Effective Strategies to Predict Churn in SaaS
What would your retention game be like if you could predict churn before it happens?
Your company will never remain the same for sure. You’ll identify and address pain points quickly, leading to increased user satisfaction. And that comes with many other benefits—free word of mouth, account expansions, increased revenue, etc.
This article dives deep into churn prediction for SaaS, showing you the strategies that work and how to implement them.
- Churn happens when a customer stops doing business with you. It can be voluntary or involuntary, but the most common is voluntary churn.
Common reasons for churn:
- Product-market fit failure
- Poor onboarding
- Delayed Aha moment
- Bad customer experience
- Poor customer service
- Product pricing plan weak points
- Customer churn prediction is the practice of using customer behavior data to predict users that are at risk of churn.
Effective strategies on how to predict customer churn and enhance customer retention:
- Segment your customers to understand them better and gather data points to identify churn patterns.
- Use NPS and CSAT to measure satisfaction along the user journey.
- Identify detractors and reach out to them.
- Track customer behavior in-app to gain insights into user attrition trends.
- Track feature usage to identify churn risk.
- Combine heatmaps with feature usage to determine drop-offs.
- Engage your customer success team.
- Use exit surveys to collect customer feedback and understand the reasons behind churn.
- Use a churn prediction model (The machine learning model works best).
- Perform customer churn data analysis.
- Best tools for predicting customer churn: Userpilot, Mixpanel, Baremetrics.
What is customer churn?
Customer churn happens when a user stops doing business with you. It can be voluntary or involuntary.
Customers could churn due to one of the following reasons:
- Product-market fit failure: This is one of the biggest reasons for churn and something that most startups deal with. P/M failure means you’re attracting customers that can’t find value in your product, hence they drop off early in the customer journey.
- Poor onboarding: For new customers, the onboarding experience is a foretaste of what’s to come, so they’ll base their judgments on it. This is especially true for trial users or customers that didn’t invest much upfront—they have nothing to lose.
- Delayed Aha moment: When the aha moment is delayed, some users wouldn’t have the patience to wait. They will leave the product thinking it doesn’t have the features they need.
- Bad customer experience: From the presence of bugs to the lack of in-app guidance, several factors constitute a bad customer experience. Users want SaaS products that are as intuitive as possible; no one wants to struggle to use a piece of software as if they’re living in 2008.
- Poor customer service: A good way to deal with customer service issues is to be proactive by creating self-service systems customers can always visit on their own.
- Product pricing plans weak points: SaaS pricing can be tricky. You have to ensure your plans are properly priced—make it too high and some users will avoid it, keep it too low and you’ll be losing out on revenue. You’ll also have to communicate hidden charges and price increases properly or you’ll scare customers away.
What is customer churn prediction?
Churn prediction is the practice of using customer behavior data to predict users that are at risk of churn. The goal is to be able to tell, with a level of accuracy, if customers will renew or cancel their subscriptions based on historical data.
Effective strategies on how to predict customer churn
Churn prediction will not only help you win the affection of at-risk customers, but it also shows you churn trends you can use for further decision-making.
There’s no one-size-fits-all approach to predicting churn—in fact, you’re better off combining different strategies. That said, below are some churn prediction strategies your company can start using.
Segment your customers
Segmentation helps you identify which customer groups churn the most. Use as many segmentation characteristics as you can—user journey stage, plan/pricing tier, frequency of support tickets, subscription date, etc.
Examine the historical data for the segments you created. Aim to find trends that will make it easy to anticipate churn in your new customers and take action before they do.
Measure customer satisfaction along the journey
Yes, there’s the immediate value of surveys like this—you can quickly spot at-risk customers and do something about it. But that’s not the best part.
The most important thing is to continue conducting in-app surveys and keep track of your results. Over time, you’ll have sufficient data to identify patterns, which makes it easier to predict churn.
A good practice is to have follow-up questions to dig into customer pain points and avoid ambiguity.
Userpilot can help you build code-free in-app surveys, including NPS. Our platform gives you templates you can customize as you want. You can also set your in-app surveys to be triggered contextually, helping you generate high engagement and accurate data.
Identify detractors and reach out to them
Detractors are the people who gave you low NPS scores. They’re the most at risk of churn, but you could still win them over.
If you used Userpilot for the survey, you’d get a beautiful dashboard that looks something like this:
From the results, you can easily see the percentage of promoters, passives, and detractors. All you have to do is reach out with personalized messages asking why they are dissatisfied.
Pro tip: When messaging these customers, check if they gave quantitative responses and let your message empathize with their complaints/pain points. It will make them more receptive.
Track customer behavior in-app
Customer behavior data gives insights into customer engagement. By regularly analyzing in-app behavior, you’ll notice changes quickly and know how best to respond.
Again, this is something Userpilot can help with. Our Pages feature allows you to track page activity to monitor the unique and total number of views by users and companies over time. This feature will help you detect unusual spikes and drops in page activity—and catch issues before your users complain about them.
You can even view page activity by user segments and trigger content for users that visited a specific page.
Track feature usage
Churn can happen when users don’t interact with the features they need to complete their goals. This applies to existing users as it does to new customers. For new customers, it can be because they didn’t know the feature existed. But even existing customers will encounter friction when a feature stops functioning as usual.
How do you detect these? It’s simple: use feature tracking to analyze the activities of your important features.
With a tool like Userpilot, you can tag features and easily track different kinds of product interactions, such as clicks, hovers, and texts.
Combine heatmaps with feature usage to determine drop-offs and predict churn
Use heatmaps with your feature tags to unveil how customers interact with the tagged feature.
This action can reveal common obstacles that can lead to churn, including:
- Bad product design
- Technical errors
- A lack of onboarding
- Users not on the happy path
Engage your customer success team
Your customer success team is actively involved in the user journey. They understand customer needs and know what makes your users tick. So that puts them in a good position to identify customers that are likely to churn and determine how best to retain them.
Use exit surveys to understand the reasons behind churn
No matter what you do to retain them, some customers will still churn. It’s just business.
However, use exit surveys to collect feedback to improve the experience for existing and new users. It will surprise you what kind of insights you’ll gather with exit surveys. That’s not to mention you have a chance of winning customers back when you offer contextual solutions to their reasons for churning.
For example, imagine a customer saying they’re leaving because they find your product pricey. By offering limited discounts or suggesting a lower plan, it’s possible to win that customer back on the spot. And if they insist on leaving, you at least know what to do to reduce your attrition rate—especially when the complaint is recurring.
Use a churn prediction model
A churn prediction model is a binary classifier that segments customers into two groups—the ones likely to churn and those that won’t. Depending on the variables and how it’s built, a predictive model can also show you how ‘sure’ it is that customers will churn.
The machine learning model is the most commonly used churn prediction model because of how effective it is. The data preparation process roughly involves the preprocessing of data from different qualitative and quantitative sources, proceeded by training and evaluation.
Perform customer churn data analysis
Churn analysis is about measuring and interpreting customer attrition data. The aim is to find the why of customer churn and use that information to drive customer retention and loyalty.
Best tools for predicting churn
No matter the approach you decide to take, churn prediction isn’t a manual process. You’ll need to use specialized tools to get the job done. And when choosing, it’s best to go for tools with all the necessary features.
Below are some of the best tools to predict churn:
Userpilot is a no-code tool that simplifies churn prediction and reduction. Some things you can do with our platform:
- User segmentation. This helps you in studying churn patterns and identifying at-risk customers.
- Create and analyze in-app surveys like NPS and CSAT.
- Use the goal-tracking feature to set milestones along the user journey and keep track of conversion rates. This can help you identify and address friction before customers churn.
- Get comprehensive in-app behavior data with analytics, feature tagging, and heat maps.
Mixpanel is a product analytics platform that will interest you if you need to analyze large volumes of customer data.
The platform is one of the best for data analytics, so you can’t go wrong choosing it. The only drawback is that it isn’t intuitive. Expert knowledge is required to maximize it. Which means it can be time-consuming if you’re new.
You can collect data from different sources and export them to Mixpanel for proper churn prediction. Here’s what the dashboard looks like:
Baremetrics allows SaaS companies to get granular with churn analysis.
For instance, you can use the platform to study the behavior of two churn segments—paying users that canceled their subscriptions, and customers that didn’t pay and just stopped engaging.
Separating your attrition numbers in this way allows you to study the details more closely and identify patterns that would have been hard to spot otherwise.
Churn prediction is just the first step in reducing customer attrition. The data means nothing if you don’t act on it in time to save incoming or existing customers from churning.
And the approach you’ll take varies from company to company. Have a meeting with your team to decide the best ways to re-engage in-active and at-risk customers. This can be anything from re-engagement emails to highly personalized in-app communication. It all depends on your product and customers.
Ready to use no-code strategies to predict churn and retain more customers? Book a demo to discuss with our team today!