Cohort analysis may be one of the most effective ways to gather information about your customer behavior, but it’s also one of the most underutilized.
To shine some light on the importance of this powerful analytic process, we’ve put together this massive guide on cohort analysis.
Inside you’ll discover:
- What are cohorts and why cohort analysis is such a powerful tool
- How you can use it to optimize nearly any customer experience
- How it can benefit YOUR industry
- How to conduct an effective cohort analysis
- And so much more!
This is truly a one-stop shop for all things cohort analysis. Read on and you’ll learn how to take your optimization to new heights and understand your customer/user behavior like never before.
What Is a Cohort?
Before we get into the specifics of cohort analysis, let’s make sure we’re all on the same page. Ostensibly, a cohort is a group of people who have a common characteristic during a period of time.
For example, people born between 1972 and 1988 who have been struck by lightning are a cohort.
However, as we’re talking about cohort analysis, we’ll need to get a bit more specific about the type of cohort we’re interested in tracking.
In the world of cohort analysis, a cohort is a group of users who have performed a common action or actions during a specific timeframe on your website or app.
Your cohorts may look something like this:
- Trial signups in the past 30 days
- Paying customers in May
- Players acquired via social media ads
Now that we’re all seeing eye to eye, let’s continue putting the pieces of this puzzle together.
So What Is Cohort Analysis?
While the previous definition may have seemed a bit elementary, getting clear on exactly what cohort analysis is will be vital to its successful implementation in your analytics program.
Cohort analysis is a subset of behavioral analytics that looks at groups of people (users) who have taken a common action during a select period of time.
Rather than looking at all of your users as a single unit, cohort analysis breaks them into groups to help identify patterns throughout the customer lifecycle. These patterns allow a brand to adapt to suit user needs more effectively and optimize their experience.
The important takeaway here is that cohort analysis allows brands to ask a very specific question, analyze only the relevant data, and take action on it. For example, below is a view of cohorts and retention by platform type.
Cohort Analysis Examples
For those new to cohort analysis, it may be easier to provide a few examples of the process to shine a light on the benefits of this type of analytics.
In the world of ecommerce, a business may only be interested in analyzing the behavior of customers who have purchased in the previous 21 days to analyze the patterns during a specific sale or promotion.
SaaS brands may find themselves needing to analyze data from customers who signed up after a new product launch, platform upgrade, or even those that use a specific tool or feature. Cohort analysis would allow them to identify key differences in behavior to the cohort of a user who signed up before the launch or upgrade.
Cohort analyses are especially important for SaaS brands to help them understand vital metrics such as churn, customer lifecycle, and customer lifetime value.
Games will be able to segment their players in the same way. They could identify cohorts of expert players and new users to determine the habits of each.
Experts may have a noticeable reaction to lag in load time that could be hurting revenue from that particular cohort, whereas if they were to lump all users together they wouldn’t have been able to clearly identify the reason for such a dramatic loss of revenue. Analyzing at such a micro level allows games to make the necessary changes to keep all of their users happy.
9 Ways Cohort Analysis Can Optimize Your Results
Now that you understand cohort analysis a bit better and have an idea of how it’s used, let’s look at some of the ways you’ll be able to apply it to your company and how it can help to optimize your overall performance and results.
1 – Understand the Effects of Unique Behaviors
While segmenting by the date which visitors become customers or trialists is useful, it’s not always specific enough to get a clear picture of the what makes each user different. Segmenting users by the behaviors taken on your app or website allows you to paint a much clearer picture of how people interact with your product throughout their lifecycle.
Cohort analysis allows you to define groups of users based on actions taken (or not taken). This allows you to analyze how their unique behaviors affect conversions, revenue, churn and retention, and much more.
2 – Easily and Effectively Test Your Hypothesis
Cohort analysis allows you to quickly and effectively test your hypothesis and to get relevant feedback far more quickly. This becomes especially easy with tools like Cooladata.
For example, if you assume a particular action taken on your site or app may be important for increasing trial signups, you can define cohorts and immediately compare your results to see how each cohort responded to said action.
3 – Lifetime Value Calculation
Cohort analysis provides you with a fairly simple way to calculate an estimated LTV. An accurate understanding of lifetime value is imperative (especially for recurring revenue brands) to be able to determine your allotted spend for customer acquisition and retention.
4 – Conversion Funnel Optimization
One of the fundamental benefits of cohort analysis is its ability to improve your conversion funnel optimization. It gives you the ability to more accurately determine how your user experience has affected your conversion rate from the top of the funnel to the bottom.
5 – Break Down Customer Acquisition by Channel
Cohort analysis allows you to get very specific when optimizing conversion funnels. Segment your per-channel revenues for unique cohorts each month and you’ll be able to determine where your optimization is most effective and where it will need to be improved.
6 – Purchase Frequency Optimization
Cohort analysis also allows you to determine whether or not your funnel optimization activities are generating more frequent purchases following a customer’s initial purchase. This can be especially useful for ecommerce brands that employ sophisticated retention and repurchase campaigns.
7- Avoid Mistaking Growth for UX Optimization
When your business grows, it has the potential to hide user-engagement issues. If you focus primarily on new user acquisition and revenues, but avoid analysis of engagement during this growth, you run the risk of a dramatic drop in engagement as users encounter issues during their initial months of use. In this case you’ll be hiding poor performance behind rapid growth and a positive bottom line, which will eventually become unsustainable.
8 – Analyze Users’ Time to Take Desired Action
If you understand which actions improve conversion or retention (by analyzing your best customers), you can work to decrease the time necessary to get newer users to take the desired action(s). In a case where, for example, you’re interested in increasing monthly visit frequency, you can determine which of your cohorts visit most frequently over time and identify commonalities between them.
How Cohort Analysis Can Improve Retention
Cohort analysis allows you to segment customers by the channel and time they were acquired and analyze the retention rate of each. You can then work to increase retention rates in poor performing channels or put your resources behind the channels that produce higher retention.
To optimize retention rates in this way you’ll want to segment your users in either of the following ways.
Cohorts by Acquisition
Divide your users by how and/or when they first purchased or signed up. You can get as specific as creating unique cohorts by the month, week, or even the day they first converted. Your level of specificity may be determined by the number of users you have signing up or buying on each day.
In the case of a software or app, this will allow you to determine exactly how long people continue to use your products before engagement drops off. It also allows you to focus your efforts on optimizing user engagement well before the dropoff point.
Cohorts by Behavior
You also have the option to segment users by the behaviors they have (or have not) taken within a specific period of time. For example, in an app this could be anything from an install, launch, or uninstall, to a combination of behaviors or transactions taken from within the app.
Your behavioral cohort would be made up of users who performed the same action within the same time frame. Using the app example from above, this could be anyone who initiated an in-app purchase within the first 7 days of download. You could use this and other unique distinctions to identify which segments of your users are most likely to become long-term users.
From there you can work to optimize the user experience to the preference of these users and increase their likelihood of long-term engagement from more of your users.
How Cohort Analysis Can Help Identify Sticking Points
When it comes to freeing up sticking points, it’s important to look at those who haven’t yet reached one. Their cohort is likely those with a higher level of engagement, or a more recent signup or purchase.
By comparing them to users who have recently churned or seen a dramatic drop in engagement, you’ll be able to determine their common actions or behaviors from those seen just in the “stuck” cohort.
The common actions taken only by the stuck users are a great place to start looking for your sticking point. While your sticking point may vary, you’ll likely see groups of users experiencing the same issues in the same places.
Industry-Specific Benefits of Cohort Analysis
The benefits of effective cohort analysis are vast. We’ve looked at some of the general ways it can help you to optimize your customer/user experience, but let’s take a moment to define how they can help businesses in different verticals.
Ecommerce Cohort Analysis
Ecommerce businesses are especially guilty of analyzing their visitors and customers as two large groups. This leads to judgments and changes based on the actions of these broad groupings. The issues that cohort analysis addresses is the fact that the customer/visitor experience isn’t the same for everyone.
You can attribute this to two main factors:
Stage in the Customer Lifecycle
Not a single one of your visitors or customers are in the exact same place in their lifecycle. Some may have just become aware of your brand and may be on their first visit. Others may have just completed their first purchase, while a different customer may buy from you on a monthly basis.
The experience of each of these customers is very different, and so are their needs and preferences given their relationship with your brand.
Changes to Your Site
As you work to optimize and market your site, you’re continually implementing changes. This will again lead to unique customer experiences based on the time they visit and each time they return.
Due to this ever-evolving experience, averaging your data across all users in a given month can deliver suboptimal insights into how your focus metrics are actually performing amongst different cohorts.
For example, let’s say that you launched an email campaign over the past 90 days that is designed to engage users on a more frequent basis, incentivise repurchases, and reduce your churn rate. In order to accurately determine how this campaign is performing, it would be important to analyze performance among different cohorts. This way you could find averages in declining monthly revenue for each cohort to make better-informed decisions about next steps.
Game Cohort Analysis
Games and online gaming are a unique industry, one where you truly need the full story on user behavior to accelerate your growth. Game cohort analysis is vital for understanding the particular user paths that lead to in-app purchases, where and why players are churning, and how to target players with the highest lifetime value.
Cohort analysis allows you to segment your users that churn and identify what common characteristics they shared. What went wrong and what caused them to decide it was time to uninstall or unsubscribe?
You can also pick apart the characteristics of your highest value players to determine which channels they’re converting from, what causes them to become long-term users, and how to optimize your experience so that other users can follow in their footsteps.
Cohort analysis is one of the most effective ways to optimize your users’ behavioral patterns, as it allows you to modify your UX to model the preferences and habits of your highest value players.
One of the verticals we have yet to mention in this guide is Fintech. However, Fintech is one of the industries that cohort analysis stands to benefit most.
Long-term engagement is one of the keys to success in the highly competitive Fintech industry. By using cohort analysis to analyze retention of user behavioral segments, you can see a timeline of the percentage of users that returned to complete actions and users that churned.
Cohort analysis allows you to analyze the customer journey over time to optimize everything from marketing campaigns to ROI and retention cycles.
Cohort Analysis and Content Marketing
Content marketing is one of the least accurately tracked forms of digital marketing. Many content marketers are happy to report on metrics like clicks and social shares rather than actually analyzing their users’ behavior with their content.
At Cooladata we don’t think that kind of analysis is enough. We believe that content marketers should be using sophisticated tools to track their users’ behavior as they interact with their content, then segmenting those users into cohorts to gain a deeper level of understanding.
Effective behavioral analytics tools allow you to track things like completion rates, virality, engagement, and subscription rates to give you clear insights into your user’s preferences and how their behavior with your content affects your conversions.
One of the many benefits of cohort analysis in content marketing is that it allows you to segment your most important users. If you want insights into how to increase loyalty and user retention, you must analyze your retention rate over time. After drilling down further to see that a specific type of user shares the most, you might decide to encourage these users to share even more.
With cohort analysis and the right tool, content marketing can be tracked as closely as your paid acquisition.
Combining Cohort Analysis With A/B Testing
Cohort analysis can provide you with insights that straight A/B testing cannot, as you track unique segments of your userbase.
Cohort analysis allows you to A/B test while controlling variables that will inevitably affect your outcomes, such as time and place. In doing so it allows you to create better tests, learning more about your customers, and potentially seeing them from a different angle based on the additional insights you were able to gain from the cohort segmentation.
Analyzing cohort trends allows you to identify shared customer characteristics, which in turn may allow you insights into how those characteristics influence positive or negative trends.
This isn’t to say that A/B testing should be avoided. In fact, we recommend quite the opposite. Cohort analysis without this level of controlled testing looks far more like sequential testing, where small changes are made and their effect is analyzed by its impact on your bottom line.
Combining the cohort analysis and A/B testing allows you to gather more accurate, detailed information in less time to make decisions on the fly. It’s a far better way to analyze what works and what doesn’t.
Bonus Tactic – Inverted Cohort Analysis
Before we get into performing your own cohort analysis, we want to leave you with a new and exciting way to approach the process. Like traditional cohort analysis, the power of this tool lies in its use in identifying behavioral trends within a predetermined period of time. However, it gives you a new perspective from which to work by turning the structure on its head.
What are we talking about?
Typically we start analyzing a first user event and proceed in tracking their actions moving forward through time. What if we told you there was a different way to go about it?
What if you were to start at the desired event and work backward?
Inverted Cohort Analysis, also known as Reverse Cohort Analysis, is the process of tracking from the desired event, like a purchase, back in time. You can then begin to analyze the patterns and behaviors of the user/customers who made it all the way to a signup or purchase and identify the commonalities between them.
We actually have a full write-up on reverse cohort analysis. Be sure to learn more about this powerful additional technique here.
Performing Your Own Cohort Analysis
Now that we’ve covered the multitude of benefits of cohort analysis, let’s take a look at how it’s done. We’ll first look at two basic examples of cohort analysis using Excel and Sheets, then take a look at how Cooladata can help you perform more sophisticated cohort analyses more easily.
Identifying Your Cohort
The first step in effective cohort analysis is accurately identifying the cohort you’ll be tracking. Here are the steps to determine which cohort you’ll need to track:
#1 – Decide on the Right Question to Answer
The point of your analysis is to return actionable information on which you can act to improve your users (and your own) end result. As such, it’s vital that you ask the right question.
What do you want to learn and how will your results be used to improve your business, user experience, etc?
If you don’t know, you may be asking the wrong question.
#2 – Decide Which Metrics Will Help Answer Your Question
Effective cohort analysis requires you to determine which event you’ll be tracking, as well as the specific properties related to that event. What event will you be tracking and what insights will this help you to gather about your cohort?
Define your metrics once you have the questions you’ll be asking and this will allow you to define your cohort(s).
#3 – Define Relevant Cohort(s)
To create a specific cohort, your must decide on whether to target all users who took a particular action within a certain timeframe OR to distinguish a defining characteristic among them, such as those who purchased more than or less than a certain amount (creating 2 or more cohorts).
In a gaming example, one might choose to segment their lowest engagement users from their highest engagement users who signed up during the same period. This way they can identify how each cohort differs in actions, purchasing behavior, usage level, and so on.
Basic Cohort Analysis with Excel
One of the most popular ways to perform basic cohort analysis is in Microsoft Excel. Given that you have been tracking the data necessary to answer the questions you’re asking, Excel allows to you manually segment your cohorts, import the data, crunch the numbers, and answer many basic questions.
Here are a couple of helpful resources to get you started:
- Here is a post by Yemi Johnson that breaks down the entire process of Excel-based cohort analysis step by step. As you can see, the process gets a little complicated and can take some learning to perform accurately.
- Here is a downloadable sample Excel spreadsheet to help you get started with the process, thanks to Christoph Jantz.
Basic Cohort Analysis in Google Sheets
Google Sheets is another great tool for performing simple cohort analysis. If you don’t currently have a behavioral analysis system in place and are looking for a way to crunch the numbers manually and perform your own cohort analysis, Sheets may be a good option to help you answer some basic questions.
Here are two great resources to get you started:
- Here is a really helpful post that breaks down the process. As you can see from the slides, the process is far more complex without the help of a tool like Cooladata to support your cohort analyses.
- Here is a cohort analysis example sheet for ecommerce businesses. It allows you to make a copy and insert your own figures to get your own results for the questions being asked within.
Making Cohort Analysis Easier and More Effective with Cooladata
If you happened to look into the resources provided above, you’ll find that cohort analysis without the help of a behavioral analytics tool like Cooladata can be a little cumbersome. Unless you have a data scientist on staff, answering anything but your simpler questions may become slightly difficult.
Yes, it’s possible, but it’s a matter of preference.
Do you have the time and resources to perform regular cohort analyses with Excel or Sheets?
Would it be easier and more efficient to have a tool doing the work for you that allowed you to answer virtually any question with very little resource expenditure?
Now you can see why at Cooladata we get so excited about cohort analysis. The power of identifying trends in user behavior is obvious, but it’s up to you to start implementing this powerful solution in your business.
Remember, the key to effective cohort analysis is your ability to accurately gather information. While the basic cohort analysis examples above allow businesses to better understand the habits of their users and the patterns that appear in their actions, a powerful behavioral analytics platform is necessary to make the most of this revelatory analytics system.
Be sure to explore our site to learn more about how Cooladata can help you to understand your customers/users better than ever before and tour our platform to see just how much opportunity for optimization you’re leaving on the table.