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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In this episode of The Metrics Brothers, Ray “Growth” Rike and Dave “CAC” Kellogg break down one of the emerging metrics in the Agentic AI era: Containment Rate - the percentage of tasks an AI agent completes (resolves) end-to-end without human intervention.

They explore multiple aspects of the Containment Rate Metric including:

• How containment rate differs from classic chatbot metric - deflection rate
• Why defining “resolved” and/or "completed" is essential to calculating containment rate
• How the metric connects directly to ROI
• Why ROI needs to include both the benefit (cost-savings) and the investment (expense) for the AI Agent

Ray and Dave also trace the history of containment from IVR to Chatbots to LLM-powered agents, debate common misconceptions, and outline benchmarks across customer support, IT, HR, and back-office agentic AI workflows.

If you’re building, buying, or benchmarking AI agents - or trying to turn AI investments into measurable ROI — this episode delivers the context, clarity, and humor only The Metrics Brothers can provide.

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Air Street Capital’s Nathan Benaich just dropped the 2025 State of AI Report — a 313-page tour de force on where artificial intelligence is today and where it’s headed next. In this episode, Dave “CAC” Kellogg and Ray “Growth” Rike break down the highlights, surprises, and bold predictions shaping the future of AI, software, and the global economy.

They explore:

• Why this report is becoming the “Mary Meeker Internet Report” of the AI era
• Key insights across research breakthroughs, model performance, geopolitics, enterprise adoption, and market maturity
• The Top 10 Predictions that could define the next 12 months — from AI agents running $5B in ad spend to the first UN emergency debate on AI security

Predictions discussed include:

• Retailers generating 5%+ of online sales via agentic checkout
• Open-sourcing frontier models to win government favor
• AI-driven scientific discoveries completed end-to-end by autonomous agents
• Deepfake or agent-led cyberattacks prompting NATO-level action
• A real-time generative video game dominating Twitch
• “AI neutrality” emerging as a new foreign policy doctrine
• AI-produced films earning major audience praise (and backlash)
• A Chinese lab surpassing U.S. AI leadership
• Datacenter NIMBYism shaping local elections
• And even AI entering U.S. presidential politics through executive orders and court battles

If you work in B2B software, this episode is your roadmap to how AI is transforming not just technology — but business models, economics, and the balance of global power.

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AI Agents (also known as Agentic AI) are quickly becoming one of the most talked-about ways for enterprises to operationalize generative AI. G2 recently released a new research report — “A Leap of Trust: AI Agents Are Winning Hearts and Wallets” — highlighting where adoption is happening and why.

In this episode, Dave “CAC” Kellogg and Ray “Growth” Rike break down the report’s findings and what they mean for business leaders evaluating AI strategies today, including:

• How efficiency and effectiveness gains translate into real business outcomes
• The connection between Parkinson’s Law and AI Agent scaling
• Which industries are leading AI Agent adoption
• The top use cases emerging across the enterprise
• Why Time to Value may be faster than expected
• Containment Rate — the new metric that matters
• And whether AI Agents accelerate a return to IT-centric projects

If you're evaluating how to bring Generative AI into your organization — including whether to deploy packaged AI applications or pursue an agentic approach — this conversation is full of practical insights and thought-provoking perspectives you won’t want to miss.

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The buzz about ROI from AI software investments is palpable, so Dave "CAC" Kellogg and Ray "Growth" Rike take on the topic of measuring Return on Investment on AI software investments. During today's episode, Dave and Ray discuss the details on measuring ROI including:

• Defining Return on Investment
• Soft vs Hard Benefits
• Productivity - the primary benefit of AI investments today
• Buy versus Build & Run - an old problem is new again
• ROI models need to go beyond payback period
• Look back reviews - the value vs the reality

Return on Investment (ROI) is not a new term - but one that is coming back into favor thanks to AI. If you are considering making or continuing an investment in AI software and project for your organization - this episode has something of value for you!

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9-9-6. A term to describe the work culture of working 9AM - 9PM six days a week. This term is most often associated with those founders, companies, and their employees that work in generational software opportunities - such as we see right now with AI Software.

Dave and Ray discuss 9-9-6 from multiple angles in today's episode including:

• What 9-9-6 is
• Where did it originate
• Why is it trending
• The importance of Winning in the early stage of a new software era
• Geoffrey Moore's segmentation of new enterprise software markets (Gorillas, Chimps and Monkeys)
• Historic examples of those companies that became category leader
• Why the grind is not the point - Winning is
• Why the choice of entering a 9-9-6 environment makes choosing the company even more important

If you are currently in an early-stage AI-Native software company or considering making the move - this episode is a must listen!

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The Forward-Deployed Engineer (FDE) represents a fundamental reimagining of the technical role in high-stakes enterprise environments. At its core, an FDE is a software engineer embedded directly with customers to solve their most complex—and often ambiguous—problems.

Palantir is widely credited as the originator and early adopter of the FDE model, initially referring to these engineers as “Deltas.” In this episode, Dave “CAC” Kellogg and Ray “Growth” Rike explore multiple dimensions of the Forward-Deployed Engineer role, including:

• The origin of the FDE
• How the military influenced the term
• Whether the FDE belongs in a technology-enabled services company or a software company
• How an FDE differs from a traditional technical services consultant
• Where FDE expenses should be allocated—COGS vs. OPEX
• How those allocation decisions impact key metrics
• The hiring trends shaping the future of the FDE

If you’re building an AI-native application or an agentic AI company with outcome-based pricing, this episode is packed with insights and ideas on why a Forward-Deployed Engineer could be your next—and most important—hire.

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