Customer Cohorts

At a time when customer acquisition is becoming increasingly expensive, it's even more important to focus on retaining existing customers. By focusing on customer retention, your brand can reduce the cost of customer acquisition and increase sales through repeat purchases and upselling. In addition, customer retention can improve your understanding of customer needs. Customer Cohorts help your brand track and understand customer behavior patterns, make informed decisions, and identify valuable customer groups for retention and acquisition.

What is a Cohort?

A cohort analysis typically includes a table or chart that displays data on different groups of customers (cohorts) over a specific period of time.

Without cohort analysis, it is difficult to establish correlations between customers and profitability, especially in terms of scale. The Customer Cohorts report breaks down your customers into related groups to gain a better understanding of their purchase behaviors. In this case, your customers are grouped by when they placed their first order.

The color of the cell is indicating the performance of that cohort compared to the weighted average for the column, with a color scale that goes from the darkest green, indicating the best performance against the average to the darkest red, indicating the worst performance against the average.

Now, let's take a look at some definitions:

LTV	Average current Lifetime Value of your customers calculated by Sum of Net Revenue - COGS acquired in the respective date of first order
Orders	Average count of orders placed by customers acquired in the respective date of first order
Gross Revenue	Average Gross Revenue generated by customers acquired in the respective date of first order
Net Revenue	Average Net Revenue generated by customers acquired in the respective date of first order
Product Return Rate	Percentage of returned items over the total gross items purchased by customers acquired in the respective date of first order; exclusively available for custom integrations,  , and .
Items	Average Items per basket purchased by customers acquired in the respective date of first order

Cumulative vs Incremental

You have the option to choose whether to monitor the KPI development of your cohorts in a cumulative or incremental way:

Let's explore what this setting implies:

• Cumulative: Shows the aggregated evolution in time for the cohort after 30, 60, or 90 days. Taking a look at Dec 2022: On average, a new customer from this month generated an LTV of $269 within their first 30 days and $308 within their first two months. Cumulative values do always include the first order.
• Incremental: Shows the change of the KPI in a specific time period.
  Taking again a look at Dec 2022, on average, new customers added $49 LTV between days 1 and 30 after their first purchase, and increased the LTV by $38 between days 31 and 60 after. Incremental values do always exclude the first order.

Average vs Total

Then, you have the option to choose if you would like to analyze the average KPI for a specific cohort, or the total value.

While selecting Average, in the Overall row you will find the weighted average per column for each KPI. If Total is selected, the Overall row will show the sum of the column.

Use Cases

Isolating and analyzing cohorts is powerful when you want to quantify the response to short-term marketing campaigns as an email campaign including a one-day voucher. This report can also show how the behavior and performance of individual user groups changes from week to week, month to month, or year to year compared to the time your customers were acquired.

How to Recognize Early Warning Signs of Churn

1. Decrease in purchase frequency: A decrease in the number of purchases made by a customer over time can indicate that they are losing interest in your products or services.
2. Low lifetime value: A low lifetime value, calculated as the total revenue generated by a customer over their lifetime, can indicate that they are not as engaged with your brand as other customers.
3. High customer service interactions: A high number of customer service interactions can indicate that a customer is experiencing problems or dissatisfaction with your products or services.
4. Decrease in purchase value: A decrease in the average purchase value made by a customer over time can indicate that they are becoming less engaged with your brand or that they are experiencing financial difficulties.
5. Inactivity: Customers who have not made a purchase or interacted with your brand in a long time are at risk of churning.
   
   

Take Action

Based on the results of the analysis, you can take action to improve the customer experience, such as creating targeted marketing campaigns, developing new products, or optimizing pricing and promotions. For this, create segments of customers you want to reactivate and synchronize it to audiences in your favorite marketing & CRM tools.

1. Reengage customers with targeted marketing campaigns: Reach out to customers who have exhibited early signs of churn risk and offer them incentives to continue doing business with your brand.
2. Address customer issues: Address any issues that may be causing dissatisfaction, such as poor customer service or product defects.
3. Personalize the customer experience: Personalize the customer experience by providing tailored recommendations and offers to retain customers longer.
4. Monitor and track customer behavior over time: Continuously monitor and track customer behavior over time to identify any changes in behavior that may indicate an increased risk of churn.
   
   

Finding patterns on your own

It's also important to keep in mind that interpreting cohort data is an iterative process. As you gain more insights and take action, it's important to continue to track and analyze customer behavior to ensure that the actions you're taking are having the desired effect.

Here are a few key steps to take when interpreting cohort data:

1. Identify key metrics: Identify the metrics that are important for your business, such as customer retention rate, purchase frequency, and purchase value. These metrics can provide important insights into customer behavior and should be analyzed for each cohort.
2. Look for patterns and trends: Analyze the data to identify patterns and trends that can inform business decisions. For example, you might notice that a particular customer cohort has a higher lifetime value than others, or that a different cohort is at a higher risk of churn.
3. Compare cohorts: Compare the different cohorts to identify similarities and differences in customer behavior. This can help you understand which cohorts are most valuable to your business and which are at the highest risk of churning.
4. Look for outliers: Identify any customers or groups of customers that have particularly high or low values for key metrics. These outliers can provide valuable insights into customer behavior and can help you identify potential areas for improvement.
5. Identify cause and effect: Identify any potential cause and effect relationships between different metrics. For example, if you notice that customers who have a high purchase frequency also have a high lifetime value, you can assume that the high purchase frequency is the cause of the high lifetime value.

What You Need

For this report to work properly, the following data must be imported:

• Order ID
• Order Date
• Stock Keeping Unit (SKU)
• Items Sold
• Item Price
• Customer ID
• Value Added Tax (VAT)
• Discount
• Shipping Revenue
• Product Returns
• Product Return Dates

Not using RetentionX yet?

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