The true success of marketing is not enabling a single transactional sale, but in building a customer relationship that spans for as long as possible. That makes customer retention a high-priority goal for any marketer.
Why should marketers focus on customer retention as a metric for measuring marketing success?
There are many reasons why your brand should focus on a strong retention strategy. For starters, new customer acquisition is five times more costly when compared to the cost of retaining existing customers Also, businesses with low customer stickiness soon run out of new customers and ultimately slip into a downward spiral of negative returns.
However, in this age of abundant choices and fleeting customer loyalty how can your business ensure customer retention? Can data analytics techniques like cohort analysis lend a helping hand? That's the premise of this blog.
To boost customer retention you must identify what makes existing customers stay. There are plenty of analytics techniques available today that can help you with that. Manifold increase in computing power, advanced analytics, and progress in behavioral science have made it possible for businesses to create new ways to retain their customers. Cohort analysis happens to be one among them.
A cohort is a group of users who share a common characteristic over a certain period of time. Cohort analysis is the study of these common characteristics of these users over a specific period.
The internet is flooded with hundreds of definitions on cohort analysis. They all make it difficult for a regular marketer to wrap their head around it. To make things complicated there is heavy use of jargon like cohorts, segments, shifting curves, and much more.
Is that too much information?
Here is an example to help you understand cohort analysis better.
Let’s take a group of users who signed up for your mobile app in the month of September. Cohort analysis for retention helps you understand how many customers continue to be active users in the days/weeks/months that follow.
In the table above, you’ll see that the first column shows the days in the month of September 2019. The column titled 'Users' shows the downloaded app users for that day. The adjacent columns with the numbers in percentages indicate the percentage of users who use the app in the following days since the day they installed the app. The top row with bold figures indicate the average values.
If the analytics tool you're using supports, you can also drill down into further specifics of user demographics like gender, location, language, device user, mobile OS platform, and much more.
Now, any analysis needs to have a specific direction to yield meaningful conclusions. In cohort analysis, this can be achieved with two different types of analyses.
There are two types of cohort analysis:
This cohort divides users based on when they were acquired or signed up for a product. Depending on your product, user acquisition could be tracked daily, weekly, or monthly.
For example, a consumer mobile app for productivity can track its acquisition cohorts on a daily basis. On the other hand, a B2B mobile app with a focused user group would focus on monthly acquisition.
Behavioral cohorts group users based on the activities that they undertake within the app during a given period of time.
For example, users who share photos using Google Photo links on a given day.
The period of time, again, varies from app to app. For a photo-sharing app, a day is a good timeframe. For an online investment platform app, 3 months would be more apt to observe user behavior.
Cohort analysis is a better way of looking at data. Its application is not limited to a single industry or function. For example, eCommerce companies can use cohort analysis to spot products that have more potential for sales growth. In digital marketing, it can help identify web pages that perform well based on time spent on websites, conversions or sign-ups. In product marketing, this analysis can be used to identify the success of feature adoption rate and churn rates.
Cohort analysis is widely used in the following verticals:
In all these industries, cohort analysis is commonly used to identify reasons why customers leave and what can be done to prevent them from leaving. That brings us to the calculation of Customer Retention Rate (CRR).
Customer retention rate is calculated with the help of this formula CRR = ((E-N)/S) X 100
The formula has three components:
To measure customer retention, we find the difference between the number of customers acquired during the period from the number of customers remaining at the end of the period. This gives a true picture of retained customers. To find the percentage of those customers who have been retained since the beginning, we divide the result with the number of customers at the beginning. This gives the customer retention rate.
A higher CRR means higher customer loyalty. By benchmarking your business CRR with the industry average, you can see where you stand in terms of customer retention. If CRR shows a bleak picture, corrective measures can be taken with the help of data analysis - this is where cohort analysis can help.
“In God we trust, everybody else brings data.” If you believe in this popular quote by W.Edwards Deming, cohort analysis will excite the marketer in you. Cohort analysis points towards a data-driven decision-making process.
As a marketer, you would be involved in multiple tasks such as — running campaigns, tweaking the customer onboarding process, introducing new product features, and so on. Cohort analysis helps evaluate the success of each of these activities.
It also has several benefits that will help you perform better as a marketer. Some such benefits of cohort analysis include:
All these activities individually and collectively help in maximizing customer retention.
Google Analytics is any marketer’s go-to tool for mining data on website traffic, key metrics and also conversions. It also has a neat cohort analysis offering (in beta mode right now) that you can use even if you are not a power user of GA.
To get started with a cohort analysis using Google Analytics, head to AUDIENCE > Cohort analysis.
At the top of the report, you will find several cohort settings that can be tweaked to generate the cohort report. The settings that you can tweak include cohort type, cohort size, metric, and date range.
Here's what each of these terms stands for:
Tip: To get the most out of cohort analysis, add more segments to the analysis. For example, you can identify where most of your users are coming from by adding website/mobile segments.
Running a cohort analysis using MoEngage’s Analytics platform is very simple. The UI is intuitive and all you’ll need to do is select just the events that you want to analyze. MoEngage Analytics is a powerful tool in terms of the analysis that can be derived through cohorts.
To get started with a cohort analysis using MoEngage Analytics, follow these steps.
Login to the MoEngage dashboard and click on the Analytics -> Cohorts in the navigation panel to your left. You’ll see the screen as shown below.>
At the top of this page, you will find options for Event Selection, Date Range, and Split Functionality. Event Selection determines the analysis and insights that you’ll get out of the report.
Tip: Unlike Google Analytics, MoEngage enables you to select the desired events to perform cohort analysis.
Here’s what each of these means:
Mo Tip: Google Analytics offers the date ranges for a month, for the last 2 months and last 3 months. However, with MoEngage, you can choose a custom time period for the cohort.
Retention Cohorts can help you understand the percentage of users who have been retained on your app retained until the defined day. This includes users who have performed the Return Event until the selected day or later.
Return Visit Cohort:
Return Visit Cohorts indicate the percentage of users who have returned to your website/app on a specific day. This can also be understood as the percentage of users, who were away from the app/website until the selected day.
A cohort table will resemble the periodic table of elements. Except that in a cohort table, instead of chemical elements, each row and column houses a value that helps arrive at a conclusion.
A cohort table is usually read one column or one row at a time for meaningful interpretation. Most cohort analysis users use color-coding to distinguish cells based on their value.
For example, let’s look at the retention cohort below for an app. The table below shows the days in the month of September 2019 in Column 1. The number of installed users on the app is shown in the second column titled ‘Users’. D0, D1, D2… correspond the number of days since the user has installed an app. You can do a cohort analysis by looking at the day column and the percentage therein top-down.
To keep the data visualization simple and to spot troublesome areas away, a cohort table uses color-coding. Typically, various shades of the same color are used to denote how values fluctuate from the maximum to the least.
Let’s circle back to the example of how many users continue to use the product in subsequent days. In an ideal world, 100% of customers who sign up should remain active users. Unfortunately, in the real world, customers keep dropping out.
You can use cohort analysis to identify spot the days when the drop has been significant. The drop can then be traced back to specific activities carried out during the month.
Now let's read the cohort analysis table shown below.
Insights from this cohort table:
|Difference||Cohort Analysis||Churn Analysis|
|Juncture||Cohort Analysis is done when the customers are still with you like they continue using your app, are buying from your store or are still visiting your website.||Churn Analysis is a probe into why customers left. It begins after the customers have left their respective cohorts.|
|Scope||Cohort Analysis helps understand the common characteristics that customers share so that your business offerings can be tweaked for the better.||Churn Analysis helps understand the weakness or shortcoming in your offerings that forced customers to leave. For example, the lack of features that competitors are providing.|
|Source||Metrics like time spent on the website, feature adoption, average order value, etc.||Negative testimonials, customer support tickets, feedback form, direct or indirect communication with customers, etc.|
There is too much information involved when you want to analyze customer retention. Cohort analysis helps put the spotlight on a handful of metrics that really matter. Some such metrics include:
Repeat Rate: There is no other metric that excels at proving success in customer retention. Repeat rate is the share of customers who transact with your business repeatedly compared to cohorts who terminate with a single purchase.
Orders Per Customer: Closely tied to the repeat rate is the orders per customer metric. More orders that customers make indicates a strong retention rate.
Time Between Orders: The time between successive orders is a subjective metric to measure. Depending on the type of products/services that your business offers, the time period could be in hours or even in months. This metric can be used to create reactivation emails that will keep the repeat rate high.
Average Order Value (AOV): The AOV metric helps in identifying high-value cohorts that can be specifically targeted with marketing campaigns. It helps eliminate spending too much time on cohorts that have low AOV.
Cohort analysis can give insights into too many behavioral traits of your customers. Connecting all the dots from the behavior and planning marketing campaigns for customer retention can be too much for any marketer. The key is to break it down into several campaigns — each one with a specific purpose — so that the sum of all efforts result in boosting customer retention.
Here are some strategies that you can try after being empowered with data from cohort analysis:
Tweak user journey: Most often, although not always, your users could churn when the user journey becomes difficult. Cohort analysis can spot the exact juncture in the user journey when the users are skipping out. The user journey can then be streamlined to make them stay longer.
Plan reactivation emails: Reactivation emails carry out the task of gently nudging customers when they are ready for the next purchase. Metrics like time intervals between two purchases help in properly planning the reactivation drip campaigns to keep customers in the loop.
Targeted offers: Cohort analysis can show what kind of customers buy the most and what they buy the most. Such data can be used to create targeted offers, coupons, free shipping, etc. that will help retain existing customers.
Introduce loyalty programs: Using loyalty points, rewards and similar gamification systems for customer retention are popular among marketers. But, the challenge in introducing these loyalty programs is identifying the right set of customers who are loyal and who will remain loyal for a certain period of time. With cohort analysis, you can narrow down on the exact set of customers who can be retained longer with loyalty programs.
Cohort analysis is an easy way of looking at your data. But, to implement it successfully you need a powerful marketing platform. A single platform where you can compile data, analyze it using cohort analysis, and act upon those insights. MoEngage it is.
MoEngage is an Artificial Intelligence-powered Customer Engagement Platform that helps businesses automate and ramp up their marketing efforts. MoEngage’s built-in analytics supports cohort analysis for various scenarios like app launch, website traffic, marketing campaigns, and so on.
MoEngage Cohorts empowers businesses with data that helps in measuring and driving user retention. You'll gain specific benefits using MoEngage, such as:
1. The virtual representation of data: You don’t have to skim through rows and columns of data to make sense of your customer behavior. MoEngage gives a quick glance at your cohort analysis in a graphical form that requires no further interpretation.
2. Color coding for easy interpretation: There is also the color graded cohort table reports where you can see tabulated data of retention or return visits. If required, you can also drill down to hourly, weekly or monthly visits for a better understanding of user behavior.
3. Download and re-use anywhere: Data becomes more powerful when it goes around. MoEngage Analytics allows you to download the cohort analysis reports in chart form or download as a PNG/CSV file for sharing.
Learn more about MoEngage Cohorts here.
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