In this episode of The Metrics Brothers, Ray “Growth” Rike and Dave “CAC” Kellogg take on one of the biggest challenges facing modern SaaS and AI-Native companies: how to measure NRR and expansion when pricing isn’t fixed anymore.

With the rise of usage-based, user-based-but-variable, and outcome-based pricing, the traditional world of ARR - long the backbone of SaaS metrics has been turned on its head. Contracts no longer tell the story. Spend does.

Dave breaks down how to rethink ARR proxies using quarterly or monthly revenue (“implied ARR”) and why longer intervals help smooth volatility, especially for “humpback” or highly seasonal customers whose spend fluctuates dramatically month-to-month.

Ray digs into what NRR was originally designed to measure and why many teams misinterpret it—especially in variable-pricing environments where a backward-looking metric can’t serve as a forward-looking forecast. The brothers explain why sequential expansion, usage behavior, and real spend patterns now matter far more than traditional ARR bridges.

Key topics include:

• Why ARR no longer maps cleanly to revenue in a variable pricing world
• How to calculate implied ARR using quarterly or monthly software revenue
• Why NRR must be interpreted differently—and why survivor bias still matters
• How volatility and seasonality distort short-interval metrics
• Why usage is the real leading indicator, not invoices
• How to rethink “expansion ARR” when base + variable spend changes continuously

Packed with examples, including sinusoidal customers, misleading GRR math, and the dangers of splitting base versus variable revenue, this episode gives operators and investors a practical framework for measuring customer growth when pricing is anything but predictable.

A must-listen for CFOs, RevOps leaders, and anyone trying to modernize SaaS metrics for the AI era.

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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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For their 100th episode, Ray "Growth" Rike and Dave "CAC" Kellogg get philosophical, inspired by the notion that many hold, which is "nothing works" in B2B GTM anymore - especially in regards to pipeline development.

They dive into the 2025 State of B2B GTM Report by Kyle Poyar and Maja Voijc to challenge this idea and find out what GTM leaders are actually prioritizing.

In this episode, The Metrics Brothers break down:

• The State of the Market: Analyzing a survey of 195 GTM leaders, including data on small companies, growth rates, and the surprising lack of correlation between GTM motion and growth.

• The "Pipeline Crisis": Discussing why scaling existing GTM motions is the number one priority, even when many GTM leaders feel their current efforts aren't effective.

• Too Much Noise: A look at the "distraction chart" [slide 12] showing the staggering number of channels and strategies B2B companies are trying, and why the report suggests this is "too much".

• The Tried and True GTM Quadrant: Highlighting the activities with the biggest likelihood of impact, including Intimate Events, Intent-Based Inbound, and LinkedIn [slide 13].

• The Winner Take All Future: Exploring the massive trend of investing in Answer Engine Optimization (AEO) [slide 18] and breaking down tactical recommendations for optimizing for ChatGPT and other answer engines, emphasizing the importance of facts and platforms like Reddit and G2 [slide 19].

• Must Try GTM Tools: Reviewing the next generation of GTM tools, with a focus on cutting-edge platforms like Clay, Lovable, Sora, and Replit for data automation, outbound, and video generation [slide 29].

Whether you're a Founder, CMO, CRO or GTM leader, this episode offers a data-driven look at where to focus your budget and attention in the year ahead.

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