Imagine you could hear the quiet whispers your customers send before they decide to leave. It would be a subtle drop in usage, a hint of frustration, or a hesitation that’s barely noticeable.
No more struggling to react after the fact or drowning in noisy alerts that don’t tell the full story. Instead, you have a system that listens carefully, interprets those early signals, and gives you the chance to step in with the right support before churn even happens.
Believe it or not, this isn’t just wishful thinking. It’s exactly what we’re building toward in Customer Success.
In our last story, we introduced the Observer, a single system designed to watch every customer’s journey so we don’t have to find data points. But to make that vision work, we had to learn how to spot the early signs of trouble—those whispers that often go unnoticed.
Today, I want to take you behind the scenes of how we defined those early churn symptoms, like usage drops, frustration, and upsell hesitation.
Spotting the First Signs: When Usage Starts to Drop
Ah, the classic early warning sign usage starts to dip. You might think, “Well, maybe they’re just busy this week,” right? But here’s the catch: when a customer who used to be active suddenly logs in less often or stops exploring your product, that’s a red flag!!!
But don’t jump to conclusions just yet. Sometimes, it’s nothing serious. It may be seasonal changes or a shift in their business cycle. The tricky part is figuring out when a drop in usage really matters.
That’s where our AI comes in. It doesn’t just notice a one-off dip. It watches patterns over time, compares with similar customers, and weighs other signals to decide if it’s time to pay attention. Pretty smart, huh?
Let me give you an example. A mid-sized SaaS company had a steady usage pattern for months. Suddenly, their login frequency dropped by 30% over two weeks.
Alone, that might not have raised alarms, but the AI noticed the pattern and flagged it. Turns out, they were struggling with a recent product update that wasn’t working well for them. Because we caught it early, our team reached out proactively and helped them navigate the issues before frustration set in.
The Silent Frustration: Reading Between the Complaints and Feedback
Now, here’s something many overlook—frustration. Customers rarely leave without showing signs of dissatisfaction first, but those signs aren’t always loud or obvious.
Sometimes, it’s a slow build-up of small complaints, or maybe they’re sending more support tickets than usual. Other times, it’s a change in tone, like emails that suddenly sound a bit curt or less friendly.
We realized early on that just scanning support tickets wasn’t enough. We needed to understand the sentiment behind the words.
So, we taught our AI to read between the lines using natural language processing to detect frustration even when customers don’t say it outright. That way, we catch those quiet cries for help before they turn into bigger problems.
For instance, one customer hadn’t escalated any major issues, but their support tickets started showing a subtle shift in language, more urgency, less patience. The AI picked up on this change, and our Customer Success Manager reached out with expert guidance and turned a potential churn situation into a renewed partnership.
The Quiet Withdrawal and Hidden Signals: Behavioral Changes and Upsell Hesitation
Here’s something you might not expect: sometimes, customers who are about to churn don’t reach out at all. They just quietly pull away.
We noticed patterns where customers stopped asking questions or seeking help. They stopped attending webinars or engaging with onboarding materials. That quiet withdrawal? It’s a powerful sign.
Plus, there are those sneaky hidden signals, like visits to pricing or downgrade pages, hesitation around upsell offers, or even changes in payment patterns. These subtle behaviors often go unnoticed but say a lot about a customer’s intent.
By combining these clues, our AI builds a detailed picture of each customer’s health and gave us a chance to step in before it’s too late.
The Importance of Context: Why One Signal Isn’t Enough
Here’s a little secret we discovered along the way: no single signal tells the whole story.
You see, a drop in usage might mean something different depending on the customer’s industry, size, or even time of year.
Frustration in one account might be a deal-breaker, while in another, it’s a temporary hurdle.
That’s why context is king. Our AI doesn’t just look at isolated signals. It analyzes them together, in the context of each customer’s unique journey.
For example, a customer visiting the pricing page might be exploring options, or they might be getting ready to churn.
The difference? Other signals like support interactions, product usage, and payment history help the AI understand the true intent.
This multi-dimensional view is what makes our approach so powerful. It’s not about reacting to every beep and buzz. It’s about understanding the story those signals tell when combined.
From Whispers to Action: How AI Decides the Best Next Move
Okay, here’s the exciting part.
It’s one thing to spot these early churn whispers. But what do you do with that information?
Our AI doesn’t just wave a red flag and leave us guessing. Nope. It helps us decide the best next move based on the context and the likelihood of success.
Whether that means sending a personalized offer, offering targeted training, or having a Customer Success Manager jump in for a chat, the AI guides us to the smartest action.
This shift is huge. Instead of reacting to problems after they blow up, we’re stepping in early, with the right solution at the right time.
And here’s a little teaser! If you’re wondering how the AI actually chooses the best move from many options, that’s exactly what we’ll dive into next. Our upcoming blog, “One Signal. Many Options. One Smart AI to Choose the Best Move,” will show you how it all works.
