Tag: Competitive Analysis

AI Search Visibility Monitoring: A Practical Framework

If your AI visibility report moves from one run to the next, you need to know whether your brand’s position changed or the sample did. A chart that cannot answer that question is noise, however polished it looks.

You can make the signal more trustworthy. Build the monitor around fixed prompts, captured answers, explicit scoring rules, and decisions someone is responsible for making. The goal is not merely to count mentions. It is to understand where your brand appears, how it is represented, what evidence supports the answer, and what you should change next.

Decide what the monitor is supposed to change

Start with the decision, not the dashboard. AI search visibility can refer to several different problems, and each requires a different measurement:

Discoverability: Does your brand appear when someone asks about a category, problem, or use case without naming you?
Competitive presence: Does the answer include you alongside the alternatives a buyer is likely to consider?
Recommendation: Does the system merely mention you, or does it actually present you as a suitable choice?
Accuracy: Are the facts about your products, services, locations, people, policies, or capabilities correct?
Reputation: Is the description favorable, unfavorable, neutral, or mixed, and what language caused that classification?
Evidence: Which pages, domains, or citations appear to support the answer?

Do not collapse those questions into one visibility score. A brand can be mentioned frequently and described inaccurately. It can receive positive language in branded prompts while remaining absent from unbranded category discovery. It can also appear in a recommendation without receiving a citation. Those are different conditions with different remedies.

Write a measurement brief before collecting data. Name the audience, market, language, products, competitors, prompt families, platforms, and business decisions in scope. A program may examine how ChatGPT, Gemini, Perplexity, and Claude describe a brand, but results from those systems should remain separate as well as aggregated. A gain on one platform can otherwise hide a loss on another.

Define the unit of observation as one exact prompt run under one recorded condition. For every run, preserve the platform, model or mode when visible, market, language, date, prompt text, session state, answer text, cited URLs, and scoring result. If account status, retrieval settings, or personalization are known, record those too. Without that audit trail, you cannot tell whether a movement came from your content, a platform change, a different prompt, or conversational context.

Most importantly, do not present monitored prompts as a census of everything users see. They are a controlled panel. Their value comes from consistency and diagnostic depth, not from pretending they reproduce the entire audience.

Build a prompt set without moving the goalposts

Your prompt set determines what your visibility score can mean. A weak set overrepresents easy branded questions, changes whenever a stakeholder has a new idea, and mixes markets or intents that should be evaluated separately.

Begin with the real language of the market. Useful inputs include search-query data, internal site search, sales questions, support tickets, product comparisons, customer interviews, and community discussions. Convert those inputs into natural questions a person might ask an assistant. Avoid adding your brand name to an unbranded discovery prompt, praising the brand inside the question, or supplying facts that make the desired answer obvious.

Prompt family	Example	What it reveals
Category discovery	What tools help a small marketing team monitor how AI assistants describe its brand?	Whether the brand is associated with the relevant category before it is named.
Problem and use case	How can I find inaccurate claims about my company in AI-generated answers?	Whether the brand is connected to a specific need or job.
Comparison	What should I compare when choosing an AI visibility monitoring platform?	Which evaluation criteria and competing options enter the answer.
Recommendation	Which options fit a team that needs citation and sentiment monitoring?	Whether the system recommends the brand under stated constraints.
Branded accuracy	What does [brand] offer, and who is it for?	Whether the assistant recognizes the entity and represents its core facts correctly.

Keep two prompt panels. The locked panel changes rarely and supplies the trend line. The exploratory panel can absorb new products, questions, competitors, and market language. When an exploratory prompt becomes strategically important, add it to the next version of the locked panel and mark the break. Do not insert it into historical totals as if it had always been present.

Tag every prompt by intent, journey stage, product, audience, market, and whether it is branded or unbranded. These labels let you find a meaningful pattern. A flat overall result might conceal rising visibility for informational questions and falling visibility for purchase-oriented recommendations.

Use fresh sessions for independent tests. Conversational history can alter later answers, so a follow-up question belongs to a different test design. If multi-turn discovery matters to your audience, monitor it as a named journey with a fixed sequence rather than mixing it with standalone prompts.

Outputs can vary even when the visible prompt does not. Repeat matched conditions before treating a single answer as a trend. First establish the normal variation of each prompt family; then judge future movement against that baseline. This prevents one favorable or unfavorable response from becoming a strategy.

Score the answer, not just the brand mention

A mention counter answers only one question: whether a brand string appeared. Your scoring model should preserve enough detail to explain what that appearance meant.

Presence: Record whether the brand or an approved variant appears. Keep aliases in an entity dictionary so spelling and product-name differences do not create false absences.
Prominence: Record whether the brand is central to the answer, included in a list, mentioned only as an aside, or introduced through a citation without appearing in the prose.
Recommendation status: Separate explicit recommendation, conditional recommendation, neutral inclusion, and explicit exclusion. Save the sentence that justifies the label.
Accuracy: Compare concrete claims with a maintained set of approved facts. Label each reviewed claim as supported, incorrect, outdated, conflicting, or unverifiable. Unverifiable is not the same as false.
Sentiment: Use positive, neutral, negative, or mixed only when you also capture the language behind the label. Sentiment without evidence is difficult to audit and easy to misread.
Citations: Save the full URL, domain, page type, and whether it belongs to your organization, an independent publisher, or a competitor. A citation is evidence of selection, not automatic evidence of endorsement or factual correctness.
Competitive context: Record every monitored competitor that appears and the role each one receives. A simple name count misses the difference between being recommended and being used as a cautionary comparison.

Define share of voice before putting it on a dashboard. One defensible answer-level definition is the share of monitored answers naming your brand among answers that name at least one monitored brand. Another is mention-level share across all monitored-brand mentions. Those denominators answer different questions and can produce different results. Publish the formula next to the metric and keep it unchanged across reporting periods.

Keep branded and unbranded visibility separate. Branded prompts test entity recognition and factual representation. Unbranded prompts test whether the brand is retrieved for a category, problem, audience, or constraint. Combining them usually inflates the headline while hiding the harder discovery problem.

Treat sentiment as a review aid, not a verdict. An answer can praise ease of use while questioning fit for a particular customer. Calling that response simply positive discards the part that could change a buying decision. Preserve mixed classifications and attach the decisive excerpt so a reviewer can see what happened.

Be equally precise with citations. Measure citation presence, domain diversity, ownership, page freshness where known, and the claims each citation appears to support. If an answer names your brand but cites only a competitor or an unrelated page, that is not the same outcome as a direct citation to a current, relevant page.

A composite score can be useful for orientation, but it should never replace the underlying measures. If you create one, document its components and weights, show the raw metrics beside it, and version the formula whenever it changes. Otherwise, an apparently stable score may be concealing offsetting gains and losses.

Turn visibility changes into specific work

A useful monitor ends in a queue of testable actions. When a metric moves, investigate in the same order each time:

Validate the observation by rerunning the same prompt under matched conditions. Preserve both the confirming and conflicting outputs.
Locate the scope. Check whether the change belongs to one platform, prompt family, market, language, product, or competitor set.
Compare the answer text and citations with the earlier baseline. Identify the claim, recommendation, omission, or source selection that actually changed.
Classify the likely problem as discoverability, entity ambiguity, factual inconsistency, weak evidence, reputation, technical access, or normal output variation.
Assign an intervention that matches that diagnosis. Record the owner, affected pages or entities, expected signal, and implementation date.
Continue the locked measurement panel after the intervention. Do not replace difficult prompts or add favorable prompts to make the result look improved.

Observed pattern	Likely interpretation	Useful next action
The brand is accurate in branded answers but absent from unbranded discovery.	The entity may be recognized without a strong association to the category or use case.	Strengthen pages that explicitly connect the brand, offering, audience, problem, and differentiating evidence. Review whether those relationships are clear in page copy, internal links, and relevant structured data.
The brand is visible, but descriptions conflict across prompts.	Canonical facts may be unclear, inconsistent, or scattered.	Create an approved fact set, reconcile conflicting pages, and make names, descriptions, relationships, and current capabilities consistent across owned properties.
A competitor appears repeatedly for one constraint or audience.	The competitor may have a clearer evidence trail for that particular fit.	Inspect the supporting pages and claims. Publish direct, substantiated material for the same decision criterion if your offering genuinely meets it.
Citations lead to outdated or irrelevant pages.	Old URLs or weak canonical paths may still be prominent in the available evidence.	Update the strongest relevant page and consolidate duplicate information. Before removing an old URL, map its links and use an appropriate redirect so you do not discard useful signals or strand visitors.
Sentiment changes while mention presence stays stable.	The visibility problem is not reach; it is representation.	Review the exact negative or conditional claims. Correct factual ambiguity in owned content, and route legitimate product or reputation issues to the team that can address the underlying cause.
Only one platform changes on an isolated run.	The movement may be platform-specific or ordinary answer variation.	Repeat the matched test and inspect that platform’s answers before changing site-wide strategy.

Your reporting view should preserve this diagnostic path. Show platform and prompt-cluster coverage, branded and unbranded presence, recommendation status, the declared share-of-voice formula, citation patterns, accuracy issues, and sentiment evidence. Add a change log underneath. Readers should be able to move from a chart to the affected prompts, full answers, citations, and interventions without asking how the number was produced.

Also separate observation from attribution. If visibility rises after you revise a page, the timing makes the revision a plausible contributor; it does not prove that the page caused the change. Look for repetition across relevant prompts, supporting citation changes, and stability beyond a single run before making a causal claim.

Key takeaways

Use a locked prompt panel for trends and a separately versioned exploratory panel for discovery.
Store the exact prompt, answer, citations, platform conditions, and scoring evidence for every observation.
Keep presence, recommendation, accuracy, sentiment, citations, and competitive position as distinct measures.
Separate branded recognition from unbranded discovery, and report results by intent and prompt cluster.
Define every denominator, especially share of voice, and display raw measures beside any composite score.
Validate changes under matched conditions before assigning site-wide work or claiming an intervention caused the result.

Start with one commercially important use case and a prompt set small enough for your team to review answer by answer. Lock the baseline, document the scoring rules, and connect every alert to a named decision. Once that loop works, expand the coverage without weakening the audit trail.

References

HiGoodie Blog — Guide to Using Our AEO Visibility Monitoring Tool

October 2, 2026

How to Run an AI Citation Source Audit That Drives Action

You can rank well in traditional search and still be nearly absent from the pages AI assistants use to support answers about your market. When that happens, publishing more content without inspecting the citation trail is guesswork.

An AI citation source audit shows which domains ChatGPT, Gemini, and Claude cite for your brand, which competitors those sources favor, and where a content or PR intervention has a realistic path to influence. The goal isn’t a longer spreadsheet. It is a defensible list of actions tied to actual prompts, answers, claims, and URLs.

Define the decision your audit needs to support

“Where does AI get its information about us?” is too broad to guide an audit. The useful version names the decision you need to make. You might need to decide which publications to pitch, which inaccurate claims to correct, which comparison pages to improve, or where a competitor has earned third-party validation that you lack.

Write that decision at the top of your worksheet. It prevents the audit from drifting into a collection of interesting but unactionable mentions.

Then separate three things that teams often collapse into one metric:

Brand mention: Your name appears in an answer, whether or not a link supports it.
Owned citation: The answer links to a page on your domain.
Third-party citation: The answer uses another domain to substantiate a claim about you, your competitors, or the category.

Those outcomes require different responses. A mention without a citation may reveal awareness but provides no evidence about which external page shaped the answer. An owned citation creates a content-maintenance task. A third-party citation can become a media, partnership, reputation, or listing opportunity.

Set the audit boundary before collecting anything. Record the market, audience, geography, language, products, competitors, and buying stages that are in scope. If the business has several unrelated product lines, audit them separately. Otherwise, a strong citation footprint for one line can conceal a serious gap in another.

Your basic record should be the individual prompt-and-answer pair, not merely the cited domain. Keep these fields:

Exact prompt
Prompt theme and journey stage
AI platform and visible mode or model label
Date and relevant account, location, or language context
Brand mentioned or absent
Competitors mentioned
Exact claim associated with the citation
Cited page URL and root domain
Citation placement, such as inline or in a linked source list
Whether the page genuinely supports the claim
Accuracy or reputation issue
Recommended owner and next action

This level of detail matters because the same domain can help in one answer and hurt in another. A simple domain tally cannot show that distinction.

Build prompts around real discovery and buying decisions

A brand-name prompt tests recognition. It does not represent the full discovery journey. If every test includes your brand, you can produce reassuring results while missing the prompts where an unfamiliar buyer first encounters the category.

Build a prompt matrix that covers different kinds of intent:

Category discovery: Questions asking what kinds of solutions exist for a problem.
Problem diagnosis: Questions describing a symptom, obstacle, or desired outcome without naming a product category.
Comparison: Questions asking how approaches, products, or named competitors differ.
Recommendation: Questions seeking suitable options for a defined use case or audience.
Validation: Questions about trust, evidence, reputation, limitations, or suitability.
Implementation: Questions about setup, migration, integration, or ongoing use.
Branded evaluation: Questions that name your organization and ask what it does, who it serves, or how it compares.

Use the language a buyer would use before they know your internal terminology. Product teams tend to write prompts with precise feature names. Buyers often describe the job, risk, or constraint instead. Include both forms and keep them as separate rows so you can see whether the citation landscape changes.

Do not cram several intentions into one prompt. A question that asks for a recommendation, comparison, price assessment, implementation plan, and risk analysis creates an answer that is difficult to classify. Each prompt should expose one main decision.

Keep the testing conditions visible

AI answers can vary with the platform, available search mode, conversation context, and phrasing. That does not make auditing pointless. It means your evidence needs enough context to be interpreted later.

Run each prompt in a fresh conversation unless conversation history is deliberately part of the scenario. Save the exact wording rather than a cleaned-up paraphrase. Record whether web access or a comparable source-discovery mode appeared to be active. If you rerun a prompt, preserve both observations instead of replacing the earlier result.

Avoid teaching the assistant about your brand before asking the test question. Pasting your positioning statement and then asking which companies lead the category measures how the assistant uses supplied context, not whether your brand is discoverable independently.

Capture the citation trail without losing the evidence

A hand links an AI answer fragment to a source-page card and an organized evidence packet on a desktop.

Collection is where a useful audit often turns into an unreliable one. Copying only the domain discards the relationship among the prompt, the answer, the claim, and the cited page. Preserve that relationship with a consistent workflow.

Run the prompt exactly as written. Do not add a clarifying follow-up until the original answer has been saved.
Capture the complete answer. Preserve the wording and citation placement, not just the sentence containing your brand.
Extract every cited URL. Keep the full page URL and add the root domain in a separate field.
Connect each URL to a claim. Record what the link appears to support: a recommendation, fact, comparison, warning, or general background statement.
Open the page. Confirm that it exists, is the intended page, and contains evidence relevant to the associated claim.
Label the result. Mark your brand as cited, mentioned without citation, omitted, or represented inaccurately. Record the same outcome for named competitors.
Assign the next action. Choose a concrete route such as correct, update, pitch, contribute, earn inclusion, monitor, or take no action.

Do not treat every displayed link as valid evidence. A URL can resolve while failing to support the sentence beside it. It can also point to an old page, a derivative summary, or a page about a similarly named entity. These are accuracy findings, not successful citations.

Also distinguish citation placement. An inline link attached to a specific claim is different from a page included in a general source list. Both belong in the audit, but they should not be interpreted as equivalent support.

Normalize URLs only after preserving the original. Remove obvious tracking parameters in your analysis field, consolidate equivalent URL variants, and keep separate pages separate. Collapsing everything to the domain level too early hides which asset type is actually being selected.

Turn the URL inventory into an opportunity map

A strategist examines a landscape of source tiles, citation paths, open gateways, and symbols for content, outreach, and reputation work.

The first useful output is not a leaderboard. It is a map of how information travels from publishers, communities, reference pages, directories, vendors, and your own site into answers that affect the buyer’s decision.

Classify every cited page by role:

Owned information: Your product, company, documentation, help, or editorial pages.
Independent editorial coverage: Reporting, analysis, reviews, or industry commentary.
Comparison and recommendation content: Roundups, alternatives pages, rankings, and buying resources.
Reference material: Definitions, standards, research, or other evidence-led resources.
Community discussion: Forums, question-and-answer threads, and other user-contributed discussions.
Directory or profile data: Listings and structured company or product records.
Commercially connected content: Partner, affiliate, reseller, marketplace, or vendor-controlled pages.

The classification tells you which intervention is plausible. You can update an owned page directly. You may be able to correct a directory profile. You can pitch an editor with evidence, but you cannot rewrite independent coverage. You can participate transparently in a community, but manufacturing endorsements would create a reputation problem rather than solve one.

Calculate a compact set of signals while retaining the underlying rows:

Signal	How to read it	Decision it supports
Citation coverage	Prompts in which your brand has supporting citations relative to the prompts tested	Shows where you are present, not whether the representation is favorable or accurate
Accuracy status	Citations whose associated claims are accurate, incomplete, outdated, or wrong	Separates visibility work from correction work
Competitive gap	Prompts where competitors receive relevant support and your brand is absent	Identifies the query themes and third-party pages worth investigating
Repeat domain presence	Domains appearing across several relevant prompt themes or platforms	Highlights relationships and placements with broader potential value
Domain concentration	The extent to which citations depend on a narrow group of domains	Reveals whether visibility is resilient or reliant on a small set of intermediaries
Source-role mix	The balance among owned, editorial, community, reference, directory, and commercial pages	Shows whether the next move belongs to content, PR, partnerships, reputation, or data maintenance

Keep results separated by platform, prompt theme, and journey stage before calculating any overall view. A combined total can hide an important pattern, such as strong citations for implementation questions but no presence in category discovery or comparisons.

Prioritize with judgment rather than a decorative score. Put each finding into an action tier:

Correct now: A cited page supports a materially wrong, outdated, or confusing claim about your organization.
Pursue next: A relevant independent domain appears repeatedly in prompts tied to an important buyer decision, and there is a legitimate route to contribute evidence or earn consideration.
Strengthen: Your owned page is cited but does not answer the associated question clearly, or a substantiated first-party resource is missing.
Monitor: A page appears in an isolated or low-relevance context with no sensible intervention.
Decline: The opportunity requires payment without clear disclosure, manufactured sentiment, or another tactic that would undermine trust.

A high-frequency domain is not automatically your best target. Relevance, claim accuracy, editorial fit, and a credible access route matter more than raw appearances. A smaller specialist publication that is repeatedly cited for your buyer’s exact concern may deserve attention before a large general-interest domain.

Convert the audit into content, PR, and reputation work

Every priority finding needs an owner, an asset, an ask, and a verification step. Without those fields, “improve AI visibility” becomes an indefinite objective that no team can execute.

Match the action to the cited page’s role:

Owned page: Correct the claim, answer the relevant question directly, show the supporting evidence, and keep important entity details consistent across the site.
Editorial coverage: Identify the coverage gap and offer verifiable information, an expert contribution, a useful dataset, or a legitimate update. Do not frame the outreach as a request to manipulate an AI answer.
Comparison page: Determine the inclusion criteria before contacting the publisher. Supply factual differentiation and evidence that helps the page serve its readers.
Reference resource: Create or expose the strongest substantiation you can stand behind. Unsupported marketing language is not a replacement for evidence.
Directory or profile: Correct missing, inconsistent, or outdated fields through the available listing process, then verify the public record.
Community discussion: Participate only where you can answer the question transparently and disclose your connection. Treat recurring complaints as product or support intelligence, not as threads to overwhelm with promotion.
Inaccurate third-party claim: Document the precise error and the evidence needed to correct it. Use the publisher’s correction route rather than demanding favorable wording.

For each target, write a one-line action brief: the prompt gap, the cited page, the claim you need to support or correct, the evidence available, the outreach or publishing route, and the person responsible. That brief is specific enough to become a task without another strategy meeting.

On your own site, make the supporting page easy to interpret. Use a stable URL, a descriptive title, a direct answer, clear entity names, visible authorship or ownership where relevant, an update date when freshness matters, and links to the evidence behind material claims. Accurate structured data can clarify what a page represents, but it cannot turn a weak or unsupported assertion into a credible citation.

Do not publish a new page for every missed prompt. Group gaps that share the same underlying intent and determine whether an existing page should be improved first. A page that clearly resolves the buyer’s question is more useful than a stack of near-duplicate pages designed around minor wording variations.

Recheck the relevant prompts after a meaningful change has had time to become publicly accessible. Preserve the earlier observation, record the new one, and compare the exact citation trail. A changed answer can be encouraging, but it does not prove that a single edit caused the change. Look for repeated movement across related prompts before treating it as a durable result.

Key takeaways

An AI citation source audit measures which pages and domains support answers, not merely whether an assistant recognizes your brand.
Test discovery, comparison, recommendation, validation, implementation, and branded prompts instead of relying on brand-name questions alone.
Preserve the prompt, answer, claim, full URL, citation placement, and testing context. A domain-only list is not enough.
Verify that every cited page actually supports the associated claim before counting it as useful visibility.
Prioritize accurate, relevant domains that recur around important buyer decisions and have a legitimate route for contribution or correction.
Translate every finding into a content, PR, listing, partnership, or reputation task with a named owner and a recheck condition.

Start with one decision-critical product area and build the prompt matrix before opening an AI assistant. Once the evidence is captured cleanly, you will know whether the next move is to repair your own information, earn third-party validation, correct a misleading claim, or leave a low-value citation alone.

References

HiGoodie Blog – AI Citation Source Audit: Which Sites Cite You

October 1, 2026

AI Search Visibility Monitoring: A Repeatable Framework
You checked an AI answer, saw your brand missing, and now you need to know whether you have a visibility problem. One response cannot answer that. AI recommendations vary between runs, and buyers can approach the same purchase through several different questions.

A useful monitoring program treats visibility as a measured distribution, not a rank. It samples real buying decisions, repeats prompts under controlled conditions, records how each brand is presented, and turns the resulting patterns into specific content and positioning work.

Key takeaways
- Monitor buyer decisions and prompt families, not a list of exact phrases that tries to imitate traditional keyword tracking.
- Run each prompt at least 10 times for a quick directional estimate. A single answer is an observation, not a baseline.
- Measure recommendation seats, prompt coverage, citations, cited pages, and buyer-fit descriptions separately.
- Keep prompt wording, search mode, environment, and run counts consistent when comparing one period with another.
- Use monitoring to diagnose the next action. A missing recommendation, an uncited mention, and an inaccurate best for description are different problems.
Define visibility before you try to measure it

AI search visibility is not simply whether your company name appears. An answer can cite your page without recommending your product. It can recommend your brand while linking to a review site. It can also place you on a shortlist but describe you as suitable for the wrong customer.

The distinction matters because AI-generated shortlists can be narrow. In one workforce-management sample, 100 responses contained an average of 5.6 recommended brands, while the referenced vendor directory contained 215 listings in the relevant category. That result belongs to one category and one test design, so it is not a universal benchmark. It does show why merely being eligible for consideration does not mean a brand will receive a seat.

Record these six layers for every completed run:
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September 29, 2026

How to Run an AI Brand Visibility Audit That Drives Action

Your search rankings can look healthy while an AI answer ignores your brand, describes it incorrectly, or recommends a competitor. That does not mean SEO stopped mattering. It means the outcome you need to measure has changed.

A useful AI brand visibility audit shows where your brand appears, what the system claims about it, which evidence supports the answer, and why another brand may be selected instead. Traditional search visibility and AI visibility can diverge, so you cannot use rankings or local-pack presence as a substitute for this work.

Key takeaways

Measure mentions, recommendations, citations, and factual accuracy separately. They are different outcomes with different fixes.
Test the questions customers ask while choosing, comparing, and validating options. A branded lookup alone cannot reveal whether AI systems discover your brand.
Check crawler access, entity consistency, factual specificity, claim support, unique information, and JSON-LD before treating missing visibility as a content-volume problem.
Treat one generated answer as an observation. Prioritize patterns that recur across relevant prompts, sessions, or AI surfaces.
Fix access barriers and incorrect facts before chasing more mentions. Being visible with the wrong information is not a win.

Build a prompt set around customer decisions

Start with the decision your customer is trying to make. A prompt such as What is [brand]? tests recognition and basic factual recall. It does not show whether your brand would be found when the customer has not named it.

Create prompts for each commercially important audience, need, location, and constraint. Keep the wording neutral. If you tell the system that your brand is the leading option or ask why it was excluded, you have already biased the test.

Discovery: Which [category] providers serve [audience or location] and meet [specific need]?
Fit: Which option is suitable for someone who needs [feature, policy, use case, or constraint]?
Comparison: How do [brand] and [competitor] differ for [specific decision]?
Fact retrieval: What does [brand] offer, where is it available, and what policies apply?
Validation: Is [brand] a credible option for [use case], and what evidence supports that assessment?

Reuse the same wording when you want comparable observations. Begin a fresh conversation where possible, preserve the complete response, and record any visible citations. Do not reduce the result to a yes-or-no mention check.

Dimension	What to record	What it reveals
Presence	Absent, named, or described without a clear name	Whether the system associates your entity with the prompt
Prominence	Primary recommendation, alternative, comparison subject, or passing mention	Whether visibility is commercially meaningful
Citation	Your site, another site, or no visible citation	Which evidence is available for inspection
Accuracy	Correct, outdated, contradictory, unsupported, or unclear	Whether visibility helps or harms the customer decision
Competitive displacement	Which alternative appears and the stated reason	Where another brand supplies stronger relevance or evidence

Paid monitoring platforms can automate structured prompts across multiple AI surfaces and track mentions, citations, competitors, and inconsistencies over time. That automation is difficult to reproduce at scale, but the initial diagnostic can still be performed manually if you preserve the evidence and apply consistent labels.

Inspect the signals behind each answer

Prompt results show the symptom. Your next job is to find the upstream reason. More content is not the default answer: an access restriction, contradictory business fact, vague claim, or missing entity relationship can undermine an otherwise substantial site.

Confirm that AI crawlers can reach meaningful content

Open yourdomain.com/robots.txt and inspect any rules for GPTBot, ClaudeBot, PerplexityBot, and Google-Extended. A disallow rule may be an intentional policy choice, so document it before changing it. The audit question is whether access matches the organization’s actual policy, not whether every crawler should automatically be allowed.

Then visit the site as a new user. Check whether the homepage or important landing pages hide their substantive content behind a cookie wall, language selector, location picker, or another interstitial. These gates can leave less-established crawlers unable to reach the facts even when conventional search crawling appears healthy.

Map the entities the brand needs AI to understand

List each distinct thing an answer may need to describe: the business, products or service lines, relevant staff, policies, locations, and location context. For each entity, record its canonical name, defining attributes, public URL, supporting evidence, and the person responsible for keeping it current.

Do not treat a passing marketing mention as documentation. A location page that says conveniently located but gives no nearby landmarks, distances, transport details, or service area leaves the location entity underdefined. A service page that promises flexible options but never names those options creates the same problem.

Compare important facts across the website, Google Business Profile, and other public representations. Different names, addresses, policies, descriptions, or availability statements create entity drift. Consistency is a foundational trust signal; decide which location is canonical, correct it first, and then align the rest.

Test whether the facts are extractable and defensible

AI systems can reuse a direct factual statement more cleanly than a sentence built from vague adjectives and unclear pronouns. Paste a priority page into an AI assistant and ask it to identify every pronoun, adjective, or phrase whose referent or meaning is ambiguous. Require a fact-specific rewrite for each flagged sentence, then verify the rewrite yourself before publishing it.

Audit claims separately. Search your pages for best, most, only, award-winning, leading, and similar language. Record the evidence behind each claim, the entity that granted any award, and the page where a reader can verify it. If the evidence does not exist, narrow the statement to a supportable fact or remove it. An uncheckable superlative gives an AI system little reason to repeat the claim.

Look for information gain and meaningful structured data

Take several sentences from a priority page and search for them in quotation marks. If competitors could publish the same wording without changing a detail, the page contributes little unique evidence. Replace generic language with information your organization can substantiate: named processes, exact policy conditions, original measurements, specific product attributes, or first-party findings.

View the page source and search for application/ld+json. No match means that page has no JSON-LD block. A match is only the beginning of the check: inspect whether the markup represents the actual entities and relationships on the page or merely supplies a thin, flat label.

Verify that names, URLs, locations, and relationships agree with visible content. Inspect sameAs values carefully and use them only for records that genuinely identify the same entity, including applicable Wikidata or Knowledge Graph identifiers. Structured data can clarify identity and relationships, but its presence does not guarantee a recommendation.

Turn response patterns into a prioritized diagnosis

A single visibility percentage conceals the difference between absence, weak prominence, missing evidence, and factual error. Diagnose each repeated pattern before assigning work.

Observed pattern	Investigate first	Action to take
Brand is absent from non-branded discovery prompts	Crawler access, category association, location facts, and incomplete entities	Resolve access barriers and add explicit, supportable facts connecting the brand to the relevant need
Brand appears only when named	Weak association with the use case, audience, category, or location	Strengthen the relevant entity pages with decision-ready facts rather than repeating the brand name
Brand is mentioned with incorrect facts	Contradictory or outdated public representations	Correct the canonical page, align external profiles, and document the changed fact for retesting
A competitor is recommended and cited	The cited page’s specificity, proof, entity coverage, and fit to the prompt	Identify the evidence your page lacks; do not copy the competitor’s wording
Your site is cited but the brand is not recommended	Evidence for customer fit, limitations, policies, and differentiators	Make the decision criteria explicit and support each material claim
A recommendation appears without a visible citation	Accuracy and reproducibility of the stated reasoning	Record the answer without guessing its origin, verify every claim, and look for the pattern in other tests

Prioritize by consequence and dependency, not by whichever gap is easiest to edit.

Remove access barriers that prevent important pages from being reached.
Correct wrong or contradictory business facts, especially facts that could change a customer’s decision.
Complete the commercially important entities and their location, product, service, staff, and policy attributes.
Replace generic claims with verifiable evidence and information the brand uniquely possesses.
Refine JSON-LD so it faithfully represents the corrected visible content and entity relationships.
Rerun the unchanged prompts and compare the complete answers, not just the mention count.

Each resulting ticket should contain the prompt, the complete observed answer, the affected customer decision, the suspected cause, the page or profile to change, the evidence required, and the retest condition. This keeps an AI visibility problem from becoming a vague request to improve the content.

Make the audit repeatable without turning it into dashboard theater

Keep a durable audit log. At minimum, capture the prompt, audience, need, location or constraint, AI surface, conversation state, observation date, full answer, prominence label, cited URLs, factual errors, named competitors, suspected cause, owner, and fix status. Preserve raw outputs even if you later calculate summary metrics.

Repeat the audit with the same core prompt set after material changes to the website, business facts, policies, products, services, or locations. Add prompts when a genuinely new customer decision appears, but do not silently rewrite old prompts and compare the results as if the test stayed constant.

Automation becomes useful when the number of prompts, AI surfaces, locations, or competitors makes manual tracking unreliable. Some platforms let teams ask natural-language questions and receive answers grounded in their own visibility data. That can speed up investigation, but the interface should still lead you back to inspectable evidence.

Before adopting a paid visibility platform, verify that it can retain raw responses, expose citations, preserve prompt wording, distinguish mentions from recommendations, compare competitors, flag entity inconsistencies, and show change history. A polished composite score is not enough if you cannot trace it to the answer that created it.

Begin with the customer decision that matters most. Capture the current answers, label what happened, and fix the first upstream failure: access, identity, specificity, evidence, or structure. Then rerun the same prompt. The practical goal is fewer missing, unsupported, and incorrect brand answers when a customer is ready to choose.

References

September 24, 2026

Goodie vs. Profound: Which AEO Platform Fits Your Team?

You are not choosing between two AI visibility dashboards. You are choosing where your team will do the hardest part of answer engine optimization: finding worthwhile prompts, deciding what to change, shipping the work, or proving that the work affected the business.

If you are stuck between Goodie and Profound, start with that bottleneck. Goodie is the clearer fit when you want prompt research, prioritized actions, execution, and revenue attribution in one operating loop. Profound is the stronger candidate when deep prompt intelligence, crawler analysis, and configurable enterprise workflows matter more than receiving a tightly prescribed action queue.

The practical answer: choose the workflow your team can run

Both platforms can help you monitor how a brand appears in AI-generated answers. That overlap is real, but it is not where the buying decision lives. The meaningful difference is what happens before monitoring and after a visibility problem appears.

Decision area	Goodie	Profound	What it means for you
Primary orientation	Closed-loop AEO operations	Enterprise AI-search intelligence and automation	Choose between a more prescribed operating loop and a deeper intelligence layer your team can configure.
Prompt research	Turns prompt opportunities into monitored topics and optimization work	Conversation Explorer emphasizes prompt demand and audience-question intelligence	Decide whether you need an actionable queue or a larger research environment.
Optimization	Prioritized actions tied to visibility gaps	Workflows and agents that can support automated content operations	Goodie reduces interpretation work; Profound can reward teams able to design their own processes.
Technical intelligence	Connects monitoring with recommended content and technical changes	Agent Analytics examines how AI crawlers interact with a site	Profound deserves close attention when crawler behavior is a central diagnostic requirement.
Business measurement	Revenue attribution is presented as part of the native AEO loop	Strong visibility, crawler, and referral analysis; revenue-level measurement needs closer validation	If finance expects pipeline or revenue evidence, test the attribution chain rather than accepting an integration logo.
Operating fit	Teams that want fewer handoffs between analysis and execution	Enterprises with analysts, marketing engineers, or established content operations	The more capable your internal operating team is, the more value it can extract from a flexible intelligence platform.

Goodie positions its product around a research-to-revenue loop, while Profound emphasizes Conversation Explorer, Agent Analytics, and agentic workflows. Those capability claims originate with Goodie, one of the vendors being evaluated, so treat them as hypotheses for your proof-of-fit rather than as an independent benchmark.

The short recommendation is straightforward. Choose Goodie when the missing link is turning visibility data into owned work and connecting that work to commercial outcomes. Put Profound first when you already have people who can interpret data and execute, but they need richer prompt intelligence, crawler evidence, and automation infrastructure.

Prompt research: decide whether you need a map or a queue

Two strategists compare a broad constellation of connected prompt signals with a focused queue of prompt cards in a digital studio.

Your prompt set is not a minor configuration detail. It defines the market the platform measures. If you track only brand-name questions, your score can look healthy while you remain absent from the unbranded questions buyers ask before they know you. If you fill the set with broad informational prompts, you can generate a large dashboard with little connection to a purchase decision.

A useful prompt library should cover distinct stages of the decision, including:

Problem recognition: questions asked before the buyer knows which category could help.
Category discovery: requests for approaches, products, providers, or methods.
Comparison: questions that place alternatives, features, constraints, or use cases side by side.
Validation: questions about proof, reliability, security, implementation, or compatibility.
Purchase friction: questions about price, migration, onboarding, contracts, and switching risk.
Post-purchase use: questions that can influence retention, adoption, and recommendation.

Profound’s Conversation Explorer is built around discovering and evaluating what people ask answer engines. That makes Profound compelling when your first problem is demand intelligence: you do not yet know which conversations matter, how questions cluster, or where the relevant opportunity sits.

Goodie’s Prompt Research is designed to feed discovered opportunities into monitoring and optimization actions. That orientation is useful when your team already understands the market reasonably well but struggles to convert research into an ordered backlog.

Make both vendors work from the same prompt brief

Do not let either demo begin with a polished sample category. Give both vendors the same brief containing your products, markets, buyer roles, competitors, and exclusions. Include questions where you expect to appear, questions where a competitor usually appears, and questions for which you do not yet know the answer.

Ask the platform to expand your seed questions without adding irrelevant informational demand.
Require an explanation for why each suggested prompt belongs in the monitored set.
Inspect the raw answer-engine responses behind every aggregate score.
Check whether prompts can be segmented by intent, audience, market, product, and stage of the buying journey.
Change the prompt set and confirm that historical reporting remains interpretable.
Ask how a discovered opportunity becomes assigned work, not merely another saved chart.

The winner is not the platform that returns the largest list. It is the one that helps you defend why a prompt matters and shows what your team should do with it. A vast prompt database can still produce a weak AEO program if no one can distinguish buyer demand from topical noise.

Optimization and attribution reveal the real split

Visibility monitoring tells you that an answer engine mentioned a competitor, cited another domain, or described your brand inaccurately. That is diagnosis. The operational value begins when someone can identify the underlying cause, choose an intervention, assign an owner, publish or deploy the change, and watch the relevant answers afterward.

Goodie puts prioritized optimization actions and revenue attribution inside the same product scope as prompt research and monitoring. For a lean team, that can remove the recurring handoff from analyst to strategist to writer or developer. It also gives leadership a more direct narrative: this was the visibility gap, this was the action, and this was the observed business outcome.

Profound should not be dismissed as a monitoring-only product. Its Workflows support automated content operations, while Agent Analytics examines crawler activity and answer-engine referrals. The distinction is that Profound’s value leans more heavily on the sophistication of the operator. A marketing engineering team may prefer that flexibility. A small SEO team may discover that it has bought a powerful system without enough capacity to design and maintain the workflows around it.

Test whether an optimization is evidence, advice, or execution

Vendors often place all three under the word optimization, but they are different deliverables:

Evidence identifies the prompt, response, cited sources, competitor, and affected page.
Advice explains the likely cause and recommends a specific change.
Execution creates, exports, assigns, publishes, or deploys the work.

During the evaluation, select a genuine visibility gap and follow it all the way through the product. Ask which page should change, what should change on it, why that intervention matches the evidence, who receives the task, and how the system detects a later answer change. If the workflow ends with generic advice such as improve authority or create better content, you are still buying diagnosis.

Do not confuse an AI referral report with revenue attribution

A referral dashboard can show visits from an answer engine. Revenue attribution has to explain how those visits, leads, opportunities, or purchases are associated with the channel. A visibility trend is further removed: a brand can gain mentions without receiving a click, and a later conversion may have several earlier influences.

Goodie’s native attribution proposition gives it the clearer advantage when proving commercial impact is a purchase requirement. You should still make the team expose the method. Ask these questions on screen:

Which outcomes are observed directly, and which are modeled?
How are direct referrals distinguished from zero-click exposure?
Can reporting separate first-touch, last-touch, and assisted influence?
Can you trace a prompt, visibility gap, optimization action, changed response, visit, and conversion without manually joining exports?
Which analytics and CRM fields are required?
Can your analysts export the underlying events and reproduce the reported total?
How does the system avoid claiming causation from a visibility increase that merely occurred before a revenue increase?

If the platform cannot answer those questions, call the feature directional measurement rather than revenue attribution. That does not make it useless. It makes the claim precise enough for your finance and analytics teams to use responsibly.

Enterprise pricing: model the total cost of operation

The headline prices create an easy trap. Goodie lists Core at $399 per month and Pro at $999 per month, while Profound lists Starter at $99 per month and Growth at $399 per month; broader enterprise packages use custom pricing. Those figures do not represent equivalent scopes.

A lower subscription can become the more expensive operating model if you must add analyst time, workflow tooling, content production, technical implementation, and a separate attribution layer. An integrated platform can also become expensive if the features you need sit above the entry plan or if usage expands with prompts, answer engines, brands, markets, and response volume.

Calculate total operating cost as the subscription plus usage expansion, onboarding, integrations, internal analysis, content and technical execution, data engineering, security review, and ongoing administration. Use the same scope for both quotes.

Quote line	What to require	Why it changes the real price
Prompt economics	Tracked prompts, research queries, generated responses, refresh frequency, and overage rules	Vendors can meter different units even when their plan labels look similar.
Engine coverage	Exact answer engines available on the quoted tier	A long platform list is irrelevant if the engines you need require an upgrade.
Organizational scope	Brands, products, markets, countries, languages, seats, roles, and workspaces	Enterprise cost often grows through organizational complexity rather than a single feature.
Data access	History, retention, raw responses, exports, API access, and business-intelligence connections	A dashboard can become a data silo if usable evidence cannot leave it.
Execution	Action allowances, workflow or agent credits, publishing paths, approvals, and task-system integrations	An action layer may be available but metered separately from monitoring.
Attribution	Analytics connections, CRM support, identity handling, models, and raw event access	Attribution may require implementation work outside the license.
Governance	SSO, permissions, audit records, data handling, and procurement documentation	Required controls can move an otherwise affordable deployment into an enterprise contract.
Service	Onboarding, strategist access, support channel, response commitments, and training	A platform that requires specialist operation should be priced with that labor included.
Commercial terms	Billing period, minimum commitment, renewal mechanics, overages, implementation fees, and exit access	The monthly figure alone does not reveal contractual risk.

Key takeaways

Choose Goodie when your main gap is turning prompt and visibility data into prioritized work and connecting the result to revenue.
Choose Profound when deep prompt intelligence, crawler analysis, and configurable enterprise automation are the priority, and you have specialists who can operate them.
Do not treat visibility, referral traffic, and revenue attribution as interchangeable measurements.
Compare quotes using the same engines, prompts, brands, markets, seats, integrations, data access, service, and execution workload.
Treat every vendor-supplied capability claim as something to reproduce with your own prompts, pages, and analytics path.

Run a proof-of-fit that produces work, not screenshots

A cross-functional team moves prompt artifacts through testing stations for discovery, content improvement, release, verification, and outcome validation.

A polished dashboard demo tells you very little about whether the platform will survive contact with your organization. A useful proof-of-fit starts with your evidence and ends with a decision or deliverable your team would genuinely use.

Write the operating problem in one sentence. For example: the content team cannot tell which unbranded buyer questions deserve work, or leadership cannot connect AEO activity to pipeline.
Provide an identical prompt set, competitor set, market scope, and group of existing pages to both vendors.
Require access to the raw responses, citations, timestamps, segmentation, and calculation behind every score shown.
Select a real visibility gap and make each platform diagnose it, recommend a change, and route the work to the person who would own it.
Run the proposed change through your approval and publishing process. Note every manual export, copy-and-paste step, missing integration, and specialist handoff.
Connect the relevant analytics environment and trace what the platform can observe after the change. Separate answer visibility, referrals, conversions, and modeled influence.
Request a production quote for the exact tested scope, including expansion rules and the controls procurement will require.

Score the result on prompt relevance, diagnostic transparency, action quality, workflow fit, measurement credibility, governance, and total operating cost. Do not create a broad feature checklist in which every row has equal value. A missing capability that blocks your operating loop matters more than several interesting features your team will not use.

Goodie should win your evaluation if it consistently turns relevant prompt gaps into work your existing team can ship, then gives your analysts a defensible path to business outcomes. Profound should win if its prompt and crawler intelligence changes your decisions materially, and your team can exploit its workflows without adding an unplanned operating layer.

If neither vendor can reproduce its claims using your prompts and data, do not force a selection. Tighten the use case, establish a manual baseline, and return when you know which part of the AEO loop deserves software. Before the next demo, complete this sentence: We are buying this platform so that a named owner can make a named decision and ship a named change without a named bottleneck. The product that proves that workflow is the better choice for you.

References

HiGoodie Blog – Goodie vs. Profound: AEO Platform Comparison

September 23, 2026

How to Segment Multi-Location SEO Data for Real Insight

Your network’s organic traffic is up, yet several location managers say calls or bookings are down. Both can be true. A few high-volume markets can lift the total, while new locations can add traffic and conversions simply because they were absent from the earlier comparison period.

You need reporting that separates portfolio expansion from SEO improvement, shows whether a problem is local or widespread, and points to the next action. That requires a stable location data model, like-for-like cohorts, and several connected views rather than one network-wide dashboard.

Start with the decision, not the dashboard

Segmentation becomes useful when each view answers a specific question. If you begin by collecting every available metric, you will usually end up with a crowded report that describes movement without explaining it.

Write down the decisions the report must support before building filters or charts. For a multi-location SEO program, the practical questions usually include:

Are established locations growing, or is total growth coming from newly opened locations?
Is a decline limited to one location, shared across a metro, or visible throughout the network?
Is the change specific to Organic Search, or are all website channels moving in the same direction?
Are people finding the business through its name, or discovering it through services, products, cities, neighborhoods, and local-intent searches?
Is search visibility weakening, or are users still finding the location but taking fewer actions afterward?
Which locations deserve intervention, and which high performers contain a practice worth applying elsewhere?

Each question needs a different denominator. A regional executive may need the total contribution from every location. An SEO manager diagnosing page quality needs location-page traffic and conversions. An analyst judging ongoing performance needs only locations that existed in both comparison periods.

Keep three kinds of network measure visible: the portfolio total, the typical location, and the distribution across locations. The total shows business impact. A median or peer-group benchmark shows whether the typical branch is improving. The distribution reveals whether the result is broad or concentrated in a few outliers. None can safely replace the others.

Build a location spine before joining SEO metrics

An isometric central spine connects storefront and map-pin modules while colored data streams join the matching locations.

Your reports need one durable record for every physical location. Think of this as the location spine: a controlled table that connects analytics, landing pages, search performance, local rankings, Google Business Profile data, reviews, and business events.

Do not use a display name as the primary key. Names change, abbreviations vary, and two branches can share similar labels. Assign an immutable internal location key, then map every platform identifier and URL to it.

Field	What it controls	Why it matters
Location key	The permanent join key across systems	Prevents renames or URL changes from splitting one location into several records
Operating status	Open, temporarily unavailable, closed, relocated, or otherwise excluded	Keeps inactive locations from being mistaken for SEO declines
Lifecycle dates	Opening, closing, relocation, and material expansion dates	Determines whether a location belongs in a like-for-like comparison
Geographic hierarchy	Province or state, region, metro, city, and neighborhood where relevant	Supports realistic market comparisons and isolates geographic patterns
Location URL set	Current page plus any mapped service or market pages	Connects page-level analytics and search visibility to the right branch
Profile identifier	The associated Google Business Profile record	Connects Search and Maps visibility, actions, and review context
Peer group	Locations with reasonably similar market conditions or operating models	Avoids judging a small market against a major metro with different demand and competition
Comparison cohort	Comparable, opening, closing, relocated, or exception	Separates organic improvement from changes in the location portfolio

Decide how a relocation should be represented before the reporting period starts. If the business considers it the same branch but its page, profile, catchment area, or local competitors changed materially, preserve the permanent location key while flagging the affected periods as an exception. That retains operational history without pretending the local search environment remained constant.

URL mapping needs the same discipline. A location may receive organic traffic through its main location page, a city page, or service pages associated with that market. Define which URLs belong to which reporting view. Do not silently attribute every site visit from a city to the nearest branch unless that allocation rule is explicit and defensible.

If URL structure permits a simple landing-page filter, use it. If it requires regular expressions, test the expression against known included and excluded URLs before interpreting the results. Then compare the filtered inventory with the location spine. A broken filter can create a persuasive but false outlier, especially after a migration or naming change.

Read every location through four evidence layers

A storefront is surrounded by four translucent layers of discovery, engagement, conversion, and neighborhood-context symbols.

No single metric explains local SEO performance. Treat analytics, search visibility, local rankings, and profile activity as connected evidence layers. When they disagree, the disagreement is diagnostic information rather than a reason to choose the most flattering metric.

Evidence layer	Useful measures	Question it answers	Common misreading
Website analytics	Organic traffic, engagement, conversion events, and corresponding all-channel measures	What did visitors do after reaching the site?	Treating a tracking or sitewide conversion change as an organic-search problem
Search visibility	Clicks, impressions, click-through rate, average position, landing pages, and queries	How often did the location appear, and what searches produced exposure or visits?	Reading a network average without separating locations or query intent
Local rank tracking	City-, service-, ZIP-code-, and near-me visibility, including grid or radius views	Where can a searcher actually see the location for priority local intent?	Using one point ranking as though every searcher in the market sees the same result
Google Business Profile and reviews	Search and Maps views, website clicks, calls, directions, review volume, recency, and rating	What happened directly in local results, and what may affect user response?	Assuming every profile action carries the same business value

Website behavior: compare organic with the whole site

Use GA4, Adobe, or the analytics platform already trusted by the business to review location-page traffic, engagement, and conversion events. Define a conversion in operational terms. Depending on the business, that may be an appointment request, booking, submitted form, phone call, or another recorded action.

Always place the Organic Search view beside the equivalent all-channel view. If conversion activity falls across every channel, investigate tracking, page behavior, availability, or a broader business change before blaming rankings. If the decline appears only in organic traffic, continue into search visibility and query data.

Do not average location conversion rates to create a network rate. Add the relevant conversion counts, add the corresponding traffic totals, and calculate the rate from those combined values. A simple average gives a small branch the same influence as a high-volume location and can distort the network result.

Search visibility: split discovery from existing demand

Use Google Search Console and Bing Webmaster Tools to examine clicks, impressions, click-through rate, average position, pages, and queries by location. Compare month over month for recent movement and year over year where the business needs a seasonal comparison.

Classify queries with documented rules. At minimum, separate branded searches from non-branded discovery. The branded group should include the approved business and brand terms relevant to the network. The non-branded group can then be divided into service or product intent, explicit city or neighborhood terms, and near-me intent where the available query data supports that classification.

This distinction changes the diagnosis. Rising branded clicks can reflect stronger existing awareness without proving that a location has become easier to discover for its services. Improving non-branded visibility is a clearer sign that the location is reaching people who have not already decided which business to find.

Keep query taxonomy rules stable between periods. If you add brand terms or change classification logic, mark that change in the report. Otherwise, a reporting edit can look like a shift in customer behavior.

Where a webmaster platform exposes AI-search performance, keep it as a clearly labeled view with its own available measures. Do not blend unlike visibility or traffic fields into a conventional web-search total. The label should tell the reader what the platform actually measured.

Local rankings: measure the searcher’s geography

Track priority local-intent searches at the city or ZIP-code level. In competitive markets, use grid or radius reporting to see how visibility changes as the searcher’s position changes. A branch may be prominent near its address and nearly absent elsewhere in the same metro; one rank captured from one point cannot represent that pattern.

When visibility moves, inspect more than the recorded rank. Check whether the results page layout changed, whether a new search feature appeared, and whether new competitors entered the result set. A lower click-through rate with stable rankings may begin to make sense once you see that the page surrounding the listing has changed.

Profile actions and reviews: interpret them by business model

Google Business Profile activity covers visibility and actions that can occur before a user reaches the website. Review Search and Maps views alongside website clicks, calls, and direction requests. Calls may be more meaningful for a service business, while directions may better reflect intent for an in-person location. Choose the primary action based on how that business actually converts demand.

Place review volume, recency, and average rating beside profile performance. These measures do not prove why a user acted, but they can explain why locations with similar visibility receive different engagement. Treat them as context to investigate, not as automatic causation.

Separate portfolio growth from comparable-location growth

A clean year-over-year report needs more than a date comparison. It needs a population definition. If the network opened, closed, relocated, or expanded locations, the locations contributing to the current period may not match those in the prior period.

Publish separate views instead of forcing every branch into one percentage:

All-network view: every valid location in each period. Use this to show the total portfolio outcome.
Comparable-location view: only locations with a stable identity and valid data in both periods. Use this to judge underlying performance.
Opening cohort: locations that began operating after the earlier period. Use this to show incremental contribution without calling it like-for-like growth.
Closing cohort: locations that ceased operating or left the portfolio. Use this to explain lost contribution.
Exception cohort: relocations, material expansions, URL migrations, tracking interruptions, or other changes that make a direct comparison misleading.

The all-network view answers, “How much organic activity did the business receive?” The comparable view answers, “Did the established footprint improve?” Those are both legitimate questions, but they are not interchangeable.

Calculate comparable change from the same location keys in both periods. For an additive measure such as clicks or conversions, sum the current values for the matched cohort and compare them with the prior values for that exact cohort. For a rate such as click-through rate, combine the cohort’s numerators and denominators first, then calculate the rate. Do not average the individual location percentages.

Also show each location’s contribution to the network change. A portfolio gain can be concentrated in a few branches even when most locations are flat or declining. Conversely, one closure or market-specific loss can pull down an otherwise healthy comparable cohort. Geographic and lifecycle segmentation exposes those drivers before they become a misleading network narrative.

Apply cohort rules consistently across traffic, visibility, profile actions, and conversions. If the analytics table excludes openings but the profile table includes them, the dashboard will invite comparisons between different populations. Put the cohort definition and any exceptions directly in the report so a stakeholder can see what the result represents.

Turn outliers into a prioritized SEO work queue

A location is not an outlier merely because it trails the network average. Market demand, competition, maturity, and the number of nearby branches all affect the opportunity. Compare locations within relevant geographic and operating peer groups first: region, metro, city, or another grouping that reflects the business.

This matters most when a network spans very different markets. A small city should not inherit the traffic target of a major metro. A dense metro with several branches may also have an internal differentiation problem: location pages can compete for the same broad city terms when neighborhood language would better distinguish their service areas.

Use the pattern across evidence layers to choose the first investigation:

Observed pattern	What to inspect next	Potential action
Impressions and local rankings fall in one market	Priority queries, Map Pack visibility, new competitors, demand, page coverage, and profile accuracy	Correct listing data, strengthen the relevant location page, or build justified market-level coverage
Impressions hold while clicks and click-through rate fall	Actual result pages, layout changes, new features, competing listings, and how the page or profile is presented	Improve the search-facing information that is within your control and monitor the changed result environment
Organic traffic holds while conversions decline	Engagement, conversion tracking, landing-page behavior, and all-channel conversion trends	Repair measurement or address the page-level conversion issue before treating it as a visibility problem
Profile views hold while calls, clicks, or directions decline	Profile completeness, business information, reviews, and whether the selected action still reflects customer behavior	Update the profile, address review weaknesses, or revise the primary action used for evaluation
Several branches in one metro weaken while the wider region is stable	Shared competitors, overlapping pages, local rankings, neighborhood differentiation, and market conditions	Use a metro-level plan rather than repeating isolated page edits at every branch
Every channel declines for the same location	Tracking, operating status, availability, page function, and broader market or business changes	Resolve the cross-channel cause before assigning an organic SEO fix
One peer location substantially outperforms comparable branches	Query mix, page completeness, profile quality, review context, competition, and local involvement	Identify a transferable practice, then validate it in another appropriate peer market

These patterns are starting points, not verdicts. Stable impressions with falling clicks, for example, can indicate a result-page change, weaker presentation, or a different query mix. Open the location, page, and query views before selecting the remedy.

Every item in the work queue should contain the affected location or cohort, the observed evidence, the working explanation, the next check or change, an owner, and a review point. Keep observation and hypothesis in separate fields. “Non-branded impressions declined” is an observation. “A new competitor displaced the location” remains a hypothesis until the result and competitor data support it.

Prioritize with three considerations: potential business impact, confidence in the diagnosis, and whether the team can act on it. A high-volume location with a clear profile error may deserve attention before a larger but poorly understood fluctuation. A strong outlier can be just as valuable as a weak one if it reveals a repeatable page, profile, or market practice.

Key takeaways

Use a permanent location key to connect pages, analytics, search visibility, rankings, profiles, reviews, and lifecycle events.
Report the all-network portfolio and the comparable-location cohort separately; they answer different business questions.
Compare Organic Search with all-channel performance before assigning an organic cause to a conversion decline.
Split branded demand from non-branded discovery, and keep the classification rules consistent between periods.
Benchmark locations against relevant peers, then use cross-layer patterns to decide what to inspect and change.

Start by creating the location spine and three saved views: all-network, comparable locations, and lifecycle exceptions. Then select one underperforming market and trace it from query visibility through page behavior and profile actions. Your reporting has done its job when that trail produces a specific, owned action rather than another network average.

References

Search Engine Land – How to segment SEO data for multi-location brands

September 23, 2026

Practical SEO Measurement: How to Prioritize What Works
You can have rankings, clicks, conversions, and a polished dashboard yet still be unable to answer the question that matters: should you put another sprint, another content batch, or another dollar into this SEO initiative?

The practical goal isn’t to prove that SEO caused every conversion. It is to build enough reliable evidence to decide what to continue, what to expand, what to repair, and what to stop. That requires a measurement contract for every meaningful initiative, explicit thresholds, and an honest separation between what you observed and what you inferred.

Measure for the decision, not the dashboard

Start by naming the decision your measurement must support. Are you deciding whether to launch, wait, expand, revise, or stop? A metric can be useful without answering all five questions.

Separate the evidence into four levels:
- Delivery evidence: Did the planned pages, templates, links, or technical changes actually ship? Until they do, you are measuring execution failure or delay, not SEO impact.
- Leading indicators: Did search engines discover and index the affected pages? Are nonbrand impressions, rankings, or other early visibility signals moving in the expected direction?
- Observed business outcomes: Did the affected traffic produce qualified leads, revenue, subscriptions, lower acquisition costs, affiliate earnings, or another unit of value that the business recognizes?
- Attributed influence: How much of that outcome can reasonably be connected to the initiative? This is usually the least certain layer because SEO changes overlap with seasonality, algorithm changes, product releases, competitor activity, and work elsewhere on the site.
Do not promote evidence from one level into another. Indexation shows that pages entered the search system; it does not show that the pages created profitable demand. More impressions indicate visibility; they do not prove incremental revenue. An organic conversion is observable, but its recorded channel does not reveal every earlier interaction that influenced the buyer.

This distinction also keeps disagreements about tools from derailing the decision. Search Console and web analytics observe different events, while Search Console totals may not reconcile when segmented. Assign one system of record to each metric, document the definition, and judge movement within that system. Do not force unlike datasets to produce an artificial match.

For every metric on your scorecard, complete this sentence: “If this crosses the agreed threshold by the review date, we will make this decision.” If you cannot finish the sentence, the metric may be informative, but it is not yet operational.

Write a measurement contract before the work starts

A forecast describes what you hope will happen. A measurement contract states how the team will decide what to do after reality arrives. Write it while everyone is still neutral, before delayed results and sunk costs make the thresholds negotiable.

The contract should contain:
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September 22, 2026

Search Marketing Performance Intelligence: A Decision System

Your CPA jumps, organic clicks soften, and visibility across AI search looks uneven. Your dashboard confirms that something moved. It does not tell you whether demand changed, a competitor became more aggressive, your ads lost relevance, or the conversion path broke.

You need more than a cleaner report. You need a repeatable way to connect business outcomes, funnel metrics, account changes, market behavior, and search-surface coverage – then turn that evidence into one defensible action. That is the practical job of search marketing performance intelligence.

Replace the reporting question with a decision question

Reporting asks what happened. Performance intelligence asks what you should change, why that change is justified, and what evidence would prove it worked.

That difference sounds small, but it changes how you build the entire analysis. If you start with all available data, you tend to produce a dashboard full of metrics. If you start with a pending decision, you can select only the evidence needed to make that decision safely.

Write a one-sentence decision question before opening your reporting tools. It should name the affected scope, the observed change, and the choice in front of you. For example: Should we restore non-brand bids, revise the ads, or repair the landing-page experience after conversion volume fell in these campaigns?

A useful decision question has five parts:

Scope: The channel, market, campaign, topic, device, audience, or landing page affected.
Outcome: The business metric that moved, such as conversions, revenue, CPA, return on ad spend, or average order value.
Timing: When the movement began and which comparison period is genuinely comparable.
Competing explanations: At least one internal cause and one external cause worth testing.
Decision: The bid, budget, targeting, creative, content, landing-page, or measurement change you might make.

This prevents a familiar failure: treating a falling line as a diagnosis. A traffic decline only becomes actionable after you identify where it began, what drove it, and what decision follows. Until then, it is an alert.

Separate outcome metrics from diagnostic metrics as well. Revenue and qualified conversions are outcomes. Impressions, click-through rate, CPC, Quality Score, ranking coverage, and AI Overview presence can help explain those outcomes, but none is a business result by itself. A Quality Score decline, for example, may surface before a later increase in click costs becomes obvious. Treat it as an early clue to investigate, not a target to optimize in isolation.

Trace every performance shift through five evidence layers

Five translucent evidence layers show business outcomes, a conversion funnel, campaign controls, market activity, and search surfaces connected by one glowing signal.

A strong diagnosis moves from the business result toward its possible causes. Do not begin with the most interesting chart or the most accessible data set. Work through the same evidence layers in the same order so that a plausible story does not outrun the facts.

Confirm the business outcome. Compare equivalent conversion definitions and comparable periods. Determine whether the change sits in conversions, revenue, CPA, return on ad spend, or average order value. Check whether it is account-wide or concentrated in a particular campaign, topic, product, market, device, or landing page.
Decompose the funnel. Inspect impressions, click-through rate, clicks, average CPC, conversion rate, and average order value. The arithmetic keeps the analysis honest: clicks are driven by impressions and click-through rate; conversions are driven by clicks and conversion rate; for commerce, revenue is driven by orders and average order value. Find the first meaningful component that changed.
Inspect internal account state. Review budgets, bids, targeting, search terms, negatives, ads, Quality Scores, landing pages, tracking, and account change history. Match each change to the affected segment and date. A coincidental account edit is not automatically the cause, but it is a testable lead.
Add market context. Look at competitor participation, competitor messaging, auction conditions, generic demand, and relevant market events. Joining account behavior with market behavior helps distinguish an internal failure from a broader shift. Timing can narrow the explanation, although it does not prove causation on its own.
Check visibility across surfaces. For the same high-value topics, inspect paid coverage, organic rankings, and AI Overview presence. A paid keyword gap has a different priority when you already hold strong organic or AI visibility than when competitors occupy every visible surface.

Keep the comparison grain consistent. If the outcome is measured weekly by market and campaign, do not explain it with a monthly global competitor trend. Align time zones, currencies, conversion definitions, attribution settings, and segment boundaries before drawing a conclusion. Otherwise, the data join can manufacture a shift that did not occur.

The table below is a diagnostic starting point, not a set of automatic conclusions. Each pattern should produce a hypothesis and a verification step.

Observed pattern	Leading hypotheses	Next check or action
Impressions fall while downstream rates remain steady	Demand, eligibility, budget coverage, or competitive participation changed	Split brand from non-brand, inspect budget and targeting status, then compare market demand and competitor presence
Impressions hold but click-through rate falls	The message no longer fits the query, a competitor has a stronger proposition, or the results page changed	Compare creative by placement and query theme; inspect competitor messaging and AI Overview presence
CPC rises while Quality Scores weaken	Ad or landing-page relevance may be deteriorating; competitive pressure may also have increased	Locate the affected campaigns, ads, queries, and pages before changing bids; add auction and competitor context
Clicks remain steady but conversion rate falls	Traffic mix, landing-page behavior, offer fit, site function, or conversion measurement changed	Segment by search term and landing page, verify tracking, and test the on-site path before buying more traffic
Conversion rate holds but average order value falls	Product, offer, customer, or order mix changed	Find the affected commercial segment before altering acquisition settings
Search terms spend without recorded conversions	The traffic may be irrelevant, but conversion lag, low volume, or measurement gaps may be hiding value	Validate the window, tracking, query intent, and assisted value; add negatives only where exclusion is justified
Generic market demand exists but non-brand coverage is thin	Budget may be concentrated on branded demand while competitors capture discovery traffic	Rank the gaps by commercial relevance and plausible return, then account for existing organic and AI visibility

This sequence also prevents channel teams from optimizing against each other. A PPC team can see a missing keyword and increase bids while the SEO team already owns the result. An SEO team can celebrate stable rankings while an AI Overview changes the visible path to the site. Performance intelligence treats those as parts of one demand landscape rather than separate scorecards.

Make every visualization perform a diagnostic job

A visual earns its place when it answers a defined question, eliminates an explanation, or supports a decision. A graph that merely makes a metric easier to look at is still reporting.

Build your diagnostic sequence as a short evidence story:

Establish the baseline. Use a trend view to show when the outcome changed. Split the line by the segment that matters, such as brand versus non-brand, market, campaign, topic, or landing page.
Expose the mechanism. Decompose the movement into impressions, click-through rate, CPC, conversion rate, and average order value. Show which component moved first and where the change is concentrated.
Test the cause. Add account changes, competitor participation, auction information, campaign launches, promotions, and relevant external events. Use them to compare explanations, not to decorate the timeline.
Mark the intervention. Annotate the date and scope of the bid, budget, creative, targeting, content, landing-page, or measurement change.
Show the resolution. Extend the same view beyond the intervention. State whether the expected signal appeared and whether the business outcome followed.

This setup-conflict-intervention-resolution structure is useful because one chart rarely provides enough context to explain both a performance change and its cause. The sequence lets each view carry one part of the reasoning.

Choose the format according to the question:

Line chart: Locate when a change began and whether an intervention coincided with recovery. Segment the line rather than relying on an account-wide average.
Metric heatmap: Find combinations that behave unexpectedly, such as strong placement paired with weak click-through rate. This is useful for creative triage because the contrast becomes visible immediately.
Calendar heatmap: Expose day- or week-level patterns around seasonality, launches, promotions, and operational events. Use it to generate a timing hypothesis, then verify the mechanism in the underlying metrics.
Word cloud: Scan dominant query or content themes, overlap, gaps, and possible cannibalization. Frequency is not commercial value, so validate promising themes against conversions, revenue, or another business outcome.
Exception table: Hand the team a finite work queue. Include only the affected entity, evidence, recommended action, expected effect, risk, and owner.

Write chart titles as questions or findings. Traffic Trend forces the reader to interpret the graph. Non-brand traffic fell after eligible impressions declined tells them what to inspect. If the evidence cannot support that stronger title, use the question you are testing: Did competitor participation coincide with the CPC increase?

Every visual should end with a short decision caption: what changed, the leading explanation, which alternatives were checked, what action is proposed, and what evidence is still missing. If no action is justified, name the next investigation and its owner. Uncertainty is acceptable; an ownerless ambiguity is not.

Turn the diagnosis into a controlled action queue

Tangled performance signals pass through a diagnostic prism and become an orderly queue of controlled actions, with one action highlighted.

The deliverable is not the dashboard. It is a prioritized queue of changes that someone can review, execute, and measure.

Each queue item should contain:

Problem: The business outcome and affected scope.
Evidence: The internal metric, account state, market context, and cross-surface coverage supporting the diagnosis.
Proposed action: The exact campaign, query set, creative, budget, landing page, or content area to change.
Expected signal: The first diagnostic metric that should respond and the business outcome expected to follow.
Confidence and gap: How strong the explanation is and what remains unknown.
Risk and rollback: What valuable traffic, data, or revenue the change could disrupt and how to reverse it.
Ownership: Who approves, who implements, and when the result will be reviewed.

Prioritize with judgment rather than a single opaque score. Start with financial exposure, confidence in the diagnosis, urgency, reversibility, and learning value. A broken landing page or measurement failure deserves attention before a speculative keyword expansion. A reversible creative test can move ahead with less evidence than a large budget reallocation. A negative-keyword upload needs careful review because an incorrect exclusion can remove useful reach across Search, Shopping, or Performance Max.

A practical order of work is to stop compounding loss, repair leading indicators, reallocate proven resources, and then test growth gaps. That usually means checking broken or outdated pages, tracking failures, and clearly irrelevant spend first; then addressing weak relevance or creative; then moving budget toward supported opportunities; and only then expanding into uncovered demand.

Automation should follow the same progression. Begin with observation, move to evidence-linked recommendations, then generate an editable implementation file, and require approval before changes are applied. Limited automatic execution should come only after you have reliable inputs, explicit guardrails, monitoring, and a tested rollback path.

Adthena describes a commercial version of this approach that joins advertiser account data with its market view and returns actions such as negative terms, copy changes, and budget moves. Its vendor-provided examples currently produce editable reports or upload-ready files, and the product is identified as Alpha. Treat that as a useful model for workflow design, not independent proof that every generated recommendation is correct.

Before approving any machine-generated action, confirm that it exposes the evidence it used, the campaigns affected, the expected result, and the reversal method. Also verify account scope, time zone, currency, attribution settings, conversion definitions, and data freshness. A recommendation that cannot show its inputs is not performance intelligence. It is an instruction without an audit trail.

Keep market context in the same evidentiary role. A competitor change that aligns with your decline is a serious lead, but timing alone does not prove the competitor caused it. Compare affected and unaffected segments, inspect the internal funnel, and use a reversible intervention where possible. The goal is not a confident story. It is a decision that can survive review.

Key takeaways

Start with a pending decision, not a collection of metrics.
Trace the shift from business outcome to funnel mechanism, internal account state, market context, and cross-surface visibility.
Treat charts as diagnostic steps: establish the baseline, expose the mechanism, test causes, mark the intervention, and verify the result.
Turn every supported finding into an owned action with an expected signal, risk, rollback method, and review point.
Use paid, organic, and AI visibility together when evaluating gaps so one channel does not buy coverage another already provides.
Keep automated recommendations editable and auditable until their inputs, guardrails, and rollback process have earned greater authority.

At your next performance review, choose one material shift and run it through the five evidence layers. Publish only the top supported action, its risk, and the signal you will remeasure. If the meeting ends with an observation but no decision or owned evidence gap, you still have a report – not performance intelligence.

References

September 21, 2026

Goodie vs Peec AI: Which AEO Platform Should You Choose?

If you are choosing between Goodie and Peec AI, the decisive question is not which dashboard looks better. It is where you want the platform’s job to end. Peec AI is oriented around monitoring and reporting. Goodie is designed to carry the work from monitoring into recommendations, content, commerce visibility and attribution.

That distinction affects more than the feature list. It determines how much analysis your team must do after the dashboard identifies a visibility gap, which other tools you will need, and whether the resulting report can be connected to business outcomes.

Goodie supplies the feature and pricing claims available for this comparison. Its descriptions of Goodie are first-party claims, while its descriptions of Peec are second-hand. Confirm Peec’s current limits, pricing, integrations and security documentation directly with Peec before signing a contract.

Key takeaways

Choose Peec AI when monitoring is the deliverable. Its reported strengths include prompt tracking, citation analysis, competitor benchmarking, unlimited users, credit allocation across projects and agency pitch workspaces.
Choose Goodie when the platform must support execution. Goodie combines visibility monitoring with prioritized optimization actions, content creation, technical AEO guidance, AI-shopping visibility and revenue attribution.
Do not compare prompt limits with credits as though they were the same unit. Goodie publishes prompt and action allowances, while Peec’s agency plans use credit pools. Ask each vendor to price the same prompt set, engines, countries, refresh frequency and client count.
Model count alone is misleading. Peec reportedly reaches a higher enterprise ceiling, but its standard plans let you choose three models from a smaller default set. Goodie’s entry plan includes five named surfaces, while its enterprise tier expands to as many as 12.
The lower subscription is not necessarily the lower-cost workflow. Include the analyst time, content tooling, technical implementation and attribution stack required after monitoring identifies a problem.

Start with the AEO workflow you actually need

A circular optimization workflow connects monitoring, analysis, recommendations, content production, and attribution, with one path ending after monitoring.

An AI visibility platform can perform two fundamentally different jobs. The first is observation: run prompts, capture generated answers, identify citations, measure brand presence and compare competitors. The second is intervention: determine why visibility is weak, decide what to change, produce or update the content, fix technical access and measure the result.

Peec concentrates on the observation layer. That can be enough when you already have an AEO strategist, content operation, technical SEO team and analytics setup. The platform supplies evidence; your existing people and systems turn it into action.

Goodie is positioned as a closed-loop system. Its published workflow covers prompt research, visibility monitoring, prioritized recommendations, content production, technical optimization and attribution. That broader scope becomes useful when the same person or small team must move from finding a gap to fixing it without rebuilding the context in several tools.

Map one real cycle before you evaluate either product:

Select the commercial questions and prompts that matter to your audience.
Run them across the relevant AI engines, country and language.
Identify missing mentions, unfavorable positioning and competitor citation advantages.
Convert each finding into a content, entity, schema, crawlability or distribution task.
Assign and complete those tasks.
Run the same prompt set again and distinguish a meaningful change from normal answer variation.
Connect the result to sessions, leads, conversions or another business measure.

Now mark which steps your team can already perform reliably. If you only need help with steps two and three, Peec’s narrower scope may be efficient. If the handoff between diagnosis and execution is where work stalls, Goodie’s broader system is the more relevant proposition.

Goodie and Peec AI feature comparison

The figures below reflect published feature and plan information from September 2026. Treat them as a purchasing shortlist, not as a substitute for a live product demonstration or contract review.

Decision area	Goodie	Peec AI	What to verify
Primary role	End-to-end AEO workflow	AI visibility monitoring and reporting	Which tasks can be completed without exporting data?
Standard model access	Core names five surfaces: ChatGPT, AI Overviews, Perplexity, AI Mode and Copilot	Standard plans reportedly let you choose three of six: ChatGPT, AI Overviews, AI Mode, Perplexity, Gemini and Copilot	Price the exact engines your customers use, not the maximum advertised count
Maximum model coverage	Up to 12 on Enterprise	Up to 13 on Enterprise, including additional models not in the standard selection	Which models require an add-on or enterprise agreement?
Prompt and competitor monitoring	Included	Included	Sampling method, geography, language, refresh cadence and export access
Sentiment analysis	Included in the published feature set	Included on Pro and above in the published plan description	How sentiment is scored and whether individual answers can be audited
Optimization recommendations	Optimization Hub with prioritized actions across plans	No dedicated recommendation layer reported	Whether recommendations name a page, issue, owner and expected outcome
Technical AEO	Recommendations for schema, site structure and crawlability	No crawlability, robots.txt or llms.txt auditing reported	Whether the platform detects issues or can also validate a completed fix
Content production	Content Studio connects prompt gaps with AI-oriented content creation	No content creation studio reported	Editorial controls, brand context, approval workflow and CMS handoff
Revenue attribution	Google Analytics attribution on Core, with broader attribution at higher tiers	No direct session, conversion or revenue attribution reported	Attribution logic, supported analytics properties and access to raw data
AI commerce	SKU-level visibility is listed on Pro and Enterprise	No AI-shopping or agentic-commerce tracking reported	Supported shopping surfaces, product matching and catalog coverage
Agency operations	Agency Growth plan, client workspaces and Enterprise multi-brand management	Unlimited seats, project-based credit pools, pitch workspaces and white-label reporting	Total cost per active client and the work required outside the platform

The apparent model-count advantage changes with the plan. Peec’s enterprise ceiling is reportedly 13 models, compared with Goodie’s ceiling of 12, but standard Peec plans are described as a choice of three models. Goodie’s Core plan names five surfaces. If Claude, DeepSeek, Grok or another non-core model matters to your audience, ask for its exact tier and add-on cost. A logo on an enterprise coverage slide does not mean it is included in the plan you are buying.

Cadence needs the same scrutiny. Goodie describes its monitoring as real-time, while Peec plans are described as supporting daily tracking, with daily or weekly options at some agency and enterprise levels. Ask each vendor what those labels mean operationally: when prompts run, whether failed runs are retried, how model changes are handled and when data becomes available for export.

Choose according to who must act on the data

For agencies selling monitoring and reporting

Peec has the clearer fit when your engagement ends with a visibility report, competitor comparison and client presentation. Unlimited seats reduce friction when strategists, account managers and clients all need access. Credit pools can be shifted between projects, while pitch workspaces let a team build prospect-facing evidence before an account becomes a retained client.

That operating model can protect agency margin, but only if reporting really is the end of the engagement. If your retainer also promises prioritized recommendations, content briefs, implementation and proof of business impact, add the cost of those activities before declaring Peec cheaper.

For agencies delivering an ongoing AEO program

Goodie’s broader workflow is more relevant when the agency owns the outcome rather than the dashboard. Its Optimization Hub is intended to turn visibility gaps into prioritized work, Content Studio addresses the production step, and attribution is intended to connect improvements with traffic and conversions.

There is an important pricing detail. Goodie’s $350-per-month Agency Growth plan includes 10 pitch workspaces per month and unlimited seats, but ongoing client workspaces run on the brand plan selected for each client. Do not treat $350 as the complete cost of operating 10 retained accounts. Ask for a scenario-based quote that separates prospecting workspaces, active client plans, model access and implementation support.

For an in-house brand team

Peec can work well when AI visibility data will enter a mature operating system. A content team can receive citation gaps, technical SEO can handle crawlability and schema, analytics can manage attribution, and a strategist can decide which findings matter. In that environment, buying those functions again inside an AEO platform may add overlap.

Goodie becomes more attractive when those handoffs are the bottleneck. A recommendation layer is valuable when it reduces the time between noticing a missing citation and assigning a concrete fix. Content tooling is valuable when it preserves the prompt, competitor and brand context that produced the recommendation. Attribution is valuable when leadership will not renew the budget on visibility scores alone.

For ecommerce and product-led businesses

SKU-level AI-shopping visibility creates the sharpest difference. Goodie lists that capability on Pro and Enterprise, while Peec is not described as offering product-level commerce tracking. If your question is whether an AI shopping experience can find, compare and surface individual products, brand-level mention tracking is not a substitute.

Test product matching during the demonstration. Use several real SKUs with similar names or variants and ask the vendor to show how it distinguishes the product, the brand and the category. Also verify which shopping surfaces are included, how frequently the checks run and whether results can be joined to your catalog or analytics data.

For enterprise procurement

Goodie says its Enterprise infrastructure is SOC 2 compliant. Peec is described as GDPR compliant, while SOC 2 or HIPAA status was not publicly confirmed in the available material. Absence from a competitor’s page is not evidence that a certification does not exist. Request current documentation from both vendors, including the exact entity and product covered, before a security or privacy review.

Compare total workflow cost, not the entry price

A balance scale compares a software tool plus extra tools, handoffs, and time with a more integrated modular workflow.

Goodie’s published brand pricing is straightforward at the first two levels. Core is listed at $399 per month with 100 prompts, 10 optimization actions per month, three seats, five named AI surfaces and Google Analytics attribution. Pro is listed at $999 per month with 250 prompts, 30 optimization actions, five seats, additional model access, full attribution and SKU-level commerce visibility. Enterprise pricing is custom, with 500 or more prompts, 60 or more monthly optimization actions, 10 or more seats and up to 12 models.

Peec’s brand tiers are described by capacity rather than dollar price in the available comparison: Starter includes 50 prompts and one project; Pro includes 150 prompts and two projects; Advanced includes 350 prompts and five projects; Enterprise is customizable. The first three let you choose three models and include unlimited users. Because no Peec dollar figures are supplied here, obtain a current quote instead of repeating an assumed entry price.

Peec’s agency tiers use a different unit:

Essential: 10,000 monthly credits, three client projects and 25 pitch prompts.
Growth: 25,000 monthly credits, 10 projects and 50 pitch prompts.
Scale: 65,000 monthly credits, 25 projects and 75 pitch prompts.
Comprehensive: custom pricing with unlimited credits, projects and pitch prompts.

A prompt allowance and a credit allowance are not directly comparable. Ask Peec how many credits your proposed schedule consumes after multiplying prompts by models, countries, languages, competitors and tracking frequency. Ask Goodie whether the same dimensions consume prompt capacity, require a higher tier or carry another charge.

Calculate total monthly cost with the same scope on both sides:

Platform subscription and required add-ons
Additional client, project, model, country and language capacity
Analyst time spent translating findings into prioritized work
Separate content, technical auditing and project-management tools
Implementation time for content, schema, crawlability and measurement changes
Analytics engineering required to connect AI referrals with outcomes
Reporting, white-labeling and client-access costs

For an agency, divide that total by active billable clients and then compare it with the gross margin of the service. For an in-house team, compare it with the internal hours removed from the cycle. This exposes the real trade-off: Peec may cost less as a monitoring layer, while Goodie may consolidate work that would otherwise happen in other systems. Consolidation only saves money if your team will use the added capabilities.

Run one full AEO cycle before you sign

A dashboard demonstration proves that a vendor can display data. It does not prove that your team can turn that data into a better answer-engine presence. Use the same controlled workflow with both products and require an exportable result.

Fix the scope. Use one commercially important customer journey, the same prompt set, the same brands, the same country and language, and only the engines you genuinely need.
Inspect the evidence. Open individual generated answers and citations. Check whether every aggregate score can be traced to the underlying response.
Create an action backlog. Ask the platform to help identify the page, entity, citation, schema or access issue behind each gap. Record how much manual interpretation is still required.
Complete a real change. Update a page, create the missing content or implement a technical fix. Note every external tool and handoff needed to finish it.
Measure again. Re-run the fixed prompt set. Look for directional improvement across repeated observations rather than treating one generated answer as a stable ranking.
Build the stakeholder report. Produce the exact report your client, marketing lead or finance team expects. Include visibility, actions completed and available business outcomes.
Price the production version. Give both vendors your actual number of prompts, models, markets, users, projects and clients. Request written confirmation of inclusions, overages, exports, support and contract terms.

If that exercise shows that your team can move cleanly from Peec’s monitoring data into its existing content, technical and analytics systems, the focused platform is likely enough. If the work repeatedly slows at diagnosis, execution or attribution, evaluate Goodie on whether its integrated tools remove those specific delays.

Make the purchase against the workflow you will operate next month, not the feature ceiling you might need someday. Take one live prompt set through monitoring, action and measurement, total every tool and hour it consumes, and choose the platform that leaves the fewest expensive gaps.

References

HiGoodie Blog – Goodie vs. Peec AI: AEO Platform Comparison

September 17, 2026

Profound Sheets Templates: Build an AI Visibility Workflow

Someone has asked you to explain why your brand appears in some AI answers and disappears from others. You do not need another dashboard screenshot. You need a working sheet that turns observations into a prioritized, defensible next step.

Profound Sheets Templates can reduce setup work because they provide a starting point for common ways teams put Sheets to work. Treat that starting structure as an analysis contract: define what each row means, keep comparisons stable, and decide what action a result is allowed to trigger before you start interpreting it.

Start with the decision the sheet must support

The easiest mistake is choosing a template because its output looks useful. A table of brand mentions, citations, prompts, or competitors can be interesting without resolving the decision in front of you. Start with the decision, then select the template whose row structure can support it.

Most AI visibility work begins with one of these questions:

Content prioritization: Which audience questions need a new page, a clearer answer, or stronger supporting evidence?
Brand accuracy: Which recurring claims about your company, products, or category require verification or correction?
Competitive analysis: On which relevant themes do competitors appear while your brand does not?
Source analysis: Which pages or domains are being cited, and what makes those resources useful for the question being answered?
Monitoring: How does a fixed set of observations change across models, markets, languages, or reporting periods?

Write the purpose of your sheet as a single sentence: “This sheet will help [owner] decide [action] for [scope] during [decision cycle].” If you cannot complete that sentence precisely, the analysis is not ready to run.

Decision	Useful row unit	Output to produce
Prioritize content	One topic or intent cluster	An ordered backlog with a reason for each recommendation
Investigate brand accuracy	One claim observed in one answer environment	A verification queue linked to evidence
Compare competitors	One brand-by-theme observation	Specific gaps that require inspection
Monitor change	One repeatable observation for a named model, interface, and period	A like-for-like change log

Do not force several incompatible decisions into one table. A content backlog, a competitor matrix, and a time-series log often require different row units. Combining them produces duplicate records, unclear denominators, and summaries that nobody can reproduce.

Define what each row represents before trusting the output

A floating blank grid contains consistent sequences of abstract objects in each row, with one fragmented row shown out of alignment.

A row is not merely a place where a result lands. It is the smallest observation your analysis treats as distinct. The same prompt run in a different model, interface, market, language, or period may be a different observation. If those contexts are collapsed, a change in conditions can look like a change in brand performance.

Create a short data dictionary before you customize a Profound Sheets Template. Your process should preserve these details, whether they live in the template itself or in an accompanying methodology record:

Scope: The brand, product, website, market, and language included in the analysis.
Prompt definition: The exact prompt or a stable cluster name, plus the rule used to place prompts in that cluster.
Answer environment: The named model or answer engine and the interface through which the answer was observed.
Observation time: When the answer was collected, so later changes are not mistaken for inconsistent analysis.
Entity rule: Which company, product, abbreviation, and accepted aliases count as the same entity.
Evidence: The answer text, cited URL, captured result, or another durable reference that lets a reviewer inspect the observation.
Review state: Whether the row is unreviewed, checked, disputed, or ready to support a decision.
Ownership: The person or function responsible for verifying the result and taking the next action.

Keep visibility concepts separate. A brand mention is not necessarily a citation. A citation is not necessarily an endorsement. Prominent placement is not proof of factual accuracy. Positive language is not proof that the correct product or entity was identified. Give each concept its own field instead of hiding them inside one broad “visibility” label.

Rates need visible denominators. Store the underlying count and the eligible observation set alongside any percentage or share. Otherwise, a filtered view can change the meaning of the metric without changing its label. Define how blank, unavailable, duplicate, and ambiguous results are handled as well; none of those states should silently become zero.

Customize the template without breaking comparability

A template is a scaffold, not a universal measurement standard. You will usually need to adapt it to your market, taxonomy, content inventory, and reporting workflow. The safe approach is to change it in controlled layers so you can still trace every conclusion back to an observation.

Preserve a baseline. Keep an untouched copy or a clear record of the original structure. Overwriting the only version can make previous calculations and field meanings impossible to recover.
Test the unmodified workflow on a representative subset. Include an expected positive result, an expected absence, and an ambiguous case. This reveals how the template handles edge cases before you commit to a full analysis.
Add only fields tied to the decision. A column should help you segment observations, validate evidence, assign work, or choose an action. If it does none of those things, leave it out.
Document derived measures. Record the numerator, denominator, filters, exclusions, and grouping logic behind every calculated metric. A label such as “share” or “score” is not a definition.
Check outliers against the underlying answer. An unusually strong or weak result may be real, but it may also reflect an alias mismatch, prompt classification error, missing result, or changed answer environment.
Freeze the method for the reporting cycle. When you change the prompt set, entity rules, model scope, or calculation logic, create a new version and record the change. Do not silently rewrite historical results to match a new method.

Run a quality check before distributing any summary. Look specifically for duplicate aliases, inconsistent topic labels, missing market or language values, citations counted as mentions, mentions counted as citations, blank cells treated as negative observations, and manual notes mixed into raw fields. These errors are mundane, but they can reverse the apparent direction of a result.

Keep exploratory prompts separate from monitoring prompts. Exploration is allowed to change as you discover new questions. Monitoring needs a stable comparison set. Mixing the two makes growth in prompt coverage look like a movement in visibility, even when the underlying comparable observations did not improve.

Turn observations into SEO, AEO, and GEO actions

Evidence tokens pass through a blank decision grid and branch toward search, direct-answer, and networked-globe action streams.

An observed result tells you what appeared under defined conditions. It does not, by itself, tell you why it appeared. A competitor citation does not prove that a particular page element caused inclusion. Your brand’s absence does not prove that your content is poor. Treat the sheet as a diagnostic queue, then investigate the relevant answer, prompt intent, cited resources, and owned content before prescribing a change.

Observation	What to verify	Possible action
An important brand fact is wrong	The exact claim, entity identity, cited resources, and corresponding information on owned pages	Correct the authoritative owned page and make the factual statement consistent across relevant properties
The brand is absent for a relevant topic	Whether the prompt represents real audience intent and whether an existing page answers it directly	Create or improve a focused resource if a genuine information gap exists
A competitor appears repeatedly	The cited URLs, answer format, evidence, scope, and task those pages satisfy	Close the specific information or evidence gap rather than copying the competitor’s page
The result changes frequently	The model, interface, prompt wording, market, language, and collection period	Continue controlled monitoring before making an expensive content change
The brand appears accurately and is supported by a relevant page	The cited asset, its freshness, and neighboring audience questions	Maintain the resource and extend coverage only where a related intent is demonstrably useful

Prioritize a finding through four gates:

Business relevance: Does the topic affect a product, audience, reputation concern, or decision your organization actually serves?
Recurrence: Does the pattern persist across comparable observations, or is it a single volatile answer?
Evidence quality: Can a reviewer inspect the answer, prompt, context, and cited material?
Controllability: Is there a specific owned asset, factual inconsistency, or content gap your team can address?

A finding that fails one of these gates belongs in investigation or monitoring, not an implementation backlog. This prevents your team from spending time on visible but low-value anomalies.

For findings that do become content work, connect the sheet to your content inventory. Assign a canonical URL or planned asset, an owner, the audience question, the factual evidence required, and a review state. The finished page should answer the task plainly, support important claims, identify the relevant entity consistently, and expose useful information in visible content.

Structured data should describe that visible content accurately. JSON-LD is not a patch for a weak answer, an unsupported claim, or an ambiguous entity. Use the most specific applicable schema only when the page genuinely contains the corresponding information, and keep the markup aligned when the page changes.

Maintain three distinct layers as the workflow grows: raw observations, reviewed findings, and approved actions. Raw evidence should remain stable. Review can add interpretation and confidence. The action register can then track the canonical URL, owner, status, rationale, and expected user outcome. Separating these layers stops an editorial opinion from being mistaken for collected data.

Key takeaways

Choose a Profound Sheets Template from the decision you need to make, not from the most appealing output.
Define the row unit, prompt rules, entity rules, answer environment, and evidence requirements before interpreting results.
Keep mentions, citations, placement, sentiment, and factual accuracy as separate observations.
Preserve raw results and version every methodological change so reporting periods remain comparable.
Require business relevance, recurrence, inspectable evidence, and a controllable next step before turning a finding into SEO, AEO, or GEO work.

Start with one decision from your current reporting cycle. Write its row definition, select the closest template, and test the workflow on a representative subset. Once another person can reproduce the conclusion from the stored evidence, you have a process worth scaling.

References

Try Profound Blog – Introducing Sheets Templates in Profound

September 11, 2026

Tag: Competitive Analysis

Decide what the monitor is supposed to change

Build a prompt set without moving the goalposts

Score the answer, not just the brand mention

Turn visibility changes into specific work

Key takeaways

References

Define the decision your audit needs to support

Build prompts around real discovery and buying decisions

Keep the testing conditions visible

Capture the citation trail without losing the evidence

Turn the URL inventory into an opportunity map

Convert the audit into content, PR, and reputation work

Key takeaways

References

Key takeaways

Define visibility before you try to measure it

Key takeaways

Build a prompt set around customer decisions

Inspect the signals behind each answer

Confirm that AI crawlers can reach meaningful content

Map the entities the brand needs AI to understand

Test whether the facts are extractable and defensible

Look for information gain and meaningful structured data

Turn response patterns into a prioritized diagnosis

Make the audit repeatable without turning it into dashboard theater

References

The practical answer: choose the workflow your team can run

Prompt research: decide whether you need a map or a queue

Make both vendors work from the same prompt brief

Optimization and attribution reveal the real split

Test whether an optimization is evidence, advice, or execution

Do not confuse an AI referral report with revenue attribution

Enterprise pricing: model the total cost of operation

Key takeaways

Run a proof-of-fit that produces work, not screenshots

References

Start with the decision, not the dashboard

Build a location spine before joining SEO metrics

Read every location through four evidence layers

Website behavior: compare organic with the whole site

Search visibility: split discovery from existing demand

Local rankings: measure the searcher’s geography

Profile actions and reviews: interpret them by business model

Separate portfolio growth from comparable-location growth

Turn outliers into a prioritized SEO work queue

Key takeaways

References

Measure for the decision, not the dashboard

Write a measurement contract before the work starts

Replace the reporting question with a decision question

Trace every performance shift through five evidence layers

Make every visualization perform a diagnostic job

Turn the diagnosis into a controlled action queue

Key takeaways

References

Key takeaways

Start with the AEO workflow you actually need

Goodie and Peec AI feature comparison

Choose according to who must act on the data

For agencies selling monitoring and reporting

For agencies delivering an ongoing AEO program

For an in-house brand team

For ecommerce and product-led businesses

For enterprise procurement

Compare total workflow cost, not the entry price

Run one full AEO cycle before you sign

References

Start with the decision the sheet must support

Define what each row represents before trusting the output

Customize the template without breaking comparability

Turn observations into SEO, AEO, and GEO actions

Key takeaways

References