Let’s talk about a practice new startup founders often underestimate: retention analysis. While Monthly Recurring Revenue (MRR) is all the rage in Twitter threads and other celebratory posts, few mention retention numbers. But if there is one metric you absolutely need to focus on right now, it’s customer retention.
Customer retention is the life and blood of your company, no matter its size or business model. Focusing solely on acquiring new customers is tempting, but keeping them is ultimately what makes you profitable: users who keep coming back to your app present a higher probability to become customers, and already-existing customers are 60% more likely to buy something new from you.
Think about it. You might spend hundreds, if not thousands of dollars each month to find and interact with users. Acquisition is estimated to be 5 times more expensive than retention: getting all these people on board is expensive, so it would be unsustainable to not nurture them to stick around.
At June, we made it our mission to simplify how people build great online businesses using product analytics. In this article, we tell you everything we know about measuring retention.
Why User Retention Analysis Matters
Retention analysis tells you how, when and why customers opt out of your product or service. It’s a data-driven process examining a variety of metrics, including how long users stay active on your product, who they are, or which feature they use most.
- To control your user retention rate: retention analysis gives us the means to measure user retention and control it, to some extent. Measuring customer retention allows you to predict future profitability and adapt accordingly. For example, having a retention rate lower than 20% is a red flag for software-as-a-service businesses.
Analytics helps you improve user retention by providing metrics to enhance the user's journey and make targeted offers. You can also use retention analytics to initiate loyalty schemes and plan reactivation mails.
- To track your marketing efforts’ return-on-investment. Being able to perform A/B tests, identifying changes, activities, or features that keep customers happy over time, and to see how well your efforts fare, you need insights from the retention analysis. Without it, you will aim in the dark, wasting your marketing budget. Without customer retention, your payback period—how long it takes to recover your acquisition costs—only keeps increasing.
- To transform users into power users. Being able to understand what makes your users love your product allows product managers to devise growth hypotheses, find your best audience segments, and develop data-driven strategies to do more with less.
Common Customer Retention Analysis Types
These are the most popular types of customer retention analysis:
Prescriptive analysis collates data to answer questions like "what's the solution to this problem" or "what's the next step to make this happen?" It's necessary for making well-informed decisions toward increasing customer retention. Prescriptive analytics is also helpful in suggesting the best approach to leverage a future opportunity. For instance, you can use data from our Retention Graph as a starting step to figuring out ways to fix your churn.
Descriptive analysis studies data in retrospect. It uses historical information to give you insights that uncover patterns and influence your business strategy. June’s reporting templates allow you to do descriptive analysis based on past user behaviour to aid growth. For instance, our New Users report helps you find out seasonal trends in user acquisition and create statements that can inform your marketing strategy (e.g., Q1 is the quarter with the most new users, while Q3 is the one with the least).
Predictive analysis is the most widely used type of customer retention analysis. As the name suggests, it focuses on using data patterns from your own data and market research in forecasting future trends and circumstances. In addition, this advanced analytics helps to identify risks and opportunities to avoid or take advantage of them.
Like prescriptive analysis, diagnostic analytics uses data to ask questions. However, unlike prescriptive analysis, which focuses on the next steps, diagnostic analytics asks "why" questions. It concentrates on finding the causes of a specific trend. Customer health score analysis and churn reason analysis are perfect examples of diagnostic analysis.
You can cross compare data from our Retention Graph with other types of information. For example, the reason B2B businesses might be churning in August might be because it's the holiday season, and there’s less demand compared to other times of the year.
Outcome analysis shows the outcome of your product releases in customer churn and retention. It can help define the popularity of your product or services, and whether your product is growing in the right direction. As a best practice, your product decisions should have an overall positive impact on the bottom-line of your SaaS company.
With that in mind, retention analysis only becomes one part of the puzzle. For instance, increasing prices might have led to increased churn but overall significantly higher revenue. Or a product decision might have led to high-paying customers canceling their subscription but also to a large influx of medium-sized customers that will help more evenly spread your revenue base. Either way, June’s reporting analytics help you identify these trends and figure out whether your decisions are positively affecting company growth.
User Retention Tracking: How To Measure And Monitor Your Retention
Calculating your User retention rate
Retention tracking requires three things:
1) a way to know when your user subscribed to your service,
2) a way to know whether your user is still using your service or not, and
3) a meaningful time scale for tracking and analyzing.
Depending on the metric you use for retention, the first two pieces of data are either provided by your user authentication system, your payment gateway, or some sort of analytics platform like Google Analytics.
For example, a B2B subscription service using Stripe as a payment system can track when a user purchases or cancels a subscription plan. A user database could also tell you when someone registered and when was the last time this person logged in. In both situations, you’d already have all you need to track your retention rate.
The last parameter depends on the kind of business you’re running: daily, weekly, monthly… and so on. If your user needs to use your product everyday to be considered active, then you’d take a day as the meaningful time period. To figure out your ideal time-frequency, check out our free user retention playbook.
Once you have obtained all this information, your retention rate is the percentage of customers who still actively use your product after starting to interact with it for a given time period. Say 10 customers bought your software service but only 5 didn’t cancel their subscription after a week, your retention rate is 50% for this week. Here’s a bare-bones retention graph showing this:
The higher the rate, the better.
Automating Retention Tracking
Doing this calculation every week is a tad repetitive, and your time would probably be better spent somewhere else anyway. All these steps can be automated by connecting your Segment account to June, it only takes a few minutes.
Segment is a platform made by Twilio to centralize all your customer data coming from different sources, such as Google Analytics, Stripe, or any other business tools using Segment’s application programming interface. Using Segment, you can automatically collect user metrics from hundreds of software tools commonly used to build SaaS businesses. By connecting it with June, you instantly receive beautiful retention reports and insights for free:
Once you’re done setting up your retention tracking process, you can move on to the interesting part: retention analysis.
How To Do Cohort Retention Analysis
Here's how to perform cohort retention analysis:
To make comparisons possible, retention analysis uses the concept of cohort: a group of customers sharing the same characteristic. A cohort retention analysis for a given characteristic is called a retention model.
3 types of user retention model are particularly interesting to start analyzing a SaaS product:
- Acquisition cohorts: group users based on when they signed up for a product
- Behavioral cohort: group users based on the activities that they undertake in the product, like using a specific feature or clicking on a certain button
- Demographic cohort: group user based on distinctive trait they have, like the plan they’re in, or the average revenue they bring
The more models you use, the more insights you will gain. Acquisition is often the standard model to get started, but you can also use demographics, used features, or traffic sources as standalone retention models as your retention analysis process gets more detailed.
June proposes two types of retention graphs for cohort retention analysis:
A retention curve is computed by calculating the retention rate for each time period and displaying them on a line chart.
This retention report offers a global overview to find changes that impact your retention rate. Customer retention models provide a great way to follow the evolution of your product strategy, and see where you need to pay attention to your retention in your marketing funnel:
Cohort Retention Chart
A cohort retention chart breaks down the retention rates by cohort. It’s basically a way to display the retention curves of each cohort for a given retention model (acquisition, in the case of the graph showcased above) in a single view.
It gives us precious insights about different user segments you can use to your advantage:
How To Use Retention Reports And Graphs To Improve Retention
Now that you know how to obtain all the data you need, it’s time to have a deeper look at how to make it actionable.
1. Find when customers drop out
Retention graphs show you when exactly customers churn throughout the user journey: during the onboarding process, after you released a product update, or even when you forgot to address a support ticket.
2. Find why customers leave
When you know where to look into your product, you can add more relevant metrics to dig deeper, or ask your users for specific feedback at this stage of the product lifetime to figure out why these people leave.
For example, if you notice users churning after downgrading their pricing plan, you could formulate the hypothesis that they can’t find a feature that was relevant to them anymore. Running a feature audit could help you figure out if changing your pricing structure, product flow, or improving onboarding (e.g., through tooltips and tutorials) could increase retention:
Depending on the retention model you look at, you can also obtain precious information about your audience: who they are, where did they learn about your product, or which features do they use, for example.
3. Identify Your Retention KPIs
KPIs are Key Performance Indicators like customer retention, lifetime value, user engagement, and net promoter score. They'll help you to discover why clients leave. This allows you to reach out to your customers and convince them to stay.
Other metrics that matter when using the cohort analysis for customer retention include the repeat rate, orders per customer, time between orders, and average order value.
You can also ask for feedback from your clients to determine how you can improve your service.
4. Establish a plan to improve retention
At this point you can attribute behaviors to customer segments to predict churn and act to increase retention. Combined with other metrics like engagement or activation, you can come up with hypotheses to prove or refute them.
Some of the most common reasons for churn include:
- Bad onboarding experience: if people don’t understand how to use your product to achieve their goals, you need to offer support systems like a FAQ section or an email contact they can use to reach out.
This is necessary because customers love the convenience and would typically avoid any product or service that looks complicated. Therefore, your brand should be customer-oriented, focusing on making your products and services easy to use. More than that, ensure that your communication channels are always open to answering any questions.
- Bad pricing: your pricing plans are too complicated, or your competitors offer higher value at a lower price.
In such cases, customers will typically go for the cheaper yet more valuable option. But this doesn't mean that you should beat your prices too low. On the contrary, offering prices that are too good to be true may make your clients think that your product is low quality.
So, how do you ensure that you fix your prices right? Start by considering the critical factors before arriving at a price. For example, factor in your target audience's purchasing power, cost of production, positioning, and profit. Also, don't forget to consider industry trends and your competitors' pricing.
- Wrong audience: look at your buyer persona, what your current cohorts are, which marketing channels referred them to you, and act accordingly. Bugs: if you cannot deliver what you promise, customers have no reason to stick around. You need to reassure them, or tell them when issues are fixed.
Finding the right target audience for your business is one of the essential criteria for growth.
Marketing to the wrong audience is a waste of money, time, and other resources as it yields no results. It's one of the costly sins of customer retention. To solve this problem, focus on getting the right customers to ensure that you’re providing value and that they will stay with you in the long run. Also, study your direct competition and consider the kind of people they're selling to. Finally, make your message crystal clear. Speak to your primary segment directly, and avoid being vague. For instance, our messaging is clear on our homepage: we offer instant product analytics for SaaS companies.
- Low Quality Products
Your product is low quality if it has defects that make it malfunction or fall short of required standards. Unfortunately, many businesses compromise on product quality because they want to reduce production costs and maximize profits. This turns out to be counterproductive in the long term.
Poor quality products negatively influence customer satisfaction. If customers don't like a product, there's a high possibility they will leave your brand. That's unless they can verify that your business has effected the necessary changes.
- Promotional Period Elapsed
Promotional offers for limited periods help to attract customers for a while. These offers thrive on urgency, and customers wouldn't like to miss out on a good deal. However, your clients can withdraw after the promotion expires because the bait that attracted them is no longer available.
In other words, promotional offers like discounts or freebies are only a short-term strategy for client retention. Therefore, you must ensure that your marketing plan doesn't hinge majorly on promotions.
- Changes in Customer Budget or Schedule
Change is constant, and your clients may need to adjust their budgets to suit new financial realities. Unfortunately, this may mean that they can no longer afford your products or services.
Sometimes, the change may be in their schedule. For example, they may get too busy to enjoy your offers. In such cases, they'll have no option but to stop paying for your products and services.
- Similar Products and Features Elsewhere
Just as your business is looking to maximize profit, customers don't like to spend money unnecessarily. So, they'll likely ditch your brand if they discover that you're offering nothing more than a competition they're already patronizing.
Get Started With Retention Analysis Fast, For Free
Retention is the most important metric you must carefully monitor to reach product/market fit. After all, acquisition costs 5x more than retention. And it doesn’t have to be hard: June offers free analytics reports in just a few clicks, including retention analytics. All you have to do is to sign up here.