Cohort Analysis is a subset of behavioral analytics. Cohort means “a class of people with uncommon characteristics.” In ancient Rome, a military unit consisting of men was called cohorts. The extended implication of this word infers any group of people with a standardly statistical factor.
While cohort refers to time-dependent group examinations, segment refers to group examinations without time dependency.
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Example of Cohort Analysis
In finance, cohort analysis can be defined as a reasoning scale to divide user data and study it. Businesses are using cohort analysis extensively to know their customers well. E-commerce businesses have easy access to their customer’s purchase history through online portals.
The information captured relates to customers’ purchases, returns, consumption, positive or negative reviews, and shopping experiences. The cohort analysis is done only on time-dependent variables grouped together. Any time-independent variables are called segments.
Another example is when acquired users are tracked and compared across different periods.
Check out this illustration:
In this example, a webpage owner wants to evaluate the traffic on his webpage and the revenue it generates. Following are some denotations:
- Series 1 – New users revenue
- Series 2 – Old users revenue
- Series 3 – Monthly revenue (Add series 1 and series 2)
He performs an analysis by segregating cohorts on a timely basis. He makes the following classifications after examination –
- Cohorts in the time-period August to October have resulted in the highest revenue in the new-user segment (as a proportion of monthly income)
- Cohorts’ analysis in the time-period January to March has the lowest revenue in the new-user segment.
- Despite higher revenue from new-user cohorts, monthly income did not rise because of low payments from old-user affiliates.
Cohort analysis is a valuable analytical tool used in various industries to understand and track the behavior of specific groups of customers or users over time. It helps businesses gain insights into customer retention, engagement, and other key metrics. Here are some real-life examples of cohort analysis:
- E-commerce Retention Analysis:
- Scenario: An online retailer wants to assess the long-term value of its customers and identify trends in customer retention.
- Cohort: Customers who made their first purchase in a specific month.
- Analysis: The retailer creates cohorts based on the month of the first purchase and tracks the percentage of customers from each cohort who make repeat purchases in subsequent months. This helps them identify which cohorts have higher retention rates and may require targeted marketing efforts.
- Subscription-based Services:
- Scenario: A subscription-based streaming service wants to analyze user engagement and churn rates.
- Cohort: Users who signed up for the service during a specific promotional campaign.
- Analysis: By creating cohorts based on the campaign sign-up dates, the service can track how user engagement and retention differ among these cohorts compared to users who signed up at other times. This informs marketing strategies and content recommendations.
- Mobile App User Behavior:
- Scenario: A mobile app developer wants to understand user engagement and feature adoption.
- Cohort: Users who installed the app during a specific month.
- Analysis: Cohorts are created based on the month of app installation, and user behavior (such as daily or weekly active users, in-app purchases, or feature usage) is tracked over time. This helps the developer identify which cohorts show the highest engagement and which features drive retention.
- Financial Services:
- Scenario: A bank wants to assess the performance of its new credit card offering.
- Cohort: Customers who applied for and received the new credit card during a specific quarter.
- Analysis: By tracking the spending behavior, payment history, and churn rates of these cohorts, the bank can evaluate the success of its credit card launch, identify potential issues, and refine its credit card offerings.
- Online Education Platforms:
- Scenario: An online education platform wants to analyze course completion rates and learner engagement.
- Cohort: Learners who enrolled in a particular course in a specific month.
- Analysis: Cohorts are created based on the course enrollment month, and the platform tracks metrics like course completion rates, time spent on the platform, and learner satisfaction for each cohort. This helps in optimizing course content and delivery.
- SaaS Customer Churn Analysis:
- Scenario: A software-as-a-service (SaaS) company wants to reduce customer churn.
- Cohort: Customers who signed up for the SaaS product during a specific quarter.
- Analysis: By analyzing cohorts’ usage patterns, customer support interactions, and product feedback, the company can identify common reasons for churn and implement strategies to improve customer retention.
Cohort analysis provides valuable insights into how different groups of customers or users behave over time, enabling businesses to tailor their strategies, improve customer experiences, and drive growth. These real-life examples demonstrate the versatility of this analysis across various industries and use cases.
Performing Cohort Analysis
Below is a step-by-step guide to cohort analysis:
#1 – Determine the Objective of the Analysis
Like every other analysis, you need to determine the goal of cohort analysis.
For example- Find the revenue generated by a website. Use cohort to solve complex issues of how to strategize to improve the webpage.
#2 – Carve out the Metrics that Associate with the Objectives
After having the determined objective, the user should look for appropriate metrics that will improve the success rate of the cohort analysis.
For example- The number of retained customers, the number of tickets sold, the per-user fee generated, etc.
#3 – Determine important Cohorts
Determine user retention rates on a webpage based on the cohort group selected. You must decide which customers to identify as a cohort between certain groups like old customers, new customers, one-time customers, etc.
This analysis using the above steps will give help in giving insights that will help formulate the right strategies for new customer acquisition and existing customer retention.
Benefits of using Cohort Analysis
Cohort analysis helps open up new ways to dig into the purchasing habits of customers. It is of great help to know what products are being preferred by the customers, their tastes and bestsellers, etc.
#1 – Know your customers well
This analysis is of great help to deep dive into your customer’s tastes, moods, and likes or dislikes. the comments or reviews help understand the pulse of the market. The future forecast becomes easier with higher accuracy.
#2 – Manage inventory
Keeping investor costs low can help you save a lot. Managing the stock of products with higher sales helps bridge the gap between demand and supply. Stock can be adjusted depending upon the buying habits of customers. Seasonal fluctuations can also be met with accurate predictions through historical data.
#3 – Strategy formulation
Right strategies at the right time can become winning shots. Data of which type of products or services are on-demand, a shift in demand, and a shift in trends are deep insights. these insights can be used further to formulate and implement strategies to win the game.
#4 – Customer acquisitions and retention
Knowing your customers well will give help you with customer acquisition and retention. New customers may be acquired with new features or products in demand and existing customers may be retained with a personal touch or improved services. Offers and discounts may be customized to suit the needs of your existing customers.
How companies use Cohort Analysis to increase their profits?
Cohort analysis is a powerful tool used by many companies to understand and maximize customer or user lifetime value. While it plays a crucial role in profitability, it’s challenging to attribute specific profits solely to cohort analysis. Nonetheless, I can provide you with examples of how some companies have leveraged cohort analysis to improve their strategies and, in turn, increase profitability.
- Netflix:
- Cohort Used: Netflix tracks user cohorts based on their sign-up dates and content preferences.
- Profit Impact: By analyzing these cohorts, Netflix can optimize content recommendations, retention efforts, and content creation. While the exact profit attributable to cohort analysis is not disclosed, it significantly contributes to Netflix’s massive subscriber base and revenue growth.
- Amazon:
- Cohort Used: Amazon uses cohort analysis to understand the behavior of Prime members and first-time shoppers.
- Profit Impact: Cohort analysis has contributed to the growth of Amazon Prime, which not only increases customer loyalty but also encourages higher spending. Amazon’s profitability has consistently improved over the years, partly due to Prime’s success.
- Spotify:
- Cohort Analysis Use: Spotify tracks user cohorts to personalize playlists and music recommendations.
- Profit Impact: While Spotify’s profitability is influenced by various factors, cohort analysis plays a role in increasing user engagement and paid subscriptions, ultimately contributing to the company’s revenue and profit growth.
- Airbnb:
- Cohort Used: Airbnb segments hosts and guests into cohorts based on their registration dates.
- Profit Impact: By understanding the behavior of different cohorts, Airbnb can tailor its marketing and incentive programs. Cohort analysis helps optimize host and guest experiences, resulting in higher booking volumes and revenue. Cohort analysis contributes to Airbnb’s overall profitability, but specific numbers are not publicly disclosed.
- Facebook:
- Cohort Used: Facebook utilizes cohort analysis to study user engagement patterns and ad performance.
- Profit Impact: Although Facebook’s profitability is linked to its advertising revenue, cohort analysis helps improve ad targeting, which in turn attracts more advertisers and drives higher ad rates. Facebook’s robust profitability is partly due to its effective ad targeting strategies.
- Google:
- Cohort Used: Google uses cohort analysis to understand user behavior across its various services, such as Search and YouTube.
- Profit Impact: Cohort analysis aids in refining search algorithms and personalized content recommendations, leading to increased user engagement and ad revenue. Google’s parent company, Alphabet, has seen significant profits driven by these data-driven insights.
- Uber:
- Cohort Used: Uber tracks rider cohorts to improve user experiences and loyalty.
- Profit Impact: While Uber’s profitability has varied over the years, cohort analysis helps identify factors that influence rider retention and driver engagement. Optimizing these aspects ultimately contributes to Uber’s profitability.
- Lyft:
- Cohort Analysis Use: Lyft uses cohort analysis to understand rider and driver behavior.
- Profit Impact: Similar to Uber, Lyft’s profitability is influenced by rider and driver retention and engagement. Cohort analysis contributes to improving these metrics, thereby impacting the company’s profitability.
- Shopify:
- Cohort Used: Shopify employs cohort analysis to understand merchant behavior and e-commerce trends.
- Profit Impact: While Shopify’s profitability stems from merchant subscription fees, cohort analysis helps optimize its platform, attract more merchants, and increase transaction volumes. This leads to higher revenue and profitability for the company.
- Tesla:
- Cohort Used: Tesla employs cohort analysis to understand buyer demographics and preferences.
- Profit Impact: While cohort analysis isn’t the sole factor in Tesla’s profitability, it contributes to the company’s marketing and product development strategies. By tailoring products to specific cohorts, Tesla has seen strong demand and profitability in various markets.
In each of these cases, cohort analysis helps companies make data-driven decisions that impact their profitability. However, it’s important to note that profitability depends on a multitude of factors beyond this analysis, including market conditions, competition, and company-specific strategies. Companies typically do not disclose specific profit shares attributed solely to this analysis. Nevertheless, this analysis remains an essential tool for optimizing customer/user experiences and driving growth.
Conclusion
This analysis helps businesses understand the trends, competition, demand, and supply gaps. It will help them strategize to maximize revenue/profits and mitigate risks.