Customer churn is the proportion of your customers that end up leaving, instead of remaining a regular buyer.
Churn analysis is the process of finding trends and patterns in the churn data to uncover which customers are leaving, at what point in their lifecycle they’re leaving, and what causes them to leave.
Understanding the who, when and why of the churn problem is the first step in implementing effective strategies in order to reduce the amount of future customers churning, and increase profits. If you’d like to learn more, read our guide on predicting and reducing customer churn.
Customer churn is the holy grail growth metric for companies. Improving the proportion of customers that become regular, consistent purchasers has a significant impact on monthly recurring revenue (MRR), due to the increase in customer lifetime value.
This is compounded because acquiring new customers is far more expensive than retaining or upselling an existing customer, resulting in customer ROI skyrocketing. Finally, the added bonus is less of your customers jumping ship to competitors.
The natural progression after churn analysis is the targeted improvement of the aspects driving customers away. This improved customer experience results in a stronger brand perception, and will ultimately reduce the churn rate as you get better at serving your customers over time.
A guide to customer churn analysis
Customer churn rate is measured by dividing the number of customers that churned by the total number of customers over a given period (usually monthly). However, the point of churn analysis is to dig deeper into the who, when, and why. So we can do a bit better.
The first way is to segment customers based on relevant demographics, which gives more insight into who tends to be churning compared to others. Possible ways to segment customers include geographical, demographical, behavioral, or any other customer information you might have. Learn how to segment customers with machine learning.
Tracking customer churn by cohort is when customers are bucketed based on when they were acquired (cohorts). This method has two main benefits; Firstly it gives insight into when in the customers' lifecycle they tend to churn. Secondly, it separates new customers from old customers, so you get a more accurate picture of how effective retention efforts are in reducing churn over time.
Other than customer churn rate, there are plenty of other metrics you’ll want to monitor as part of your churn analysis.
Revenue churn rate: Another way of measuring churn is by tracking monthly recurring revenue (MRR). It isn’t a replacement for customer churn but is complementary. Revenue churn is useful when a business has a large price range because it gives more weight to the important, high-ticket customers.
CAC, LTV, and ROI: Customer acquisition cost (CAC) and customer lifetime value (CLV), work together to determine how viable the business model is, detailing the ROI of marketing spend. A rule of thumb is to have a 3:1 LTV to CAC ratio. Any lower you’re probably spending too much, any higher you’re probably not spending enough.
Gathering qualitative data from individual customers, on the reasons behind a customer leaving is an important source in customer churn analysis.
Implementing a customer exit survey either just before or just after a customer has left, is an efficient way to understand the trends in customer responses. It’s best to keep the survey short to make sure you maximize the response rate, but they’re already churning, so you’ve got nothing to lose. The qualitative data can help you prioritize improving the underlying reasons causing churn, such as:
Pricing: Does your competitor's product or service have a greater perceived value?
Customer Service: Did a poor customer service experience influence their decision to leave?
Missing functionality: Did the marketing of your product make promises your product or service can’t keep?
Usage frequency: Is the customer using the product or service frequently enough to justify paying for it?
Onboarding: Are there enough resources to teach the customer how to get value from the product or service?
The AI & Analytics Engine is a machine learning-powered tool, that makes it possible to predict customer churn by analyzing large customer datasets with machine learning algorithms.
The latest update includes a tailor-made customer churn prediction template to guide you in getting churn predictions.
The Engine can tell you specifically which customers are exhibiting churn signals, and the feature importance tool allows you to see what factors contribute towards that churn likelihood (useful when measuring segment-based churn).
Determining which customers are likely to churn is the first step in implementing an effective retention strategy.
The final step in the puzzle is implementing the retention strategy, informed by your knowledge of when and why a customer most frequently churns, for the customers above your churn probability threshold. Some examples to get you started include:
Improving (or creating) a customer onboarding process
Engage customers with proactive, personalized communication
Reward customers by offering incentives
Remind customers of your value with social proof
Implement gamification to keep customers engaged
Demonstrate your company values to your customer
So that’s it, hopefully you’ve found this guide useful in starting your churn analysis. If you’re looking to predict customer churn, consider using the AI & Analytics Engine’s new template, and let us know how you go.
Not sure where to start with machine learning? Reach out to us with your business problem, and we’ll get in touch with how the Engine can help you specifically.