What is churn analytics?
Churn analytics is the process of measuring the rate at which customers quit the product, site, or service. It answers the questions “Are we losing customers?” and “If so, how?” to allow teams to take action. Lower churn rates lead to happier customers, larger margins, and higher profits. To prevent churn, teams must first measure it with analytics.
What is customer churn?
Customer churn, also known as customer attrition, is when a customer essentially stops being a customer—ie, they choose to stop using your products or services. The customer churn rate is the rate at which your customers stop doing business with you. Every company experiences churn—the key is to understand why your customers are churning and decrease the churn rate.
Nearly all companies experience churn, and actually, not all churn is bad. Customers don’t have to be paying customers to churn; a user forgoing Google or Facebook is considered customer churn, as is a large corporation canceling its office building lease. Because churn hurts company growth, many brands are (rightly) obsessed with reducing it.
Why does churn analytics matter?
Churn analytics helps companies plug the leak in their customer bucket, to borrow a common analogy. Many businesses prioritize attracting new customers over retaining existing ones and don’t notice the toll churn takes until it seriously erodes profits. By some measures, as many as 97 percent of customers who churn do so silently, without leaving feedback or clues as to why.
If a company acquires new users at a loss—not uncommon for business or consumer apps that spend heavily on ads—and those users don’t evolve into paying customers, it’s tough for the company to make money. With churn analysis, the team there can quantify the value of customers, the price at which it makes sense to acquire them, and develop ideas to increase retention and customers’ lifetime value. Benefits of a customer churn analytics tool:
- Prevent revenue loss
- Lower customer acquisition costs
- Reduce marketing and sales costs
- Improve quality of customer service
- Increase opportunity for up-sell and cross-sell
How does churn analytics work?
Churn analytics is a software that explains why churn occurs. They integrate into a company’s existing CRM and support systems to measure the loss of customers. Most churn analytics track individual user events to reveal the user’s journey—the steps users took before they quit—and allow teams to compare this behavior with that of retained customers to reveal what went wrong.
There are three types of churn companies can measure: Subscription churn: Companies that charge a recurring fee define churn as the point at which a customer cancels or suspends the subscription. For example, when a customer calls to end their subscription to a news site, SaaS product, or fitness app. How to measure subscription churn:
# of customers that quit / total # of customers over a time period
Non-subscription churn: Non-subscription churn is trickier to measure than subscription churn. E-commerce companies and ad-supported news sites rely on a steady stream of recurring visitors and define churn as the percentage of those visitors who don’t return for a period of weeks or months. In the non-subscription model, there’s lots of what’s known as rotational churn, where visitors who were considered churned return and no longer count as churned. How to measure non-subscription churn:
% of customers that have dropped below the threshold and are considered inactive, within a time period
Revenue churn: Measures the percentage or dollar-value of revenue that’s churned over a time period. This is a common measure for SaaS companies. How to measure revenue churn:
$ of recurring revenue lost / total $ of recurring revenue at start of period
Churn analysis examples
Churn analysis is useful to any business with many customers, or to businesses with few, high-value customers. Which is to say, nearly every company. Companies in different industries use customer churn analytics for a variety of reasons:
- Financial services: Measure account holder lifecycle, detect users thinking of switching banks
- Consumer packaged goods: Develop a support model that encourages loyalty
- Consumer tech: Measure app churn
- Energy: Measure how much revenue is at risk of being lost to other providers
- Healthcare: Calculate the value of patients lost to other providers
- Insurance: Predict a user’s likelihood to close a policy
- Life sciences: Measure churn for device or equipment buyers
- Manufacturing: Measure churn for direct and downstream buyers
- Media and entertainment: Measure subscriber churn
- Retail and e-commerce: Predict when shoppers pose a high churn risk
- Telecommunications: Detect when customers are shopping other carriers
- Travel: Measure churn among repeat web visitors
Why is it difficult to predict churn?
Churn isn’t always straightforward to calculate, especially when it’s measured based upon past data. The future may resemble the past, but nothing is certain. Unforeseen events, from the emergence of new competitors to black swan market fluctuations, can prove old models wrong and lead companies to take the wrong actions. It’s also difficult for teams to apply the findings of churn analytics to individuals.
While the law of large numbers may prove churn statistics correct for an entire population, what does it mean when an individual presents a 20 percent chance of churn? What actions should the customer service agents take, if any?
Finally, companies often apply customer churn analytics to datasets that are too limited, such as only reviewing the last touch customers had with the companyThis rarely tells the full story. A customer that calls a support number to cancel their subscription isn’t canceling because they called support—they’re calling because they’ve accumulated grievances over many months. The call is merely a symptom.
Teams that want to get to the bottom of their customers’ churn must view the entire customer journey, plot the low and high points, and determine its true cause. When selecting a churn analytics tool, consider:
- Does it integrate with the company’s CRM and customer support system?
- Does it offer one central repository for customer data?
- Does it feature an interface simple enough for non-experts to access?
Proactive retention with churn analytics
To prevent churn, teams must identify it. Teams can use a churn analytics tool to view the actions users took throughout their lifespan and develop hypotheses for what led them to quit. Follow-up surveys and questionnaires can provide critical details and suggest actions teams could have taken to prevent the churn. Teams can segment their churn data for greater clarity. No two customers quit for precisely the same reason, but classes of users, known as cohorts, often behave similarly.
For example, users that signed up for a SaaS tool during a particular conference where they heard the CEO speak could have all shared a sense of awe that wore off after a few weeks, at which point they churned en-masse. Or a group of political campaigns could have adopted the tool for the duration of an election, then summarily dumped it once the outcome was decided.
Sometimes unprofitable or uncooperative users churn, and that’s good. Segmentation can help teams discover which customers are most valuable and seek to retain them above others. It can also help teams consider whether the users they’d like to keep from churning can actually be retained. In the example of political campaigns, those customers expire after the election no matter what and the churn may be inevitable.
Teams can also segment churned users by how long they were customers. Are new users churning at higher rates than medium or long-term users? Has something about the service changed to cause it? With data, teams can develop hypotheses that they can test to reduce churn.
How is churn modeled?
Churn modeling, as known as predictive churn analytics, provides teams with a sense of the events that cause churn that they can develop a model to predict it for segments of users or, ideally, for individuals, based on their demographics and behaviors.
Churn modeling can explain whether, say, a particular zip code suggests that a user is a greater risk of churn. Some tools allow teams to score all customers based on their likelihood to churn, known as a churn score, to allow marketing and customer service teams to prioritize their time. Teams can test measures for reducing churn at key points in the customer journey. For instance, teams can A/B test:
- New messaging
- Selective discounts
- Eliminating steps in the buyer journey
- Assigning more support to at-risk accounts
All the while, teams should collect satisfaction data through surveys, chat widgets, and feedback buttons, and tie that data back to particular individuals or accounts.
How to optimize and reduce customer churn
Many of the reasons customers leave can be rectified before a user hits “cancel,” but it can be challenging to understand these reasons with 97% of users churning silently. No matter your company size or industry, your churn reduction strategy must begin with some amount of churn rate analysis, or churn analytics.
1. Identify what’s causing your customers to churn
Along with customer surveys and qualitative user research, consider using a product analytics tool like Mixpanel to pinpoint where users drop off in a specific funnel. You can then use the data you uncover to confirm your hypothesis, and test solutions in your product.
Though the reasons that customers churn will be unique to your product, they can include things like:
- Customers are not getting value (or finding success) from your product.
- Your onboarding funnel is too complicated, or there are too many steps.
- Your product doesn’t encourage a usage frequency that’s regular enough (e.g., daily, weekly, monthly), causing customers to forget about it.
- The cost of your product is too high relative to competitors, or to its real or perceived value.
- The product doesn’t align with the message customers were sold, or they had a negative experience with it.
- A bug or a broken UI element doesn’t let users complete an important action.
2. Create solutions that help reduce customer churn
Once you know why users churn, begin testing solutions. Make changes, analyze their impact, and keep optimizing until you improve your product, offering, or customer journey.
For example: let’s say that your product is a subscription music streaming service. After conducting a churn analysis with the help of an analytics tool, your team identifies a cohort of subscribers who log in only a few times a month. You determine that, historically, customers who log in fewer than, say, four times per month have a higher chance of canceling their subscriptions or churning.
As a churn reduction strategy, you send that user cohort an email offering a free month of service. Then, you use cohort analysis to see how that group of users responded to outreach efforts. After a month, you’re able to determine that they were less likely to churn after receiving the promo offer. Success!
In this instance, taking the time to understand the “why” behind your customer churn rate provided actionable insights to help you recoup initial customer acquisition costs—and keep hard-earned customers engaged.
Depending on the drivers behind your churn rate, remember that churn reduction strategies can be applied at any point in the customer journey—from how your product is built or architected, to how it’s sold, marketed, and supported.
How using Mixpanel’s product analytics reduces customer churn
Product analytics helps you fully understand why customers are churning and then figure out the best way to reduce your churn rate and improve retention.
Let’s look at two quick examples of how Mixpanel has helped companies from three different verticals reduce churn.
Online food ordering/food delivery
Deliveroo is a London-based leading online food delivery company operating in 500+ cities worldwide. To ensure its restaurant partners are successful and get a high ROI from using the company’s services, Deliveroo looks closely at the conversion funnel from sign up to first order and, for each market, sets targets they want partners to hit so that Deliveroo knows its reducing churn and increasing conversion.
Deliveroo used Mixpanel to analyze one- and three-week retention rates to better understand how restaurants were engaging with the platform and if they were finding value in it. Their discoveries led them to improve retention rates and reduce churn.
Viber is Rakuten’s cross-platform instant messaging and Voice over Internet Protocol (VoIP) application. To understand what makes Viber more fun for its users, the company used Mixpanel to get a holistic understanding of messaging patterns and how they could potentially change the product to move the needle of their most important business drivers—increasing engagement and improving retention.
These are just a few examples, of course, but product analytics can be used across industries to gain a competitive advantage by gaining the deepest possible insights into what makes your customers stick or stay.
Preventing churn is everyone’s job
Preventing churn is the responsibility of every team within the company. It’s up to the marketing team to properly educate new buyers, the sales team to not overset expectations, the customer success team to provide high-quality support, and the product team to build a service that continues to delight customers. Companies can improve their churn rates by sharing churn data throughout the company. The more insights each team has into how it can reduce churn, the more customers the whole company keeps.
Remember that not all churn is bad and that there’s always a chance to go back, change things, and try again. The ultimate goal is a slow but steady reduction in churn rate and that the reasons for churn will always be unique to your product. Understanding the “why” is the key, and product analytics is a powerful resource for that piece of the puzzle.
Gain insights into how best to convert, engage, and retain your users with Mixpanel’s powerful product analytics. Try it free.