How to Calculate Retention Rate (With Formulas, Examples & Use Cases)
Many product leaders claim that retention is the holy grail of all product KPIs. Most of us make sure to actively track and improve our retention rates. However, we also usually end up tracking it incorrectly or using the wrong tools and processes to fix it.
So, this guide will help you understand what retention rate is across different contexts, how you need to track it (the right way), and how to make impactful improvements.
What Is Retention Rate? (And Why It Matters)
Retention rate shows the percentage of customers who are still using your product over a specific period (e.g., 4 weeks, 6 months, etc.) among all of your users. So, essentially, it tells you how many of your users stay with you.
Since it shows the number of people who’ve stayed, retention is the exact opposite of churn. So, if you say that your product’s 3-month retention is 80%, your churn for the same period would be 20%.
Retention and churn are two sides of the same coin, but product teams track both because they tell different stories. Retention shows how well your product drives habit and delivers ongoing value, while churn highlights where and why users drop off. Together, they give a fuller picture of product health.
Retention is only one of the “great trinity” of product growth metrics that you need to measure (the other two being acquisition and engagement). However, based on my experience of managing 15+ successful and a few failed (miserably) products, I would argue that retention is the “one metric to rule them all”.
The logic here is simple. If your retention is unhealthy, you can’t grow your user base, and you will eventually lose all of the users you’ve gained through acquisition.
There is another reason why retention is important. Retention increases customer lifetime value (LTV). It is a great way of achieving that coveted healthy 3:1 ratio between LTV and customer acquisition cost (CAC) - making your business model sustainable.
When Growth Masks Retention Problems
I have to admit that I learned to appreciate the importance of retention the hard way. There was a marketing tool I was leading a long time ago that was enabling web push notifications as a marketing channel for websites.
Put simply, the retention there was bad. We were losing 100% of our users after ~4 months of them signing up. The way this product was set up was by adding its code snippet to your website. The snippet would then enable web push notifications on your site and let your visitors use it.
The problem with this code snippet was that it was using a single domain to manage push notifications. So, when some of the clients used push notifications to distribute explicit or illegal content, Google would flag this snippet as malware. So, all clients, including those using the product in good faith, would have massive SEO ranking drops for their websites.
We did not pay too much attention to the bad retention as our acquisition channels were bringing so many new users that we were still seeing overall growth, despite the egregious churn metrics.
Naturally, as with any other new tool, the period of high-volume acquisition came to a lull. This is when we started noticing and feeling the effects of poor retention. Our user base not only stopped growing, it started shrinking!
Luckily, we were able to find a fix. We implemented filters blocking bad content, as well as implemented domain segregation. So, the bad behavior of one client would not affect others. But we should have paid attention to it in the first place to avoid the user loss.
The final important note here is that retention is important, but it doesn’t mean that you should ignore other product success metrics. Instead, you should track and optimize all of them, but simply give retention the most attention.
How to Calculate Retention Rate (With Formula and Examples)
Retention rate calculation is fairly straightforward. Calculate it on the starting cohort only—exclude new users acquired during the period, since they haven’t completed the full window.
After that, you apply this formula.
What you get at the end is a percentage.
100% retention = no leaks. 0% retention = empty bucket.
Let’s look at an example. You’re measuring the monthly retention, and you have the following figures.
- 1,000 users at the start of the month
- 920 users at the end of the month
- 150 new users joined you during the month.
The retention rate in this case would be:
So, we can say that 77% of our users have remained with us during the month, and we have lost 23% of them.
You can use this formula for other periods too (e.g., weekly, daily, quarterly, etc.). Here are examples.
It’s important to note that you need to keep track of your retention for every period, not just once. If it’s monthly retention, then you need to track it for every single month.
Going Deeper: Cohort Analysis for Better Retention Insights
Looking at your overall retention rate is useful for a big-picture view, but remember—it’s just an average across all users who joined at different times. That average can blur the impact of specific product changes.
For example, say your retention was 50%. You then launched a feature that dramatically improved customer experience. To see its true effect, you’d want to measure only the retention of users who joined after the feature release. Their behavior reflects your new product reality, not the old pain points.
This approach—measuring retention by groups of users who joined during the same time period—is called cohort analysis. Here’s what it looks like for a 6-day retention tracking:
Cohorts are based on the day of signup.
Here, you can see that day 3 retention has dropped significantly on the October 22 cohort. It might be worth investigating what happened around that time to cause that drop off.
How Retention Rate Varies by Context
It’s probably unfair to generalize retention rate and consider it a universal metric for every context. The reason is that the retention rate works differently based on where you use it. So, let’s look at different types of retention rates to better understand their intricacies.
Customer Retention (SaaS / eCommerce)
In SaaS, retention is measured through subscriptions—whether customers continue, renew, or expand their plans. Beyond customer count, Net Revenue Retention (NRR) is equally critical, since it shows how much revenue you keep and grow from existing customers. Both metrics directly drive your MRR (monthly recurring revenue) and ARR (annual recurring revenue), making them core health indicators for SaaS companies.
In eCommerce, retention looks at repeat buyers—the percentage of customers who come back and purchase again. Even small lifts in repeat purchase rates compound into significant revenue growth, since it’s often cheaper to keep an existing customer than acquire a new one.
User Retention (Product/Apps)
User retention is not about the money they pay you continuously. Instead, it’s about their repeated usage of your product.
Depending on the natural frequency of the pain your product covers, you will measure daily, weekly, monthly, or even quarterly retention. Social networks, for instance, have a daily natural frequency. So, for them, you would measure the Day 1, Day 7, and Day 30 retention rates.
The difference between Day 1 and Day 7 usually lets you measure your adoption milestones. Day 30, on the other hand, shows your product’s stickiness and the quality of habit loops.
Employee Retention (HR)
Employee retention tells you how good the employee experience is at the company. A low retention rate means that a lot of people leave the company.
Here’s how you calculate employee retention:
Traditionally, HR teams use quarters to measure the employee retention rate, considering that it takes a while for people to leave or join the company. You should also exclude any of your short-term contracts from this calculation, as the retention rate is irrelevant for them.
It’s important to understand the difference between employee retention and 2 other metrics that HR teams use to understand employment change in the company.
Employee Turnover Rate: Unlike retention, which shows what % stays. Turnover shows what % of all people who left during that period.
Attrition: It focuses on the drop in team size. If your team grows, then attrition is 0%.
Where Retention Rate Gets Miscalculated (And How to Fix It)
Many of us look at the formula for retention and think that it is so simple that there’s no way you can miscalculate it. Well, based on my miserable experience calculating it manually during my junior days, I can assure you that you can definitely do it wrong.
But don’t worry about it. The majority of user behavior analytics tools calculate it for you automatically. If you do need to do it by hand, here is a list of common mistakes along with the ways of fixing each.
- Including new users in the formula. It artificially increases the retention rate as new users have not churned yet. You can avoid this by tracking the number of new users and removing them from the users at the period end.
- Mismatching timeframes. You cannot compare a 7-day retention to a 14-day one. Those are two different states for your users and are not equivalent to each other.
- Ignoring reactivations. When users leave the product and return after some time, they mess up your retention chart as retention drops, then increases over time. To combat this, I suggest you create separate cohorts in Amplutide or your other product analytics dashboard, one for retained, new, and reactivated cohorts.
The last one is something I have faced numerous times. Generally, you should expect the retention rate to become static at some point in time. It means that people are staying with you. When you notice the retention increasing over time, that’s a sign that the data is dirty and can mislead you.
At least it has misled me more than once. This one time, when I was leading a call summarization tool, I got excited seeing an increase in the retention rate, thinking that it was showing the quality of our product efforts. Well, when I showed it to my CEO, he looked at me with a disappointed face. I was so wrong. The growth in retention showed the result of our retargeting efforts and masked a drop in retention during the same period, giving me the false impression that everything is fine.
Retention Benchmarks by Industry
What you might call a high retention rate depends on the industry and the type of product you have. For some, an 80% churn rate is absolutely fine, while for others it would indicate a catastrophic level of customer loyalty.
Here are the average retention rates for the most common industry and product types.
Don’t forget that it’s absolutely ok for the early versions of your product to have a low retention rate. You will improve it over time. After all, the benchmarks here are mainly based on mature products.
How to Improve Retention Rate (Checklist + Playbook)
I wouldn’t consider a retention guide complete without a set of practical tips to improve your retention rate. Here are six that I know work well based on my own experience and that of well-known digital products.
1. Improving Activation and Onboarding
Activation is the first part of the funnel. The more people activate, the more they will get the chance to use your product and stay with you. So, increasing your activation rate is crucial.
For that, I suggest you:
- Do progressive discovery when you’re showcasing your features one by one.
- Provide users with templates so they don’t have to set up your product from scratch. Miro’s Miroverse is an excellent example of this.
- Use checklists to guide users on the setup and first usage of your product.
In terms of checklists, Trello does it the best.
They have a “Starter Guide” board with a checklist of 6 tasks. If you complete them, you experience the main value of the product.
2. Personalize Engagement With Users
You can take advantage of channels like email, in-app, and push notifications to keep your users engaged with your product.
The most effective use of these 3 channels is by including them in a habit loop.
The call summarization tool that I mentioned earlier I was leading, had a serious problem with retention. After the call was over, people would forget that we had made a summary for them and not view it. All it took was adding a small desktop notification right after the call - telling people that their summary is ready. The result - retention rate almost doubled!
3. Using Feature Adoption Tips
This is another way to make sure that users have experienced the value of your feature. Again, the more people do it, the higher the chances of them staying with you.
These tips can be anything from tooltips or info icons beside buttons to tours around your product. Tools like Gainsight help you set up these kinds of tips and tours. Just make sure that they are not too invasive. Some users prefer figuring things out themselves without your active help.
Finally, make sure that you’re tracking the tour effectiveness using a funnel on any visual reporting tool.
4. Gather Feedback with NPS Surveys and Exit Interviews
To increase your retention rate, you need to first understand the reason behind customer churn. There are two ways to do this.
- NPS Surveys that you can find in most user feedback tools. It will show you how likely people are to recommend your tool and why they love or hate it.
- Exit interviews. When people delete their account, ask them to book an interview with you and tell you the reason they are leaving.
There’s an interesting case study about Bonobos, a male clothing eCommerce site that highlights the value of NPS surveys. According to the Bonobos team, when they rolled out a feature that added an extra step in checkout, they saw an immediate drop in their NPS score. It lets them make a data-driven decision to roll back the change and avoid losing customers.
5. Segmenting Churn by Reason
Here’s a pro-level product analytics setup for you. You can set up a multi-tool chain for gathering and analyzing feedback. For instance, HubSpot can gather feedback on churn by asking users the reason they leave when they delete their account.
Then, you can pass this data to a product analytics tool such as Amplitude or Mixpanel. This lets you create cohorts based on churn reason and see user behavior that has led to churn.
For instance, you can compare these cohorts with healthy users and see that churned users reached their aha! Moments later, or did not reach it at all (a very common reason).
You can also pair it with a user tracking tool to see how they were navigating your product and what kind of features they were using.
To learn more about advanced analytics setups, you can check out our curated list of product analytics courses.
FAQs
What is a good customer retention rate?
It depends on the product and industry. For B2C SaaS, it’s around 90-95%. For mobile games, 20% is considered good retention. Expected retention for eCommerce is around 70%.
What’s the difference between retention and churn?
Retention is about who stays with you. Churn is about users who leave. They are two sides of the same coin. However, they let you get different insights. Retention lets you focus on the effectiveness of your habit formation and adoption. Churn lets you focus on identifying and fixing issues with value delivery.
How do you calculate employee retention rate?
You divide the number of employees at the end of the period (minus new hires) by the employees at the start of the period. Please also exclude temporary contract staff, as retention is irrelevant to them.
Why is cohort analysis better than overall retention?
It lets you see the effect of your product efforts on the retention rate. New users experience the product in an improved state and retain better than old users – showing that your efforts have fixed retention.
How often should I measure retention?
For employee retention – quarterly. For product retention – match with the natural frequency of the pain your product solves. Social networks track daily retention while Airbnb tracks yearly retention.
Can AI help improve retention?
Yes, modern AI tools can help you find user segments where churn is likely, analyze and summarize user complaints, and predict churn based on previous data.
Join For More User Retention and Engagement Insights
User retention rate is one of the most important metrics for you to track and optimize for. It’s much cheaper to retain a user than to onboard a new one.
I hope this guide helped you understand and effectively track retention for your products.
But retention is only one of the many metrics to track and improve. To learn about the rest, make sure to subscribe to our newsletter.
