Stop Reacting to Customer Churn—Start Predicting It

Customer success (CS) leaders live and breathe customer churn. Ask them for last quarter’s churn rate, and they’ll undoubtedly respond in seconds. Though customer churn is a collectively owned metric, discussed in company all-hands meetings and displayed in cross-functional shared dashboards, customer success teams often feel the most pressure.

While product teams bear the weight of product-related churn, not all churn is product-driven. Success teams have the overarching responsibility to foster relationships, promote positive behaviors that drive satisfaction and stickiness, and ultimately keep customers happy.

Customer success teams aren’t surprised by churn—they know it’s going to happen. Low levels of churn are natural and sometimes even healthy (think, bad-fit customers and unprofitable segments). The problem is that CS often spots churn too late. Let’s explore why and how CS teams can use digital analytics to take a proactive, predictive approach to churn, stopping it before it happens.

See risk before it surfaces

Customer success manager, strategic account manager, technical account manager, client relationship manager—there are endless title variations for the crucial role these individuals play in your customer experience. With a focus on helping customers achieve their business outcomes, they know their customers better than anyone else in your organization. In many industries, CSMs act as an extension of their customers’ teams, becoming an integral part of their organizations. They advocate for and champion their priorities internally, guide them through challenges, and help them get the most value out of your products.

Customers are constantly sending signals that can be early indicators of churn. Yet, even their CSMs, who speak with them frequently and review their account details daily, often miss these signs. By the time the customer actually says something, they’ve already emotionally churned, making account recovery more challenging.

When green accounts churn, it means you’ve got an analytics blind spot. The signs were there in the customer’s usage, sentiment, feedback, and behaviors, but you missed them. You might have a traditional CS analytics tool that analyzes the content of customer interactions, or even helps you predict and address CX-related issues, but these tools won’t catch changes or decay in account-level product behavior.

Only a unified digital analytics platform that unifies CS, product, and marketing data, like Amplitude, can give your teams the ability to see risk before it surfaces. Because when it comes to churn, behavior speaks louder than words.

The behaviors that predict churn every time

At Amplitude, we’ve been discussing customer retention and churn with product teams for over a decade. Specifically, using in-product behaviors and segmentation to understand and identify the key actions that signal churn and loyalty to promote or discourage those behaviors across your customer base. However, we haven’t spent as much time helping customer success teams understand how they can use those same insights (and more) to prevent churn.

In working with thousands of companies to stanch the flow of customers leaving their customer ecosystem, we’ve identified the leading indicators of churn.

Decline in daily active users (DAU) and weekly active users (WAU)

DAU and WAU count how many unique users engage with your product in a single day or week. It’s used to track short-term engagement and monitor daily usage trends. A drop in DAU suggests that your customer isn’t experiencing as much value from your product as they once were. Less engagement often precedes actual churn.

Dropped feature usage and feature abandonment

Users don’t stop using a feature for no reason. For example, if customers turn off notifications, that’s a sign they’re losing interest. Dropped feature usage, or abandoning a feature altogether, signals that value is waning—and when that happens, customers will start looking for other ways (or products) to solve their use case.

Behavioral analytics and segmentation can help you identify the features most critical to retention. If your most loyal customers send five messages to peers through your product per day, your messaging feature is important to retention. In that case, you’d want to monitor messaging usage closely and take action when it drops.

Support volume spikes

Your customers like you, but not enough to reach out for no reason. So spikes in support volumes are rarely a good thing. No matter how well you’ve designed your service processes, soliciting support takes effort—and if customers have to expend too much effort to use your product, it will eventually become not worth the hassle. Spikes signal product issues and customer frustration. Keep a close eye on support volumes and delve into support data to understand and resolve problems before they compromise your customer experience.

Negative sentiment in feedback

Companies with strong retention listen to users from a qualitative and quantitative perspective. Both data types are critical and complement each other. Fully understanding and detecting changes in sentiment is powerful, but not always straightforward. A customer complaint is obviously negative. However, sentiment is often more nuanced, embedded in the content of the conversation. Leading companies use tools like AI Feedback to analyze all these conversations, revealing what customers are saying and surfacing insights that bring negative sentiment to the forefront.

Additionally, today’s customers are constantly giving feedback, whether you realize it or not. There are solicited feedback channels that you’re likely tracking (surveys, support tickets, conversations with CSMs, etc.), but there are also organic feedback channels that many CS leaders miss. For example, community discussions, online forums, product reviews on third-party sites, and more. AI Feedback helps you listen to what users are saying at scale across all these channels. This makes it easy to identify negative sentiment early and take action to make things right with those customers.

What proactive CX looks like

A CS leader’s dream is to see into the future and prevent problems before they happen. However, without magic, institutionalizing a proactive approach to CX is second best. Everyone talks about proactive customer service, but what does that actually look like?

• Health scores based on behavior, not gut feel. The CS team at Amplitude uses a scoring system to assess customer health based on a combination of metrics like usage, engagement, and satisfaction. This aggregate provides an accurate and well-balanced picture of customer health, enabling CSMs to investigate and take action if scores drop.
• Alerts when accounts deviate from normal patterns. Proactive CS teams implement an early warning system that flags accounts showing signs of potential risk, such as decreased product usage or negative feedback. A modern digital analytics platform, such as Amplitude, makes it easy to set up automated alerts for key metrics, enabling timely intervention.
• AI-generated “at-risk signals” based on cluster analysis. CS teams can use machine-learning-based tools to group users into behavioral clusters and then flag when a user moves into a cluster that historically correlates with negative outcomes, such as low engagement or churn. When a customer starts exhibiting high-risk behaviors, the system alerts you.

Using Amplitude to predict and mitigate churn

Amplitude empowers CSMs with a complete, connected view across all touchpoints—product behavior, surveys, and sales/support interactions—to truly understand customer health. It makes it easy for CS teams to identify red-flag behaviors and sentiment, determine the root cause, and intervene with the customer. Without Amplitude, those red flags may go unnoticed.

Some of the ways CS teams use Amplitude include:

• Setting up behavioral cohorts. Behavioral cohorts group users based on the behaviors they perform in your product within a specified timeframe. Cohort analysis enables you to investigate why specific groups or segments (cohorts) of users leave your app—because if you know what’s making people leave, you can take action to avoid it.

Amplitude makes it easy for CSMs to create and analyze these segments based on any combination of actions taken (or not) in the product. It can help you form and test hypotheses about why people churn. For example, behavioral cohorting could reveal that users who sign up for the “basic” plan churn because they lack access to sufficient features, or that users who don’t activate reminder notifications churn because they forget to use the app and derive no value from it.

• Automated alerts to CSMs when thresholds are hit. Amplitude’s alerts system uses advanced data mining and machine learning to automatically detect any anomalies in your data and instantly bring these hidden trends to your attention. Amplitude creates an automatic alert for every event you instrument to help you track all events for anomalies and unexpected trends. CS leaders can also configure custom alerts with specific conditions related to customer health and churn.
• Using Journeys to map where users drop off. Amplitude’s Journeys feature creates a visual path analysis, enabling CSMs to explore user actions to or from any point in your product. Path analysis can be instrumental in identifying what users do once they drop out of a funnel. Do these users abandon an onboarding workflow altogether, or are they engaging in an alternative activity instead?
• AI narratives that explain the “why” behind declining usage. Amplitude uses AI to combine behavioral changes, feedback themes, and pattern recognition. This enables CSMs to understand why usage is slipping and what to do next. It analyzes all touchpoints and data, translating them into a concise explanation with actionable recommendations. That means less time figuring out the problem and more time fixing it and fostering the customer relationship.

Reduce customer churn and create customers for life with Amplitude

Churn isn’t going anywhere, but it doesn’t have to keep you on your back foot. Stop reacting to customer fires that result in churn and start predicting and preventing them. Leading CS teams take a proactive approach and partner with Amplitude to:

• Use behavior to inform QBRs and renewal strategies
• Run targeted adoption campaigns powered by Amplitude insights
• Partner with product and marketing to remove friction points

Turn fragmented customer signals into retention. Connect feedback, sentiment, and real product behavior in a single view, so you can spot risks before they escalate.