The snowflake (two-year look back) method

In this episode, "The Metrics Brothers," Growth (Ray Rike) and CAC (Dave Kellogg), dive into a critical challenge for modern SaaS and AI-Native companies: accurately calculating Net Revenue Retention (NRR) in environments that utilize variable pricing models (usage-based, outcome-based, etc.).

They begin by defining NRR, emphasizing its importance as a key metric and its high correlation with Enterprise Value-to-Revenue multiples.

The brothers then dissect the primary challenge: the absence of traditional Annual Recurring Revenue (ARR) in non-annual contract models. They explore different proxies for ARR, including MRR x 12 and Implied ARR (Quarterly Revenue x 4), and discuss the pitfalls of each, particularly the risk of overstating annual revenue due to seasonality or significant one-time deals.

Finally, they offer their preferred, cohort-based method for calculating NRR—the "Snowflake Method" or "Two-Year Look Back"—which compares the current revenue of a specific group of customers (cohort) to their revenue from a year ago. They conclude with a discussion on how this method helps dampen the "noise" and variability inherent in usage-based data when trying to measure expansion and contraction.

📊 Key Takeaways & Discussion Points

• NRR Definition & Importance: NRR measures how much recurring revenue you retain and expand from your existing customer base over a period, factoring in upsells, cross-sells, downgrades, and churn. It's a top-tier metric for investors, correlating highly with enterprise valuation.
• The ARR Proxy Problem: In usage-based and outcome-based models, true ARR (based on annual contracts) doesn't exist, requiring the use of proxies
• MRR x 12 and Implied ARR (Q4 Revenue x 4) are common but suffer from issues like seasonality or the timing of large deals, often leading to an overstatement of forward-looking revenue.
• Trailing Spend is presented as the most reliable underlying truth, as it reflects the actual usage and revenue generated by the customer.
• Best Practice: The Cohort Method for NRR:
• The recommended approach is a cohort-based calculation that eliminates the need to rely on potentially flawed ARR proxies.
• The Calculation: Take a specific cohort of customers who existed one year ago (e.g., all customers as of December 31, 2024). Divide their revenue today (December 31, 2025) by their revenue one year ago.
• The Two-Year Look Back Method (Snowflake): This method is "self-correcting" as it naturally excludes new customer revenue, ensuring the NRR accurately reflects only the existing customer base.
• Dealing with Usage-Based Variability (Noise): Variable usage can lead to "noise" in quarterly expansion/contraction metrics. Using a trailing 12-month period (year-over-year) for the NRR calculation is safer than a quarterly view, as it dampens this volatility and provides a clearer signal of long-term customer value.

If you are responsible or measured on NRR in a variable pricing model environment, this episode is a great listen to understand the pitfalls and best practices of calculating Net Revenue Retenion.

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