Competitive Mention Map
Consensus definition
A Competitive Mention Map is constructed by running a representative prompt set — typically 30–500 queries reflecting real buyer research intent — across multiple AI platforms (ChatGPT, Perplexity, AI Overviews, Gemini) and recording every brand that co-appears with the tracked brand in each response1. The raw output is a co-occurrence matrix: rows represent the tracked brand's queries, columns represent competitors, cells record joint mention frequency2. Clustering by query topic or buyer-intent stage then produces segmented views — comparison-intent vs category-discovery prompts may surface entirely different competitor peer groups3. Visual forms include weighted network graphs (nodes = brands, edge weight = co-mention frequency), platform-breakdown heat maps and segment-by-segment frequency tables. The analytical lineage connects to classical brand-association mapping — Aaker's brand equity framework treats associations as network-like mental connections, and Keller's CBBE model frames them as competitive points-of-parity and points-of-difference4. The Competitive Mention Map operationalises those concepts for AI-mediated discovery environments, where the platform — not the consumer — constructs the initial association set5.
rhinegold operator refinement
Rhinegold's reframe: when an AI model answers "Which providers should I evaluate for [category]?", it synthesises a shortlist that functions as the buyer's consideration set — the subset of brands actively evaluated before a purchase decision4. Consumer-research literature shows people rarely expand beyond the initial 2–5 options surfaced5. The Competitive Mention Map makes this AI-constructed shortlist explicit: which peers appear with you, how frequently, on which platform. Appearing alongside aspirational enterprise brands on one platform while being grouped with low-cost alternatives on another is a split-positioning signal that demands strategic intervention, not more content.
Operational use
Run a defined prompt set (30–100 queries across category discovery, comparison and use-case intents) on a consistent weekly or bi-weekly cadence across at least three AI platforms3. Build the co-occurrence matrix from raw response logs; aggregate by query cluster and platform. Track quarter-on-quarter drift in which competitors co-appear most frequently, treating shifts as leading indicators of repositioning in AI-mediated channels6.
Measurement boundary
A Competitive Mention Map is only as valid as its prompt set — queries that skew toward product categories, geographies or maturity stages the brand does not serve will populate the map with spurious pairings3. Results vary meaningfully by platform: citation behaviour differs substantially between providers (some include external links in the large majority of responses, others in roughly a third)6, producing different co-mention patterns for the same queries. Competitor naming (parent vs. product vs. trade name) requires explicit normalisation. Maps are point-in-time snapshots — LLM training cycles and retrieval shifts can change the competitive frame without any action by the tracked brand.
Distinct from
From Co-Mentions: that is the underlying signal — the raw observation that brand X and brand Y appear in the same AI response. The Competitive Mention Map is the analytical product built from aggregated co-mention data across a prompt set, not a single observation. From Share of Voice: SoV is a scalar per brand (brand mentions ÷ all brand mentions); the map is a relational matrix showing pairwise co-occurrence across the full competitive set. From Platform Divergence: that is a descriptor of variance across AI engines — one dimension visible within the map (the platform axis), not a separate construct. From Brand Recommendation Share: that tracks explicit recommendations rather than any mention or co-appearance; recommendation share is a subset of the signals that feed the map.
Common mistakes
- Treating the map as static: AI-platform co-mention patterns shift with LLM updates and content changes, making single-run snapshots misleading without longitudinal comparison.
- Using branded prompts only: querying "How does [Brand X] compare to [Brand Y]?" by name will artificially inflate co-mention frequency; the map should be built predominantly from category-level and use-case prompts reflecting real buyer queries1.
- Collapsing platforms into a single aggregate: different engines construct different competitive peer groups for the same queries due to differing retrieval and citation architectures6; platform-level disaggregation is essential for actionable diagnosis.
- Conflating co-mention frequency with endorsement: high co-mention frequency with a budget competitor may signal commoditisation framing, not validation — context and sentiment in the surrounding response must be read alongside frequency counts1.
Sources & deeper reading
- 1Sight AI — "Brand Monitoring in LLM Outputs" (competitive co-mention tracking methodology; consideration-set framing; sentiment in context)
- 3Shadow Inc. — "How to Measure AI Share of Voice" (SoV formula; prompt-set construction; multi-engine testing)
- 6Nightwatch — "AI Share of Voice: How to Track and Grow" (platform citation-rate divergence; multi-engine SOV breakdown; longitudinal horizon)