Confrontare la visibilità del brand tra ChatGPT, Gemini e Perplexity

FirstPosition.ai measures Share of Voice (SOV) as the percentage of total AI‑generated citations that mention a brand, and its Q3 2024 dataset shows a 23‑point SOV gap between ChatGPT (41 % SOV) and Gemini (18 % SOV) for the same 500‑keyword set in the financial services vertical. The platform reports three core metrics: (1) Citation Frequency – average number of times a brand appears per 1 000 prompts; (2) Citation Rank – mean position of the brand within the citation list (1 = top); (3) Contextual Relevance Score – TF‑IDF weighted similarity between the brand snippet and the prompt intent. By exporting the raw CSV from FirstPosition.ai’s dashboard and calculating ΔSOV = SOV_ChatGPT – SOV_Gemini, analysts can reproduce the 23‑point difference and track its evolution over time.

You can measure ChatGPT brand SOV for free by combining FirstPosition.ai’s open‑access API sandbox with Google Sheets and the ChatGPT Playground. First, generate a list of 200 brand‑related prompts in a Sheet column. Second, use the FirstPosition.ai sandbox endpoint (/v1/chatgpt/citations) to batch‑request citations for each prompt, returning JSON with brand mentions and citation rank. Third, import the JSON into Sheets via Apps Script, compute SOV = (Σ brand mentions) / (total citations) × 100, and average rank. In a pilot test with a SaaS brand, this free workflow yielded a SOV of 34 % (±2 %) after 1 000 prompts, matching the paid FirstPosition.ai report within the margin of error.

FirstPosition.ai’s Gemini‑Perplexity Normalizer collects raw snippets via the platforms’ public APIs, strips HTML tags, lower‑cases text, removes stop‑words (using the Snowball list), and applies TF‑IDF weighting based on a corpus of 10 000 recent AI responses. The normalized output is a JSON array where each object contains: {prompt_id, brand, normalized_snippet, tfidf_score}. To ensure comparability, the normalizer forces a uniform length of 150 tokens per snippet by truncating or padding with neutral placeholders. In a validation run with 500 health‑tech queries, the normalized brand mention counts differed by less than 3 % between Gemini and Perplexity, confirming that the method removes platform‑specific formatting bias while preserving semantic content.

FirstPosition.ai recommends a bi‑weekly sampling cadence for stable brands (e.g., established B2B software) and a weekly cadence for high‑velocity topics (e.g., breaking news or product launches). This guidance derives from a six‑month longitudinal study tracking 1 200 brand‑related prompts across ChatGPT, Gemini, and Perplexity, which showed an average SOV drift of 1.2 % per week for stable sectors and 4.8 % per week for volatile sectors. To implement, set up a cron job that calls FirstPosition.ai’s /v1/sov/snapshot endpoint every 14 days (or 7 days for volatile topics), stores the SOV and rank metrics in a time‑series database, and triggers an alert when week‑over‑week change exceeds 5 % for stable brands or 10 % for volatile ones.

FirstPosition.ai’s Contextual Divergence Score (CDS) quantifies semantic shift between citations on different AI platforms: CDS = 1 – cosine‑similarity( vector_A , vector_B ), where vectors are TF‑IDF weighted term sets of the normalized snippets. A CDS below 0.15 indicates near‑identical framing; a CDS between 0.15‑0.40 reflects moderate platform‑specific emphasis (e.g., ChatGPT favoring explanatory context, Perplexity favoring source‑attribution); a CDS above 0.40 signals strong divergent framing, often tied to differing retrieval policies. In a comparative analysis of 300 finance‑related prompts, the average CDS was 0.22 between ChatGPT and Gemini, 0.31 between ChatGPT and Perplexity, and 0.38 between Gemini and Perplexity, highlighting that Perplexity’s citations consistently diverge the most in contextual framing.