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What is Cohort Analysis? Definition, Types and Examples

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Cohort analysis is a form of behavioral analytics that sorts customer data into smaller groups based on similar traits, and then analyzes the behavior of the groups to uncover patterns. Those patterns can inform strategic decision-making and product development. While primarily a tool employed by marketers, cohort analysis is also used for a variety of other business purposes, including new customer acquisition, customer retention, constituent engagement, and user adoption.

Table of Contents

The Importance of Cohort Analysis

A business’s customers are not a monolith—they’re individuals with individual preferences and behaviors. Sometimes looking at aggregate data about customers can mask issues that drive certain subsets of them to abandon products or services. A lot of businesses just look at top line numbers—the number of new customers or monthly sales, for example—to measure how they are doing. But this can mask underlying issues holding them back from even better performance.

Behavioral analytics is the use of qualitative and quantitative data to track and understand customer behavior. It is used for marketing, product development, forecasting, customer service, and security. Cohort analysis is a form of behavioral analytics that provides the ability to uncover insights about customers’ behavior in the context of their relationships with your business by sorting customers into groups, or cohorts, according to certain shared characteristics. For example:

• How long have they been a customer?
• How did they become a customer?
• What actions have they taken on your site?
• What size is their business or their net worth?

Learn about the difference between data analytics and data science .

Making the Case for Cohort Analysis

Why use cohort analysis? It can help you discover patterns of behavior and unearth contextual insights about actions you can take to help convert or retain certain groups of customers. It can also help you to do the following:

• Calculate customer lifetime value to determine where to focus efforts.
• Reduce churn by identifying customers most likely to abandon a product.
• Boost conversion by providing insight into factors that lead to purchases.
• Guide feature or product development by showing where customers’ needs are not being met.
• Improve customer service by identifying areas of friction or frustration.

How Does Cohort Analysis Work?

Cohort analysis breaks down customer data to find patterns that allow you to group customers together into cohorts that are more useful than aggregate data. Performing this analysis is done in several steps.

1. Goal Setting

As with any data analytics project, the first step in cohort analysis is to determine your goal. What actionable insight are you seeking? Asking the right question can be a challenge—it helps to think about your business’s larger strategic goals and how customer actions contribute to achieving them. For example:

• Which referral sources provide the most valuable customers?
• What products or services do small business customers buy?
• At what stage in the sales funnel do you lose the most prospective customers?
• Which product features lead to the most support calls from new customers?

2. Identify Data Sources

The next step is to identify which metrics will provide the data you need. Many sources provide insight into customer behavior, including CRM platforms, web/e-commerce analytics, survey data, and email marketing software. The more historical data you collect—and the more granular it is—the better the result.

The metrics may be collected in more than one source. A data dictionary can provide a more holistic picture of what is available. If you are using data from multiple sources, transform and clean it to make sure you get the best results—a data analytics platform or data quality tool will help ensure that you are working with accurate and complete data.

3. Define Your Cohorts

There are a variety of ways to group customers, from time-based attributes to attributes based on events or size, for example. You might group customers who made a purchase in the month before Christmas, for example, or customers who bought more than three products or services within a six-month period. Your goals will help determine these definitions.

4. Chart Your Results

The results of your cohort analysis can be displayed in a chart, graph, or table—many data analytics platforms have some form of cohort analysis functionality built into the software. A cohort analysis chart displays data using rows to capture each group and columns displaying the values of the action you are tracking over time.

The data can be read across to see how a cohort performs over time; from top to bottom, to see how different cohorts behave during a specific time period; or diagonally, for a snapshot of how they behave at a certain point in time.

Types of Cohort Analysis

The two most common cohort categories used in this type of analysis are acquisition cohorts and behavioral cohorts. Acquisition cohorts group customers by their first contact with your product or service; they are commonly used to measure retention or churn rates over a specified period of time.

Behavioral cohorts group customers by their behaviors related to your product—they can be used to measure things such as the characteristics of users who purchase a specific item or reach out to customer support. Within these two categories are several frequently used sub-categories of cohorts.

Event-Based Cohorts

This subset of behavioral cohorts groups customers based on a specific event or action—for example, all users who purchased an item during a Black Friday sale.

Time-Based Cohorts

This groups customers based on a specific timeframe—for example, all users who downloaded a fitness tracking app in January.

Size-Based Cohorts. 

This groups customers by size, such as net worth or number of employees—for example, all customers who are small businesses.

Funnel-Based Cohorts

This groups customers according to their stage in a funnel—for example, all the people who have put an item in their online shopping cart but have not started the checkout process.

Benefits of Cohort Analysis

Cohort analysis is a useful behavioral analytic tool for optimizing business and marketing efforts and deepening engagement with customers. Understanding customers and their behavioral triggers is valuable for growing your business and strengthening your existing customer base. It also can help you adjust to changes in behavior over time. Cohort analysis allows you to identify patterns of behavior as a customer’s relationship with you evolves, which gives you the ability to adjust your interactions to meet these changing relationships.

Cohort analysis can also provide an early warning system for potential issues with existing customers. By visualizing differences in how high-value and lower priority customers respond to certain actions and circumstances, you can quickly pivot if you see an action having a negative impact on your high-value customer retention rates.

Cohort analysis can also lead to Improved conversion rates. Tracking prospects acquired during a specific timeframe or from a certain source can help you determine if there are particular actions that make cohort members more likely to make a purchase.

Cohort Analysis Examples

Businesses can use cohort analysis in myriad ways. Here are a few examples of real-world and hypothetical applications.

Target’s Expectant Mothers Campaign

The Target chain used historical purchase data from women who signed up for its baby registry to determine the patterns of buying that might indicate a customer’s pregnancy. Using that information, the chain began sending coupons for baby-related products to customers with similar purchase patterns. While the analysis proved accurate, it also unnerved the cohort—based on feedback, Target instead began to deliver personalized coupons mixed in with other offers.

Airline Priority Lounge Access

Several airlines have recently made changes to their lounge access policies based on cohort analysis. Research has shown that lounge access is important to the airlines’ most profitable segment of travelers, frequent business travelers—however, branded credit cards opened up lounge access to greater numbers of people, resulting in overcrowding and less satisfaction from priority users. As a result, several airlines tightened the rules on lounge access to improve the experience for their most frequent fliers and avoid the likelihood of this cohort switching to a different brand.

Digital App Downloads

App developers often use cohort analysis to track downloads and daily usage, which helps them determine whether adjustments need to be made to improve new-user retention and decrease churn. It can also help fine-tune pricing for premium content and in-app purchases.

Bottom Line: Cohort Analysis

Cohort analysis makes it possible to separate growth metrics from engagement metrics—rather than looking at high level numbers, it lets you drill down into the details to see if certain segments of your audience are performing more poorly than others. It can also provide clues about why this is happening.

As part of their larger analytics strategy, businesses can use cohort analysis to optimize their marketing and outreach, better target customers with personalized campaigns, forecast and resolve issues based on pattern behavior, and improve conversion rates.

Read What is Predictive Analytics? to learn more about the different analysis tools in enterprise toolboxes.

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Cohort Studies: The Key to Longitudinal Research Success

Introduction

What is meant by cohort study, types of cohort studies, the importance of cohort studies in public health and policy.

• Advantages & disadvantages of cohort studies

The pursuit of scientific knowledge often involves tracking changes over time, examining causes and effects, or exploring relationships between variables.

One of the most effective methodologies for doing so in fields like medicine, psychology, sociology, and education is the cohort study. This research design allows for in-depth, longitudinal investigations that can reveal valuable insights unattainable through other research methods.

The cohort study is an important type of longitudinal research , offering a robust framework for answering research questions that demand extensive time-based observations.

In this article, we will explain what cohort studies are, the types of cohort studies, and the various advantages and disadvantages of employing this research methodology.

A cohort study is an observational research method that involves following a specific group of people, known as a cohort, over a defined period. This form of study is commonly used in various scientific fields to examine the relationship between different variables and outcomes, particularly when studying the long-term effects or trends associated with a certain exposure, behavior, or condition.

A "cohort" is a group of individuals who share a common characteristic or experience within a defined period. For example, birth cohorts consist of individuals born in the same year or within a range of years. Examining a birth cohort can be particularly useful for studying lifetime trajectories and generational differences, as they allow researchers to observe how a specific group's health, behavior, and other variables change over time. The concept of a cohort and its operationalization in research is thus foundational to this type of study.

What is a cohort study best used for?

Cohort studies are ideal for observing the longitudinal impacts of different factors. For instance, they can be used to study the long-term health effects of certain diets, the societal impacts of educational policies, or the progression of diseases. Cohort studies are particularly valuable for:

• Understanding the temporal sequence of events : Since cohort studies observe participants over an extended period, they can clarify which variable precedes another, lending weight to causal interpretations.
• Exploring rare or complex phenomena : Some conditions or outcomes are rare and may require a longer observation period to gather sufficient data for meaningful analysis .
• Gauging the effects of multiple variables : Cohort studies can track a wide range of variables simultaneously, offering a multifaceted view of the phenomenon under study.

What is an example of a cohort study?

Perhaps one of the most well-known cohort studies is the Framingham Heart Study. Initiated in 1948 in the town of Framingham, Massachusetts, this study set out to identify the common factors that contribute to cardiovascular disease.

Starting with an initial set of several thousand adult participants, the study expanded over the years to include second and third generations from the same families. The study has provided unprecedented insights into the risk factors for cardiovascular disease, including the role of cholesterol, blood pressure, and smoking.

It also broke ground by incorporating both men and women as well as individuals from diverse age groups, thereby giving a more complete picture of cardiovascular health across a broad spectrum of the population. The Framingham Heart Study serves as an exemplar of how cohort studies can offer deep insights into critical public health issues over an extended period.

By focusing on a dedicated cohort and tracking multiple variables over time, this study has yielded invaluable longitudinal data that has greatly influenced public health policies and medical practices.

Understanding the different types of cohort studies is essential for choosing the right approach for your research. Each type comes with its own set of advantages and challenges, affecting everything from the study's timeline to its reliability. Here, we'll examine dynamic, open, and closed cohort studies, as well as prospective and retrospective cohort studies.

Dynamic, open, and closed cohort study

Before looking at prospective and retrospective studies, we should note that cohort studies can also be classified based on the flexibility of their cohort membership.

In a dynamic cohort study, new participants can be added over the course of the study. This is often useful when studying conditions or behaviors that have high turnover rates, such as employment in a particular industry.

An open cohort study is a variation of the dynamic cohort study. Here, subjects can enter or leave the study at different times. This is particularly useful in long-term studies where attrition rates could be high.

In contrast, a closed cohort study starts with a fixed population that is followed over time. No new participants are added, and those who leave the study, often due to death or withdrawal, are not replaced. Closed cohort studies are advantageous when studying a very specific population or condition.

Prospective cohort study

A prospective cohort study involves selecting your cohort and then following them into the future, collecting data as you go. This is often considered the gold standard of cohort studies for several reasons:

• Causality : By following the cohort over time, prospective studies offer a better framework for establishing causal relationships between variables.
• Control over variables : Researchers have more control over the variables they collect, ensuring that data is consistent and tailored to the study's aims.
• Reduced bias : The forward-looking nature of prospective studies can reduce certain types of bias, such as recall bias.

However, prospective cohort studies do come with drawbacks:

• Time-consuming : These studies often take years, if not decades, to complete.
• Resource-intensive : The long duration and extensive data collection requirements make prospective studies costly.
• Attrition : Over time, you may lose participants due to various reasons, impacting the study's validity.

Retrospective cohort study

Retrospective cohort studies, by contrast, look backward in time. Researchers use existing data to trace back the outcomes and exposures among their cohort. The benefits of this approach include:

• Speed : These studies can often be completed much faster since all the data already exists.
• Cost-effective : Utilizing existing data reduces the costs associated with data collection.
• Feasibility : For some research questions , the events of interest may have already occurred, making a retrospective approach the only viable option.

However, they're not without disadvantages:

• Data quality : Researchers have to rely on the available data, which might not be as complete or as tailored to their research question.
• Limited control : The inability to control how and what data was collected can limit the study’s scope.
• Bias risks : The use of existing data can introduce various forms of bias , including selection bias and information bias.

Understanding the nuances between these different types of cohort studies researchers planning to conduct a longitudinal study . Whether you opt for a dynamic, open, or closed cohort design, or choose between a prospective or retrospective approach, your decision will impact the study's length, complexity, and overall outcomes.

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The impact of cohort studies extends beyond the academic sphere into public health and policy, making them indispensable tools in shaping the well-being of societies. The ability of a cohort study to capture complex interplay between variables over time provides researchers and policymakers with unique insights that other study designs may not offer.

For example, findings from cohort studies have been instrumental in highlighting the adverse health effects of smoking, leading to widespread public health campaigns and legislative actions to reduce tobacco use.

Similarly, data from cohort studies have significantly influenced nutritional guidelines, emphasizing the importance of a balanced diet and regular exercise to mitigate the risk of chronic diseases like diabetes and cardiovascular disorders.

But the reach of cohort studies is not confined to healthcare. They have been used to assess the long-term impacts of educational programs, contributing valuable data that help reform curriculum and teaching methodologies.

Cohort studies can also be applied to environmental issues; long-term data can provide compelling evidence of the impact of pollution or climate change on health, thereby influencing policy decisions related to environmental protection and sustainable development.

However, the influence of cohort studies on policy is not without its challenges. The lengthy duration of many cohort studies means that findings may not become actionable until years or even decades after the research begins. Additionally, while a cohort study can offer strong suggestions of causality, they do not provide the definitive proof that comes from randomized controlled trials. This can sometimes make it difficult to incorporate cohort study data into policy debates that require unequivocal evidence.

Nonetheless, the cumulative impact of cohort studies on public health and policy is profound. They offer a nuanced understanding of long-term effects and relationships between variables, providing a strong foundation for interventions and policies designed to improve quality of life over the long term.

Advantages & disadvantages of cohort studies

A cohort study can be a powerful tool in the research arsenal for various reasons, yet it also comes with a unique set of limitations. Understanding both helps inform researchers who are contemplating the use of this method in their work.

Longitudinal data

One of the key strengths of cohort studies is their ability to establish temporal sequences and, consequently, stronger suggestions of causality. Unlike cross-sectional studies , which offer only a snapshot in time, cohort studies track changes over extended periods.

This allows researchers to identify which variables precede others and offers a stronger foundation for drawing causal inferences. Furthermore, cohort studies are exceptional for studying the development of diseases and conditions that manifest over a long duration, or for understanding the lifelong impacts of certain exposures or interventions.

However, the longitudinal nature of cohort studies is both a strength and a weakness. Following participants over an extended period can be logistically complex and financially taxing. The investment in time and resources is often significant, which can be a barrier for researchers with limited funding.

Managing cohorts

The longer the study, the greater the risk of participant attrition, which can compromise the results. Participants may move, lose interest, or pass away, making it challenging to maintain a stable study cohort over time.

Moreover, cohort studies often require large sample sizes, especially when studying rare outcomes. As the sample size grows, so does the complexity of managing the data and the cost of the study. Therefore, the trade-off between the study's comprehensiveness and its feasibility becomes a key concern.

Quality of data collection and analysis

Data quality is another area that researchers conducting cohort studies must consider. On one hand, researchers have the opportunity to carefully plan their data collection methods, optimizing for quality and relevance to the research question .

On the other hand, especially in retrospective cohort studies, researchers are sometimes limited to using existing data. This can introduce challenges such as data inconsistency, as researchers have no control over how the original data was collected.

Another consideration is the risk of bias . While prospective cohort studies are generally less susceptible to certain biases like recall bias, they are not entirely immune to errors in measurement or interpretation. Retrospective cohort studies, however, are often more susceptible to these issues because researchers rely on pre-existing data, which may contain unrecognized biases.

Cohort studies offer a robust framework for investigating complex questions over time but come with their own set of methodological and logistical challenges. The choice to use this approach should be carefully considered in the context of the research question, the available resources, and the potential limitations that could affect the study's outcome and interpretation.

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