What Marketing and Measurement Models Can You Learn from Avinash Kaushik's Blog?

Avinash Kaushik's blog, Occam's Razor, publishes practical frameworks for marketing, analytics, strategy, measurement, experimentation, and AI. If you want reusable models rather than theory, the site organizes them into named sections — Analytics, Marketing Models & Frameworks, Strategic Thinking, and Leadership — and several posts present complete, named playbooks you can adapt to your own work. The models below are drawn from the site's own post listings and descriptions.

Marketing Models & Frameworks

This section collects the most directly reusable models. Three stand out from the current listings:

• Loss Recovery Growth Model: Answer Engine Optimization (AEO). The premise is that as search behavior shifts from keyword queries to resolution questions typed into answer engines, you will lose traffic and revenue. The model frames AEO as a loss-recovery growth strategy rather than a pure acquisition play — useful if you are trying to quantify what answer engines are taking away and build a response around it.
• AI Ready? Google Ads Maturity Model. A staged maturity model for Google Ads, built on the argument that continuing to run the platform the old way is like using horses when fuel-efficient long-range motorcycles exist. Use it to assess where your account sits and what the next stage requires.
• Sports Marketing Measurement Playbook. Built on the observation that nearly all of the top 50 telecasts in the US were connected to sports. It covers incrementality and measurement for sports-linked campaigns — relevant if you are deciding how to measure sponsorship or event-driven marketing.

Measurement and Experimentation

The Analytics section carries the measurement-specific material:

• Google's Statistics: Heads They Win, Tails You Lose. Examines Google Brand Lift and Conversion Lift studies, which share the same underlying idea of statistical certainty. The key argument: certainty is not the same thing as answering your actual question. Read this before treating a lift study's significance as proof of business impact.
• Robust Experimentation and Testing | Reasons for Failure. A catalog of why controlled experiments fail, aimed at readers already familiar with testing. Useful as a pre-flight checklist before you launch a test.
• Google Analytics 2026: Unlocking New Features, Analysis. Covers a cluster of GA features released over the past year that extend the platform's ability to support higher-altitude analysis — relevant if your measurement work runs on GA.

Strategy, Creative, and Leadership

• High-Impact Marketing: Rock Creative Pre-Testing. A framework for testing creative before it runs, framed around a conversation about the gap between what a brand believes its challenge is and what the ad actually communicates.
• Scapegoating Analysts | Recognizing & Preventing A Bad Idea. In the Leadership section, this covers how analysts get blamed for outcomes and how to recognize and prevent it — a management-side complement to the measurement models.

How to Use These Models

Each model is presented as a post rather than a downloadable template, so expect to read the argument and extract the structure yourself. The site's own framing — "actionable web analytics," "actionable analytics," "key performance indicators" — signals that the intended output is a decision or a change in practice, not a summary. Browse the full section listings under Marketing Models & Frameworks and Analytics to see the complete set, since the site adds posts over time.