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As a Customer Success Manager (CSM), you're no stranger to data – heaps of it. Every day, you're met with numbers, trends, and customer feedback that could hold the key to improving customer satisfaction.
But here’s the challenge: making sense of it all. How do you transform these endless rows of data into something useful? Something that helps you boost engagement, reduce churn, and actually improve the customer experience?
The truth is, segmenting your customers into meaningful groups is a great first step, but finding the patterns that actually matter is where it gets tricky. Without the right tools, it's easy to feel stuck trying to identify why one group of customers thrives while others quietly slip away.
This is where cohort analysis steps in. It offers a clear, structured way to break down your customer base into smaller, more manageable groups, allowing you to spot trends and behaviors that might otherwise go unnoticed.
With cohort analysis, you can move from feeling overwhelmed by data to understanding it in a way that directly impacts your strategy – giving you the insights you need to take action where it matters most.
In this blog, we’ll cover everything you need to know about cohort analysis to leverage it for Customer Success, starting with the basics.
What is cohort analysis?
Cohort analysis is a method that allows you to break your customers into smaller, more focused groups based on shared characteristics or behaviors.
Instead of looking at your entire customer base as one large entity, cohort analysis helps you track specific groups over time, revealing patterns that would otherwise be difficult to spot.
At its core, cohort analysis is about grouping customers who share something in common during a specific time period – like signing up for your service in the same month, making a purchase around a similar event, or interacting with a specific feature of your product.
By analyzing these cohorts over time, you can see how their behaviors evolve, which helps you understand the customer journey more clearly.
For Customer Success teams, this is valuable because it allows you to pinpoint exactly where your customers are thriving and where they might be struggling. If you notice that a certain cohort has a high churn rate, for example, you can investigate what’s different for that group and take steps to address the issue.
On the flip side, if a particular cohort has a high engagement rate, you can explore what’s working and replicate those strategies across other groups. Ultimately, cohort analysis equips CSMs with the information needed to make data-driven decisions that can boost retention, improve engagement, and increase customer satisfaction.
Now that you have a better understanding of what cohort analysis is and how it works, let’s dive into the benefits it brings to your role as a CSM.
Benefits of cohort analysis for Customer Success managers
Whether you’re focusing on retaining customers, refining your onboarding process, or mapping out the customer journey, cohort analysis provides specific benefits that can help you make smarter, data-driven decisions. Here’s a closer look at what they are:
1. Understanding customer retention
One of the most critical applications of cohort analysis is understanding customer retention. By examining how different cohorts behave over time, you can spot patterns in who’s staying, who’s leaving, and why.
For example, you might notice that customers who signed up in a particular quarter have a lower retention rate, which could point to issues with your product or customer service during that period.
Armed with this information, you can design more targeted retention strategies. Maybe one cohort responds well to more frequent check-ins, while another group might prefer additional training on key features.
By segmenting your customers this way, you can create personalized plans to keep them engaged.
2. Improving onboarding and engagement
Cohort analysis can also be a powerful tool for improving your onboarding process and tracking user engagement. When you analyze different groups, you can see which onboarding practices work best and which might need adjustment.
Perhaps one cohort consistently struggles with a particular step in the onboarding process, while another sails through it with no issues. These insights allow you to fine-tune your onboarding and engagement strategies to fit each cohort's needs.
For CSMs, this means that cohort analysis can guide you in tailoring onboarding and engagement to different customer segments. By adjusting the process based on cohort behavior, you can provide a more personalized experience that boosts engagement from the start.
3. Optimizing the customer journey
Beyond retention and onboarding, cohort analysis is incredibly useful for optimizing the entire customer journey. By tracking how different groups of customers interact with your product over time, you can identify key moments when churn is most likely to occur.
For example, if you see that a particular cohort tends to drop off after the first three months, you can focus on improving engagement during that critical period.
Once you’ve identified these bottlenecks, you can implement specific changes that address the needs of each cohort. Whether it’s offering additional support, fine-tuning features, or increasing communication, the insights from cohort analysis help you make improvements where they matter most.
With these benefits in mind, let’s explore the different types of cohort analysis and how they can be applied to improve your Customer Success strategy. Whether you’re looking at when customers joined or how they behave, different approaches can provide unique insights.
Types of cohort analysis
As you dive deeper into cohort analysis, it's important to understand that not all cohorts are created equal. Different types of cohort analysis focus on various aspects of customer behavior, giving you unique insights depending on the lens you use.
Two of the most common types are acquisition and behavioral cohort analysis. Each provides a different perspective, allowing you to track and improve different elements of the customer experience.
Acquisition cohort analysis
Acquisition cohorts group customers based on when they first signed up or made their first purchase. This type of analysis is particularly useful for tracking long-term customer behavior tied to specific time periods.
For instance, you might want to compare how customers who joined during a product launch behave versus those who signed up during a regular sales cycle.
Acquisition cohort analysis can help you uncover how customer engagement, retention, and satisfaction evolve depending on when they joined, and whether certain marketing or onboarding efforts had an impact.
If you notice that customers from a specific cohort show a decline in engagement over time, it might be an indicator that your marketing or onboarding efforts during that period need to be reviewed. This type of analysis allows CSMs to look beyond surface-level data and understand the long-term trends that shape the customer journey.
Behavioral cohort analysis
Behavioral cohorts, on the other hand, are based on specific actions customers take – whether that’s using a feature, making a purchase, or interacting with support. This type of analysis focuses on how customer behaviors impact their overall experience and can reveal patterns that are directly tied to engagement and retention.
By grouping customers based on their behaviors, you can track how different actions lead to different outcomes. For example, you might discover that customers who actively use a certain feature of your product are far more likely to stay engaged long term, while those who don’t engage with that feature tend to churn.
Behavioral cohort analysis allows you to dig deeper into what’s driving customer satisfaction or dissatisfaction, giving you the tools to create targeted interventions.
Now that you have an understanding of the types of cohort analysis, the next step is learning how to implement it effectively. From defining your cohorts to tracking the right metrics, applying cohort analysis to your Customer Success strategy requires a structured approach. Let’s walk through the key steps for making cohort analysis work for you.
How to implement cohort analysis for Customer Success
Now that we’ve covered the benefits of cohort analysis, the next question is: how do you actually put it into practice? Implementing cohort analysis within your Customer Success strategy involves a few clear steps:
Step 1: Define your cohorts
The first and most important step in implementing cohort analysis is defining your cohorts. You need to decide what makes the most sense for your business goals. Cohorts can be grouped based on several characteristics, such as the date of customer acquisition, how they use your product, or a specific behavior like signing up for a webinar or purchasing an add-on feature.
The key is to define groups that help you track the trends that matter most to your objectives. For example, if you're focused on retention, an acquisition cohort might be useful because you can track how customers who signed up in the same period behave over time.
On the other hand, if you're looking at product adoption, behavioral cohorts that examine usage patterns will offer more relevant insights. Defining the right cohorts upfront ensures that the data you collect and analyze aligns with your business goals.
Step 2: Track key metrics over time
Once you’ve defined your cohorts, the next step is to track their performance over time. This means monitoring metrics like customer retention, churn, and satisfaction. By following these metrics, you can get a clear view of how each cohort behaves at various stages of the customer journey.
If you notice a significant drop-off in retention after a few months for a particular cohort, that’s a signal that you may need to intervene with tailored strategies for that group.
CSM tools like Velaris can help you monitor customer health scores and other key metrics across these cohorts, giving you a deeper understanding of their satisfaction levels and engagement.
Velaris also offers built-in surveys, making this step easier by allowing you to track customer satisfaction and see how different groups are experiencing your product and what changes might improve their engagement.
This approach gives you actionable insights, allowing you to identify specific areas where your cohorts are either thriving or struggling.
Step 3: Identify trends and take action
With your metrics in hand, the next step is to identify trends within the cohorts and use that data to make improvements. This is where cohort analysis becomes especially useful for CSMs.
By looking at how different cohorts perform over time, you can uncover trends that help you refine your approach. Whether it’s adjusting your onboarding process, tweaking product features, or improving communication, these insights let you take action where it’s needed most.
Velaris’ customer health score and sentiment analysis tools make it easy to identify patterns across cohorts, helping you spot potential issues before they become bigger problems.
The sentiment analysis tool automatically analyzes customer interactions and uses this data to interpret how your customers are feeling while the health score tool helps you monitor the overall well-being of each customer or cohort.
With these tools working in tandem, you’re equipped to not only identify potential issues early but also refine your Customer Success strategies. Whether it’s improving a product feature, personalizing communication, or adjusting onboarding, you can offer a tailored experience to each cohort based on real data, all while ensuring that no critical details slip through the cracks.
By following these steps, you can start leveraging cohort analysis to make data-driven decisions that boost customer satisfaction and retention. Next, we’ll explore some of the common challenges you can expect to face when it comes to cohort analysis and go over practical ways to overcome them.
Challenges with cohort analysis
As powerful as cohort analysis can be, it’s not without its challenges. Two major issues that often arise are data fragmentation and the complexity of manual analysis. However, with the right tools and processes in place, these challenges can be overcome.
Data fragmentation
One of the biggest hurdles when conducting cohort analysis is data fragmentation. Often, customer data is scattered across multiple teams and platforms – your sales team might use one tool, marketing another, and product development a third.
When data is siloed like this, it becomes difficult to build accurate cohorts, as you’re missing key insights from various parts of the customer journey. For example, without input from marketing or product usage data, you may not have the full picture of how different customer groups are engaging with your business.
The solution is centralizing this data so that all the relevant information is accessible in one place. By connecting tools used across your teams, you can create more accurate cohort models and make decisions based on a comprehensive view of the customer experience.
Velaris helps with this by uniting siloed data from sales, marketing, onboarding, support, and product teams, automatically populating the information you need for accurate cohort analysis. This centralization ensures you’re not working with incomplete data and gives you a more holistic understanding of customer behavior.
Analysis complexity
Another challenge is the complexity of manually analyzing cohort data. When you’re working with large datasets, manually sorting through information, identifying trends, and drawing conclusions can be time-consuming and prone to human error.
For CSMs with limited time and resources, this process can quickly become overwhelming. This complexity often means that important insights are missed, or decisions are delayed because of the time it takes to analyze the data.
Automating the cohort analysis process can significantly reduce these complexities. By leveraging automation, you can quickly generate insights that might otherwise take hours or even days to uncover manually.
Velaris’ can streamline this process by allowing you to automate key tasks, saving time so you can focus on making data-driven decisions based on reliable information. With automation, you not only make cohort analysis faster but also ensure more accurate and consistent results.
By addressing these challenges, you can unlock the full potential of cohort analysis and make it a seamless part of your Customer Success strategy.
Conclusion
Cohort analysis offers Customer Success Managers (CSMs) a valuable way to gain deeper insights into customer behavior, enabling more informed, data-driven decisions. By breaking customers into meaningful groups and tracking their behavior over time, CSMs can uncover trends that help improve retention, engagement, and overall satisfaction.
While cohort analysis can sometimes feel like a complex process, it doesn't have to be. With the right tools, you can simplify your analysis and make it actionable. Velaris helps automate and streamline the cohort analysis process by centralizing data from various teams and using AI-driven insights to help you focus on what matters most – your customers.
If you’re looking to take control of your data without getting weighed down by manual processes, Velaris offers the solution. Book a demo today to see how you can start using cohort analysis to make more informed decisions for your Customer Success strategy.
