What is Cohort Analysis?
The term Cohort refers to a group of people or data that exhibit or possess similar characteristics or behavior over a specific period. Eg: The number of students graduating, prospects downloading an app for the first time, or users subscribing to an OTT platform forms a division of Cohort.
Cohort Analysis is studying or concentrating on a set of user/customer groups or Cohort types based on their shared characteristics to understand their actions and behavioral patterns over a defined period. Cohort Analysis helps in segregating huge data or customers personas based on specific attributes and makes the study and analysis easier. It allows you to form conclusions or to ask more specific questions around the problem statement and helps you to take appropriate decisions followed by desired actions that will reduce or bring down the customer churn and increase profitability.
Customer Retention & behavioral analysis is one of the key elements in business that requires a lot of effort and oftentimes turns out to be a nightmare for a lot of business entities. Hence, companies put in a great amount of effort & resources to understand the customer journey and reason for what makes or breaks the game of Customer Retention.
Cohort Analysis plays an important role in understanding the behavior of different sets of customers or we say Cohorts to improve retention and sustainability.
What are the types of Cohort Analysis?
This type of Cohort segregates users based on their acquisition time and date of the service or when they signed up for product/service. User acquisition can be tracked daily, weekly, or monthly depending on the product or service.
Example: OTT platforms can track daily acquisition on the face of daily new subscriptions.
Behavioral Cohort divides the user base into groups based on the activities or actions they undertake while using the product or service during a given time. These cohorts can further be analyzed based on their demographics as per the requirements.
Example: Users navigating through the eCommerce stores, purchases they make, products they re-order, etc.
What are the applications & importance of Cohort Analysis?
As we deep dive into the subject and mark upon the usability & importance of Cohort Analysis, it could be used in different pockets of businesses across the industries.
Cohort Analysis in Marketing can help you analyze the success or trend of Digital Ad Campaigns, Response to Promotion Page, Target Audience, and Social Media Channels. You can understand the patterns and responses of different cohorts (group of customers) and jot down a successive marketing plan accordingly.
Assessing compensation and promotion of employees is a tricky business and requires a lot of time and effort. By using data-driven Cohort Analysis you can scrutinize each employee’s detailed work, and also could co-relate the work and effort of other employees or employee groups, ensuring an ethical reward system.
With the help of Cohort Analysis online stores & websites can understand their campaigns by analyzing return users, average time spent, footprints of users from different locations, and comparison between demographics.
Where gaming companies can figure out the behavioral patterns & demographics of new users and monitor metrics like Daily Active Users, Average Revenue Per User, Average Revenue Per Paying User, and Monthly Active Users.
Cohort Analysis is widely used in the following industries & verticals:
- Digital Marketing
- Online Gaming
- Human Resources
- Software as service companies (SaaS)
Example of Cohort Analysis
Let’s take a group of customers who signed up for an eCommerce grocery store in January 2020 in a particular city. Cohort Analysis for retention would help the stores to understand how many new customers they have and how many of their old customers have unsubscribed from the service on a weekly, monthly, or quarterly basis. It’ll also give insight into which group of customers has given them the highest revenue and what time range.
How to use or perform Cohort Analysis
Step 1: Extracting raw data
Considering the above example, first, we need to pull the required data from the database about customers, revenue, sales, demographics, time & date, and streamline the same into a spreadsheet or similar software.
Step 2: Creating cohort identifier
Now we’ll put these customers into different groups based on their date of subscription, first purchase, amount of purchase or last purchase, etc to perform Cohort analysis.
Step 3: Calculating lifecycle stage
Once we identify the cohort, we now need to determine the “lifecycle stage” at which every event happened for the particular cohort. For example, if a customer made his first purchase in January, and the second purchase in April. Their first purchase would be in the “Month 1” lifecycle stage and their second purchase would be in the “Month 4” lifecycle stage. These stages can differ depending on the requirements and objectives of the company.
Step 4: Creating tables and graphs
One can easily analyze and understand the data with the help of visual representations of user data and draw conclusions or do comparisons between different cohorts.
Understand Retention Cohort Analysis
Many companies have been using Cohort Analysis to improvise their customer acquisition techniques and onboarding process ensuring lower churn rates & improved customer retention. Cohort Analysis aids them to understand customer behavior and reach the root cause of why customers are abandoning their business and moving to others. To analyze the retention rate of users, the metric is used called Customer Retention Rate which is calculated with the help of key factors such as the number of new customers acquired during a period (N), customers at the beginning (S), and end of the period (E).
Customer Retention Rate (CRR) = ((E-N)/S) X 100
A higher CRR denotes strong customer loyalty or repeat customers. Comparing CRR for different user groups/ cohorts can help the business to take required actions to increase customer retention & satisfaction.
Key Metrics for Retention Cohort Analysis
Note: following metrics are special only for the consumer market, and they might differ from industry to industry.
Repeat Rate or Repeat Customer Rate helps in the analysis to identify users who interact or purchase with the business regularly compared to other users who drop out after the first or second purchase. Depending on the calculation companies then can come up with different policies to reward or engage these repeat customers.
Orders per customer
This metric allows businesses to understand the number of orders each customer or a customer group is making in a particular time frame. It’s also an indicator of good retention or loyalty rate for given products or services.
Time between orders
Companies can use this metric to measure the average time/duration between the orders placed by the customer in a given period. The value or time frame differs depending on the service or product offering. For example, the time between two Swiggy or Zomato orders could be a couple of hours or a day and on the other hand, it could be months for an online apparel store.
Average order value
This metric can help businesses to identify high-value customers and loyalists. Based on this metric companies can introduce programs and promotions for these customer groups or cohorts and win over their trust and loyalty.
Tools used for Cohort Analysis
Following are the tools that can be used to perform Cohort Analysis