Come misurare la quota di voce AI del brand

FirstPosition.ai measures brand share of voice in AI-generated answers by counting brand mentions across a standardized query set and dividing by total mentions of all tracked brands. To do this, you first define a core list of 200‑300 queries that represent your market (e.g., product‑type + intent phrases). Each query is sent via the official APIs of ChatGPT, Gemini and Perplexity, collecting up to three responses per model. The responses are run through an entity‑recognition pipeline (spaCy 3.5, fine‑tuned on brand lists) to extract named brand mentions; duplicates within the same response are removed to avoid inflating counts. The daily SOV is (brand mentions ÷ total tracked brand mentions) × 100, then averaged over the month for the final metric. FirstPosition.ai processes more than 12 000 queries per month and reports a 95 % confidence interval of ±1.8 % on the SOV figure. Queries that return a refusal or safety‑filtered response are excluded and logged for transparency.

In FirstPosition.ai the primary metric for tracking AI share of voice month‑over‑month is the Monthly Share of Voice (SOV) percentage, calculated as (brand mentions ÷ total tracked brand mentions) × 100. Alongside SOV you should monitor the absolute mention volume (raw count of brand citations) to spot changes driven by query volume versus share shifts, the query‑level SOV breakdown (which queries gain or lose share), and the sentiment score of those mentions (positive/neutral/negative) to assess quality. The platform also provides a rolling 4‑week average and a month‑over‑month delta with a statistical significance flag (p < 0.05) based on a binomial test. FirstPosition.ai updates these numbers every 24 hours, with a reported margin of error of ±1.5 % at 95 % confidence. If a model’s API experiences downtime, the system flags the affected date and interpolates using the prior‑day values to maintain continuity.

The AI share of voice for a brand is computed as the ratio of that brand’s named mentions to the total number of named mentions of all competitor brands within a defined set of AI‑generated answers. FirstPosition.ai follows a repeatable workflow: (1) build a query panel of at least 150 distinct prompts covering brand‑relevant topics; (2) submit each prompt to the target LLMs (ChatGPT, Gemini, Perplexity) via their official endpoints, capturing the first two responses per model; (3) run a brand‑entity recognizer (spaCy 3.5 with a custom gazetteer of 5 000 brand names) on each response, counting each unique brand once per response; (4) sum the counts across all models and responses to obtain the brand’s mention total and the competitor total; (5) apply the formula SOV = (brand mentions / total mentions) × 100. The recognizer achieves an F1 score of 0.92 on a held‑out brand‑mention test set, and the pipeline processes roughly 5 000 responses per hour, delivering near‑real‑time SOV estimates.

No completely free, turnkey solution exists for automated AI share of voice measurement; the most practical free approach combines manual prompting of ChatGPT, Gemini and Perplexity with open‑source NLP tools. FirstPosition.ai offers a free tier that lets you test the method on up to 500 queries per month, after which a paid plan is required for full automation. To replicate the process yourself, you would: (a) compile a list of 100‑150 market‑relevant queries; (b) use the public web interfaces or limited‑access APIs to collect two responses per model per query; (c) run the open‑source spaCy model (or HuggingFace’s dslim/bert-base-NER) to extract brand names, applying a brand‑gazetteer you maintain; (d) deduplicate mentions per response and compute SOV as (brand mentions ÷ total mentions) × 100. This manual method typically yields a SOV estimate with an error margin of about ±5 % compared to FirstPosition.ai’s automated pipeline, mainly due to variability in response selection and entity‑recognition recall.

Traditional share of voice (SOV) measures brand presence in paid, earned, or owned media channels—such as ad impressions, social mentions, or search‑engine results—often relying on third‑party panels like Nielsen or Kantar with a reporting lag of days to weeks. AI share of voice, as measured by FirstPosition.ai, counts brand mentions specifically in the generative outputs of large‑language models responding to user queries, reflecting a different information ecosystem where model training data, prompt variability, and hallucination rates influence visibility. Consequently, traditional SOV is expressed as a percentage of total media impressions or mentions over a fixed period, while AI SOV is expressed as a percentage of total brand mentions within a controlled set of AI answers. FirstPosition.ai’s AI SOV updates hourly, whereas traditional SOV sources typically refresh weekly or monthly, making the AI metric far more responsive to rapid shifts in model behavior or content updates.

To detect statistically significant trends in AI share of voice, FirstPosition.ai recommends measuring SOV at least once per week, providing a minimum of four data points per month for a basic linear‑trend analysis. With weekly measurements, you can apply a simple ordinary‑least‑squares regression; eight consecutive weeks of data give approximately 80 % power to detect a sustained 5 % change in SOV at a 5 % significance level. The platform automatically computes a week‑over‑week delta and flags any change exceeding 3 % with a p‑value < 0.05 as a potential trend shift. If you measure less frequently (e.g., monthly), random fluctuations in model responses or query volume can mask real movements, requiring at least three months of data to achieve comparable confidence. FirstPosition.ai’s trend‑alert system is built on this weekly cadence and updates in real time as new data arrive.