You don’t need us to tell you how the long-term success of your business depends on your ability to retain customers or how much cheaper it is to retain users rather than acquire new customers. You wouldn’t be reading this article otherwise.

But it’s one thing to know how important it is to retain your customer base and another to understand how to retain them. Unfortunately, every company is different when it comes to handling retention. A strategy that helps retain customers for one brand may not work well for another. That’s why it’s crucial to analyze your efforts — and that’s where customer retention analysis comes in. 

In this blog post, we’ll be delving into the world of customer retention analysis and discussing why it’s so important for SaaS businesses to get it right. We’ll show you how to analyze your customer retention efforts step-by-step and end with best practices you can use to take your retention efforts to the next level. So, without further ado, let’s dive in!

What Is Customer Retention Analysis?

Customer retention analysis (survival or churn analysis) analyzes why and how customers churn. It also measures what percentage of your customers are loyal and why.

It is a much deeper analysis than just calculating your churn rate. By doing a customer retention analysis, you’ll understand the following:

  • When customers churn during their lifecycle
  • How many customers churn
  • Where users churn in the customer journey
  • The specific reasons customers churn
  • Which parts of your customer retention strategies are working
  • Which parts aren’t working
  • How customer satisfaction levels impact retention
  • How to improve revenue churn

Customer retention analysis isn’t just about finding out how many of your customers you retain monthly — that’s relatively straightforward. It’s about discovering why customers choose to stay or leave when they are most likely to make that decision and what you can do to improve retention. 

Types of Customer Retention Analysis

Customer retention analysis comes in a variety of different formats. These are the five most common types of customer retention analysis:

Five types of Customer Retention Analysis

Prescriptive Analysis

Prescriptive analysis is a form of customer retention analysis that focuses on identifying the best course of action for a given situation. It involves using mathematical and statistical algorithms to analyze data and recommend actions to achieve a specific outcome or goal.

For example, you could use prescriptive analysis to answer questions such as: “What will stop customers from churning in the first three months?” and “What are the most common causes of customer attrition?”

Descriptive Analysis

Descriptive analysis is a type of customer retention analysis that focuses on summarizing and describing events in retrospect. It involves statistical and visualization techniques that explore and understand a dataset without drawing predictions or making conclusions. 

For example, you can use descriptive analysis to understand patterns in average customer behavior, like at which month they’re most likely to churn or the actions that power users take. This will usually be one of the first forms of customer retention analysis you’ll perform. 

Predictive Analysis

Predictive analysis is the most common form of customer retention analysis. It is a form of analysis that uses machine learning algorithms to analyze data sets, understand patterns, and predict future events. 

For example, you can use predictive analysis to understand which users are most likely to churn in the future or the impact improving your onboarding flow may have on retention rates. 

Diagnostic Analysis

Diagnostic analysis is a type of customer retention analysis that focuses on identifying the root cause of a problem or issue. It involves analyzing data to determine why a particular event or outcome occurred and identifying any underlying factors contributing to it.

Diagnostic analysis typically involves a combination of statistical analysis and qualitative research methods, such as interviews and surveys, to gather information and identify patterns in the data. 

For example, you can use a combination of customer feedback surveys and quantitative data sources to understand why customers churn in the first place, or which product features cause them to remain a customer. 

If descriptive analysis understands what is happening, diagnostic analysis understands why it is happening. 

Outcome Analysis

Outcome analysis is a type of customer retention analysis that focuses on evaluating the impact and effectiveness of a particular action. It involves measuring the outcomes of a specific action or intervention, and determining whether it has successfully achieved its intended goals.

Outcome analysis involves collecting data both before and after the intervention or treatment and comparing the two sets of data to determine if there has been a change. The outcomes measured may vary depending on the intervention but can include factors such as patient health outcomes, academic achievement, or financial performance.

Outcome analysis is the ideal way to measure the effect of any improvements you’ve made due to customer retention analysis.

For example, if you’ve revised your onboarding process, you can use outcome analysis to understand the impact of that change on churn rates within the first few months of a customer subscription. 

Why Does Retention Analysis Matter?

Customer retention analysis can have wide-ranging benefits for your business. First, and most obviously, it can help reduce churn

The better you understand why customers churn or remain, the more effectively you can reduce churn in the future. For example, if customers churn because your product lacks a certain feature, you can prioritize developing that feature. If new users churn within a few months of signing up, you can improve your onboarding experience.  

This can significantly reduce customer acquisition costs, as a result. When retaining more customers, you aren’t forced to invest so heavily in acquisition strategies. You still can, of course, but it’s not necessary to keep your company growing. 

Customer retention analysis can also significantly improve revenue growth. That’s customer retention and churn compound. So a company with a 5% churn rate will outgrow a company with a 10% churn rate by over 500% in just two years. 

Strong retention rates can lead to higher funding valuations if you are a VC-backed startup.

“Retention, both from a gross and net dollar standpoint, is probably the number one metric that investors and buyers are honing in on today”

– Kristopher Beible, Vice President at Software Equity Group

Customer retention analysis can ultimately help you boost your bottom line.

Retention analysis can help you identify who your power users are and what makes them love your products. Knowing this information, you can focus on acquiring more customers that match this persona and providing the features or services they love. 

High levels of customer retention can even improve employee morale. When customer engagement is high and active users get a lot of value from using your product, employees will feel their efforts are validated and that they are working for a company that provides real value beyond paying their salary. 

5 Steps to Customer Retention Analysis Success

Now that you know what customer retention analysis is and why it matters, follow the steps below to execute a customer retention analysis strategy. 

Define Retention and Calculate Customer Retention Rate

Customer retention differs for every business. So start by defining what retention means for you. For a subscription business, that might be a customer who keeps their subscription for a year. For an e-commerce brand, it might be a customer who makes two purchases within a year. 

The next step won’t change from one business. Calculate your customer retention rate. Understanding the percentage of customers leaving is fundamental to executing customer retention analysis. 

The equation is simple. Divide the number of customers you had at the end of a given period of time (taking into account the number of customers you gained) by the total number of customers at the start of that period, and multiply by 100.

Customer Retention rate = [(number of customers at the end of the period – number of new customers during this period) / Total customers at the start of the period] x 100

So if you had 5000 customers at the end of a period, gained 200 customers during that period, and had 5500 customers at the beginning of the period, then your customer retention rate would be:

(5000 – 200) / 5500 x 100 = 87.27%

Use Cohort Analysis to Understand Which Customers Churn

Next, you need to understand which types of customers churn and which don’t. The best way to do this is to divide users into customer segments (or cohorts) based on one or more characteristics. 

Start by using an acquisition cohort analysis to understand how long customers take to churn on average after sign-up. As you can see from the image below, users are split into cohorts based on the month they signed up. Within three months, 25% of ‌users who signed up in February have churned. 

MRR Retention Cohort

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Next, use a behavioral cohort analysis to understand what makes certain groups churn more than others. A behavioral cohort analysis groups users by behavior-based characteristics like certain features they use, buttons they click, or actions they take. 

Unlike acquisition-based cohorts — which tell you when churn takes place — behavioral cohorts will tell you why churn happens. Continuing the example from above, you can use behavioral cohort analysis to find out what made 25% of your churned customers leave after month three by comparing how those users behaved. 

If you have an onboarding checklist users can complete, for example, you could create a cohort for users who did complete the checklist and one for users who didn’t. If the churn rate for users who didn’t complete the checklist is significantly higher, you’ve identified a problem.

Unfortunately, it’s rarely just one issue that causes customers to cancel their subscriptions. As such, you’ll need to compare multiple behavioral cohorts to identify multiple reasons.

Use Customer Retention Metrics and KPIs

Key performance indicators and other metrics are an excellent supplement to your customer retention analysis efforts. Understanding how these metrics change over time and between different cohorts can help you analyze what’s working and what needs to be improved. 

Identify and track the following metrics:

Customer Churn Rate

Your customer churn rate is the number of customers who have churned over a given period. It is essentially the opposite of your customer retention rate figure. This headline figure will highlight whether more or fewer customers are leaving between periods, but it won’t tell you why they are leaving. 

Customer Lifetime Value (CLV)

Calculate CLV by multiplying your average monthly subscription by the average number of months you retain customers. Your CLV should grow when your customer retention efforts are successful, although this metric can take a while to bear fruit. 

Understanding customer lifetime value also helps you understand the potential revenue customers can generate if you retain them. You can use this value to budget customer retention efforts. 

Net Promoter Score (NPS)

Net Promoter Score, or NPS, is a common method to collect customer feedback and generate qualitative data. It is based on a simple question of how likely customers are to recommend you to a friend on a scale of 1–10. 

An improvement in your NPS should result in improved retention rates and vice versa. 

Monthly Recurring Revenue (MRR) Churn Rate

Your company’s Monthly Recurring Revenue (MRR) Churn Rate is the percentage of MMR you’ve lost from existing customers over a given period. 

A negative MMR churn rate means you are gaining customers, while a positive MRR churn rate means you are losing customers. 

It’s important to note that the MRR churn rate isn’t just caused by customers leaving. Downgrading or pausing a subscription is also a form of MRR churn. 

Ask Customers for Feedback

There’s nothing like qualitative analysis to understand what customers did and didn’t like about your products. One of the best ways to get honest feedback from churning customers is to implement customer feedback surveys into your cancellation flow. 

Take a look at the example from the SaaS company Delighted. They provide a short space where customers can explain why they are leaving before canceling their account.

Example from the SaaS company Delighted

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Make sure you pose qualitative rather than quantitative questions to generate useful feedback. It may make the feedback harder to process, but it will be much more valuable. 

Identify At-Risk User Behavior Patterns

Using the data you’ve gathered from cohort analysis, KPIs, and customer feedback, you should have a pretty good idea of the kind of scenarios that cause customers to churn. 

Those reasons could include things like:

  • Customers not using your platform within 14 days of joining
  • Raising multiple support tickets
  • Having a payment method declined

Whatever they are, make a note of all at-risk behavior patterns and use them to personalize your customer retention efforts. 

Customer Retention Analysis Best Practices

Take your customer retention analysis further with these best practices.

Leverage Software

Amassing large amounts of customer retention data isn’t easy without a digital platform. Managing that data without a platform isn’t easy, either.

Fortunately, a wide range of analytics tools can help you improve your customer retention analysis efforts — some of which are designed specifically for the task. 

Churn Breakdown Report by Activation Date

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Take Chargebee’s subscription analytics feature, Revenuestory, for instance. Revenue story lets you drill down into churn metrics, which makes it easy to track the KPIs we’ve discussed above, analyze historical data, and identify the reasons customers churn.

Connect Multiple Data Sources to Improve Efficiency

Modern subscription businesses rarely store customer data in a single location. From CRMs, and customer success platforms to product analytics and business intelligence tools, different departments use different software solutions. But you need to access that data from all of them to execute the most accurate customer retention analysis possible. 

Not just any subscription analytics software will do as a result. You need a platform that can integrate with dozens of different CRMs and business tools. 

Incorporate Analysis into Your Customer Retention Roadmap

Customer retention analysis isn’t useful unless you turn your data into action. Use the outcome of your retention analysis to guide your future retention strategy.

That could improve the customer experience during onboarding if customers churn within the first few months of their subscription. Or it could be redesigning a feature that multiple customers cite as a reason for leaving. It could even mean adjusting your pricing model.

Incorporate Analysis into Your Customer Retention Roadmap

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It could even be integrating a smart dunning strategy to recoup lost revenue and avoid involuntary churn after a failed payment.

Identify Your Power Users

Use behavioral analysis to understand who your most loyal customers are. Identify their demographics and what actions they all take. 

The better you can understand power users, the easier it will be to position your marketing strategies to attract them in the future. At the same time, you should focus your efforts on building the best possible product or service for these users. The more value you can add, the longer they will stay with you. 

Take Retention Analysis to the Next Level

Customer retention analysis is a key component of any successful business strategy. By understanding the factors that drive customer loyalty and developing effective retention strategies, businesses can increase revenue, reduce churn, and build lasting relationships with their customers.
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