Your customer base is growing. Your revenues are increasing. Maybe your SaaS business has even started turning a profit. The future is looking bright. What more could you ask for?
If you’ve been through this stage of building a SaaS business in the past, though, you might be aware that promising growth can actually make it harder to spot inefficiencies and missed opportunities that hurt your brand’s long-term prospects.
This is where a SaaS cohort analysis can offer valuable insights to guide a SaaS brand’s strategic planning. As your business scales, customer retention becomes more critical to meet forecasted growth and profitability.
A granular view of your diverse customer base can help you understand the specific and varying needs of your existing customers to improve your customer experience, lower your platform’s churn rate, and realize new revenue opportunities to push profits higher.
What Is A SaaS Cohort Analysis?
A cohort analysis is a data-driven evaluative process that examines the behavior and financial performance metrics across smaller segments within your overall customer base.
These customer segments can be defined and separated in many different ways. Customer personas, subscription levels, platform behaviors, acquisition channels, and customer sign-up dates are all possible criteria by which cohorts may be created.
In a SaaS cohort analysis, businesses should be seeking insights into how different customers use the platform in different ways. Do longer-term customers spend less time taking advantage of newer features? Do certain subscription tiers churn at a higher or lower rate than others?
By conducting a SaaS cohort analysis, your business can uncover actionable insights that enable new efficiencies, greater revenue generation and, above all, higher customer retention rates for your platform.
Data Use Cases
As SaaS platforms grow, so do the troves of customer data at your disposal. But a high volume of data doesn’t do your business any good if you don’t have a way to leverage that information for deeper, actionable insights.
A SaaS cohort analysis offers a straightforward template for processing this data and using it to inform your business strategy. Given the flexible ways a SaaS cohort analysis can be conducted, your business can use this evaluative process to serve a number of goals and use cases, including the following:
● Identify product decisions that create value for your customers and improve retention rates, including new feature releases, pricing changes, and UX upgrades.
● Assess customer engagement over time and across segments to better understand your customer lifetime value (LTV) potential.
● Define customer personas and segments to improve tailored messaging to these groups.
● Enable proactive monitoring for patterns that may indicate customers are about to churn. Early identification can help your business implement interventions to retain each customer’s business.
● Evaluate the success of new product upgrades, UX enhancements, and other changes affecting the customer experience.● Learn more about why customers churn and identify strategies to improve customer engagement and retention.
Time-Based Vs. Segment-Based Cohort Analysis
A SaaS cohort analysis can be conducted to generate insights related to a wide range of financial and other performance metrics. But the actual customer cohorts analyzed are defined and grouped together through one of two methods.
Also known as acquisition cohorts, time-based groupings are a straightforward approach to segmentation: cohorts are defined according to when those customers joined your platform. Whether on a monthly, quarterly, annual, or other time-based axis, this approach makes it easy to analyze how customer relationships have changed with your SaaS product over time.
● Best for: SaaS businesses that want to evaluate the impact of new product developments and/or the overall user experience over time. This strategy can also uncover insights into how customer relationships have changed as the SaaS company has scaled its user base.
Segment-based cohorts define customer groups by behavioral data, demographics, or other commonly shared data points not bound to time considerations. Customer personas, SaaS usage patterns, subscription tiers, and similar criteria can be used to create segments that can be analyzed to learn more about the differences between these cohorts.
● Best for: Understanding which types of users generate the most value and revenue for your business. Segment-based cohorts can be analyzed to discover reasons why your platform may be struggling to engage certain types of users, what types of customer personas offer the best LTV and marketing ROI, and many other insights made possible through your own first-party cohort data.
How To Perform A Cohort Analysis
Is your most engaged audience your highest-value audience? Do your highest-paying subscription tiers deliver the best customer LTV? Which cohorts offer the best bang for your marketing bucks, and which customer groups are saddling your business with high rates of churn?
A SaaS cohort analysis can help you answer all of those questions and more. While it’s important to remember that correlation doesn’t always equal causation, a simple cohort analysis can be the starting point for a deeper dive into A/B testing and other strategies aimed at proving your hypothesis.
When starting a cohort analysis, you’ll need to access a software tool that offers built-in cohort reporting tools. Google Analytics is one of the most popular resources for this, but ProfitWell and Mixpanel are among a number of proven tools to make cohort reporting easy.
Once you’re set up with one of these tools, you can conduct your own analysis using one of the following approaches:
To conduct a behavioral analysis, define customer groups based on trackable actions within your SaaS platform. For example, if you want to know whether high-tier subscribers are using the expensive features they’re paying for, separate groups based on whether they’ve engaged with that feature within a specific period of time.
Focus on narrow time frames to reduce the variables that may disrupt these analytics. For example, consider analyzing customer behavior over a two-week or 30-day period rather than a six-month period, when seasonal ebbs and flows, along with other platform events, could compromise the quality of your data.
Once you’ve defined the time period, compare different cohorts to see if any group of users shows a correlation between the targeted behavior and their retention rate.
Churn analysis is great for analyzing what types of engagement patterns and platform usage are causing customers to churn.
To calculate churn, identify the users signed up on a particular date, or over a particular time period, and compare that with the number of customers still active at a specific date. The percentage represents your churn rate.
A churn analysis can be conducted by then creating cohorts based on their sign-up date, subscription tier, business type, or other criteria to isolate the variables that may be causing, or contributing to, high rates of churn. Multiple cohort analyses may be conducted to further test these results and validate their conclusions.
A revenue analysis begins with setting a timeframe for analysis and determining the churn rate over that period of time. Then, you set up cohorts based on the financial grouping you want to examine—common segmentations will focus on subscription tiers, the size of the customer, and ranges of revenue generation in cases where your pricing model is based on the number of users or platform usage.
Over the churn rate timeframe, examine how revenue generation has expanded for each of these groups. Depending on the stage of your company and the insights you’re looking to generate, you may choose to view this in terms of gross dollar retention or net revenue retention (more on these below). Identify which cohorts are seeing the fastest rate of growth, and which ones see stagnant or declining revenue expansion.
From here, you can analyze the differences in these cohorts—potentially through the use of additional segment-based analyses—to identify the behavioral, demographic, and other reasons why these revenue differences are occurring.
Gross Dollar Retention
Gross dollar retention (also known as gross revenue retention) reflects your SaaS company’s success in retaining existing customers. It’s a great data point for younger SaaS businesses because it helps you evaluate the stability of your existing customer base.
Gross dollar retention is calculated by subtracting revenue churn from your monthly recurring revenue (MRR) - without accounting for new revenue - and then dividing that smaller number by MRR. If your MRR is $25,000 but you experience $5,000 in lost revenue due to churn, you divide $20,000 by $25,000 and come up with a gross dollar retention rate of 80 percent.
Net Revenue Retention
While gross dollar retention only focuses on your company’s success in retaining existing revenue, net revenue retention also accounts for revenue expansion driven by new customers. This slightly more complex calculation is a more complete reflection of your company’s revenue growth.
To follow the above scenario, let’s say your business started the month with $25,000 in MRR, suffered $5,000 worth of churn, but also ended the month with $32,000 in MRR due to the addition of new users. After subtracting your churn from the end-of-month MRR (including new revenue), you would divide the net revenue of $27,000 by the original $25,000 figure and come up with a net revenue retention figure of 108 percent.
While gross dollar retention can never surpass 100 percent, a healthy net revenue retention figure should be well above 100 percent, reflecting your company’s growing revenue base.
Understanding Cohort Analysis Metrics
No matter how you’re segmenting and analyzing your customer base, any SaaS cohort analysis is going to be primarily concerned with one or more of the following three SaaS metrics:
● Churn rate: As mentioned above, a high churn rate—even among only one cohort within your customer base—is a problem that needs to be addressed. A cohort analysis can help spot high rates of churn and their potential causes.
● Customer lifetime value (LTV): A high churn rate will drag down customer LTV. Strong customer retention, combined with strategic pricing, upselling, and other revenue generation strategies, will maximize the revenue earned from each individual customer.
● Customer acquisition cost: The more expensive it is to acquire customers, the higher your customer LTV needs to be. If you have a high churn rate and high customer acquisition costs (CAC), your company could quickly end up bleeding money as it spends to try and make up for lost revenue.
Implementing Your Findings
A SaaS cohort analysis is invaluable in identifying troubling patterns and their possible explanations. But with so many variables affecting churn, customer engagement, and the overall user experience, it’s impossible to be certain about the cause of poor customer retention until you implement changes that address the issue.
For example, if you conclude that premium-tier subscribers are churning because they aren’t properly utilizing your most powerful platform functionality, you should consider building an onboarding tutorial or in-platform training module to increase adoption and familiarity.
Whatever problems you identify, develop and implement a workable solution, and monitor cohort metrics going forward. If churn rate and/or customer revenue generation increase, you can be confident you correctly identified the issue.
Be Fearful When Others Are Greedy
Alongside customer reviews, surveys, and other forms of feedback, a SaaS cohort analysis uses reliable first-party data to uncover telling trends and patterns among your customer base. The more your SaaS solution grows, the more valuable this strategy will be in sorting through cohort data and pulling out insights you might not find anywhere else.
Even in the best-case scenario, though, addressing the findings of a SaaS cohort analysis can be a bit of a guessing game. One of the best ways to approach this challenge is to consult with other members of your financial team, as well as other experienced CFOs who have faced similar challenges in building and protecting their own SaaS revenue streams.
Discover more insights and resources empowering today’s tech CFO leaders—subscribe to The CFO Club newsletter today.