Growth
Cohort Analysis: A Startup's Secret Weapon
Written by
Lineke Kruisinga
Published on
October 21, 2024
Written by
Published on
When a VC fund is considering an investment in a startup, one of the first things they’ll ask for is a cohort analysis. It’s more than just a standard metric—it’s a powerful tool for understanding how user behavior evolves over time. As a company grows and refines its product, it continuously tests new strategies, which means the experience of early adopters can be vastly different from that of customers who come on board later. Cohort analysis allows entrepreneurs and investors to track these differences, providing a clearer picture of whether a company is truly improving and scaling effectively.
Cohort analysis is a behavioral analytics technique that groups users or customers into distinct cohorts based on shared characteristics. These cohorts might be defined by the time they started using a product, the specific service they purchased, or even their company size. By analyzing the behavior of these cohorts over time, businesses can identify trends, pinpoint problems, and optimize their offerings to better serve different customer groups.
For example, a cohort might consist of all the users who signed up for your product in January, or those who completed a specific action in the app during their first week. Cohort analysis involves comparing the behavior and outcomes of these different groups over time. You could look at how the January cohort stacks up against the February cohort in terms of retention, engagement, and conversion rates, or how users who took that first-week action compare to the rest of your user base. This approach lets you spot trends, identify what’s working, and make data-driven decisions to optimize your growth strategy.
Without cohort analysis, businesses risk treating all customers the same, which can result in missed opportunities to understand customer retention and lifetime value. Using this tool, companies can answer critical questions: Why are some customers leaving sooner than others? How does customer behavior change over time? Which customer segments are the most loyal or profitable?
Take an e-commerce site as an example. Imagine it brings in 500 new buyers each month. Most pitch decks would only show this raw growth number, but cohort analysis dives deeper, comparing distinct groups of users—such as those who joined in the first month versus the third month—to uncover meaningful insights. It reveals patterns in customer retention, engagement, and revenue generation, offering a more nuanced view of a startup’s progress and potential for growth. Let’s explore how this works and why it’s a vital part of any data-driven pitch.
Cohort analysis allows businesses to break down overwhelming amounts of data into more manageable groups, making it easier to identify trends and patterns. It’s particularly helpful for tracking customer retention, identifying churn, and improving customer lifetime value.
By grouping customers into cohorts, businesses can:
Cohort analysis helps you see how revenue or user retention shifts over time by comparing different groups of users (cohorts) against each other. This gives you a clear picture of how behavior changes across these groups, letting you spot trends and make smarter decisions.
Image: Almanac.io
Understanding Cohort Analysis like a PhD: a founder's cheat code to startup financials
Investors want to back companies that understand their customers and can predictably grow. Cohort analysis allows you to go beyond vanity metrics like total user count or gross revenue, providing a detailed view of how your customer base evolves. It helps answer critical investor questions: Are customers coming back? Are they spending more over time? What actions lead to longer retention? Preparing these insights and presenting them clearly can significantly strengthen your pitch, making your company more attractive to potential investors.
Cohort analysis is a valuable tool for businesses looking to dig deeper into customer behavior and understand why users make certain choices in your app. Here are six key benefits of conducting cohort analysis:
Cohort analysis is a great way to get a pulse on how healthy your business really is. Even if you're not acquiring new customers, increasing revenue from existing users is a strong indicator of growth. It helps you figure out which customer groups are driving revenue, allowing you to focus on upselling to them.
Want to really understand your customers? Cohort analysis lets you track their behavior over time, making it easier to spot patterns and trends that might not be obvious at first glance. This goes beyond surface-level metrics and allows you to get to the heart of what’s driving user engagement.
By dividing your users into specific cohorts, you can create more targeted marketing campaigns. This means you're delivering a more personalized experience to your customers, which leads to better engagement and satisfaction.
Keeping customers around is just as important as acquiring them in the first place. Cohort analysis is a key tool for analyzing retention rates and spotting potential churn risks early. Armed with this data, you can take proactive steps to keep your users happy.
Cohort analysis helps you pinpoint trends in customer behavior, so you can tweak your app to keep users engaged. Whether it’s streamlining onboarding or improving a specific feature, these insights will help you boost customer lifetime value by improving the overall experience.
Investors are always looking for data-driven evidence of a company’s growth and potential. Cohort analysis provides a clear picture of user retention, revenue growth, and customer satisfaction over time. Presenting these insights can strengthen your fundraising pitch, showing investors that your company has a solid understanding of its customers and a sustainable path to growth.
There are different ways to group cohorts depending on what insights you’re looking for. Here are the three main types of cohorts businesses often focus on:
These cohorts are grouped based on when users signed up for a product or service. By tracking customer behavior over specific time periods (such as monthly or quarterly), businesses can see how users who joined at different times behave over the long term.
For example, a SaaS company might notice that customers who signed up in the first quarter of the year have a higher retention rate than those who joined in the second quarter. This could indicate that marketing efforts in Q2 need to be revised, or that a competitor ran a successful promotion during that time.
Time-based cohorts are especially useful for tracking churn rates—the percentage of customers who stop using a service after a certain time. By comparing different time-based cohorts, companies can identify key moments when churn spikes and take action to retain more customers.
Segment-based cohorts focus on the type of product or service a customer signs up for. In a SaaS company, this could mean analyzing customers based on whether they chose the basic, premium, or enterprise-level package. Each segment has different needs and expectations, and understanding these can help businesses fine-tune their offerings.
For instance, if a business sees that customers in the premium segment are churning more quickly than those in the basic segment, this could indicate that the premium offering is overpriced or doesn’t deliver enough value. With this insight, the company could adjust pricing or introduce more features to better meet the needs of high-paying customers.
Size-based cohorts analyze customers based on their size, such as startups, small businesses, and enterprise-level organizations. This is particularly useful for SaaS businesses that cater to a wide range of customers, as different-sized businesses often have very different expectations and budgets.
Smaller businesses, for example, might have a higher churn rate because they are testing different solutions, while larger enterprises may be more committed to long-term contracts. Understanding the needs and behaviors of each size-based cohort can help companies design solutions that cater to each group more effectively.
When it comes to fundraising, investors are primarily interested in understanding how your business is performing and growing. Cohort analysis can play a crucial role in demonstrating the metrics that matter to them. Investors want to see strong signs of customer retention, evidence of growth potential, and a deep understanding of your customer base. Here’s what they typically look for:
Retention rates can be one of the most telling metrics for an investor because they indicate the stickiness of your product. High retention rates show that customers find value in your offering, which translates into more predictable and recurring revenue. Investors look for startups that understand not just whether customers stay, but why they stay or leave, and cohort analysis helps demonstrate this clearly. Highlighting positive trends in cohort-based retention can boost investor confidence, while identifying and addressing spikes in churn will show your awareness and responsiveness to potential weaknesses.
The lifetime value of a customer tells investors how much revenue a company can expect from a single customer over the entire relationship. Cohort analysis can help calculate and demonstrate CLV by analyzing how different groups of customers behave over time. A growing CLV across cohorts signals to investors that the company is getting better at retaining high-value customers and generating more revenue from existing ones. It also shows that your marketing and sales efforts are effectively targeting profitable segments.
Investors often compare CLV to the cost of acquiring new customers (CAC). By using cohort analysis to determine how long it takes to recoup CAC, you can demonstrate the sustainability of your growth strategy. If CLV is much higher than CAC and cohorts show a consistent pattern of increased retention over time, it’s a strong indicator that your business model is scalable and efficient.
Early-stage investors, especially at the seed and Series A stages, are looking for signs of rapid growth. Cohort analysis allows you to show how user growth or revenue has evolved over time for different customer segments. This can be particularly useful when demonstrating the impact of product changes, new features, or marketing campaigns. If more recent cohorts exhibit faster growth or higher engagement than older ones, it signals to investors that your company is learning and improving.
Let’s take a fictional software company as an example. By analyzing customer retention, the company notices that users who signed up in July and December have higher retention rates, with over 95% of customers staying past the four-month mark. In contrast, customers from other months drop off after two months. This insight could suggest that promotions or product improvements in July and December were particularly effective in keeping users engaged.
On the flip side, the company also notices a spike in churn for customers who signed up in April. This could indicate an issue with the product during that time, such as a glitch that frustrated users and caused them to abandon the software. By identifying the root cause of this problem, the company can work to prevent similar issues in the future.
Ready to dive into cohort analysis? Here’s a simple four-step process to get you started:
The first step is figuring out when churn happens. Often, your data will reveal when users are dropping off, giving you a clue about what’s causing it. To start, you’ll need an acquisition cohort analysis chart that tracks users over a set period of time (days, weeks, or months). Pay attention to key retention periods like early, middle, and late stages in the user lifecycle.
Pro tip: Keep your analysis focused on smaller chunks of time. Zooming out too much can blur the details you really need to see.
Image: appcues.nl
Once you’ve mapped out when churn happens, the next step is figuring out why. Look at the big drop-offs and ask yourself what users are doing (or not doing) at that point. For example, if you notice a 23% drop-off on day 3, what are users encountering then? Are you asking for a tricky action, like syncing data?
The goal here is to identify the specific features that are either keeping users engaged or turning them away. Instead of looking at overall engagement, zoom in on individual behaviors that correlate with churn.
Chances are, churn isn’t due to a single feature—it’s usually a combination of factors. That’s where comparing behavioral cohorts comes in. You can create different cohorts based on user actions, like completing an onboarding checklist, and compare their retention rates. Tools like Amplitude make this process easier, allowing you to quickly compare how different user behaviors impact retention.
Remember, your aim is to find patterns that help you understand what’s working and what needs fixing.
Once you’ve identified the issues, don’t rush into massive changes. Start with small tests. If you’ve found that users who don’t complete your onboarding checklist are churning at a high rate, try a gentle nudge or reminder rather than overwhelming them with notifications. Test your hypotheses and keep refining based on the data.
Let’s walk through a practical example of how to conduct a cohort analysis using a fictional productivity app. The goal: identify where users are dropping off and figure out how to keep them engaged.
Start by conducting an acquisition cohort analysis, where you group users based on when they first started using the app (e.g., by month). This will help you track how different groups of users behave over time. Here's how to get started:
For example, if you acquired 100 users in January, you would track how many of those same users continue using the app in February, March, and beyond. Repeat this for each month to build a comprehensive dataset before diving into the analysis.
With your data in hand, look for patterns to understand where users are dropping off. Create a retention curve that visualizes how many users stick around each month. Pay special attention to moments where there are significant drops—these are the points where users are losing interest or encountering friction.
Once you've pinpointed where drop-offs are occurring, dig into the possible reasons behind them. Are users encountering a complicated feature at that stage? Is there a lack of value provided early on? Formulate some hypotheses based on your findings.
To get a better sense of what's working, compare the retention rates of users who interact with a specific feature (e.g., completing a checklist) versus those who don’t. If users who engage with the feature have a much higher retention rate, it's a sign that the feature is driving value. This insight can guide you in optimizing the user experience.
Armed with insights from your cohort analysis, start making small, data-driven changes to your product. For instance, if onboarding is where users drop off, improve the process by simplifying it or providing additional guidance. Track the impact of these changes with new cohorts to see if retention improves.
By following these steps, you can transform raw data into actionable insights, allowing you to understand where users are struggling, which features are keeping them engaged, and how to optimize for growth.
This template includes the acquisition month, cohort month, number of users acquired, users retained in subsequent months, and the retention rate. You can use this structure to organize your own data and perform cohort analysis, helping you understand user retention patterns and identify areas for improvement.
Cohort analysis is a game-changer for understanding your startup’s product performance and uncovering growth opportunities. To make the most of it, start by choosing the right cohort type and metric that align with your goals, and stick to a consistent, relevant time frame. Make sure you’re working with a large enough sample size that truly represents your user base, and set benchmarks to compare your cohorts effectively. Most importantly, stay flexible—experiment with different combinations and variations to surface new insights and fine-tune your strategy as you go.
Now that you know how to apply cohort analysis, it's time to take it a step further and use these insights to prepare for fundraising. If you're looking to attract investors at the seed or Series A stages, you need more than just good metrics—you need a compelling story backed by data. Cohort analysis can be your secret weapon, giving you the ability to show growth, retention, and customer behavior trends in a way that resonates with investors. Here’s how to leverage cohort analysis to make your fundraising pitch stand out:
Prepare a visual representation of cohort retention curves, demonstrating how newer cohorts have better retention than older ones. This can highlight improvements in product-market fit, customer satisfaction, or onboarding processes. It’s not just about showing growth but proving that you’re addressing churn and learning from customer feedback to keep users longer.
Use cohort analysis to show how specific product changes have positively affected user behavior. For example, if a recent update led to higher engagement among certain cohorts, present this data to investors as evidence that your company is iterating quickly and responding to customer needs effectively.
If you’ve identified that certain segments have a significantly higher CLV or retention rate, present this data to investors. Explain how you plan to target and acquire more customers from these profitable segments. This shows a strategic approach to scaling the business and maximizing growth potential.
Use insights from cohort analysis to build realistic projections for customer growth, revenue, and retention. Show how past trends inform your future strategy. For example, if you see a pattern where customers acquired through a specific marketing channel have better retention rates, you can emphasize plans to double down on that channel to attract more high-value users.
There are many tools available for cohort analysis, and each tool has its own pros and cons. Here’s our top 8 selection of tools that can help startups analyze and optimize user behavior:
Houseware makes cohort analysis super simple by automatically capturing all event data in your product, whether it’s clicks, swipes, or form-fills. It’s quick and easy to visualize patterns and retention, making it ideal for product teams that want to see which users are most (or least) likely to churn and which features are used the most.
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This free tool from Google is ideal for small businesses and marketers. The cohort analysis feature lets you visualize user behavior and retention. While setting it up can be time-consuming, once everything is configured, it provides valuable insights.
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Mixpanel is a popular product analytics tool that lets you track user engagement. Its cohort analysis feature helps you discover patterns and engage users based on their behavior, such as login dates or purchase history.
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Amplitude enables flexible cohort analysis based on user behavior and profile information. This is ideal for startups looking to make data-driven decisions to improve their product experience.
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Kissmetrics offers powerful analytics with a focus on customer segmentation and email campaigns. It can run cohort analyses based on demographics, behavior, and time frames, which is useful for websites looking to boost conversions.
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If you’re technically savvy, you can build your own cohort analyses with SQL. By grouping customers based on signup dates and tracking their return behavior, you can calculate retention and analyze trends over time.
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Heap provides an intuitive way to conduct cohort analysis for both web and mobile apps. By grouping users based on their activities, you can gain valuable insights into how they interact with your product and adjust marketing campaigns accordingly.
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Userpilot helps drive user engagement and retention. The cohort analysis feature makes it easy to segment users based on behavior or subscription type and analyze retention in detail for each cohort.
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Cohort analysis provides entrepreneurs and potential investors with a clear, data-driven overview of a company’s progress, making it an invaluable tool during fundraising. Whether you’re tracking revenues, user growth, retention rates, average revenue per user, or even expenses, this approach allows you to uncover crucial insights that traditional metrics might miss. Since it’s a favored method among VCs for evaluating a startup’s potential, founders preparing for seed or Series A investments should be well-versed in using it to showcase their company’s strengths.
By breaking down complex datasets into smaller, actionable cohorts, businesses can better understand their customers, identify opportunities for improvement, and fine-tune their strategies. Ultimately, cohort analysis not only helps companies make smarter decisions but also strengthens their position when seeking investment, driving growth, and building a sustainable, successful business.
Also good to read:
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Mastering B2B Startup Branding for Success
Transforming Ideas into Impact: The Power of MVP Development for Startups Growth
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