indicator and becomes a strategic advantage.
