When evaluating startups, we review customer acquisition, behavior, and retention. For this purpose, we often ask to see a cohort analysis. In this blog post, we dive a little deeper into what is a cohort analysis, what it tells, how to build one, and what should be taken into account when using it.

Key Benefits of Cohort Analysis

• Track customer retention: Cohort analysis can help businesses identify which cohorts are retaining customers and which cohorts are losing customers. This information can be used to target marketing and support efforts at the cohorts that need the most help.
  
• Identify churn drivers: Cohort analysis can help businesses identify the factors that are leading to churn. This information can be used to improve products, services, and customer support to reduce churn rates.
  
• Analyze customer lifetime value: Cohort analysis can help businesses estimate the lifetime value of each customer cohort. This information can be used to make informed decisions about pricing, marketing, and product development.

Types of Cohort Analysis

There are two main types of cohort analysis:

• Time-based cohort analysis: This type of analysis divides customers into groups based on the date they acquired the product or service. For example, you could create a cohort of all customers who signed up in the first month of a new marketing campaign.
  
• Event-based cohort analysis: This type of analysis divides customers into groups based on the date they completed a certain event, such as making a purchase or signing up for a newsletter. For example, you could create a cohort of all customers who purchased a product in the last 30 days.

How to Conduct Cohort Analysis

Here are the steps involved in conducting a cohort analysis:

1. Define your cohorts: Determine which characteristics you want to use to divide your customer base into cohorts. You may want to consider factors such as date of acquisition, channel of acquisition, product usage, or customer demographics.
   
2. Collect your data: Gather the relevant data for each cohort. This may include data on customer engagement, purchase history, churn rates, and customer support interactions.
   
3. Create a cohort analysis chart: Construct a chart that shows the performance of each cohort over time. This chart should include metrics such as churn rate, average order value, and customer lifetime value.
   
4. Analyze the results: Examine the chart to identify trends and patterns in customer behavior. This will help you understand which cohorts are performing well and which cohorts need improvement.
   
5. Take action: Based on your analysis, develop strategies to improve customer retention, increase engagement, and drive revenue.

Do you have a data-driven startup with strong cohorts? We would be happy to hear more about it.