Cohort analysis: How to measure user retention?

Ever downloaded an app, loved it at first, but then kinda forgot about it? It happens.

But what if you could figure out why and keep people hooked?

It would be great, right? That’s exactly what cohort analysis does.

Imagine throwing a party. You wouldn’t just invite everyone randomly. You’d think about different friend groups and what they like. Cohort analysis does the same thing but for your product or website users.

Let’s break it down and see how it works.

What is cohort analysis?

Cohort analysis is a data-driven method that groups users based on shared characteristics or experiences within a specific time frame. By tracking these cohorts over time, businesses gain a deeper understanding of user behavior, allowing them to make informed decisions to improve user experience and retention.

Cohort analysis example

Say you run a social media platform. You create a cohort of users who signed up during your latest marketing campaign. By tracking this cohort’s activity, you can measure their:

Retention rate: What percentage of campaign users remain active after a specific period (e.g., one month)?
Engagement metrics: How frequently do they post, comment, or interact with other users?
Conversion rate: Do they upgrade to premium accounts at a higher rate than other cohorts?

This data helps you evaluate the campaign’s effectiveness and identify areas for improvement.

Different types of customer retention cohort analysis

Understanding how your customers stick around is crucial for any business. Customer retention cohort analysis helps you do just that by splitting your users into groups based on shared characteristics and tracking user behavior over time. Let’s explore the different types

1. Acquisition cohorts: when they joined the party

It’s like if you go through a party every month. Each group attending on a specific month forms an acquisition cohort. By tracking how many people from each party stick around for future celebrations, you can see which marketing campaigns attract the most loyal guests.

Example: An e-commerce platform compares its quarterly cohorts. They find that users acquired during holiday promotions churn faster than those acquired organically. This tells them to focus on building long-term brand loyalty rather than just seasonal discounts.

2. Behavioral cohorts: what makes them tick

Understand it like dividing your party guests based on their activities. The dance floor crew, the chatty corner group, and the dessert enthusiasts are your behavioral cohorts. Analyzing their behavior throughout the night reveals their preferences.

Example: A streaming service groups users by the type of content they watch. They find that viewers who watch documentaries tend to stick around longer than those who only watch comedies. This suggests they can target documentary fans with exclusive content or longer subscription offers.

3. Prediction cohorts: Peeking into the future

Imagine having a magic mirror at your party that predicts who will leave early. By analyzing past party data, the mirror identifies patterns and flags potential early leavers. You can then approach them and make their experience better, encouraging them to stay.

Example: A fitness app analyzes past user data to predict who might cancel their subscription soon. They proactively offer them personalized workout plans or discounted deals, potentially convincing them to stay.

Remember, customer retention is a journey, not a destination. By understanding your different cohorts and their behaviors, you can tailor your approach to keep them engaged and coming back for more!

Calculate retention through cohort analysis

In website and product analytics, cohort retention helps you understand how different groups of users stick around over time. Imagine running a website or product and having new visitors every week. You want to know which groups become loyal users and which ones drop off quickly.

Here’s how cohort retention works:

Group users: You divide your users into cohorts based on a shared characteristic like the week they signed up. Each cohort is like a mini audience with its own behavior to track.
Track engagement: Over time, you monitor how many users from each cohort stay active on your website or app. This could mean things like logging in, making purchases, or using specific features.
Calculate the rate: For each cohort, you can calculate the retention rate by dividing the number of active users after a certain period (like a month) by the total number of users in that cohort. Multiply by 100 to get a percentage.

For instance, you have a class of 20 students who are starting a new online game. After a week, 15 students are still playing regularly. The retention rate for that week’s cohort is 75% (15 / 20 * 100).

Remember: It’s important to respect user privacy when analyzing their behavior. Always anonymize data and follow ethical data collection practices.

Analyze cohorts to improve customer retention rate

Measure the effectiveness of new users’ onboarding experience: Understanding how well new users are onboarded can significantly impact their long-term engagement.
Pinpoint successful behaviors that increase user engagement and retention: Identifying actions that correlate with higher retention helps tailor strategies to encourage these behaviors.
Identify drop-offs in the user journey: Recognizing points where users tend to drop off allows businesses to address pain points and improve the overall user experience.
Track how a new feature launch impacted retention rates: Assessing how new features affect user retention provides insights into feature adoption.

Measure customer retention through cohort analysis

You can utilize many product and website analytics tools to measure customer retention through cohort analysis. One of these tools is Usermaven, which helps you understand how well you’re keeping customers interested.

Usermaven-cohort analysis tool

Usermaven, a powerful cohort analysis tool, provides businesses with insights into user behavior, facilitating effective strategies for customer retention. Let’s delve into key practices to leverage the capabilities of cohort analysis, drawing inspiration from the Usermaven approach.

Analyzing retention in Usermaven

Overall retention curve: It’s like a rollercoaster showing how many people stay with your product over time. Hover your mouse over spots for info.

Cohort analysis: This one breaks down how groups of people who signed up in the same month are doing. It’s like checking if friends from July 2023 still enjoy your product months later.

How to read a cohort analysis table

Understanding this table is like reading a story. Let’s break it down:

Cohort month: When the user first joined. Like July 2023.
Initial count: How many of them joined in July 2023?
First-month retention: How many customers stayed after the first month?
Subsequent months: Each column shows how many users stuck around in the months that followed.

How to get started with cohort analysis

To get started with cohort analysis using Usermaven, follow these steps:

Analyze retention by customer acquisition cohort: Use cohort analysis to identify drop-off points and areas where users are disengaging from your product.
Define core user actions: Identify the most important actions that users take within your product, such as signing up, logging in, or completing a specific task.
Use behavioral cohorts to analyze, invert, and combine them: Group users based on their behavior, such as sign-up date, product usage, or purchase history, and analyze how different groups interact with your product over time.
Formulate hypotheses based on acquisition and behavioral cohort analysis: Develop theories about why users are dropping off or disengaging.
Iterate the process based on learnings: Continuously refine your cohort analysis and product improvements to optimize user retention and overall product success.

By following these steps, you can leverage the power of cohort analysis in Usermaven to better understand your users, identify areas for improvement, and make data-driven decisions to enhance your product’s performance.

Conclusion

Cohort analysis, when done right, enables businesses to understand user behavior, fostering data-driven decisions and, ultimately, enhancing the customer experience.

Usermaven helps you find where the gems are, like what’s working in your product and where users might be dropping off. So, whether you’re a tech whiz or just starting, Usermaven makes figuring out cohort and user retention a breeze.

Book a demo today!

FAQs

1. What is the purpose of retention analysis?

Retention analysis helps businesses understand how likely users or customers are to stick around over time. It gives insights into what keeps people engaged and what might lead them to leave. This information can be used to improve products,
services, and marketing strategies to increase retention and build a stronger customer base.

2. What does an 80% retention rate mean?

It means that for every 100 users or customers acquired, 80 remain active after a certain period, like a month or year. This is just one example, and a “good” retention rate varies depending on the industry and business goals.

3. Are cohort analysis and churn analysis the same thing??

No, they’re related but distinct; cohort analysis groups users or customers by acquisition time (e.g., users who signed up in January) and tracks their activity over time. This helps identify patterns and trends within specific groups. While churn analysis focuses on measuring how many users or customers stop using a product or service within a given timeframe. It helps understand the overall rate of customer loss.