Retention Curve

A Complete Guide to Retention and Churn Rates for B2B SaaS

Retention Curve

Retention is the king of growth, but it can also be the silent killer if you don’t pay enough attention to it. 

One thing I’ve noticed when talking to people in SaaS is that everyone seems to have a different definition of retention. That’s understandable because, well, there are indeed many ways to measure it.

The problem, however, is that people are often unaware of the distinctions. As a result, they end up making misinformed decisions based on the wrong metrics or apple-to-orange benchmarking. 

I haven’t found an article that clearly explains the differences between various retention metrics, so I decided to write one myself. In this guide, I will dive into the different ways of measuring retention in B2B SaaS and explain how each one should be used.

NOTE: The following metrics are designed for subscription-based B2B (and likely B2C) SaaS. They might not be suitable for other revenue models, such as e-commerce or pay-per-use products.

Key concepts 

Before I get into the calculations, here are some key concepts you should understand:

Retention vs. Churn

Retention and churn rates are two sides of the same coin. They both measure how well your customers are being retained but are inverses of each other.

For example, if your retention rate is 80%, your churn rate will then be 20%.

You can use either metric depending on whether you want to emphasize the percentage of customers lost or the percentage of customers retained in a given context.

Cancellation ≠ Churn (yet)

Cancellation happens when a customer asks to end their subscription at the next renewal, but they won’t be considered churn until the subscription actually expires. 

For example, if ACME Corp upgraded to a yearly plan in Jan 2022 and canceled in Mar 2022, the customer will be considered active until Jan 2023. This is because the plan is already paid for and will be valid for the entire year.

What this means: It is not possible for a new customer to churn within the first month. If you have a “free cancellation within X days” policy, customers who exercise it also shouldn’t be counted as churn because they never truly become customers (still meaningful to track how often this happens though).

Now that you understand the basic concepts, let’s dive into the different ways of measuring retention along 2 dimensions:

1) Reference time 

2) Unit of measurement

Reference time

Calendar month

The most common way to report retention is by calendar month. It gives you an overview of how your retention rate changes over time, which is suitable for high-level reporting. 

Here’s the formula to calculate it:

Monthly Retention Calculation

Example: 

You have 100 paying customers at the beginning of October. 20 of them churned throughout the month. Among those who churned, 3 reactivated before the month ended. Your October retention rate will be: (100-20+3)/100) x 100% = 83%.

How about other reporting periods (daily, weekly, quarterly, yearly)?

In theory, you can apply the same formula to get daily and weekly retention. However, I wouldn’t recommend doing so because these durations are too short to show meaningful trends. You will likely end up overreacting to normal fluctuations. 

On the other hand, if you use a reporting period that is longer than monthly, you must consider new customer churn (again, not possible in monthly retention calculation), which makes the math extremely complex. Don’t take my word for it – even Shopify’s data team struggled to find a perfect solution.

Therefore, if you want to report longer periods, I recommend using the average monthly retention rate (unweighted) instead.

For example, if you want to report your Q3 retention:

  • Calculate the retention rate for June, July, and August separately.
  • Add them up, then divide by 3.

Lifecycle (cohort retention)

While monitoring retention by calendar month gives you a clear view of overall business health, it doesn’t provide actionable insights.

To implement an actionable retention strategy, you must understand when customers usually churn in their lifecycles (aka cohort analysis).

Here’s how to do it:

  1. Group customers who joined within the same calendar period (usually week or month) as a cohort.
  2. Find out the percentage of customers retained in each subsequent period* (presented as “period N”), relative to the amount in the starting period (period 0).
    • A period here should be defined as “rolling X-days”. For example, “Month 1” = Day 31-60 after the date a customer joins the cohort. It’s not the end of the world if you use calendar months. Just note that the data can be skewed by the distribution of cohort joining dates.
  3. Repeat for each cohort.

The end result is usually presented in the following format:

Retention Cohort Analysis

You can also visualize the data as a retention curve (based on weighted average). If the curve flattens at a high percentage point, it means you have healthy retention:Retention Curve

With this graph, you can also tell which lifecycle stage requires the most attention (spoiler: the earlier stages). 

To understand whether your overall retention improves over time, turn each cohort into a retention curve and plot them on the same graph. If newer curves appear to drop more gradually, it means your retention is improving. 

Retention Curve by Cohort

Unit of measurement 

Customer retention vs. Net MRR retention

We commonly think of retention as the number of customers retained (aka logo retention). However, not all customers contribute to revenue equally in B2B SaaS, especially as usage-based pricing becomes increasingly popular.

This is why net MRR retention (aka net revenue/dollar retention) has become the standard retention metric for SaaS companies. Here is how you calculate it:

Net MRR Retention Formula

Because net MRR retention factors in not only churn MRR but also contraction and expansion MRR, it is possible to achieve a net MRR retention rate >100% (aka negative MRR churn) – a goal all SaaS companies should aim for.

To identify your biggest lever for net MRR retention, you can break it down by movements in:

  • Seat
  • Usage (however you define it)
  • Plan
  • Professional services

It’s important to look at customer retention together with net MRR retention to get a complete picture of “how” your company is growing, so it can better inform your GTM and product strategies. 

Usage retention

Whereas revenue retention measures how well you are capturing value, usage retention measures how well you are giving value. 

Although revenue is what matters at the end of the day, it is a lagging indicator. If a customer stops using the product, churn will follow soon. This is why it’s also important to measure retention from a usage perspective.

It’s not very actionable to analyze usage retention by calendar month, so cohort analysis is the way to go. The main question we want to ask is:

What % of users who signed up/upgraded are still experiencing value after X period of time?

Before you can answer the question, you must first define a few elements:

Value signal

Most SaaS companies’ definition of active users = users who log in.

But “logging in” might not be enough of an indication that users are getting value from the product. In fact, some products don’t even require users to log in to get value. 

Example: Once a company has built a website using Webflow, they won’t need to edit it that frequently. However, they’re getting value whenever someone visits their website.

For this reason, you must pick a value signal that makes the most sense for your specific product. Here is a simple process for doing so:

  • Select 3-5 potential value signals based on informed hypotheses.
  • Perform correlation analysis* to find the correlation between each candidate and long-term revenue retention (per your definition of “long-term”).
  • Pick a winner based on the result.

*Most analytics tools, such as Mixpanel and Amplitude, offer features to perform correlation analysis. But even better if you have a data team that can do it.

It’s worth noting that a metric with the highest correlation coefficient should not automatically win (remember, correlation ≠ causation) – you must also apply some qualitative judgment. As a rule of thumb, the best value signals tend to be those that can be easily understood, accurately tracked, and acted on. 

My suggestion is to not get too hung up on finding the perfect value signal. Pick something that is directionally correct and revisit it periodically. 

Period

Unlike revenue retention, which is measured on a monthly basis, usage retention might require a different measurement period depending on your product’s natural usage frequency. It could be either:

  • Daily
  • Weekly
  • Monthly
  • Quarterly / Yearly
  • Custom brackets (e.g., 1-3 days, 4-7 days, 8-14 days, etc.)

It’s important to define the natural usage frequency based on how often you believe the product should deliver value rather than how your users are currently using it.

For example: If you offer a to-do list app that is supposed to be used at least weekly, but most of your users only use it once a month, that doesn’t mean your product’s usage frequency should be monthly; It simply means you have a lot of work to do.

Day-N vs. Unbound retention

There are two ways to measure usage retention:

  • Day-N retention (most people call it N-day, but I think day-N is more accurate): Users who come back exactly in a specific period
  • Unbound retention (aka rolling retention): Users who come back in or after a specific period

Unbound retention might be useful for some B2C products (although I have my doubts), but for B2B SaaS, I recommend using Day-N exclusively. 

Company vs. User

For most B2B SaaS products, tracking usage retention at the company level is enough. Having said that, it could still be useful to track usage at the user level to gain additional insights. 

For example, a whiteboarding tool like Miro is supposed to be used by multiple teammates for remote collaboration. An account can’t be considered healthy if all the activities come from a single user, and the only way you can catch this behavior is by tracking usage retention at the user level.

Bonus concepts

Since we are on the topic of retention, let me also explain some relevant concepts:

Voluntary vs. Involuntary churn

Voluntary churn happens when a customer cancels willingly. This is the most common type of churn you will see.

Involuntary (aka delinquent) churn happens when a customer’s credit card gets declined for unintended reasons (expired, stolen, limit reached, etc.). According to ProfitWell’s study, 20-40% of churn is involuntary, so it’s definitely not a group you can ignore.

Retention vs. Engagement

Usage retention measures whether a customer still uses the product after X period of time. The result is either a yes or a no.

Engagement, on the other hand, goes much deeper to measure the intensity of product usage among retained users. There are no universal engagement metrics because they must be tailored to the nature of each product, but here are some high-level examples:

  • # of times a value signal is triggered 
  • # of times a key feature is used
  • # of unique features used
  • % of active users / paying users

Retention starts with activation

Before you go, I’ll give you one extra piece of advice on improving retention. 

Many SaaS companies try to reduce churn by adding frictions to their cancellation flows or having customer success teams talk customers out of leaving. These tactics have never worked, and never will. By the time a customer has decided to leave, it is already too late.

In order to truly improve retention, you have to start with activation

Get new users to experience your product’s value early and form a habit of using it. Once they have been activated, you will have to continue leveling them up to power users as well as shipping game-changing features to build up switching costs. None of these is easy work, which is even more of a reason to prioritize activation over “last-minute hacks.”

If you want to know how your retention rate stacks up against the benchmark, here are some resources you might find helpful:

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Hi, I’m Austin!

I love exploring new ways of building and growing products. If this sounds like your cup of tea, feel free to follow me or subscribe.

Hi, I’m Austin!

I write about product management, SaaS, growth, plus anything that comes to mind during hot showers.