Cohort Analysis in eCommerce: Why You Need It and How to Create One

In this article, Promodo Analytics experts explain what is cohort analysis, why it is essential for eCommerce, which cohort analysis tools can be used, and how to set up a cohort analysis report in Google Analytics 4.

What Is Cohort Analysis?

Cohort analysis is a method of grouping customers into segments (cohorts) based on shared characteristics or actions over a specific period of time.

Cohorts are groups formed based on certain metrics or actions, such as the date of the first purchase, traffic source, geography, or user behavior. By analyzing these cohorts, businesses can track behavioral changes, identify trends, and make data-driven decisions.

For example, a cohort could consist of customers who made their first purchase in January or those who signed up on the platform during a summer promotion.
‍

Why Is Cohort Analysis Important?

For better understanding cohort analysis, let’s view key benefits of using cohort analysis:

• Optimizing Marketing Campaigns – Enables businesses to analyze how marketing decisions impact customer retention rates, return on investment (ROI), customer lifetime value (LTV), customer acquisition costs (CAC), and other critical metrics.
  
  
• Identifying Trends – Helps track how customer behavior changes over time, allowing businesses to adapt their strategies accordingly.
  
  
• Increasing Customer Loyalty – Pinpoints areas where the customer experience can be improved. Helps develop strategies to engage less active users and reduce churn rates.
  
  
• Revenue Forecasting – Assesses the value each customer cohort brings to the business, optimizes processes to improve conversion rates, and examines how seasonal factors affect sales volumes.
  ‍

How to Perform Cohort Analysis eCommerce: A Step-by-Step Guide

Promodo analytics experts have prepared a cohort analysis tutorial for eCommerce. 

1. Data Collection and Preparation

• Define the metric you want to analyze (e.g., purchases, registrations, or repeat visits).
• Collect data using tools like Google Analytics 4, CRM systems, databases, or other analytics platforms.
• If using Google Analytics 4, conduct the analysis directly in the interface or export data to BigQuery or Google Sheets for further examination.

2. Grouping Customers into Cohorts

Cohorts can be formed based on various criteria, such as:

• First purchase date – to track how customer behavior evolves over time.
• Traffic source – to identify which channels attract the most loyal customers.
• Product category – to determine whether buyer behavior differs based on product type.

Cohort setup varies by tool:

• In Google Analytics 4, navigate to “Explore” → “Cohort Exploration” template.
• In Google Sheets, use pivot tables to segment data.

3. Behavior Analysis

Analyze collected data by evaluating key metrics such as:

• Purchase frequency – how often customers return for repeat purchases.
• Average order value (AOV) – whether the average transaction size changes over time.
• Lifetime Value (LTV) – total revenue generated by a customer over their relationship with the brand.

Compare cohort behaviors to identify patterns and their causes. For instance, do customers acquired through email marketing have a higher LTV than those from paid ads? Enhance visualization with heatmaps to better understand trends.

4. Interpreting Results

Identify weak points or successful campaigns and adjust strategies accordingly:

• If customers acquired during a sale event have lower retention, discount-driven campaigns may attract less loyal buyers.
• If customers from a specific traffic source show higher LTV, increasing investment in that channel might be worthwhile.
  ‍

Common Mistakes in Cohort Analysis

1. Insufficient Data Volume – Small cohorts (under 50–100 users) may produce unreliable insights due to random fluctuations. Recommended minimums:
   
   
   • eCommerce: 500–1000 buyers per cohort.
   • SaaS: 200–500 users who signed up or activated the service.
   • Mobile apps: 1000 active users per period.
2. Analysis Duration
   
   
   • Short cycles (eCommerce, mobile apps) → 1–3 months.
   • Long customer lifecycles (SaaS, B2B) → 6–12 months.
   • If data is insufficient, combine multiple cohorts or extend data collection.
3. Ignoring External Factors – Seasonal trends or promotional campaigns may significantly impact customer behavior.
   
   
4. Wrong Metric Selection – Focus on metrics that truly impact business outcomes. (See our article on Key KPIs in eCommerce).
   
   
5. Lack of Regular Analysis – Cohort analysis should be conducted systematically to detect trends early. Automating reports in Google Analytics 4 or Google Sheets enables real-time monitoring.
   
   

How to Set Up Cohort Analysis in Google Analytics 4

Let’s view how to do cohort analysis using a step-by-step guide. 

1. Setting Up a Report in GA4

1. Log into Your GA4 Account.
2. In the left-hand menu, select "Explore" (Дослідження).
3. Choose the "Cohort Exploration" (Когортне дослідження) template.
4. Click here to access the Google Analytics 4 Template Gallery, where you can create and customize your report using the Demo Account.
   ‍

2. Configuring Key Parameters

You will receive a template with all standard parameters already added.
‍

Adding Segment Comparisons

If you want to compare different user segments, use this field.
For example, compare transactions of users from different traffic sources (organic, paid, referral).

Cohort Inclusion

In the "Cohort Inclusion" field, select:

• "First touch" (First interaction) or "First visit" (First session) to analyze new users.
• "Any event" to track all user interactions after the first contact.

Return Criteria

Select the return criteria:

• "Any event" – to measure any user activity.
• "Transactions" – to focus on users who made a repeat purchase.

Cohort Granularity

Choose the appropriate granularity:

• "Daily" – for short-term trends.
• "Weekly" – for mid-term analysis.
• "Monthly" – for long-term trends.

Calculation Methods

Select the desired calculation type:

• "Standard" – counts users who performed the action in a given period, regardless of previous activity.
• "Rolling" – includes users who performed the action in both the given and previous periods.
• "Cumulative" – considers all users who performed the action at least once during the analysis period.

Breakdown

This allows segmenting users within each cohort based on specific characteristics or parameters, providing deeper insights into user behavior over time.

• "First user source" – Identifies which traffic source is most effective for attracting users.
• "First user medium" – Analyzes the performance of marketing channels like CPC, organic, or email.
• "First user campaign" – Evaluates the effectiveness of marketing campaigns in acquiring first-time users.
• "Gender" – Examines behavioral differences based on user gender (male, female, unknown).
• "Platform" – Analyzes user interactions based on their device (web, iOS, Android).
  ‍

3. Adding Metrics and Indicators

In the right panel, configure the metrics you want to analyze:

"Values" (Indicators)

• Add the "Transactions" metric or other relevant events you need to analyze.

"Metric Type"

Choose the appropriate metric type:

• "Sum" – Analyzes the total number of events.
• "Per cohort user" – Displays the percentage of the metric based on the number of users in the cohort.
  ‍

‍

What Conclusions Can We Draw from This Report?

Earlier, we created a cohort analysis report, and now we have the data to analyze. It’s critical to know not only how to do cohort analysis but also how to interpret cohort analysis data. Let’s break down the key insights and recommendations.

1. Most Successful Cohort: December 8–14, 2024

• This cohort had the highest number of transactions in Week 0 — 391 transactions.
• However, in the following weeks, transactions dropped sharply to 58 in Week 1, and then to 4 and 3 transactions in subsequent weeks.

💡 Insight: The campaign was highly effective in the first week, likely due to holiday sales or special promotions. However, customer retention dropped significantly in the following weeks.

✅ Recommendation:
Analyze why the December 8–14, 2024 cohort performed best.

• What marketing campaigns or products were offered?
• Try to replicate and scale this success during other periods.

2. Overall Transaction Trends

• Most cohorts see the highest transaction volume in Week 0, followed by a steep drop from Week 1 onward.
• Examples:
  
  • November 3–9, 2024 cohort: Transactions dropped from 328 (Week 0) to 57 (Week 1).
  • October 24–26, 2024 cohort: Transactions dropped from 49 (Week 0) to 10 (Week 1).

💡 Insight: Users are highly active immediately after first engagement but lose interest quickly. This suggests that while initial offers or promotions attract customers, long-term loyalty is low.

✅ Recommendation:

• Introduce subscriptions or loyalty programs for repeat customers.
• Conduct surveys to understand why users don’t return after their first transaction.

3. Seasonality Effect

• The most active cohorts occur in November and December (e.g., the December 8–14, 2024 cohort).
• However, activity drops sharply in January, indicating that seasonal campaigns influence transactions, but brands don’t maintain engagement after the holidays.

✅ Recommendation:
Plan post-holiday engagement campaigns:

• After the holiday rush, launch special retention campaigns in January (“New Year Sale,” “Gift with Purchase”).
• Use push notifications and email reminders with exclusive promotions.

4. Retention Rate is Low

• Only a small percentage of users return after their first transaction.
• Example (December 8–14, 2024 cohort):
  
  • Week 0: 391 transactions
  • Week 1: 58 transactions (~14.8% retention)
  • Week 2: 4 transactions (~1.02% retention)

💡 Insight: Users engage initially but don’t return for repeat purchases.

✅ Recommendation:

• Improve re-engagement strategies:
  
  • Implement loyalty programs
  • Use email campaigns and push notifications
  • Offer personalized recommendations based on user behavior

By implementing these strategies, businesses can improve customer retention, drive repeat purchases, and maximize revenue from each cohort.

Cohort Analysis Tools

Although we demonstrated how to create a report for cohort analysis in Google Analytics 4 (GA4), there are many different cohort analysis tools that can be used for this purpose. Here are a few popular ones:

1. Excel/Google Sheets: Suitable for basic and deeper data analysis and chart creation.
   ‍

‍

2. Power BI/Tableau: Powerful cohort analysis tools for data visualization and deeper analysis.
‍

‍

3. CRM systems (e.g., HubSpot): Help segment customers and analyze their behavior.
4. Other analytics services (e.g., Amplitude or Mixpanel): Allow automating data collection and analysis for cohort analysis.

Each of these tools has its own advantages, so the choice depends on the size of your business and your objectives.

The table below will help assess the advantages and goals of each tool.
‍

Conclusion

Cohort analysis in eCommerce is an indispensable tool for businesses that aim to be flexible and customer-centric. It allows you to better understand your customers, optimize marketing strategies, and make data-driven decisions.
‍

Make Data-Driven Solutions!

Benefit from Cohort Analysis with Promodo.

Contact

FAQ

[[FAQ-START]]

How to define cohort analysis?

Cohort analysis definition refers to a method of analyzing data by grouping users or customers into segments, or cohorts, based on shared characteristics or behaviors during a specific time period. These cohorts are tracked and analyzed over time to identify patterns and trends in their actions, such as purchasing behavior, engagement, or retention. By isolating these groups, cohort analysis helps businesses gain deeper insights into customer behavior, optimize marketing strategies, and improve customer retention and lifetime value (LTV).

What is a cohort in business?

A cohort in business refers to a group of customers or users who share common characteristics or experiences within a specific time frame. This could include factors such as the date of their first purchase, the source of acquisition, or their behavior over a particular period. By analyzing cohorts, businesses can identify patterns, track customer journeys, and make informed decisions to improve customer retention, optimize marketing strategies, and boost overall performance.

What is cohort analytics?

Cohort analytics is the process of analyzing and comparing groups of users or customers who share a common characteristic or experience over a set period. These groups, known as cohorts, are typically based on factors like the date of first interaction, purchase, or behavior. By tracking how these cohorts behave over time, businesses can gain insights into customer retention, lifecycle trends, and the effectiveness of marketing campaigns. Cohort analytics helps businesses understand patterns, identify high-performing segments, and optimize strategies to improve customer engagement and overall performance.

At Promodo, our experts leverage cohort analytics to provide actionable insights and optimize strategies, helping your business make data-driven decisions and achieve sustainable growth.

What is the difference between cohort analysis and traditional analysis?

Cohort analysis groups users based on shared characteristics or behaviors, such as the date of first purchase or interaction. Traditional analysis, on the other hand, looks at aggregate data across all users without distinguishing between different user groups. Cohort analysis provides more granular insights by allowing you to track specific cohorts over time, helping identify trends and patterns that might be missed in traditional analysis.

How can cohort analysis help improve customer retention?

Cohort analysis helps you understand the behavior of different customer segments over time. By tracking their interactions with your business, you can identify which cohorts have the highest retention rates and which ones show signs of churn. With this data, you can implement targeted strategies, such as personalized offers or loyalty programs, to improve retention and reduce customer attrition.

Can Promodo help with cohort analysis for my business?

Yes! At Promodo, our experts specialize in cohort analysis to help businesses gain actionable insights and optimize marketing strategies. We analyze your customer data to identify key trends, evaluate retention rates, and improve your overall performance. Whether you're a small startup or a large enterprise, we can help tailor cohort analysis to meet your specific business needs.

[[FAQ-END]]

‍