What is Cohort Analysis, and Why Should Marketers Care?

As marketers, we’re constantly striving to understand our audience better, and how they engage with our brand over time. This is where cohort analysis comes in. 

If you're not familiar with the term, don't worry! In this post, we'll define cohort analysis, provide some examples of how it's used in the industry today, and explain why it matters to marketers.

What is cohort analysis?

Cohort analysis is a type of data analysis that groups customers or users into segments based on a common characteristic shared over time. More specifically, a cohort. 

A cohort is a group of customers used to create user segmentation. These common characteristics are usually separated into two types:

1. Acquisition cohorts

These divide users into groups based on their actions, e.g. first acquisition or scope of retention.

2. Behavioral cohorts

These divide users into groups based on their common characteristics, e.g. age groups, installed apps, or location data.

This segmentation aims at not only tracking and monitoring a customer’s actions over time but also – and especially – understanding their activities and ultimately providing ground for decision-making policies such as how to target or engage with the different types of audiences the customer portfolio is made of.

Customers can be sorted into cohorts by acquisitional or behavioral traits.

What does that mean?

By grouping people into cohorts, you can analyze and compare a huge range of factors depending on the data you have available, such as buying patterns, conversion trends, or product popularity. You can then use the output of this evaluation to unearth insights into performance across multiple customer segments.

This means cohort analysis is one of the most effective ways for you as a marketer to lead successful experiments. It allows you to run a campaign (in a set timeframe) with different attributes you want to test, such as marketing channel, ad creative design, or a certain product, and compare the results to see which factors were contributing to your performance and which were not.

If that all sounds a little too theoretical, here are a few examples of what it could enable you to scope out:

• Whether customers who first read a product review have a higher conversion rate than those who don’t.
• If audiences coming from a specific ad campaign have a lower bounce rate, and any signs of a similar high churn pattern in terms of retention for website users arriving at your site from a specific channel.
• Whether customers who used a discount code converted from a single to a repeat purchase.
• If your new customers acquired last quarter generate enough value to break even the cost of their own acquisition — and if not, when will that be achieved?

To find the answer to any of these questions, you’ll need to start by grouping your customers into cohorts. Providing an explanatory answer to these complex questions usually requires more specific analyses — but cohort analysis is the starting point of several subsequent analyses, such as break-even analysis, and customer lifetime value analysis.

Why should cohort analysis matter to marketers?

Cohort analyses are a great way for marketers to better understand customers’ segments and their performances over time. Analyzing the several cohorts helps you to:

• Improve retention by tailoring marketing messages to customers how are likely to repeat their purchases with personalized communication to keep them engaged and loyal to your brand.
• Reduce churn, same as above but in the opposite direction, by creating customized messages to customers who are more likely to drive away from your business with retention strategies.
• Marketing campaigns optimization, by providing you with a powerful tool to understand which campaigns, ad groups, or ads performed the best across all customers’ cohorts.
• Growth opportunities identification, by showing which cohorts better responded to a certain stimulus, e.g. a discount code, and conversely which cohorts could still have room for improvement.

Examples of cohort analysis:

Here are some examples of the role a cohort analysis plays in different industries:

• Mobile app developers track their users’ behavior over time in order to optimize in-app experiences, ultimately improving monetization and user retention.
• SaaS firms can track user engagement over time, assessing behavioral patterns, leading to a higher user retention rate and, conversely, lower churn rate.
• eCommerce shops are able to identify which groups of customers respond better to a particular stimulus, e.g. acquisition campaigns or discount codes, resulting in a higher efficiency in driving engagement and future sales actions.

Quick case study:

Let’s say you’re the manager of a clothing brand. You’re running a campaign across multiple regions of the US, and want to target a marketing campaign to particular regions where there’s a higher conversion rate. To do this, you’ll need to create behavioral cohorts of customers in each location you want to investigate. Once you’ve done this, it’s easy to see where particular messaging is resonating with cohorts in particular areas and ramp up spend accordingly.

In fact, that’s exactly what Jeff Coleman, Leader of Digital Marketing Science at kids clothing company Carter’s did. His team was able to look at specific performance within different tactics and campaigns, and how people were interacting in different locations. 

“You start to get down to who the customers are, where they are, what they like. We were able to see in real time that a certain campaign, on the whole, is having this type of conversion rate. But we could also dig deeper to see that, for whatever reason, at that point in time, our customers in Chattanooga were really responding to what we were doing. So we ramped up more dollars there.” 

If you want to watch Jeff explain how he took Carter’s from low-value data tasks to high-value insights, you can check out the video here.

Challenges with cohort analysis:

While cohort analysis can be a powerful tool for marketers, there are also some challenges to consider when implementing this approach. These include:

1. Data quality: make sure you can trust the dataset’s content.
2. Time, resources, and complexity: cohort analysis is no easy feat, in terms of time needed for creating, maintaining, and especially understanding their outcomes. Make sure you have the necessary competencies and tools.
3. Tools: given the aforementioned requirements, make sure your data pipelines are solid and reliable, otherwise, you’ll find yourself spending more time creating and maintaining the data, rather than working with the data itself. There are plenty of tools out there aimed at automating most, if not all, the processes for data collection, transformation, and plotting into consumable insights.

Conclusion:

All in all, cohort analyses are powerful tools for not only marketers, but also data scientists, to better understand what their customer bases are made of and, more importantly, how best to engage with them. 

It’s no secret that cohort analyses can be difficult to set up. However, once they’re properly put to work, the value they can bring will far outweigh the initial implementation cost. Cohort analyses are a must-have in your toolkit if you want to step up your game in terms of customer understanding, engagement and loyalty, churn prevention and ultimately driving better results for your business.