When we start a business, all we care about and are focused on is getting people to use our service or product. The process begins with acquiring new customers, and after a while, we have a database of customers. However, sometimes things don’t work out as planned and users or customers keep churning from our service, so we end up spending fortunes on getting new customers to know us.
Well, here comes the concept of “retention”. Customer retention refers to the ability of a company or a product to retain its customers or users over some specified time period. High customer retention means customers of the product or business tend to return to and continue to use it. This doesn’t mean that all our efforts should be focused on getting retention, but let’s face it, a lot of it should be.
Retention strategies can be applied only after you know about your customer retention rate and decide which strategy might work best for your case.
Retention rates help you understand how well your web app is performing over time, per user. Generally speaking, a higher retention rate is better overall because users who stay active for longer provide more engagement, as well as greater monetization opportunities.
Retention rates are also useful to understand why many users disengage from web apps, and where you stand in the grander scheme of app performance. This can help pinpoint what you can improve and how to extend your users’ lifetime value (LTV).
With your analysis, you could look at how retention develops in your web app over a 30-day period. If retention drops at a certain point, a goal may be to increase the number of users who are interested and engaged enough to retain past that date. In gaming, this is often the end of the onboarding period. In verticals like e-commerce, you could find out when purchases occur and optimize for that point in time.
Retention rates give you a potential insight into the longevity of your app. It’s also important to note that benchmarks for retention rates will change depending on app vertical. For example, users may install a travel app before their flight, use the boarding pass and then uninstall once their journey is complete. In this case, a lower retention rate after Day 7 wouldn’t be a cause for concern because it’s common behavior for that vertical.
Benchmarking provides the best answer to this question.
Naturally, retention varies across niches (consumer vs. B2B software) and segments (SMB vs. enterprise).
We have collected several benchmarking studies, which should help guide your understanding:
Over the last few years, we’ve seen SaaS become even more competitive.
The emergence of trends such as product-led growth demonstrates how professionals in the industry are trying to find new ways to achieve sustainable success.
All that means that it is even more critical than ever to find ways to align your product with the value you generate for your customers and find the right monetization structure that allows you to grow along with them.
We often see this reflected in the discussion about value metrics and the fixation on (achieving) positive net MRR retention (i.e., growing even without adding new customers).
One of the most common questions that people have after learning about a new metric is what a “good” or “bad” number is. “What is a high retention rate?” they ask. While there isn’t a single number that we can all benchmark ourselves against, there are ways to learn about what’s normal in your company and industry.
It’s worth pointing out how important it is to track yourself against yourself.
While there are ways to see how other companies are performing, and we’ll explore them in a moment, your top competition is your past self. Tracking your retention rate month-over-month and year-over-year is the best way to understand what your efforts are yielding.
We set a time as the basis of our calculations and we call it “Day 0” — usually the first day of the month. The most important thing about classic retention is that it is calculated independently for each day.
We know that we have gained a specific number of new users on our basis day; and for each day after that, the classic retention can be calculated in the following form:
Note that the two individuals who came back on Day 2 could be all, some, or none of the three that came back on Day 1.
This measurement is very easy to calculate and understand; however, it is very time-sensitive and can change due to external factors. If you’re going to launch a one-day campaign and want to measure the stickiness of the users acquired through that campaign, classic retention is what you should use.
If you want to look at overall day-to-day retention rather than retention of a specific day, then average several days together to minimize the daily noise. For example, averaging the last 15 Mondays will give you the average behavior of new users who first use the app on a Sunday. Alternatively, averaging every day in a month would give you the average retention rate of new users who first used the app that month.
To calculate this metric, we need to set a time period as the basis. Most common calculations are weekly (7-day basis) and monthly (30-day basis).
Range retention is not as time-sensitive as classic retention, but it is also not as specific as that. Range retention is good for figuring out the weekly or monthly trends, but it doesn’t indicate if changes happened in the beginning or at the end of the time period. It is better to use this approach to monitor the health of your business at a high level over periods.
If you just want to know how many customers you’ve successfully built a long-term relationship with, rolling retention will give you the answer. Rolling retention reflects the stickiness of your app in one metric.
It doesn’t matter if a user comes back one time or 100 times after the day you’ve selected. At the same time, if you select Day 7 as the day to measure against, it doesn’t matter if the user comes back on Day 7 or Day 700. Also, if you find that you have a low Day 30 rolling retention and high sessions per user, it could mean your app is grabbing user interest at first but struggling to keep it after a month of use.
Common mistakes in calculating user retention in SaaS include ignoring MRR retention, assuming canceled customers to be churned customers, and calculating retention without considering customer lifetime or subscription plans
If you’re looking to improve retention rates, here are a few quick tips on how to get started:
If your web app is difficult to understand, users may uninstall or quickly bounce from it. Improve the usability of your app by assessing how the information is laid out, from the design to content. Encourage and help users navigate through the app by showing them which icons need to be tapped or swiped.
Use other channels to reach customers and get them to come back to your app. This can be done through email, social, and search ads.
How are you rewarding users for their continued loyalty? If you have a mobile game, you could create an event or limited-time tournament with prizes and ways to socially connect with other players.
Retention rates help prepare your team with how users are engaging with your app so you can plan for the future, make adjustments to your strategy, and meet your business goals.
As you may already be aware, this is a surefire way to discover what works for your audience. A/B testing new features gives you the insight you need without alienating your audience and consequently lowering retention.
For many verticals, Day 1 is the best chance you have to stretch a user’s lifetime value (LTV). Make sure your users are able to make full use of your app by optimizing the onboarding experience from the point of install.
The SaaS model is simple when stripped of all layers of complexity added by technology, measurement, etc.
At its essence, it is about a promise that your solution will solve a pain that the customer is experiencing.
Your retention rate measures how well you are delivering on that promise.
You can apply multiple marketing and business tactics to acquire a customer — on a free trial or a freemium plan — but retention is the definitive proof that you are delivering on the promise you attracted them with.
Following about two years of research and model testing, we're proud to finally release Smart Signals ✨, small logical indocators that reflect the state of each account/user within a PLG motion. Leveraging Machine Learning 🤖, yet flexible enough to be altered by customers, they eventually shape the foundation for **automatically** detecting signups that are most likely to buy, to expand, or the churn.
Whether setting up workflows for freemium and trial signups to convert more, or for paying customers to be expanding towards a higher tier, or preventing to churn, being able to compare and optimise different workflows for maximum goal conversion is pretty much on every sales and CS leader’s list.
We’ve released Inbox, a place where each platform account/user that needs supervised intervention gets a case with to-do tasks and best-next actions. Built from the ground up for sales and customer success teams, they now have the power to orchestrate outreach throughout the entire organisation... the PLG way!
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