What Is Cohort Analysis?

When you look at data, it feels like they speak a language that you don’t understand. Cohort analysis helps you translate this language. 

Cohort analysis does not treat all your customers the same, it groups them into smaller units to help you understand their behavior better. Let’s uncover what Cohort analysis is and how to perform one!

What Is Cohort Analysis?

Cohort-analysis might sound like a complicated term, but it’s not. It’s a data-oriented process that helps you figure out user behavior.

This is done by grouping customers into different groups based on similar characteristics or actions during a given timeframe.

Looking at customers as a uniform group is not always beneficial, so cohort analysis breaks them into smaller segments. These segments can be anything such as month of signup, first purchase, or engagement level.

By tracking these cohorts and how they behave over time, you can analyse trends and improve strategies.

For example, if there’s more loyalty visible in people who signed up for your product in January than those in June you can analyse what led to the difference.

Cohort analysis is helpful for businesses in marketing, customer success and product development particularly. 

In short, it’s like holding a magnifying glass and spotting actionable insights into customer behavior.

How to Do Cohort Analysis

Executing a cohort analysis is as simple as it gets. Here’s how to do it:

Step 1: Define Your Cohorts

Begin by selecting the criteria for your cohorts. Based on your product, it can be any specific action or a shared characteristic (first purchase, signups, location, age, etc)

You can group users who joined your app in the same month or carried out a specific action within the same week.

Step 2: Select Metrics

You need to know what you’re looking at, so select the key metrics that you want to track and analyse for each cohort. 

Some examples can include retention rates, purchase frequency or even churn rates. They should align with your business goals.

Step 3: Collect Data

Gather the applicable date from analytical tools, CRM and customer database. Make sure the data is not rigged, it should be accurate and organised. Unstructured data can skew the analysis.

Google Analytics, Mixpanel or any custom dashboard can help you with the process.

Step 4: Visualise the Data

Presenting your findings in a neat manner is equally important. A heatmap is a good choice to analyse such data.

It shows important trends such as increased / decreased engagement within a cohort.

Step 5: Analyse Trends

Watch out for patterns or peculiarity within the data / cohort. For instance, a cohort from a particular campaign can show lower retention rates, indicating that it’s time for improvement.

Similarly a stronger campaign can show that the targeting was successful.

Step 6: Action

Make changes to the campaigns, features or customer support strategies based on your analysis. Cohort analysis isn’t a one time thing, you need to regularly revisit them to assess the effect of the adjustments.

What Is the Importance of Cohort Analysis?

Cohort analysis can benefit your business in several ways. It’s a valuable tool if you aim to understand customers and optimize performance.

It helps you understand and master customer retention. You can group users based on different factors pertinent to your business model and figure out trends in behavior. 

Once you locate where customers drop off, you can improvise your retention strategy accordingly to boost long-term loyalty.

Next, cohort analysis is great for checking the effectiveness of marketing campaigns. Campaign interaction cohorts and its analysis can help you figure out the strategy that led to higher engagement and better lifetime value. 

It allows efficient marketing and helps you target customers more successfully.

Moving on, it also stands out in product performance evaluation. A business can supervise multiple cohorts and how they interact with new features and services.

This helps in polishing offerings that fit customer needs better.

If you’re a business running on subscription models, cohort analysis is critical for analyzing churn rates. Once you understand when and why a specific cohort cancels, you can come up with retention strategies to tackle the problem.

Last but not the least, cohort analysis promotes data-driven decision-making. You can divide big datasets into smaller fractions to identify action-points and make sure the decision is backed by evidence.

What Are Some Examples of Cohort Analysis?

Cohort analysis is a versatile tool that can be used in different business settings to understand customers and how they behave. Here’s some examples:

E-commerce Retention

Online retailers can create cohorts with respect to the month customers made their first purchase. 

They can figure out if recent marketing campaigns have been improving for long-term customer retention or not this way.

SaaS User Engagement

Software-as-a-service (SaaS) companies can use cohort analysis to trace user engagement.

One can analyze the percentage of users from every cohort who complete certain actions. This can include stuff like inviting a referral or even uploading a file. 

Mobile App Retention

Mobile App Developers also use cohort analysis keeping the date of download as their tag. DAU (Daily Active Users) or MAU (Monthly Active users) can be tracked within each cohort to help understand retention rates to improve user experience.

Product Feature Adoption

You can even group users based on whether they’ve tried a new feature you’ve released. Comparing engagement and satisfaction rates across cohorts help in improving the feature.

Churn Analysis

One can also understand which user groups are likely to cancel the subscription. This can be done by analysing churn rates by signup cohorts.

You can improve retention strategies based on these insights.

Conclusion

Cohort analysis is your data’s best friend – always letting you in on secrets about your customers, users and sales patterns.

It doesn’t focus only on knowing where you stand but also helps you figure out how you got there and where to go next.

With insights as clear as a crystal-ball, it helps you improve decision-making. The next time you’re overburdened with data, remember cohort analysis has your back!