What Is Cohort Analysis? A Beginners Guide, Types and Benefits

In decision-making processes for product development, ecommerce businesses often rely on analytics. These analytics typically assess all users uniformly, overlooking individual differences. This approach treats all users as a single group, which fails to provide the detailed insights necessary for precise decision-making. This is where the importance of cohort analysis emerges. Cohort analysis helps you see the data with more context and detail, which can lead to better decisions.

In this guide, we will understand cohort analysis, why it matters, and how you can use it to make better data-based decisions and tackle problems effectively.

What is cohort analysis?

Cohort analysis involves grouping users based on similar characteristics and observing their usage patterns unfold. This method helps to uncover how specific traits can impact user behavior, providing clearer insights into what drives their actions. A 'cohort' in this context is simply a term for a group of people who share these common traits.

Types of cohort analysis

Now that we understand what cohort analysis means let's understand its types to understand better how we can use it.

Acquisition cohorts

Acquisition cohort analysis is all about timing. They group users based on when they first signed up or started using your product. This is useful for determining how long people stick around and how quickly they drop off.

For example, you might notice that users who signed up during a certain promotion are likelier to stay longer than those who came on board at other times.

Behavioral cohorts

Behavioral cohorts, on the other hand, are grouped by the actions users take within your product. This approach lets you see how different user groups interact with your service. It’s an effective way to uncover why certain users might stop using your app or why they interact in specific ways.

So, while acquisition cohorts help you understand when particular behaviors or engagement levels occur, behavioral cohorts provide deeper insights into why these behaviors happen. These are particularly useful for addressing issues like user churn or shifts in user engagement.

Benefits of cohort analysis

If you’re an e-commerce store owner, understanding the benefits of cohort analysis can boost your store’s performance. It unlocks insights into customer habits and helps refine your strategy.

• Better product development: Cohort analysis can significantly influence product development by identifying specific features or services that resonate with your customers or different customer groups. This insight allows you to tailor your product to specific customer needs more closely, improving satisfaction and encouraging longer retention.
• More efficient marketing: Cohort analysis is crucial for pinpointing which customer groups are most profitable and which marketing strategies work. By analyzing different cohorts, you can see who brings in the most revenue and refine your upselling strategies accordingly. It also allows you to assess the effectiveness of marketing campaigns by tracking whether new sign-ups increase following specific promotions.

Keen to grow your customer base? Check out our "How to Use Direct Response Marketing to Attract More Customers" guide for effective strategies.

• Increased customer retention: By understanding the behavior and preferences of different groups, cohort analysis helps you implement targeted strategies to keep your customers. This can involve adjusting your product offerings or improving customer service based on the feedback and habits of specific cohorts.
• Identifying high-value customers: Cohort analysis sheds light on the customer lifetime value in different groups, providing crucial data on how much they are likely to spend over their time with your product. This information is invaluable for making informed decisions about resource allocation and pricing strategies in an ecommerce setting.

Steps to perform a cohort analysis

Cohort analysis provides your ecommerce store with an understanding of what drives your customers, helping it to improve and grow. This is how you can go about doing it.

Step 1: Set your goal

Start your cohort analysis by zeroing in on what you hope to learn. Consider your overall business objectives and how understanding specific customer behaviors could help reach those goals. Here are a few questions you can explore:

• Referral analysis: Which sources are bringing in your top customers?

• Product appeal: What do your customers tend to purchase from you?

• Funnel troubleshooting: Where are you losing potential customers in the sales process?

• Feature feedback: Which features are causing the most calls to customer support from new users?

Step 2: Identify data sources

Identify where you can gather the data that answers these questions. You’ll likely pull from various sources like CRM systems, website analytics, surveys, and email marketing tools. The more data you can compile, the better. Here are some tips:

• Combining data: Make sure to merge and clean your data from different sources for accuracy.

• Data dictionary: Create or use an existing data dictionary to understand what data you have and how it's organized.

3. Define your cohorts

Think about how you can categorize your customers into cohorts. This could be based on their purchase timing, the number of items they bought, or their actions. How you group them should align with the goals you outlined earlier. Here are some grouping ideas:

• Time-based: Customers who shopped during a specific season or sale.

• Volume-based: Customers who purchase multiple items within a certain period.

4. Chart your results

Decide how you'll visualize the results of your cohort analysis. You can use graphs, charts, or tables to make the data easy to digest. Here’s how to read different layouts:

• Horizontal reading: Tracks how a cohort’s behavior changes over time.

• Vertical reading: Compares different cohorts at the same time.

• Diagonal reading: Provides a snapshot at specific intervals.

Next, learn how to leverage customer profiles further to propel your business growth with our comprehensive guide.

How to read a cohort chart analysis with examples

Let’s examine some examples and use cases of cohort analysis. We’ve also discussed what each example's cohort analysis chart represents, which will be useful even if you don’t know how to read a chart.

1. Visualizing customer retention and churn

The Cohort analysis table in this example tracks the number of new user groups that sign up each month and the number that stick around over time. Each row in the table represents a new user group or cohort based on the month they signed up. The columns track these groups month by month to see who's still in business with us.

Let's take the January cohort as an example. We started with 184 fresh faces with the first purchase. Fast-forward ten months, and we're down to 70 in the vertical axis. So, we've kept 38% of our original January users in ten months. That's our retention rate for this cohort. Meanwhile, the other 62% have moved on—this is our churn rate.

The colors in the table usually highlight key data points, such as a notably high retention rate in one month or a concerning drop in another. For instance, our October group kicked off with 373 active users, and by the end of the first month, we held on to 284, which is quite impressive.

We can see how retention rates evolve by comparing rows and following the diagonal trend. This could reveal if we're improving at keeping our user group or if some months are naturally better. This business analytics is crucial—it can point to the success of marketing strategies or reveal the best times to release product updates.

2. Assessing the value of customers

The table provided is a great example of using a cohort analysis report to monitor the average cumulative revenue per user over time. Each row represents a month in which a new group of customers was acquired, starting with January and moving through to October. The columns correspond to the number of months since the users first joined. For example, the number of users who joined in January contributed an average of €47.81 in the first month. By the ninth month, this figure rose to €411.87 per user. This reveals how much we're making and how user value grows over time.

The figures climb as you move to the right, indicating that the longer users stay, the more they spend. This pattern is crucial for understanding the long-term value of customer relationships. The color highlights likely indicate particularly high revenue per user, which can signal successful up-selling or engagement strategies. For instance, the May cohort size shows a significant increase from the first to the ninth month, suggesting that users who joined in May are especially valuable.

This cohort table helps businesses pinpoint when users will likely increase their spending and identify the most profitable conversion rate. This information can inform acquisition strategies and customer retention, shaping resources for marketing and customer service initiatives.

Conclusion

Starting with cohort analysis can seem difficult due to the complexity of the data and the need for strong analytical skills. However, you can effectively overcome these challenges by breaking your data into manageable segments and committing to ongoing learning. The buisness intelligence you gain will be invaluable, helping you make smarter decisions and deeply understand your customer’s behaviors.