Cohort analysis is a type of behavioral analytics in which you take a group of users, and analyze their usage patterns based on their shared traits to better track and understand their actions. A cohort is simply a group of people with shared characteristics.
Cohort analysis allows you to ask more specific, targeted questions and make informed product decisions that will reduce churn and drastically increase revenue. You could also call it customer churn analysis.
What is Cohort Analysis?
Cohort Analysis is a form of behavioral analytics that takes data from a given subset, such as a SaaS business, game, or e-commerce platform, and groups it into related groups rather than looking at the data as one unit. The groupings are referred to as cohorts. They share similar characteristics such as time and size.
Companies use cohort analysis to analyze customer behavior across the life cycle of each customer. In the absence of cohort analysis, businesses may experience difficulties in understanding the life cycle that each customer goes through over a given timeframe. Businesses use cohort analysis to understand the trends and patterns of customers over time and to tailor their offers of products and services to the identified cohorts.
A business sees a lot of data coming in on a daily basis. Analyzing such large volumes of data is not only complex but also an expensive task that requires dedicated staff. However, a business can break customers down into more manageable and actionable cohorts.
Types of Cohorts to Analyze
Cohorts can be grouped into the following categories:
1. Time-Based Cohorts- Time-based cohorts are customers who signed up for a product or service during a particular time frame. Analyzing these cohorts shows the customers’ behavior depending on the time they started using a company’s products or services. The time may be monthly or quarterly, depending on the sales cycle of a company.
For example, if 80% of customers who signed up with the company in the first quarter stick with the company in the fourth quarter but only 20% of customers who signed up in the second quarter stick with the company up to the fourth quarter, it shows the Q2 customers were not satisfied. The company could’ve overpromised during Q2 promotions, or a competitor may be targeting the same customers with better products or services.
Analyzing the time-based cohorts helps in looking at the churn rate. For example, if customers who signed up for the company’s product in 2017 churn out faster than those who signed up in 2018, the company can use this data to find out the cause. It could be that the company is not keeping up with its promises, a competitor offers better quality products, or a competitor is directly targeting your customers with better incentives.
For a SaaS business, the churn rate tends to be high at the start of a given timeframe, and drops as the customers get used to the products. Customers who stay longer with the company tend to love the product and churn at a lower rate than at the start of a time frame. In the absence of cohorts, a company may not identify the exact cause of a high number of customers abandoning the products within a given timeframe.
2. Segment-Based Cohorts- Segment-based cohorts are those customers who purchased a specific product or paid for a specific service in the past. It groups customers by the type of product or level of service they signed up for. Customers who signed up for basic level services might have different needs than those who signed up for advanced services. Understanding the needs of the various cohorts can help a company design tailor-made services or products for particular segments.
A SaaS company may provide different levels of services depending on the purchasing power of the target audience. Analyzing each level helps in determining which kind of services fit particular segments of your customers.
For example, if the advanced level customers churn at a much faster rate than basic level services, that is an indication that the advanced services are too expensive or that basic level services simply better meet the needs of most customers. Understanding what customers are looking for in a package helps the company in optimizing its notifications to focus on relevant push emails that customers will open and read.
3. Size-Based Cohorts- Size-based cohorts refer to the various sizes of customers who purchase a company’s products or services. The customers may be small and startup businesses, middle-sized businesses, and enterprise-level businesses.
Comparing the different categories of customers based on their size reveals where the largest purchases come from. For categories with the least purchases, the company can review any issues with the product and service offering and brainstorm areas for improvement that can boost the level of sales.
In a SaaS business model, small and startup businesses usually churn at a higher rate than enterprise-level companies. Small and startup businesses may have a small budget and be testing low-priced products to see what works for them. Enterprise-level businesses have a larger budget and tend to stick with a product for a longer period of time.
benefits of cohort analysis
- Determine business health. A great indicator of a healthy business is increasing revenue even if you aren’t acquiring new customers. that cohort analysis “can help you determine which cohorts/groups of customers are contributing the most to revenue.” This, in turn, allows you to focus on upselling other products or services to them.
- Understand customers better-Cohort analysis allows businesses to gain a deeper understanding of their customers by tracking their behavior over a period of time. This can help you identify patterns and trends that may not be immediately apparent from looking at vanity metrics.
- Enhanced customer segmentation- By dividing user groups and creating specific cohorts, businesses can create more targeted and effective marketing campaigns and offer personalized customer experiences.
- Increased customer retention.-cohort analysis helps by analyzing retention rates and identifying potential churn risks. With this information in hand, you can take proactive steps to improve customer experiences.
- Optimize your app for increased interest-You can use cohort analysis to optimize the user experience and increase customer lifetime value by identifying trends and patterns in the customer lifecycle
How to Use Cohort Analysis
Cohort analysis tools for marketing professionals, provided by services such as Google Analytics, generate a cohort analysis report in the form of a cohort table with the website’s acquisition date range by user retention rate. Examples of popular metrics by which to analyze a cohort include: conversion rates, goal completions per user, page views per user, revenue per user, sessions per user, session duration per user, and transactions per user.
Advanced cohort analysis tools also provide further segmenting options, such as acquisition cohorts vs behavioral cohorts, or mobile users vs desktop users, to further narrow down data groups. The date range and cohort size are fully customizable and can be adjusted depending on the scope of the project.
The steps typically involved in the analysis process include:
- extracting raw data: raw data is pulled from a database and exported into spreadsheet software, where user attributes can be joined and further segmented.
- creating cohort identifiers: group user data into different buckets, such as date joined, date of first purchase, graduation year, all mobile devices at a particular place and time, etc.
- calculating lifecycle stages: once users have been divided into cohorts, the amount of time between events attributed to each customer is measured in order to calculate lifecycle stages.
- creating tables and graphs: pivot tables and graphs create visual representations of user data comparisons, and help calculate the aggregation of multiple dimensions of user data.
Steps
1-Determining the objective- Like most analyses, cohort analysis also needs to define certain objectives that it has to fulfill. Here, the examples could be finding the revenue generated by a website, or building something complex, like strategizing for improvements to the webpage traffic.
2 – Carving out the metrics- After identifying the objectives of the analysis, the analysts must look for appropriate metrics. The data is separated using metrics that also define the features of cohorts. Some simple examples of metrics are the number of retained customers, the number of tickets sold, the per-user fee generated, etc.
3 – Determining the necessity- If the study is about finding customer retention rates on a webpage, the analysts must appropriately determine which customer cohort would best serve the study’s objective. For example, the available options could range from certain old customers, new customers, one-time customers, etc.
4 – Conducting the analysis- After diligently carrying out the above-mentioned steps, the analysts can start their analysis. They can repeat the process with the same example. The web page owner can ascertain how the webpage has fared in different metrics over a period of time. Such metrics could include customer views, customer retention, call to action, etc.
The analysts must carefully determine the research’s actionable insights from the analysis. As a result, the research will always give a true picture. The business, thus, must not hold any biases that could hinder the objectiveness of the findings.
5 – Preparing and presenting the results– The next step is to record the results of the analysis in an appropriate format. They could be in the form of charts, tables, or summarized text. The results of the analysis must be communicated to the people likely to be influenced by the results. The cohort analysis helps in customer retention besides onboarding the new ones.
Cohort Analysis vs Segmentation
Cohort analysis, when defined, sounds similar to segmentation. Though these terms imply dividing customers based on specific criteria, they are still different.
While segmentation deals with classifying consumer groups irrespective of time, cohort analysis deals with classifying consumers into different groups for a defined period. When both segmentation and cohort analysis are applied, businesses get an opportunity to identify friction points within a time frame, which might lead to risk aversion.
FAQs
Why is cohort analysis important?
Cohort analysis provides insights into user or customer behavior patterns, retention rates, and overall performance over time. It helps businesses identify trends, optimize marketing strategies, and enhance user experience by understanding how different cohorts respond to various factors.
What types of cohorts can be analyzed?
Cohorts can be based on various criteria, such as the acquisition source (marketing channel), sign-up date, geographic location, product usage patterns, or any other relevant segmentation that helps in understanding user behavior.
How is cohort analysis different from traditional analytics?
Traditional analytics often focuses on aggregate metrics, while cohort analysis drills down into specific groups over time. Cohort analysis provides a more granular understanding of user behavior, enabling businesses to make data-driven decisions on a more personalized level.
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