{"id":3146,"date":"2026-10-13T03:22:30","date_gmt":"2026-10-13T03:22:30","guid":{"rendered":"https:\/\/maxaeo.ai\/blog\/competitor-share-model-template\/"},"modified":"2026-10-13T03:22:30","modified_gmt":"2026-10-13T03:22:30","slug":"competitor-share-model-template","status":"publish","type":"post","link":"https:\/\/maxaeo.ai\/blog\/competitor-share-model-template\/","title":{"rendered":"Competitor Share of Model Evaluation Template: A Practical SoM Framework"},"content":{"rendered":"

By maxaeo.ai \uff5c Published 2026-10-13 \uff5c Updated 2026-10-13<\/em><\/p>\n

A competitor share of model evaluation template<\/strong> turns AI search answers into a repeatable comparison of brands, prompts, models, citations, and recommendation position. Instead of asking whether a company \u201cshows up,\u201d it measures how much competitive answer space each brand captures and where that visibility comes from.<\/p>\n

Most AI visibility frameworks cover share of voice, prompt gaps, model-level reporting, and cited sources. The harder measurement problems are usually left open: keeping the denominator stable, weighting recommendation position, separating buyer intent, and distinguishing a passing mention from a commercially useful recommendation. (aisearchtoolrank.com<\/a>)<\/p>\n

This framework fills those gaps with a copy-ready evaluation structure for SaaS and other considered purchases.<\/p>\n

What is share of model in competitive AI search?<\/h2>\n

Share of model (SoM) is the percentage of weighted AI answer visibility captured by a brand across a fixed set of prompts, models, and competitors.<\/strong> It is similar to traditional share of voice, but it accounts for how AI systems mention, compare, cite, and recommend brands.<\/p>\n

A basic formula is:<\/p>\n

Brand SoM = Brand weighted visibility\n            \u00f7 Total weighted visibility for all tracked brands\n            \u00d7 100\n<\/code><\/pre>\n
A simple mention count is useful for directional tracking, but it treats every appearance equally. A brand listed first in a recommendation answer should not receive the same score as a brand mentioned once in a long comparison.<\/p>\n
The evaluation should therefore record four separate signals:<\/p>\n