How Do I Calculate Retention if I Have Consumption Revenue? | SaaS Metrics School | Retention
How Do I Calculate Retention if I Have Consumption Revenue? | SaaS Metrics School | Retention
Welcome back to another edition of SaaS Metrics School with Ben Murray, also known as The SaaS CFO. In today’s video, we’re diving deep into a crucial topic that many SaaS founders, CFOs, and finance teams often ask about: How to calculate retention with consumption-based revenue.
This topic came up in one of my recent SaaS Metrics Foundation course sessions, where a student asked, “How do we calculate retention when we have variable or consumption-based revenue models?” Whether you're working with consumption revenue, transactional revenue, or other variable revenue streams, this question is incredibly relevant.
Unlike traditional MRR (Monthly Recurring Revenue) or ARR (Annual Recurring Revenue), consumption-based revenue doesn’t follow a fixed pattern. It can fluctuate based on usage rates, transaction volumes, or seasonal factors, which makes calculating revenue retention a more complex challenge.
In this video, I walk you through the process I use to calculate retention in a consumption or variable revenue model. Here’s what you’ll learn:
1. Aggregate Revenue Retention Schedule
One of the first steps I recommend is to run an aggregate revenue retention schedule. This involves taking your MRR schedules for those variable revenue streams and compiling them to analyze retention on different levels:
Customer-level retention
Gross revenue retention
Net revenue retention
By doing this, you can begin to identify trends and patterns that show how variable revenue is impacting your business in the long run.
2. Identifying Patterns in Variable Revenue
In traditional MRR models, it’s straightforward to see growth trends — your P&L shows revenue going “up and to the right.” However, with consumption revenue, it’s harder to spot patterns. I’ll discuss different cases where variable revenue streams are either predictable (with consistent ARPA – average revenue per account) or subject to seasonal changes.
When there’s no clear pattern, we may need to look at longer measurement periods, such as year-over-year comparisons. This helps us see if, over time, variable revenue is contributing to growth or if we need to refine our retention strategies.
3. Cohort Analysis for Consumption Revenue
Another powerful tool for measuring retention in consumption-based models is cohort analysis. By tracking a customer group over 12, 24, or even 36 months, you can compare their revenue contributions over different periods. This analysis helps you answer key questions:
Are customers increasing their usage over time?
Is there a clear upward trend in variable revenue for specific cohorts?
The ultimate goal is to ensure that retention is increasing in these variable revenue streams, especially if they are a significant part of your overall revenue mix.
4. The Role of Pricing Models
I also cover how pricing models come into play when analyzing retention. If your variable revenue accounts for a small percentage (e.g., 10%) of your overall revenue, then it may not be a major focus. However, if consumption revenue is material to your total revenue, understanding and improving retention is vital for long-term success.
5. Month 13 Retention Analysis
Another interesting method I discuss is Month 13 retention analysis. This involves looking at customer retention over a full year, from the moment they were acquired. For example, if you acquired a customer group in January of last year, what percentage of them are still generating revenue 13 months later? This analysis can give you critical insights into whether customers continue to engage with your product and grow their usage over time.
6. The Importance of Time in SaaS Metrics
Throughout the video, I emphasize the importance of the period of measurement when analyzing SaaS metrics. Whether it’s monthly, quarterly, or year-over-year comparisons, understanding how time frames impact your revenue data is key to identifying trends, seasonality, and growth opportunities.
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