In this episode of The Metrics Brothers, hosts Ray “Growth” Rike and Dave “CAC” Kellogg provide a critical deep dive into the 2025 SaaS Benchmark Report published by High Alpha. Known for their analytical, and sometimes "crusty" approach, the metrics brothers dissect the data behind 800+ SaaS companies to separate real market trends from report commentary.

Key Highlights & Benchmarks

The brothers break down the report’s most significant findings with their signature skepticism regarding "correlation vs. causation."

• The AI Growth Premium: Companies with AI at their core are growing significantly faster than those using AI as a supporting feature. For instance, in the $1–5M ARR band, AI-core companies achieved a median growth of 110%, compared to 40% for their peers

• The "Lean Team" Era: Efficiency is surging as headcount falls. Median revenue per employee has jumped to $129K–$173K, with top-tier public companies hitting over $283K. The hosts note that engineering and support have seen the largest headcount reductions due to AI automation

• Venture Rebound (with a Caveat): While quarterly VC deal value has returned to near 2021 levels (~$80B), the capital is highly concentrated. Over half of all VC funding is currently flowing into AI startups, often in massive "mega-rounds."

• In-Office vs. Remote: For the second consecutive year, the data suggests that in-office or hybrid teams are growing faster (42% median) than fully remote teams (31% median).

As always, Ray and Dave offer practical advice for founders and GTM leaders:

"Read the data, but watch out for the commentary." While the data is good, some commentary and conclusions in the report imply causation where there is at best some level of correlation, such as why companies stay private longer or how AI "drives" growth.

• Retention is King: The strongest growth outcomes are found where high Net Revenue Retention (NRR) meets short CAC payback periods.
• Outcome-Based Pricing: The brothers highlight the shift toward outcome-based and hybrid pricing models as a primary driver for best-in-class NRR in 2025.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.