Cohort Retention Analysis Guide on How to Improve User Retention

Cohort retention analysis answers a very crucial question app marketers are most concerned about. How frequently are users engaging with my app?

While app downloads can indicate initial interest, they don’t necessarily reflect long-term success. Cohort retention analysis digs deeper, providing insights into user behavior that help evaluate the effectiveness of your engagement strategies and pinpoint challenges in user retention.

This article will dive deep into understanding cohort retention analysis, explain its types, and provide strategies for how it improves user retention rate.

What is Cohort Retention Analysis?

Cohort retention analysis is the process of tracking user engagement with your app over a period of time. It segments data from your app or website into distinct groups based on user behavior, usage patterns, and common traits, facilitating detailed analysis. This strategy is instrumental in customer engagement marketing for B2C marketers, aiming to reduce customer churn and enhance retention.

Cohort retention analysis distinguishes genuine engagement improvement from misleading vanity metrics like download numbers.

A notable advantage of cohort retention analysis is its ability to distinguish engagement metrics from growth metrics. This distinction is crucial because it prevents the misinterpretation of engagement levels, which can often be confused by high growth rates. This provides a cleaner, more actionable understanding of user behavior, which is essential for growth.

The Different Types of Cohort Retention Analysis

Cohort retention analysis is invaluable for precisely monitoring user behaviors on your app. It uses behavioral analytics to compare these behaviors by pinpointing when users were acquired and observing their retention over time. Based on this approach, there are three types of cohorts:

Acquisition Cohorts

Cohorts can be categorized based on the timing of when users downloaded and signed up on your app. This segmentation is instrumental in analyzing user retention from the moment they begin using the app.

Example of an Acquisition Cohort Chart

Based on the data observed, we can see that:

• Of all the users acquired between the 1st and 6th of March, only 10% remained active by the end of Day 5.

• There is a consistent daily decrease in the number of users returning to the app.

By comparing different cohorts, you can discern the patterns of user return rates on specific days—Days 1, 3, and 5, for example.

This analysis reveals the retention rates during the initial days and provides a broader view of the longevity and health of user engagement.

Behavioral Cohorts 

Cohorts can also be defined on user behaviors and actions within your app during specific times, like the first seven days post-installation.

They are based on users taking specific actions within your app, like completing purchases, dropping off, and signing up for loyalty rewards. It gives more granular insights than acquisition cohorts by not only telling us the number of users retained but also why.

Prediction Cohorts

These are groups of users who are likely to show certain behaviors in the future, like churn. Robust customer engagement platforms can determine this using AI and ML algorithms that analyze past behavior.

This type of cohort allows teams to tailor strategies that can proactively change user behavior. For example, marketers can cross-sell or up-sell products to users by sending in-app messages to reduce churn and increase conversion rates.

Cohort Retention Analysis Explained with an Example

Let’s understand cohort retention analysis better using an example. 

Assume you are running a major ecommerce store and have recently launched a new mobile app. You want to identify the number of users who have launched your app and measure your app stickiness from the acquisition date over a 7-day period.

Start by performing an acquisition cohort analysis of users who first downloaded your app and launched it in the time period.

Here is what the cohort data chart looks like:

Cohort Chart Showing App Launch

From the above table, we can infer the following.

• On 1st March, 11,478 users launched the app. Day 1 retention rate was 58.1%, day 4 retention rate was 19.8% and day 7 retention was 16.5%. This means that after day 7 of using the app, 1 in 5 users who launched the app on 1st March were active users.

• Out of all the new users till 6th March (54,018), 42.2%(average of day 1 retention) users are retained on day 1, and 16.5% are retained on day 7.

As the days progress, the number of users returning to the app declines. To better understand the reasons for drop-offs, you analyze the data by segmenting it into behavioral cohorts. It is revealed that users are abandoning their carts during the shopping experience from the cohort chart below.

Behavioral Cohort Chart of Users Abandoning Carts

• Almost all users who add a product to their cart proceed to complete a purchase on the same day.

• Conversely, less than 60% return to the app once a user abandons their cart.

The most effective way to interpret the data is by creating a retention curve (given below), illustrating the retention cohort of users over a period of time. Doing this makes it remarkably straightforward to understand when users disengage from your product.

This visual simplifies the analysis and highlights critical points for intervention to enhance user retention.

Retention Curve Graph

Interpreting The Cohort Analysis To Increase Customer Retention Rate

The retention curve clearly indicates that over 50% of all users stop using the app just after day 1. Following this significant initial decline, user numbers consistently decrease day by day.

Such a trend suggests that users are not realizing the core value of the app, leading to drop-offs. This insight stresses the urgent need to enhance the early experience within the app to prevent users from drifting away.

Consider making the onboarding experience smoother and more eye-catching. Also, launch a welcome series campaign with benefits like big discounts, free shipping, or high-end services to captivate your audience. Addressing these issues early on can significantly improve user retention and engagement.

They also emphasize the critical need for immediate retargeting strategies. On the day a user abandons their cart, prompt cart abandonment recovery strategies need to be implemented. This could involve sending a special discount coupon or a limited-time offer through direct engagement channels like triggered app push notifications, emails, SMS, or WhatsApp.

Case Study: How Meta’s Threads App Hits Record Downloads but Faces Challenges with Daily User Engagement

In July 2023, Meta (formerly Facebook) launched Threads, a new micro-blogging platform to share text updates. The platform was launched to take on X (formerly Twitter) and was considered one of the biggest bets after Facebook.

With Meta’s astounding user base, the platform witnessed an astounding start, amassing 10 million users signing up in just 7 hours. To provide perspective, Facebook took 852 days, Twitter 780 days and ChatGPT 40 days to reach the same number of users.

Time Taken for Platforms to Reach 10 Million Users – Source

However, this acquisition thrill was short-lived. On its busiest day, the number of users on Threads was half that of Twitter. On its launch day, July 6, 2023, Threads reported 41.79 million daily active users (DAU) which peaked to 49.3 million the next day.

But, on July 14, 2023, Threads’ daily app users (DAU) dropped to 23.6 million. That’s a sharp 52% decline in one week. By August 2023, the DAUs further plummeted to 10.3 million, marking a decline over 79% from its peak. This clearly highlighted challenges in sustaining user engagement over time.

DAU of Threads vs Twitter – Source

While multiple reasons could be attributed to this downfall, this scenario underscores how app download figures can misrepresent actual app growth. Relying solely on downloads can distort the true picture of an app’s success and user retention.

Focusing on app stickiness is equally, if not more important. While developing a high-quality app is important, ensuring users return and continuously engage is vital. The measure of app stickiness reveals the true potential for growth of your brand.

Conducting regular cohort retention analysis is crucial. Apart from showing how well your app retains users over time, it offers other important benefits, which are given below.

Benefits & Impact of Cohort Retention Analysis to Improve Customer Retention

1. Better Onboarding Experience

First impressions are pivotal, and for many customers, these impressions are shaped during the onboarding process.

If cohort retention analysis indicates that a considerable number of users disengage soon after signing up, this strongly indicates that the onboarding experience may be deficient. The process might be overly complex, lack intuitiveness, or fail to clearly demonstrate the product’s value.

By pinpointing the sticking points across different cohorts, brands have the opportunity to enhance and reorganize their onboarding process. The goal is to make it more user-friendly, engaging, and informative, ensuring a long-lasting customer lifecycle and engagement with the app.

2. Customize Product Offerings

In the diverse world of consumer preferences, one size doesn’t fit all. Cohort analysis can illuminate patterns in purchasing behaviors.

For example, if a certain group of users consistently shows a preference for organic products, businesses can curate special bundles for that cohort.

Similarly, insights into the purchase patterns of different cohorts can guide upselling or cross-selling strategies.

If a cohort frequently purchases a specific type of product, businesses can recommend complementary items, creating a holistic shopping experience that caters to the user base’s tastes and needs.

3. Get Higher Average Order Values

Customers who are consistently retained tend to develop a strong sense of loyalty. This loyalty manifests in increased purchasing frequency and higher average order values. 

In fact, retained customers often spend 33% more per order compared to new customers, illustrating the significant financial benefits of nurturing long-term customer relationships.

4. Run Engagement Campaigns for Loyal Users

While acquiring new customers is crucial, retaining existing ones is often more cost-effective and beneficial in the long run.

Cohort retention analysis can highlight which user groups are more inclined toward brand loyalty. Armed with this data, brands can introduce tailored loyalty programs, offering exclusive benefits, discounts, or referral bonuses to increase customer lifetime value.

For cohorts showing higher loyalty tendencies, these programs can be a way to further deepen their connection to the brand, making them not just customers but brand advocates.

A modest 5% boost in customer retention can result in a significant revenue increase ranging from 25% to 95%. This statistic underscores the critical role that customer retention plays in enhancing both revenue stability and growth.

5. Hyper-personal Customer Journey Mapping

Cohort retention analysis is integral to orchestrating the customer journey. Since different cohorts experience the product or service uniquely, it becomes crucial to map, optimize, and standardize their journeys—from the initial interaction to the loyalty hyper-loop. 

This detailed mapping ensures a rich and engaging experience that is pivotal for boosting user retention. By understanding and enhancing each touchpoint, businesses can ensure that every customer segment enjoys a seamless and satisfying journey, ultimately fostering loyalty and long-term engagement.

6. Provide Detailed and Insightful Feedback from Existing Customers 

Retained customers are a rich source of valuable feedback that can propel business enhancements. Statistics show that 77% of customers have a more favorable view of businesses that actively seek and implement customer feedback. 

By retaining customers, businesses gain deeper insights into areas needing improvement and the overall quality of products or services and app experience.

Conclusion

Cohort retention analysis digs deep into customer behavior to measure app stickiness. It indicates how users are adopting your app and provides insights that are crucial to improving customer retention. 

They help identify trends and give directions to create marketing strategies that ensure customers are continuously engaged and your brand has maximum recall.

By analyzing different cohorts, you can tailor your updates and features to meet the specific needs and preferences of each group. This targeted approach significantly boosts user satisfaction, drives app repeat usage, and improves customer referrals.

Ultimately, understanding and acting on cohort retention analysis can dramatically increase customer lifetime value and drive sustainable growth.