Brand Visibility Scorecard
Measure brand presence across products, customers, regions, prompts, and engines
- Difficulty
- Moderate
- Time to result
- ~days to results
- Steps
- 6
- Confidence
- 99%
The Brand Visibility Scorecard begins by defining the names an answer engine might use for the brand, then maps products, competitors, ideal customer profiles, regions, and intent stages. Those inputs generate a representative portfolio of prompts rather than an arbitrary keyword list. Each prompt is sent repeatedly to selected answer engines, and the system records the actual responses. Brand visibility is calculated as the percentage of answers in which the brand appears. Teams can then segment the score by product, customer, region, intent, engine, competitor, and time period. This creates a stable operational metric for AEO work while preserving the detail needed to diagnose gaps. The score measures presence, not necessarily positive sentiment or commercial impact, so it should be paired with citation analysis and outcome data.
Origin
Extracted from Marketing Against The Grain during a live walkthrough of HubSpot's answer-engine optimization tool using Dell as the demonstration brand.
Core principles
- 01Visibility is the percentage of relevant answers containing the brand
- 02Prompt selection determines what the score represents
- 03Products and ideal customers require separate measurement
- 04Real engine responses are stronger evidence than ranking proxies
How to run it
- 1
Normalize the brand identity
Define the company name, product names, abbreviations, aliases, and common answer-engine references. Use these variants when detecting appearances in responses.
Pro tip Review false positives and missed variants before trusting the score.
Watch out Simple text matching can misclassify ambiguous names.
- 2
Map the market dimensions
List products, competitors, ideal customers, regions, and intent levels. Decide which combinations are strategically important enough to monitor.
Pro tip Separate products that serve materially different customer groups.
Watch out A single undifferentiated brand score can hide weak visibility in priority markets.
- 3
Build the prompt portfolio
Generate realistic questions across awareness, evaluation, and decision intent for each important combination. Review them for relevance before measurement begins.
Pro tip Use customer language from sales, support, and research data.
Watch out A biased prompt set will produce a misleading visibility score.
- 4
Collect real answers
Send the prompts to selected answer engines and retain the full answers, mentions, citations, engine, and date. Repeat collection on a consistent cadence.
Pro tip Preserve raw responses so analysts can audit how each score was produced.
Watch out Answer-engine outputs can vary, so one run per prompt may be noisy.
- 5
Calculate and segment visibility
Calculate the percentage of answers in which the brand appears, then break it down by product, ICP, region, intent, engine, and competitor. Highlight important gaps and trends.
Pro tip Show both the aggregate score and the denominator of prompts behind it.
Watch out Presence does not establish that the answer described the brand favorably.
- 6
Set improvement targets
Choose priority segments where better visibility is commercially valuable and establish a baseline and review date. Pass those gaps into citation analysis and content planning.
Pro tip Target a small number of high-value prompt clusters before expanding coverage.
Watch out Optimizing the overall percentage can distract from the prompts that influence actual purchases.
In the wild
The tool identifies Dell's brand, competitors, product lines such as XPS, Alienware, and Latitude, the customers each product serves, and regional differences. It generates prompts across intent levels, sends them to ChatGPT, Gemini, and Perplexity, and calculates how often Dell appears.
→ Dell receives a structured visibility baseline that can be analyzed beyond the company-wide average.
A software company builds separate prompt sets for UK and US mid-market buyers because product terminology and requirements differ. It discovers strong awareness visibility in both regions but weak decision-stage visibility in the UK.
→ The team directs AEO work toward a specific regional and intent-level gap.
Common mistakes
Tracking arbitrary prompts
Prompts that do not reflect real products, customers, regions, and intent produce an impressive-looking but strategically weak score.
Reading visibility as sentiment
A brand can appear because an answer praises, criticizes, or merely lists it.
Ignoring score denominators
A high percentage based on a tiny or narrow prompt set may not represent the wider market.
Is it for you?
Best for
Marketers who need a consistent AEO baseline and competitive share-of-voice view.
Not ideal for
Teams expecting one aggregate percentage to explain sentiment, citation quality, or revenue impact by itself.
From the transcript
“But then we're actually gonna go and look at what are the prompts you should be tracking across different levels of intent to really understand…”
“And what brand visibility says is it tells us in what percentage of the answers does our brand appear.”
“This is the metric you want to improve.”
From the episode
We Found Where AI Gets Its Answers (It’s Not Your Website)