If you’re looking to improve your customer retention rate, this guide is for you. I know from personal experience how frustrating it can be to lose customers. That’s why I’m sharing my knowledge on how to do retention analysis. By understanding the key factors that affect customer loyalty, you can take steps to keep your customers coming back.
How to Do Retention Analysis
There are a few different ways how to do retention analysis. The most common way is to look at a cohort of users and see how many of them are still using the product after a certain amount of time.
This can be done by looking at a graph of retention over time, or by calculating a retention rate for each cohort.
Another way to do retention analysis is to look at the factors that affect retention.
Look at data on why people stop using a product or survey users about why they continue to use a product.
This can help identify areas to improve to increase retention.
What is Retention Analysis?
Retention analysis is a process of understanding why customers use your app and how long they continue to use it. By understanding these factors, you can develop strategies to improve customer retention and grow your business.
These insights into how to retain customers are the foundation of growing your business.
User retention is critical for any business, especially SaaS products. The time and resources spent on acquiring new users can be quickly negated if those users churn shortly after signing up. To combat this, you need to have a strong retention strategy in place that keeps users engaged and coming back for more.
Churning customers is a lot like fighting a losing battle against a relentless enemy. Without the proper tools, you’ll never be able to gain the upper hand.
With customer retention analytics, you can identify why customers leave and what you can do to stop them from doing so.
What is a Cohort Analysis?
A cohort analysis is a method of analyzing metrics by grouping them based on user behaviors.
Segmenting your data by the date when users first signed up with your business can help you better understand your customer’s life cycle and the health of your company.
How Cohort Analysis Helps Increase Product Adoption
Cohort analysis is a great way to see how well your product works for different groups of people.
By analyzing cohorts of users who leave your product, you can identify common characteristics among that group. By understanding those characteristics and why they left, you can make changes to the product that will prevent them from churning in the future.
Here is how it should be used.
A cohort analysis tracks how users perform during their initial use of your product.
By looking at which users respond best to different features, you can determine which ones to provide support, send emails, or offer in-app tooltips.
After launching, you can measure the success of each approach by tracking how many users adopt each new feature. This data can also be used to adjust your long-term product strategy.
As people continue to use your product over weeks and months, cohort analyses can reveal when different groups of people are likely to leave.
Companies can see how often customers used their products before they eventually abandoned them.
They can then dig deeper into the characteristics of those with the lowest retention rates to find the cause of their high rates of churn.
How to Get Started With Cohort Analysis
Cohort analysis groups people together based on when they first signed up for your service. The goal is to find actionable differences between them, such as what actions they took, what features they used, etc.
One way to start analyzing cohorts is by first comparing retention rates among different groups. You can compare groups by things like location or how they came to your site. This can help you identify which groups are retaining the least. You can then dig deeper into the group, looking at which specific actions are causing users to churn.
Paid search tends to have a higher drop-off rate than other acquisition sources. You can further analyze this by looking into the actions that people take after signing up.
What is a Retention Report?
A user retention rate is a metric that measures how many of your users come back after their first session. This helps you determine how successful you are at retaining new users.
It can help your sales team keep track of retention, identify trends in subscriber behavior, and know when most new customers drop off.
Here’s an example of a customer retention graph, also known as a “cohort” or “flow” diagram.
This chart illustrates the percentage of new users that download and start using your app on a given date and how many return to the app within the next 10 days after that.
Another way to think about retention rates is as a customer lifecycle.
A customer retention report provides insight into how users behave and where the biggest drop-offs happen. By studying cohorts, you can focus on marketing to new customers to retain them longer.
How to Perform Customer Retention Analysis
Retention rate analyses are used to analyze how long a customer remains an active customer before they churn. This data can be used to improve acquisition and customer retention efforts.
Survival analysis is a chart that tracks a cohort of customers over a given period.
Cohorts are static groups as no new members are added once a cohort is created.
The most common type of cohort is a group of customers who all bought from you during the same time frame, such as on a single day, during a week, or during a quarter.
Two Survival Analysis Methods
There are two ways of analyzing customer retention rates.
This chart illustrates the activities of five different users on a hypothetical site over two weeks. The top of the graph shows how many total days each user was on the site.
Method 1: Periodic Survival Analysis
For this example, we monitored whether customers were active each day.
We measure how active customers are in each period, from among all of our customers in the given time range. This helps us identify trends in how our customers behave over time.
In our example above, we can see the 7-day survival rate and the number of active users for that day in the orange box.
For e-commerce stores, it could be weeks or months before a prospect makes a purchase.
The Periodic Method is a simple way to track cohorts of customers over time. It’s based on actual customer data, so it provides real-time insight into how active each customer is.
This metric does not accurately depict a regularly-active user, such as our example of “Jane”, who exhibits consistent behavior (activity once every 4 days). This percentage will fluctuate daily, as some users exhibit more sporadic behaviors.
Even though Jane is a regularly engaged user (showing up on the app four times a week), this will not show on the dashboard daily.
Method 2: Retrospective Survival Analysis
The Retrospective Method assumes that a customer will remain active until they have been inactive for a certain period.
In the above example, the 10 days of customer inactivity was used to designate them as “churned.” This period may vary from company to company or industry to industry.
Both Robert and Frank were churning customers, meaning that they canceled their subscription after only 4 days.
The blue line shows the number and percentage of “survivors” in each period.
The Retrospective Method of calculating customer lifetime value differs from the Periodic Method in that it calculates a specific period in which a customer has churned.
However, we can only determine when customers leave by waiting for 10 days after they last visited.
This method only works if you look back at a customer’s activities after a certain amount of time has elapsed.
The advantage of using the retrospective method is that it allows you to accurately determine how frequently your customers are leaving you. It can also help you get a better sense of how often you are losing business.
Comparing the Two Methods
The advantages and disadvantages of each method are detailed below:
Why Retention Rate Analysis Is Important in Customer Analytics
Customer survival analyses are one of the mainstays of data analytics.
Customer behavior is a key to understanding your customer’s relationship with your company. This knowledge can lead to “A-ha!” moments which result in major changes to your product and marketing strategy.
Some of the immediate benefits you can reap from survival analysis are:
- Focus churn prevention efforts on high-value customers with low survivability rates
- Evaluate customer acquisition channels according to their retention rates
- Focus on the timing of customer acquisition marketing campaigns according to the day of week and date of month which exhibits the highest-value customer cohorts
The two methods of analyzing customer behavior described here both provide unique insight into how loyal your customers are. Each method has its own advantages and drawbacks.
We recommend using both methods on how to do retention analysis to maximize the amount of customer data you can collect.
By monitoring your retention rates, you can better understand your customer base, optimize your marketing, and increase your revenue.