Cohort analysis is an excellent tool if you want to improve retention in the company. What it means is that if you wish to continue your company's steady growth or sustain the current one, you need the tools to analyze who your customers are and how they are behaving. 

Cohort analysis is one of the best tools, with groundbreaking solutions that change how we perceive customer groups. When it was introduced into marketing more than 70 years ago, it was completely new and different. In some ways, it changed the world of commerce.

Nowadays, it’s used everywhere. Small, big businesses, you name it. But, as most things are, it’s still an unfound gem for some. For those of you, we’ve prepared a quick guide on creating your own cohort analysis report for all your commerce needs. Enjoy!

Understanding cohort analysis will have to wait. The first thing we want to focus on is the kind of software you should use and some templates example to pair it with.

As usual, in office-related cases, there is only one answer. Microsoft Office.

Microsoft Office gives you a great set of tools to combat any office environment problems, from simple tasks like getting your paper going to such complicated ones as cohort analysis.

And for this specific task, we highly recommend using Microsoft Office. It’s great for getting your high in detailed cohort analysis, especially if you think about running it yourself.

One of the significant assets of the Microsoft Office suite is its ergonomics; it can accomplish many tasks, and most people are already familiar with it.

Here, at Royal CD Keys, we can promise you the latest Microsoft Office suite for an affordable price. Check out the latest sales on our website to be up to date with the latest ones.

But, enough with theory, let’s focus on the practice. Here are some cohort analysis templates you may be interested in.

It’s the most basic of them all, but - an exciting one as well. This one focuses on the subscribers' rate during different amounts of time and specific months, but in all honesty - you can pretty much put anything up there. It’s looking good if you’d like to run a small or medium-sized business efficiently.

You can get it for free at MagniMetrics.com.

This one is more complicated, with many more detailed brackets and data points. You can use this cohort analysis data template to get even more done.

As you can see, it’s good at collecting data and scaling them, but it can be pretty overwhelming sometimes, so if you’re not into cohort metrics details, you may want to sit this one out.

It’s available to download for free at finmodelslab.com.

If you want to perform cohort analysis, it would be beneficial to know what it is. You can use cohort analysis in many ways. To create a data range by user retention rate, you can identify trends, find out the customer lifecycle, or check out relevant differences between all the users in an average time.

A cohort chart is a great tool, especially when paired with Google Analytics. But, before getting ahead of us, let’s define cohort analysis.

In most basic terms, it’s the process of understanding and evaluating the behavior of cohorts (groups of customers) over some time. You can do weekly cohorts; you can change the cohort size, date range, or whatever you need to do. The central concept stays the same.

Now that we know some of the different types of cohort analysis, we can get into the weeds.

As with most things, cohort analysis has different types and different ways of application. With so many various cohorts, it’s no surprise that we’ve decided to group them. Here are different types of cohort analysis.

Time-Based

Time-based cohort analysis is probably the most popular one. This one divides the customers into cohorts based on precise dates. If you’re running a media company, this could mean that you have a different subscriber count for each month. If you’re selling ice cream, you will probably see a spike in your sales during the summer.

It’s helpful in behavioral analytics as it shows user engagement during different months or weeks. Compare it with your actions during that time, and you have a great cohort analysis example.

Size-Based

Or, more importantly, the size of the budget of potential customers. We all know that the best life cycle of a product is if it’s getting to all related groups of customers. There are some common characteristics between each one of them, but the main product stays quite similar. You just add some tweaks here and there to make it more or less “premium.”

You can see those kinds of analyses in practice in most of the subscription services. You can create an essential product that is free of charge. Then, add a premium version with no ads for a higher price. And if you have an uninhabited cohort to fill with content, add another with some premium tools, like better visuals, a more excellent selection of X factor, and just overall - a more-or-less the same but better-on-paper product.

As we’ve stated before, cohort analyses let you approximate your profits because they can evaluate customer retention, one of the most critical metrics in the business world.

Why is that? Well, if you can get the customer's attention and keep it for a prolonged period, you can gain a loyal customer base that will keep your business running for a long time.

It’s a probability game if you think about it more deeply. Let’s say you have a product that you’re selling. You need to spread the word. So, you send an e-mail to your customer base telling them - hey, I have a new product; get familiar with it.

On the flip side, if you send out those e-mails to hundreds of people, you can measure the increase in the possibility of something happening.

And we can see it in real-time. People do this with everything, lottery tickets, gambling, or even doing anything in real life. Everything is a probability game. And cohort analysis lets you understand the key metrics of each cohort you come across.

So, to better understand what kind of cohort data you can collect, let's determine the types of cohorts to look out for.

Acquisition Cohorts

Those are the types of divides that let you understand which cohorts signed up for your product during what time. And then, you can segregate this data and simply track each user.

This is great for the retention aspect of cohort analysis as it allows you to see how much time people spend on your app/site/with your product and gives you some insight into what you should do to keep them glued to it. You can divide it into weekly, daily, or monthly cohorts; however, you need to do it.

The best way to start with this one is to divide it between people who purchased the product on the first possible acquisition date and new users who came to the same stage later.

You probably understand that there are actionable insights you can take based on this information. For example, if you see in your retention table that customer lifetime changes during the winter months, think of some way to attract new customers. Add an app to your product. Create a new “winter” edition of whatever you propose to the world. There are a lot of things you can do to create the very best experience for your customers. And you should use it to your advantage.

Behavioral Cohorts

This one is more on the sociology/psychology side of things. Acquisition cohort analysis usually does the more basic job of seeing what happens with your product in relation to a specific time period when customers use it.

Behavioral cohort analysis helps you understand what people do with your app/product in a specific period. And that gets a little more complicated. For example, if you create an app, the behavioral cohorts will quickly show you how many people clicked the button on the left side and how many people clicked the button on the right side.

It may sound simple, but it gets better with time. You will also know how much time people spend with your app. What kind of activities do they do? What kind of things make them use the app more? What are the user segments relating to age, sex, and profession? You can study growth metrics in the cohort table and see if anything gets more clicks than the previous cohorts.

In some cases, it feels like literal social engineering. You put out a data range to people and change it from time to time to see how the biological specimen reacts.

Different cohorts will react differently and do other things, so you need to make everything you can to make it as easy as possible for them to stay with the app and as hard as possible to leave it.

To create a cohort analysis in practice, you need to understand three main things about this.

The first thing is - to define your cohorts. Those would be customers who entered your business at a particular time. And bind it to this time strictly. 

Observe how each of them changes, see what goes on each day, month, and week, and analyze and summarize that information. Then, you need to add additional details, like what you did during a given date range, what kind of products you were selling, and what services you were offering; everything needs to be in your cohort analysis.

If you have that, it’s time to add the second factor, the period you want your analysis to focus on. In any given e-commerce platform, you can usually see that the defined period is around one year. But, there are subcategories to subcategories, and every complete cohort study has to be conducted with that in mind.

And in the end, you need to know when you want to stop. Meaning - what date do you want to end your analysis and introduce your results to the public? When you want to attribute the contribution of user behavior to the app launch or any other project? Then, and only then, you’ll be able to create particular cohorts to influence your business significantly.

As we’ve proved in this article, cohort analysis is an essential tool that can make or break any business. You can get ahead of your competition with the right approach, or with the wrong one (meaning - not doing cohort analysis), you can make your business fail.

Everything is relative, of course, but if you have such a great tool, it’s a shame not to use it properly. Most big companies in the world rely on cohort analysis; based on that, they choose to either change their behavior or let everything stay the same.

You can probably see it in your everyday life. Take your smartphone. It tells you how much time you spend on it, analyzes what kind of things you do with it, and sends this data to the manufacturer’s headquarters, where it is being analyzed and processed. Its core feature is to send a higher user base, to be honest.

How often have you felt like thinking - this would be great if they did it and then suddenly, a few months later, it happens?

Everything is related to the great algorithm, and your cohort is just one of many analyzed.

Thank you very much for checking out our article. As usual - it would be much appreciated if you’d spread the word about it. It dramatically helps reach out to other people interested in the fantastic world of office-related guides!

We hope you’ve enjoyed this dive-in into the fascinating world of analyzing data. As always, we hope you enjoyed this one, and we’ll see you at the next one! Stay safe!