Cohort Analysis is a method of analyzing customer data in groups, often based on time or common behavior. This method helps businesses understand customer trends, habits and loyalty levels, thereby devising appropriate marketing and product strategies. In this article, we will introduce the application of Cohort Analysis in data analysis.

What is Cohort Analysis?

Cohort analysis is a method of analyzing customer data based on groups of customers with common characteristics (cohort). Cohort analysis helps businesses understand customer behavior, preferences and trends over time, thereby devising appropriate marketing and product optimization strategies.

Cohort analysis can be applied for many different purposes, but the most common is to measure customer retention rate and repeat purchase rate. These indicators indicate the level of customer loyalty and satisfaction with a business’s products or services.

Why should business use Cohort analysis?

There are many reasons to use cohort analysis, including:

• Helps understand customer behavior: Cohort analysis helps businesses understand customer behavior throughout their lifecycle, from when they first interact with the business to when they stop using the product or service of business.
• Identify opportunities to improve customer experience: By analyzing customer behavior, businesses can identify opportunities to improve customer experience, thereby increasing customer retention and increase revenue.
• Compare customer groups: Cohort analysis helps businesses compare different customer groups, such as customer groups exposed to different marketing campaigns or customer groups with different characteristics. This can help businesses identify factors that influence customer behavior.

Basic Cohort analysis methods

There are two most common types of cohort analysis: time cohort and behavioral cohort. Time cohort is a method of analyzing data by groups of customers based on when they first interacted with your product or service. For example, you can analyze the behavior of customers who signed up in January, February, and March to compare each group’s retention rate, purchase rate, or engagement rate.

Behavior cohort is a method of analyzing data by groups of customers based on specific actions they take on your product or service. For example, you can analyze the behavior of customers who purchased product A, product B, or product C to compare the satisfaction, loyalty, or average order value of each group.

What business should Cohort analysis be applied?

Cohort analysis can be applied to many different fields, but some of the most popular are:

• E-commerce: Cohort analysis helps e-commerce businesses track customer shopping behavior over time, such as number of purchases, order value, repurchase rate and cancellation rate. Thanks to that, businesses can optimize pricing, promotion, delivery and customer care strategies.
• SaaS (Software as a Service): Cohort analysis helps SaaS businesses evaluate customer satisfaction and loyalty with their products, such as number of logins, usage time, usage features and conversion rate from trial to paid. Thanks to that, businesses can improve product quality, increase product appeal and value.
• Online education: Cohort analysis helps online education businesses track students’ learning progress over time, such as number of registered courses, number of completed lessons, scores, and course completion rate and re-enrollment rates. Thanks to that, businesses can improve content quality, increase student interaction and commitment.

Applying Cohort Analysis in analyzing Retention rate

Retention rate, or customer retention rate, is an important indicator in marketing, indicating the number of customers who continue to use a business’s products or services after a certain period of time. A high retention rate shows that businesses are doing well in retaining customers, thereby increasing revenue and profits.

Cohort analysis is a useful data analysis method for analyzing retention rate. For example, businesses can divide customer data into cohorts based on time, such as monthly, quarterly, or yearly cohorts. This will help businesses track the retention rate of different customer groups over the same period of time.

Or, businesses can divide customer data into cohorts based on behavior, such as cohorts by number of purchases, amount spent, or number of times a product or service is used. This will help businesses identify customer groups at high risk of leaving and take measures to prevent them from leaving.

Here are some ways to use cohort analysis to analyze retention rate:

• Track retention rate over time: Analyzing retention rate over time will help businesses determine retention rate trends of different customer groups. This can help businesses identify factors that affect retention rate and take measures to improve retention rate.
• Compare retention rate between customer groups: Analyzing retention rate between different customer groups will help businesses identify customer groups at high risk of leaving. This can help businesses take measures to prevent these customer groups from leaving.
• Identify factors that affect retention rate: Analyzing retention rate by different factors, such as behavior, customer segments, or marketing campaigns, will help businesses identify factors that affect retention rate. This can help businesses improve retention rate by focusing on these factors.

See more: Customer psychology in marketing

Cohort analysis is a powerful tool that can help businesses improve business performance. Businesses should consider using this method to better understand customer behavior and improve customer experience.