From Prompts to Priorities: A Practical AI Visibility Workflow

This workflow dives profound into the prompts you track, uncovers rivalrous gaps, examines the citations ample tongue models depend on for recommendations, and reveals your brand’s true visibility in Moz Pro.

Marketing professionals are used to being overwhelmed by data. We're used to sifting through data points from disparate systems and construction a narrative that proves we are doing a fine job for our clients.

However, immediate tracking tests marketers and SEOs in a way that feels nearer to the fabled Madison Avenue promotion executives of the 1960s:

• No straightforward conversion data ties to an idiosyncratic tracked prompt.
• No public-facing hunt quantity data exists for prompts.
• Prompt responses lone offer directional guidance. Responses are tailored according to all user’s own habits and former conversation history.

The measure playbook that SEOs have memorized complete the final 15 to 20 years has been thrown out the window.

Fortunately, you can analyze directional AI Visibility data and rotate it into an act plan. Unlike most digital promotion programs, there are no easy, quick fixes. Brand marketing? You improved accept that’s rear on the menu. Buying non-branded links to manipulate hunt results? Yeah, not so much, unless you’re into scorch & churn projects.

The goal isn't to industry tidy attribution stories from example data. The goal is to nexus the questions your customers ask alongside the answers they receive, the brands surfaced in those recommendations, and the sources that power those answers.

That association volition not create a tidy conversion rate. It volition display you anywhere to examine and what to enhance next.

Tracking your brand's visibility without a apparent goal is akin environment up an analytics dashboard without knowing which conversion actions most effect your business.

Define what you are trying to resolve and nexus the inspection to a endeavor outcome, enabling you to construe data in a way that drives strategic decisions.

For example:

• A product promotion team may desire to cognize whether its brand appears as a advice whenever possible buyers ask LLMs to difference the finest products for a particular use case.
• A content promotion team may desire to comprehend which satisfied types power LLM recommendations and which related fan-out queries appear from a precedence topic. Fan-out queries are the connected searches or subquestions an AI scheme may use to examine a wider request.
• A digital PR team may desire to acknowledge third-party sources that create dependable opportunities for brand coverage, inclusion, or outreach.
• An SEO & GEO team may desire to display executives how their brand appears in AI alongside traditional hunt performance.
• An executive may desire a focused dashboard that tracks how the brand is discussed throughout a defined set of high-value prompts.

Once you cognize the chief intent for environment up a dashboard, choosing what to track gets much easier.

Define:

• Your mark spectators or ideal client overview (ICP).
• The products or services that can credibly fulfill the need.
• Your precedence topics.

The mark market and language. Where customers are in the purchasing cycle.The possible activity your squad expects to do following reviewing the data (e.g., reaching out to authors, creating new content, etc.)

Now all squad in your promotion institution has item it can use, fairly than starting from a blank slate. Content writers can scheme about it; merchandise promotion can sharpen positioning; media buyers can create ads about particular features and ache points; PR can acknowledge anywhere stronger third-party safety may matter; and SEO/GEO can build a reporting perspective tied to a genuine objective.

It additionally prevents a average mistake: treating all missing brand citation as a problem. If a immediate does not fit your spectators or merchandise offering, appearing in the answer may have small business value.

Start alongside top-level metrics, but don't halt there.

• Visibility measures coverage. How many tracked responses included your brand?
• Average rank measures stance whenever present. Absences are excluded, so peruse position beside visibility.
• Share of voice measures existence inside a defined rivalrous set. It does not portray the complete market or all brand named in the reply text.

The AI Visibility dashboard in Moz outlines these data points in a digestible format, highlighting how frequently brands are mentioned, how first the brands are mentioned in responses, and how frequently all brand “wins” the competition to be shown first. 

These metrics answer distinct questions. 

• A powerful average position alongside low visibility method the brand performs fine whenever selected, but doesn’t appear often. 
• High visibility alongside a weaker position method the brand appears regularly but may not clasp a foremost position. 

This is why I’d never put one of these numbers on a glide by itself and call it the story.

Example: Coverage and position can item in distinct directions.

In this post, I volition citation a prompt-tracking dashboard I set up to difference my favorite headphone brand against its fiercest competitors. As a dense metal element enthusiast, I have tested a broad range of headphones and earbuds complete the final decade, and Bose has continually arrive out on top. P.S. This is not sponsored in any way, but I cannot province my gratitude adequate for Bose creating headphones that rotate the cacophonous audio of dense metal element into sweet, bass-induced sonic delight. Ok, adequate of that; let’s get rear to the data & the example:

In a supplied set of 72 headphone prompts, Bose appeared in 61.1% of ChatGPT responses and 44.4% of Perplexity responses. However, whenever current in prompts, the average position was 1.95 in ChatGPT and 1.84 in Perplexity.

Bose appeared small frequently in Perplexity, but tended to clasp a slightly stronger stance whenever included in the immediate response. That is a logic to examine the missing appearances, not province one example universally stronger or weaker.

The general visibility mark is lone the starting point. Export the data, afterward interrupt it distinct until you can see anywhere the form is really coming from.

Start by segmenting prompts into a few helpful buckets:

• Topic: Where is the brand strongest or weakest throughout merchandise categories, use cases, features, pricing, and support?
• Intent: Does the brand appear in informational, commercial, transactional, or navigational prompts?
• Prompt format: Are gaps concentrated in recommendations, comparisons, how-to questions, or characteristic constraints?
• Model: Does the identical inquiry create a distinct brand set or advice form throughout ChatGPT, Gemini, Google AI Mode, and Perplexity?
• Competitor presence: Which tracked competitors appear whenever your brand does not?

On Intent:

You can afterward internally tag your prompts according to traditional search intent.

Pro tip! Keep immediate counts beside percentages. If a topic contains six prompts, one reply can materially alter the result, so you do not desire outliers to coat a narrative that power guide you downward a rabbit hole.

Don't section everything fair since you can. Look for three findings:

1. A recurring power value defending.
2. A model-specific difference value investigating.
3. A repeated gap throughout crucial prompts or models.

Your goal at this phase is to change a elephantine figure of exports into a fistful of patterns value investigating. Segmentation should disclose what the general mark hides, not provision you six additional charts to explain. Consider which patterns power key client segments, have the top possible endeavor impact, and which you can realistically location alongside accessible resources. This method helps your squad concentration on the patterns that matter most alternatively of getting misplaced in engaging but small impactful findings.

If your current immediate set is too lean to disclose any themes or actionable data, Moz’s Prompt Suggestions can exterior additional prompts according to topic sets. Review the baseline first, afterward add applicable suggestions and tag them separately.

Visibility tells you whether your brand appeared. Sentiment helps you comprehend how favorably it was discussed.

Let’s obtain a appearance at how you can analyze brand opinion in the Moz tools.

Within AI Visibility, open the Sentiment tab for the example and immediate catalog you desire to analyze. Start alongside the Overall Score and its confidence label, but use it as a starting point, not the verdict. It reflects conversation inside the tracked prompts, not worldwide brand perception.

Next, assessment the Mentions Breakdown. The total figure of mentions and the positive, neutral, and negative mix provision essential environment whenever comparing brands. Two brands can district on akin scores alongside extremely distinct citation counts, so don't treat those scores as equal without checking the quantity rearward them.

Then move from the total mark to Top Themes and Sentiment Drivers:

• Use Top Themes to acknowledge perceived strengths and weaknesses.
• Select your brand or a competitor under Sentiment Drivers, afterward develop idiosyncratic themes to inspect the summaries and applicable reply snippets.
• Investigate blended or feeble themes before proposing a fix. Determine which prompts, claims, and sources contribute to the pattern.

Example: The general mark is lone the archetypal layer

On the ChatGPT dashboard, Bose had an Overall Score of 73 alongside elevated confidence throughout 169 mentions: 79 positive, 90 neutral, and none negative. Noise cancellation scored 88, and listening comfort scored 95 among its top themes.

That doesn't average the net universally loves Bose. It method those strengths showed up consistently inside this tracked immediate set. The 90 neutral mentions are additionally a logic to keep digging fairly than halt at the header score.

Not all citation is grouped into a theme, so the theme counts may not absolutely equivalent the header metrics. Use the in-app perspective to place the pattern, afterward peruse the reply content to comprehend what's driving it.

Once you discover a pattern, move from the metric to the genuine response.

Compare all of the following:

• The products or services recommended.
• The criteria used to create the recommendation.
• The competitors included.
• The claims made concerning all option.
• The cited sources.
• Whether your brand credibly fits the need.

Example: Similar metrics can conceal distinct problems

For “best over-ear headphones for glasses wearers,” Bose held an average position of 1 in three models and was absent from Google AI Mode. For “best headphones for video calls,” Bose appeared lone in Perplexity, at exported position 2.

The archetypal gap points you toward comfort criteria and the sources supporting them. The second points you toward microphone and call-quality evidence. Neither proves you need to dedicate resources to build a new page.

Ignore the essential intuition to create a 2,000-word blog article to location not showing up for a sole prompt. A missing appearance may item to out-of-date information, feeble third-party coverage, unclear merchandise positioning, a genuine merchandise limitation, or mediocre immediate fit.

A missing appearance isn't the diagnosis. It's the clue that tells you anywhere to examine next: the prompt, the response, the merchandise fit, and the sources directing the recommendation.

First, categorize all applicable source: Is it an editorial review, difference page, retailer, community, video, social media post, investigation report, owned resource, or competitor-owned page?

Then analyze how that citation is used and how it relates to your brand:

1. Does your brand appear on the cited origin page?

2. Does your brand appear in the LLM reply that cites it?

Those combinations guide to distinct actions:

Example: An total citation site is not the final answer

Cited pages from Perplexity showed a TechRadar over-ear headphone guide as a origin throughout 20 prompts, alongside Bose mentioned in fractional of them. The live leaf already contained a substantive Bose section, which apt influenced Perplexity’s responses.

The incorrect move would be sending a generic throw to TechRadar to add additional data concerning Bose. The defensible move is to acknowledge the connected prompts and responses, verify which attributes are highlighted, and discover a specific, evidence-supported betterment you can display alongside new content.

Important clarification: a citation isn't the identical as a backlink, and it certainly isn’t an outreach approval slip. A leaf can citation your brand without linking to you. That can create an outreach opportunity, but lone whenever a nexus would genuinely enhance the page. Big publications and famous authors are drowning in templated pitches; “you mentioned us, delight link” is not a sustainable strategy.

Studying the data is lone fractional the job. Most reports end up in purgatory since they never inform the squad what to do next. The representative study barely explains what happened, adds multiple fancy-looking charts, and quietly expires in a shared directory following a 30-second administrator review. Your AI Visibility study should complete alongside a abbreviated catalog of activity the squad can really start.

Turn all verified finding into one of four types of work:

• Fix the facts: Correct any obsolete data on the two owned pages and the third-party sources from the citations report.
• Earn consideration: Craft pitches for your brand to be mentioned on cited pages anywhere competitors are featured, and you’re not.
• Protect a strength: Keep the supporting claims and pages exact and current.
• Keep digging: Investigate whenever merchandise fit, immediate value, or origin evidence remains murky.

Not all finding deserves its own project. Some patterns can rotate into activity that really moves revenue; another tasks may be intriguing but not value pursuing. To prioritize effectively, use this checklist:

• Assess endeavor impact
• Consider the frequence alongside which the form appears.
• Evaluate your confidence in the supporting evidence.
• Estimate the attempt required.

Pick three to five priorities according to these criteria. Don’t grade all possible project as a elevated precedence unless you desire to emphasis out your colleagues and scorch the midnight oil alongside your project administration system.

Assign all project an owner, a tangible deliverable, a supporting immediate or URL, a assessment date, and a status. For most teams, a shared spreadsheet is enough; larger teams can move the activity into their existing project administration system.

One final rule: don't keep moving the goalposts. Preserve the existing prompts, exports, topics, cited pages, and gathering settings. Compare the identical range next period and study any new prompts separately. Otherwise, you can industry a theatrical addition in brand visibility merely by changing what you track.

For example, let’s say you desire to pivot your brand visibility dashboard to track only difference prompts against competitors, using lone tagged terms. Your next gathering may indicate a notable addition in brand visibility, but that’s since you guided the scheme to citation your brand name in all sole prompt. Based on your first endeavor objective, you may desire to track non-branded prompts to gauge improved whether LLMs propose your products without being prompted by the brand name. On the another hand, a ample figure of tagged prompts can provision you additional evidence to collect intel on how your brand is perceived throughout the web.

Steps 1 through 7 display you how to analyze your AI visibility inside Moz Pro and the corresponding exports. If you’re managing multiple dashboards, the true issue is scale. Once you're reviewing hundreds of prompts throughout multiple models, topics, responses, and cited pages, the manual activity gets ugly fast.

That is why I built the AI Visibility Analysis skill. It applies the identical workflow logic in bulk: comparing models and topics, spotting recurring strengths and gaps, summarizing reply themes, construction a citation assessment queue, and turning the strongest verified findings into prioritized actions. 

It afterward packages the inspection into:

• An administrator overview that gives leaders the scorecard, the most crucial gaps, and a abbreviated catalog of priorities.
• A detailed study that explains the findings throughout models, topics, competitors, prompts, reply themes, and citations.
• An editable act workbook that maps prompts and sources to actions, owners, effort, confidence, status, and next steps during keeping the supporting evidence attached.
• Supporting CSV and JSON records that maintain the immediate appendix, cited-page inventory, citation-review queue, and calculated metrics for deeper analysis.

Example: The inspection matters additional than the document count

In the Bose example, the accomplishment processed the identical immediate and citation exports used throughout this post, segmented achievement throughout models and topics, surfaced repeated gaps, built a citation assessment queue, and translated the strongest findings into prioritized actions. It afterward packaged that activity into a three-page administrator summary, an eight-page detailed report, a 13-sheet workbook, and supporting evidence files.

The accomplishment is platform-agnostic and can run in tools specified as Claude Code, Codex, or Cursor/Grok. Because the accomplishment uses a reasoning-heavy workflow, stronger models mostly create deeper analysis. While new models keep launching, it’s finest to rod alongside elevated or additional elevated reasoning, despite of the model, to justify the best-quality output.

Note: You can download the AI Visibility Analysis accomplishment as part of a wider Marketing Skills community repository I have created here. In supplement to the AI Visibility Analysis skill, you can download the Backlink Gap Analysis & SEO Workbook Builder, the two of which use Moz data to assistance you in mundane SEO work.

Start alongside the scorecard or use the accomplishment to measure the analysis, but don't run it formerly and call it a day. Immerse yourself in the data, unearth any possible patterns, and try to discover 3-5 important opportunities for your brand complete the next 90 days.

The supreme goal alongside this data is to answer the three questions:

• Where is your brand consistently winning (Defend your position)
• Where are competitors being recommended alternatively of you (Build your visibility)
• What evidence appears to be influencing that decision (Understand the market)

That is anywhere AI Visibility data stops being another mark and starts becoming useful.

You may detect a positioning problem, a gap in third-party coverage, outdated information, feeble merchandise evidence, or merely prompts anywhere your brand was never a fine fit in the archetypal place. The crucial part is knowing the difference.

AI Visibility volition not provision you the spotless attribution marketers have spent the final 20 years chasing. However, these reports can display you item traditional ranking reports never could: how AI systems comprehend your brand, who they difference you against, and which sources are shaping those recommendations.

The brands that get the most value from this data won’t be the ones obsessing complete whether their visibility mark moved three points this month. They’ll be the ones using the data to fig out why they are or are not part of the answer, and what they can do concerning it.