Customer Cohorts

As customer acquisition becomes more and more expensive, focusing on customer quality and retaining existing customers is more important than ever. Especially when scaling marketing efforts, customer value often declines compared to your early, loyal fans. By investing in retention, your brand can boost return on ad spend and drive more revenue. Customer Cohorts help you do exactly that—by tracking the quality of newly acquired customers and how it evolves over time.

What is a Cohort?

A cohort is a group of customers who share a common characteristic. RetentionX focuses on acquisition cohorts, grouping customers based on the date of their first purchase. This helps uncover patterns in customer quality by acquisition period.

The analysis shows how KPIs evolve over time for each cohort, broken down into monthly progress (30-day intervals). Each cohort is displayed as a row, with KPIs shown for different intervals (e.g. after 1, 2, or more months). The color of each cell reflects performance compared to the weighted average of that column:

• Dark green indicates above-average performance
• Dark red signals below-average performance

This color coding makes it easy to spot standout cohorts—both good and bad.

Build your Cohorts

By default, RetentionX groups your customers based on the date of their first order. Still, you have the following options to adjust which customers are included in your cohort analysis:

• Segmentation: Apply your customer segments to the cohort analysis to only include customers who match specific criteria, e.g. customers who purchased a certain product in their first order or signed up for a subscription from the start.
  
  
• Dimension: Decide how your cohorts should be broken down. By default, RetentionX uses monthly cohorts, but you can switch to weekly, quarterly, half-yearly, or yearly views. Please note, that his setting does not affect the horizontal time axis; it will always remain monthly intervals, regardless of how your cohorts are built.
  The number of new customers indicates the cohort size, meaning the number of customers who placed their first order in the respective month, quarter, year, etc.
  
  
• Time Period: The selected time period determines which cohorts are displayed. Only cohorts that fall within this period are included when calculating the overall average, which is used to evaluate each cohort's performance and apply the color coding accordingly.

Choose the Metric you want to Measure

To understand how your customer cohorts are performing, start by choosing the metric you’d like to analyze. Typically, Customer Lifetime Value (LTV) is the go-to choice, as it provides a powerful overall view of how profitable, and therefore healthy, your cohorts are.

But LTV is just the beginning! To dig deeper and understand what’s driving the differences between cohorts, you can also monitor influencing factors to see whether a high LTV is driven by more orders, larger baskets, or lower return rates. Available metrics include:

• LTV: Customer Lifetime Value measures the total value a customer brings to your brand. LTV is calculated by (Sum of Net Revenue - Sum of COGS) per customer. Learn more here.
  
  
• Orders: Number of orders placed by customers belonging to the cohort. By default, product exchanges are excluded. Learn more here.
  
  
• Revenue: Revenue represents the income per cohort. By default, both gross and net revenue exclude sales tax, shipping fees and are calculated after discounts. Learn more about RetentionX's revenue definitions and how to change them here.
  
  
• Product Return Rate: Percentage of items that were returned by the cohort—in relation to all items sold.
  
  
• Items: Number of items purchased by customers belonging to the cohort. By default, product exchanges are excluded. Learn more here.
  
  
• Retention Rate: Percentage of customers who placed another order. For this KPI, you can choose how to build your cohorts: either based on their first order date or on any order date (including both first and repeat purchases).

Select the View

After you’ve chosen the KPIs you'd like to analyze, you can also select how the KPI is presented per cohort. Your options are:

1. Cumulative vs Incremental

You can choose how KPI progress is displayed over time: either cumulative or incremental.

• Cumulative: Shows the total KPI value up to a certain time.
  For example, it answers the question: How much LTV has the average customer generated within their first two months?
  → Cumulative values always include the first order.
  
  
• Incremental: Shows the change in the KPI during a specific time period only.
  For example, it answers the question: How much LTV did the average customer add between month 1 and month 2?
  → Incremental values always exclude the first order.

2. Average vs Total

You can also toggle between Average and Total views:

Average: KPIs are shown as averages per customer. The "Overall" row shows the weighted average across all displayed cohorts. Total: KPIs are shown as summed totals for the entire cohort. The "Overall" row shows the total across displayed all cohorts.

Use Cases

Isolating and analyzing cohorts is especially powerful when you want to measure the impact of strategic changes in customer acquisition, such as short-term marketing campaigns, audience adjustments, or budget shifts.

Since the cohort analysis shows how healthy and engaged each customer group remains over time, it helps you identify early warning signs of at-risk or even churning customers:

1. Decrease in purchase frequency: Fewer purchases over time may indicate that customers are losing interest in your products or brand.

2. Low lifetime value: A low LTV suggests weak engagement and may indicate that customers aren't seeing long-term value in your offering.

3. Decrease in purchase value: A drop in the average order value may point to reduced engagement or financial constraints on the customer's end.

4. Inactivity: Customers who haven't purchased with your brand for an extended period are at high risk of churn.
   
   

Take Action

Based on your findings, you can take proactive steps to improve the customer quality and reduce churn. Start by creating segments of customers you want to reactivate and sync them to audiences in your favorite marketing and CRM tools.

Finding patterns on your own

It’s important to remember that interpreting cohort data is an ongoing process. As you gain insights and take action, you should continue tracking and analyzing behavior to ensure your efforts are paying off.

• Identify key metrics: Focus on the metrics that matter most to your business, such as retention rate, LTV, and number of orders.

• Look for patterns and trends: Analyze the data to uncover patterns that can inform your decisions. For example, if you’re able to acquire a high volume of customers during BFCM promotions, but those customers tend to show low quality and remain one-time shoppers, it’s a clear signal to rethink your retention strategy for that audience.

• Compare cohorts: Identify similarities and differences in behavior across time periods. This will help you understand which cohorts are most valuable– and which may need attention.

• Look for outliers: Spot unusually high or low performance. For example, if one cohort shows a significantly higher LTV, ask yourself: What did we do differently when acquiring these customers?
  

• Identify cause and effect: Make connections between metrics. For example, if high purchase frequency consistently correlates with high LTV, you can assume that increasing order frequency is a key driver of value.

Not using RetentionX yet?

Read our full blog post about cohort analysis for Shopify stores here.