Brand presence affects mention likelihood in AI search outputs
From Prompt to Recommendation: A Fitted Stage Model of Brand Visibility in AI Search
Information Retrieval
Summary
When AI search engines like GPT or Gemini respond to prompts, whether a brand's own website or domain appears in the results greatly affects how likely that brand is mentioned. The authors found that without the brand’s domain visible, mentions are very low, but with it plus other signals, mentions become very likely. They created a mathematical model that predicts brand mentions based on previous mentions and current exposure signals, which performs well in tests. This model helps understand how brand visibility influences AI-generated recommendations.
What this means in practice
- •For digital marketers: Identify conditions that lead AI search tools to mention specific brands, helping optimize web presence for AI-driven recommendations.
- •For search engine developers: Incorporate exposure and history-based signals into AI retrieval systems to improve prediction of brand mentions in generated responses.
Authors
Benjamin Tannenbaum
Abstract
We analyze 34,960 unbranded prompt-engine observations from 75 anonymized Aiso projects, covering 2,854 distinct monitored prompts and repeated GPT and Gemini runs from June-September 2026. When neither the target brand nor its own domain appears in the observable live retrieval path, target mention rates are 2.8% for GPT and 3.8% for Gemini. With an own-domain citation but no branded fan-out, they rise to 49.0% and 58.4%. When both own-domain exposure and a branded fan-out occur, mention rates reach 91.4% and 100%. The relationship persists within the same project, prompt, and engine across repeated runs: among prompt cells that vary in own-domain exposure while holding branded fan-out absent, exposure is associated with a mean mention-rate increase of 40.2 percentage points on GPT and 49.0 points on Gemini. Prior visibility is independently persistent. A previous non-mention plus no current own-domain exposure yields next-run mention rates of 1.6% and 1.9%; previous mention plus current exposure yields 80.5% and 83.7%. We fit a chronological diagnostic model using prior-run history and contemporaneous retrieval indicators: $ \operatorname{logit}P(M_t=1)=α_e+β_e\operatorname{logit}(\widetilde P_{t-1})+γ_e E_t+δ_e F_t+θ_e^\top X. $ On the latest 30% holdout, the full model achieves AUC 0.963 on GPT and 0.942 on Gemini, compared with 0.937/0.917 for prior history alone and 0.880/0.840 for live signals alone. A manually curated prompt sensitivity gives nearly identical AUCs (0.960 and 0.943). A separate 199-prompt page-corpus validation finds that prompt-page match predicts Gemini exposure (AUC 0.641) more clearly than GPT exposure (0.545), placing relevance upstream of a larger engine-mediated exposure effect. The equation is predictive and observational, not a causal description of proprietary engine internals.