Churn Analytics: The Need to Analyze Churned Customers

Last Modified: December 29th, 2022

Understanding why customers don’t or do — buy more services or goods is pivotal to any company’s success. Extracting this insight starts with analyzing how many clients are taking their business elsewhere. This would allow you to pinpoint similarities among these clients, uncovering opportunities to improve and avoid losing more customers. 

But is this all there is to it? If your churn rate crawls up, what does it mean? How would you come up with an action plan to handle it? All these questions will be answered in the upcoming sections as we highlight the key facets of customer churn analytics.

Table of Contents

What is Churn Analytics?

Customer Attrition or Churn is defined as the rate at which clients back out of buying more of a company’s services or products. Customer Churn Analytics is a method that lets you measure this rate.

Churn Analytics goes beyond telling you what percentage of your customers don’t come back. It focuses on understanding the underlying cause and curtailing churn in time by leveraging data. Customer Attrition, despite being inevitable, invites you to learn how to boost customer retention and mend any leaks in your revenue streams.

Methods to measure Customer Churn include calculating this KPI (Key Performance Indicators) across various timeframes and trending those results; high-performing firms can also measure the financial repercussions of customers abandoning ship, followed by benchmarking those numbers against KPIs essential to the business’s profitability.   

Key Benefits and Importance of Churn Analytics

Churn Analytics will help you discover pain points in the customer journey while opening up avenues to bolster your services, products, or communication. Here are a few essential benefits of deploying Customer Churn Analytics:

Finding Opportunities for Better Communication

Improving customer experience comes with a constant understanding of customer expectations and meeting them. Churn Analytics unravels trends in customer behavior at every point of contact. Tailored engagement across the communication channels that your customers prefer is one way to make customers feel appreciated and valued.

Discovering Strengths and Weaknesses of Your Product

Churn Analysis allows you to find patterns that depict common propellers for customers to leave you, such as poor product adoption or price sensitivity. It also shows how customers engage with your product throughout their lifecycle. You can leverage these learnings to maximize what customers already love and improve on what they don’t.

Predicting and Reducing Future Churn

Churn Analytics focuses on analyzing historical customer data to make churn prediction possible. You can also leverage Customer Lifetime Value (CLV) analysis to understand customers at every lifecycle stage and who’s going with your product. This means that you can be proactive with your approach and pour your energy into improving retention when you find the red flags that depict churn. 

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When should you deploy Customer Churn Analytics?

While your ERP might calculate Customer Churn every day in the background, doing a formal customer churn analysis too often can diminish the effects of improvement efforts. To obtain the most from Customer Churn Analytics, it is recommended to monitor it regularly.

Many companies choose to update their Customer Churn metrics on a quarterly or monthly basis. Businesses might deploy financial planning along with analysis professionals to execute specialized churn analyses on a more alternating or frequent basis. For instance, a company with five main product lines could update all those churn metrics every month or rotate them, so each gets a quarterly refresh. 

What are the Different Types of Churn?

The different types of churn you can come across are as follows:

Involuntary Passive Churn

This type of churn is indicative of a leak in your revenue stream. Involuntary churn takes place when the customer’s payment isn’t completed for the following reasons:

  • Hard declines might occur when a card is reported stolen or lost.
  • When an expired card is used.
  • Banks might decline the card (due to frozen accounts, suspected fraudulent activity, etc.)
  • Soft declines could occur when a credit card has maxed out its limit.

This churn is relatively easier to deal with and can be resolved by implementing smart dunning workflows.

Voluntary Active Churn

This refers to customers that cancel your service or product. This type of churn can occur due to various reasons, such as poor customer service, poor onboarding, or taking their business to a competitor. Voluntary Active Churn comprises a major chunk of your lost revenue, and you should direct the majority of your strategic initiatives on preventing it.

Downgrade Churn

Downgrade Churn occurs when customers downgrade to a lower-tier plan, leading to downgrade MRR. This might be caused due to:

  • Value proposition misalignment
  • Price sensitivity

You can reduce this churn by experimenting with your product pricing and packaging. To decrease Downgrade Churn, you need to think of ways to pack more features and value into your customer’s current plan. 

Good Churn

Churn isn’t all bad! Sometimes churn might remove customers that were a bad fit for your product, business model, or service. Another instance of ‘good churn’ is when customers leave after their short-term need for your product has been met, such as a short-term project or an event. It is also known as Happy Churn. These customers tend to reactivate their subscription later, therefore, one way to keep an eye on Happy Churn is to track reactivation MRR.

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Customer Churn Analytics Examples

Here are a few instances of Customer Churn for a large manufacturing company and a midsize subscription service.

Example: Large B2B Product Company

A large pool bottle manufacturer sells its products through 900,000 resellers across the United States. Last month, it added 24,000 resellers and lost 12,000. So, for this company, the monthly reseller churn rate is as follows:

12,000 / 900,000 = 1.33% 

Example: Midsize Business

Online retailer Sacramento Sanitizers sells sanitizer bottles to its customers at $12/bottle. It currently has 12,000 customers and makes $200,000 in monthly revenue. Over the last year, the company lost 1,200 customers. Therefore, the annual Churn rate would be:

1,200 / 12,000 = 10%

Understanding the Customer Churn Analytics Process

Here are the steps you should follow to set up Churn Analytics for your pipeline:

Step 1: Invest in Churn Analytics Tools

Before you can carry out any type of Churn Analysis, you need to have data to analyze. For this, you’ll have to invest in Subscription Analytics tools, since they allow you to take a look at all your metrics — including churn — at a single glance. You have all your metrics and data in one place, with various ways of slicing and dicing it.

Churn Analytics: Step 1 Illustration
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Churn Analysis allows you to proactively identify customers that are likely to churn. Creating alerts to inform you about any adverse change in real-time, is a splendid way to stay on top of your churn metrics.

Churn Analytics: Create Alert
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Step 2: Cohort-based Analysis of Customers

Customer Segmentation or Cohort-based Analysis is defined as the process of collating your customers with various similar traits. It can allow you to discover trends in customer churn. It is recommended that you pick a tool that permits configurable segmented analysis of churn. You should be able to analyze churn by business type, revenue, or demographics.

You can perform churn analysis across customer segments in the following ways:

  • Churn Analysis by Industry: Churn Analytics by Industry allows you to prevent churn by implementing specific measures for every sector. For instance, the global downturn brought forth by the COVID pandemic hit the travel industry hard, but e-learning saw a dramatic rise as students took to remote learning. You can execute the following measures to decrease churn in sectors facing a slowdown.
  • Churn Analysis by Revenue: This segmentation will group customers based on their revenue. Early-stage startups might be churning because of budgetary issues, and you can decrease this churn by offering them flexibility and discounts in payment terms. For enterprises, you should make sure that your product has scaled along with the company’s growth.
  • Churn Analysis by Geography: Knowing your customer’s location lends context to why they would be churning. Payment gateways, tax regulations, and payment processing differ for different countries, therefore, affecting your product’s adoption. For SaaS businesses, it is pivotal to adhere to the local sales tax guidelines. Your subscribers could be churning due to a lack of payment options or due to a dearth of compliance with regulations, and Churn Analytics poses a good way to carry out that analysis.

Step 3: Determine Why and When is the Churn Occurring

To discover the answers to these questions, you need to pose these questions. One way of doing this is to simply send out an email after customers cancel. You can either ask customers why they canceled via a questionnaire or directly in the email. If you are a small-scale SaaS company, you can try this out.

But as your company grows, you’ll need a more scalable way to find out why customers are churning. Understanding what % of churn was involuntary and voluntary gives splendid insights into churn prediction workflows and strategies you would be setting up.

Apart from this, analyzing the timing of the churn will add depth to your Churn Analytics. There are various ways to take a look at this. You can start by analyzing churn via activation dates. It lets you know how soon the customer churned after activating a product.

Churn Analytics: Step 3 Illustration
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You can also analyze this by taking a look at the MRR retention cohorts. This cohort can help you visualize growth, MRR addition, and churn behavior based on both when you acquired the customer and what transpired within a month.

Churn Analytics: MRR Retention Cohort
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As you scroll down from the first row, it depicts how much new revenue you could acquire month on month while going across columns demonstrates how much that cohort contracted or expanded. In the cohort mentioned above, you can see an adverse impact on revenue growth across customers for April. An interesting observation here is that customers acquired in the recent months seem to have churned more than the older ones — indicative of a high early-stage churn.

Why is it Difficult to Predict Churn?

Churn isn’t usually straightforward to calculate, especially, when it’s measured based upon past data. The future may resemble the past, but nothing is fixed. Unforeseen events, like inexplicable market fluctuations to the emergence of new competitors, can prove old models wrong and lead companies to perform the wrong actions. It can also be difficult for teams to apply the findings of Customer Churn Analytics to individuals.

While the law of large numbers may prove churn statistics correct for an entire population, what can you infer when an individual presents a 20% chance of churn? What actions can the customer service agents take, if any?

Finally, companies often apply Customer Churn Analytics to datasets that can be too limited, such as only reviewing the last touch customers had with the company. However, this doesn’t reflect the entire 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 collated grievances over many months. The call should merely be deemed as a symptom.

Teams that wish to unravel the root cause of their customers’ churn need to look at the entire customer journey, plot the high and low points, and ascertain its true cause. When choosing a Churn Analytics tool, you should consider:

  • Does it provide one central repository for customer data?
  • Does it integrate with the company’s customer support system and CRM?
  • Does it incorporate an interface simple enough for non-experts to access?  

How to Reduce and Optimize Customer Churn?

The majority of the reasons why customers leave can be corrected before a user hits “cancel”, but it might be challenging to understand these reasons with 97% of users churning silently. Irrespective of your company’s industry or size, your churn reduction strategy should begin with some amount of Churn Analytics or Churn Rate Analysis.

Create Solutions that Help Reduce Customer Churn

Once you understand why users churn, you should start testing solutions. Make modifications, analyze their impact, and keep optimizing until you’ve improved your offering, product, or customer journey.

For instance, let’s say your product is a subscription music streaming service. After conducting a Churn Analysis via an analytics tool, your team pinpoints a cohort of subscribers who log in only a few times a month. You determine that, historically, customers who log in fewer than, say, five times per month have a higher chance of churning or canceling their subscriptions.

As a churn reduction strategy, you could send that user cohort an email offering a free month of service. Next, you can use cohort analysis to check how that group of users responded to outreach efforts. After a month, you can ascertain if they were less likely to churn after getting the promo offer.

In this instance, taking the time to understand the “why” behind your Customer Churn Rate offered actionable insights to help you recover initial customer acquisition costs — and keep hard-earned customers engaged.

Based on the drivers behind your churn rate, keep in mind that churn reduction strategies can be applied at any point in the customer journey — from how your product is architected or built, to how it’s marketed, sold, and supported.

Identify what’s Causing Your Customers to Churn

Along with qualitative user research and customer surveys, you should consider using a product analytics tool to pinpoint where users drop off within a specific funnel. You can then use the data you discovered to confirm your hypothesis and test solutions in your product.

Despite the reasons that customers churn will be unique to your product, they can include things like:

  • Your product doesn’t encourage a usage frequency that’s regular enough (e.g., weekly, daily, or monthly) causing customers to forget about it.
  • A broken UI element or bug doesn’t let users execute an important action.
  • Customers aren’t getting value (or finding success) from your product.


In this article, we’ve covered the salient aspects of Churn Analytics, including its importance, benefits, types, and steps to reduce and optimize churn among others. All in all, there are various ways the churn data can be diced and sliced. The 3-step process for Churn Analytics covers the primary aspects of churn: who is churning? when and why are they churning? and where are they churning? Armed with this information, you’re all set to decrease customer churn and boost customer retention.

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Content Marketing Manager, Hevo Data

Amit is a Content Marketing Manager at Hevo Data. He enjoys writing about SaaS products and modern data platforms, having authored over 200 articles on these subjects.

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