GTM Report · Methodology

Intro

Hi, I'm Adam. I'm an independent builder & industry analyst. My goal is to help GTM leaders make sharp decisions in uncertain markets.

This is my methodology for the GTM Tech rankings and category maps. Three principles guide this work:

1. Mindshare matters. We can gain an edge by understanding the conversations and perceptions within our market. This goes beyond reviews or narrow expert opinions.
2. Curation is judgement. Most of my time is spent curating the panel and coverage universe. This is my judgement encoded into a research system that is differentiated from vanilla Claude queries.
3. Everything must update and evolve constantly. The traditional analyst model doesn't keep pace. This system is continuously updating and evolving.

Coverage

I cover products and companies that have a central application for B2B go-to-market in the AI era. The majority are GTM focused (e.g. Gong, Clay), but I also cover broader solutions (e.g. Claude Code, Vercel, Exa).

On the edges my judgement determines what fits (and what doesn't). Like all things, this evolves as I learn and as the market changes.

Panel Mindshare Score & Rank

Rankings and scores are based on "Panel Mindshare." It's a dynamic, leading indicator that I developed to understand this space. Here's how it works:

1. I've carefully curated a "panel" of over 2,000 sources across social media, newsletters, blogs, and podcasts. The panel is NOT intended to represent the broad market, it's designed to represent the sharpest minds in GTM (operators, authors, builders, executives).
2. I track vendor mentions and context of those mentions across the entire panel (tens of thousands of pieces of content and posts).
3. I weigh mentions based on a variety of engagement and relevance factors and then compile into a normalized score across channels and sources. Affiliated posts are ignored.
4. I compute scores across multiple timeframes, with a focus on quarterly and monthly change.
5. All scores are indexed to the top product for each scoring period. That creates a final 0-99 score for every product. The index follows a power curve rather than a straight line.

The 0-99 score shown in the master dataset is based on a trailing 180 day total, calculated nightly. The Q/Q change compares a product's index across its last two complete quarters (measured in index points) to deliver a durable trend instead of monthly noise.

Category Definitions

GTM Tech Types

My highest level organizing framework has a strong bend toward AI. I believe the traditional categorization is breaking down quickly. This is how I see the space at a high level:

GTM Tech Categories

Categories are generally organized around use cases, go-to-market motions, or the intersection of the two. That said, there is a balance of many factors in this framework and it ultimately comes down to my judgement.

Rules & Guidelines

Products can fit types and multiple categories, but there must be a strong case.

Agent Readiness Score

I score Agent Readiness as a secondary factor because it's highly relevant in today's world: in an agent era, whether your systems can actually build on a product matters more than the UI. It's an analyst-judged 0-99 score (with an A-F grade) across four surfaces — API, MCP server, SDK, and CLI — plus the quality of the docs behind them. The Surfaces column shows exactly which a product ships. In this case the score comes from multiple agents with different personas and perspectives, each assessing the product's agent surfaces and docs.

Feedback & Corrections

Send any suggestions or issues to adam at adamgtm.com. I verify them, write the fix back to the system and publish.

If you're building something interesting in this space, but it's not covered yet, please suggest a product here.