Use Cases for Cohort Analysis

Cohort analysis provides insights into user behaviors by tracking groups over time. Here are practical use cases that show how to apply cohort analysis effectively.

A) Sustaining Return Rates for New Visitors Acquired Through SEO

By examining how frequently new visitors return over time, you can measure the long-term success of acquisition channels like SEO. This use case focuses on understanding the loyalty and retention patterns of users who were initially acquired through the SEO channel, providing insights into the channel’s effectiveness in attracting repeat visitors.

Analysis: Individual Analysis

B) Tracking Development of Visitor Value by Cohort

Tracking how much value each new visitor contributes on average after their initial acquisition helps assess the effectiveness of engagement strategies. By analyzing the development of Visitor Value over time, you can gain insights into how effectively your marketing efforts convert visitors into high-value customers. Unlike simply tracking total Order Value, this metric focuses on the average revenue generated per visitor, providing a clearer view of user engagement quality.

Analysis: Individual Analysis (using custom metrics)

Key Concept: By using Visitor Value, you can measure how much revenue each new visitor generates on average. This metric is calculated by dividing the total Order Value by the number of Visitors, allowing you to track the average contribution per user.

Custom Formula Setup

Visitor Value (Week N) = Order Value (Filter: Cohorts (Lifespan Week N)) / Visitors (Filter: Cohorts (Lifespan Week N))

C) Analyzing Return Patterns by Custom Time Clusters

While it’s common to analyze user behavior using standard intervals like daily, weekly, or monthly, Mapp Intelligence provides the tools to go beyond these predefined metrics. This flexibility allows you to create fully customized time clusters tailored to your specific business needs. By setting up custom metrics and formulas, you can analyze user return patterns in intervals that align with your unique engagement strategies, rather than being limited to traditional time frames.

This capability enables you to identify the optimal timeframes for re-engaging users, enhancing the effectiveness of your marketing efforts.

Analysis: Individual Analysis (using custom metrics)

Key Concept: By leveraging Mapp Intelligence’s flexibility, you can define custom time clusters to pinpoint when users are most likely to return after their initial visit. This allows you to fine-tune your re-engagement strategies, focusing your efforts on the timeframes that maximize user retention.

Custom Formula Setup

Returning Visitors % (Days 3-6) = Visitors (Lifespan Days between 3 and 6) / Visitors (Lifespan days 0) * 100

D) Measuring the Time to Registration After First Visit

Understanding how quickly users register after their first visit provides insights into the effectiveness of your customer journey. Tracking the time span between the initial visit and registration helps identify potential barriers, allowing you to optimize your strategies for converting visitors into known users.

With Mapp Intelligence, you can measure the time it takes for visitors to complete registration by tracking the number of users who register within specific lifespans (e.g., days, weeks, or months). The Website Goal “Registration” is a custom goal that needs to be set up, unlike the predefined Website Goal “Order”. To learn more about configuring website goals, refer to our Website Goals Training Chapter. By applying a filter on this goal, you can analyze how users are engaging over different lifespans.

Analysis: Individual Analysis

E) Measuring Weeks to Reach Visit Milestones

Understanding how long it takes for users to reach a certain number of visits provides valuable insights into user engagement. By tracking how many weeks it takes for users to achieve a specific visit count, you can identify patterns in user behavior and determine how effectively your platform encourages repeat visits. This information can help refine strategies to improve user retention and frequency of visits.

Analysis: Individual Analysis

F) Mapping User Lifespans Across Campaign Channels

This use case helps answer the question: What is the distribution of user lifespans across different campaign channels currently driving traffic? By analyzing how long users have been engaging with your platform when they interact with specific channels (like SEO, display ads, or email), you can determine whether these channels are more effective at reaching newer users or those with a longer engagement history.

Analysis: Individual Analysis (using custom metrics)

Key Concept: This analysis segments users based on their engagement history. The custom metrics calculate the percentage of users who return within specific timeframes (less than a week, one week, or two weeks).

Custom Formula Setup

Lifespan Week 1 %  = Visitors (Lifespan Week 1) / Visitors * 100