Tag: Proprietary Data

  • Credibility-First Link Building: How We Earn Lasting Authority

    Credibility-First Link Building: How We Earn Lasting Authority

    Link building for lasting brand authority

    At Resolve, we define link building for legitimacy as earning authoritative backlinks, brand mentions, and media coverage that demonstrate trust, expertise, and credibility to search engines and AI systems. Instead of chasing link volume, we use digital PR, original research, thought leadership, and journalist relationships to earn genuine editorial citations. These are the authority signals behind Google’s E-E-A-T framework, and they can help us appear in AI Overviews, earn citations from large language models, and build visibility that survives ranking swings.

    We believe a little competition is healthy. It challenges us, sharpens our thinking, and pushes us to pursue bigger and better results.

    However, today’s search environment is changing faster than ever. Large language models, AI-generated answers, and frequent algorithm updates are reshaping how people find information, making it increasingly difficult for us to rely on yesterday’s playbook.

    The metrics we once used to keep brands afloat — traffic, domain authority increases, and keyword rankings — no longer define SEO success on their own. We can reach the top of a search results page and still see very few conversions.

    If we continue chasing those numbers in isolation, we risk being left behind. We have to adapt.

    We now widen our view to the outcomes that matter most: trust and brand authority. Unlike a temporary ranking or traffic spike, trust and authority are not earned quickly or easily.

    We need time to spread the word about our brand, and we need even more time to prove that people can rely on us. Once we establish that trust, however, it becomes much harder to dislodge.

    An algorithm update can cut our traffic overnight. It cannot erase genuine trust overnight.

    Our challenge is learning how to build trust and strengthen our brand while every competitor is trying to do the same. We also need meaningful ways to measure concepts that can initially seem difficult to quantify.

    We have found that the answers involve some nuance, but they are simpler than they appear. The process begins with a shift in perspective.

    For years, we treated link building like a popularity contest. The site that collected the most votes, in the form of backlinks, was often rewarded with a prominent position in the search results.

    Infographic showing 86% of journalists use PR-pitched stories, 54% rarely answer pitches, and 61% of Gen Z use generative AI instead of Google.
    Relevance and trust beat volume: 86% of journalists use at least some PR-pitched stories, yet 54% seldom respond, while 61% of Gen Z turn to generative AI instead of Google.

    Over time, Google and other search engines updated their algorithms to improve the search experience. With each change, Google cracked down on more sites that tried to manipulate the system with backlink volume instead of earning links with real editorial and audience value. Countless sites lost traffic, and many still feel the effects.

    Today, we see Google place more emphasis on relevance, industry trust, and authority. That helps explain why a familiar brand can attract more searchers than a smaller competitor even when both publish similar content and target similar keywords.

    Large language models and Google’s AI Overviews have widened this divide. These systems can use retrieval-augmented generation, or RAG, to retrieve relevant sources, often favoring authoritative publications and proprietary information. If we merely repeat a statistic already cited by a top-tier publication, an AI system may choose the better-known source to reduce the risk of spreading inaccurate information.

    We also see younger searchers moving toward AI tools. In a 2025 Resolve study, 61% of Gen Z respondents said they used generative AI instead of Google.

    None of this means every form of link building looks like spam to Google or an LLM. It means we need backlinks to work alongside a broader set of authority signals.

    When publications and journalists cite our brand, they signal authority. When we publish original content and proprietary data, we signal authority. When we create useful graphics and informative videos, we signal authority again.

    Once Google and AI systems recognize these signals, the backlinks supporting them become meaningful votes of confidence. Our site may then be more likely to rank prominently, appear in AI Overviews, and receive citations in LLM-generated answers.

    How we use E-E-A-T in a competitive search environment

    In 2018, Google updated its quality-rater guidance to place greater focus on expertise, authoritativeness, and trustworthiness, commonly shortened to E-A-T. In 2022, Google added another E for experience. Together, these qualities provide a framework for understanding how Google considers credibility and legitimacy.

    • Experience: We demonstrate that an author has personally engaged with the subject. Examples include a forum where people describe testing a product or a gardener documenting firsthand pest-prevention trials.
    • Expertise: We show that the author has relevant knowledge, qualifications, or credentials supporting the information and advice.
    • Authoritativeness: We earn recognition from credible sources and industry voices that cite or link to our work, helping establish us as a respected participant in the field.
    • Trustworthiness: We remain transparent, accurate, and honest. We avoid deceiving readers or using manipulative link-building practices.

    We apply E-E-A-T both on and off the page. Author biographies can demonstrate expertise, while accurate sourcing can demonstrate trustworthiness. Off the page, we strengthen E-E-A-T signals through the quality of the sites that link to us and the journalists who rely on us as a source. Both dimensions influence how search engines assess whether our information is useful, accurate, and credible.

    If we consistently earn backlinks from dozens of irrelevant websites, that pattern can look like a low-quality or manufactured signal. If several respected journalists mention our brand because we published a valuable study, those mentions are much more likely to function as genuine votes of confidence.

    Infographic comparing vanity SEO metrics like traffic and backlinks with durable authority metrics such as media placements, conversions and branded search.
    Move beyond fragile SEO numbers. This side-by-side graphic shows how earned media, branded searches, industry citations and conversions build authority that can survive algorithm updates.

    For us, link quantity is no longer a reliable proxy for legitimacy. We look for backlinks that demonstrate real relevance and value.

    We cannot earn those links half-heartedly. We need a coordinated strategy that strengthens credibility both on and off our site.

    The off-page SEO tactics we use to demonstrate value

    When we ask how to earn links that search engines and LLMs will treat as signs of trust, we do not look for a single outreach tactic. Strong links usually emerge from several activities that we sustain over time.

    We create genuinely linkable assets

    To prove that people genuinely want to reference our site, we first create content worth referencing. If we are accustomed to quick and easy links, this may require a larger investment in content than we have made before. A routine how-to article or listicle is rarely enough by itself.

    We define linkable content as something journalists, publishers, and readers find distinctive and useful — something they have not already encountered dozens of times. We often draw from the following content formats.

    • Original data and proprietary research: We publish information people cannot find elsewhere. In a crowded search environment, that means conducting original research rather than recycling familiar statistics. When a journalist needs a statistic and our site is the primary source, we can earn a natural backlink.
    • Thought leadership and expert commentary: We share an original perspective from a credible expert within our organization, giving publishers a useful idea or quotation they may cite in future coverage.
    • Authoritative long-form guides: We answer the main question thoroughly and anticipate the follow-up questions a reader is likely to ask. This depth can help us earn links as audiences move further into their research.
    • Engaging visuals and infographics: We invest in visual assets that make complex information easier to understand and share. Ahrefs found that YouTube mentions strongly correlated with inclusion in AI Overviews. Videos can be especially valuable, but informative infographics also give publishers a useful visual for their own audiences.

    These formats demand more time, effort, and money, but we have found that they are often more sustainable than disposable content. They help us earn credible editorial citations and build industry authority that is more resilient to algorithm updates.

    We connect link building with digital PR

    We place digital PR at the center of authority building because it connects brand development with link acquisition. It helps us earn coverage, attract links, and introduce our organization to new audiences through credible journalists. Those are precisely the kinds of signals search engines can consider when assessing legitimacy.

    Unlike traditional PR, our digital PR work focuses on online coverage and backlinks from news organizations and media outlets. We create useful assets or proprietary data, identify the journalists most likely to care, and pitch stories that fit their established beats.

    Many of these publications carry significant influence and reach large audiences that can introduce our brand to more people. When a highly authoritative outlet covers our story, other journalists may discover and cite it organically. Syndication can amplify the effect further when a media group republishes an article across its network, potentially producing many relevant links from one story.

    Our strongest digital PR campaigns typically use one or more of the following approaches.

    Infographic outlining five steps for a credibility-focused SEO strategy, from targeting trusted publications and creating linkable assets to measuring results.
    Build lasting brand authority in five steps: target trusted publications, create citation-worthy assets, launch digital PR, nurture journalist relationships, then measure and refine your approach.
    • Data-led PR campaigns: We begin with what journalists and their readers care about, not simply what we find interesting. We review local news, Google News, and current coverage to understand which subjects are gaining attention. By considering journalist intent from the start, we improve our chances of receiving responses and earning placements.
    • Newsjacking or reactive PR: When we can move quickly, we contribute expert opinions, data, or commentary to breaking stories that relate to our brand. This gives journalists material they can use while the topic is still timely.
    • Proactive PR: We anticipate trends before they break and prepare insights around recurring news cycles, holidays, and other relevant media moments.
    • Contributed content and guest features: We place useful content written by our experts in relevant publications, allowing us to speak directly to established audiences and earn recognition.

    When we combine these tactics effectively, we can elevate our brand to a level that competitors cannot reproduce with a batch of low-value links.

    We build relationships with journalists and publishers

    We know that even fascinating proprietary data, packaged in an expertly designed analysis, can fail if our journalist outreach is poorly targeted.

    Resolve data about journalist outreach and PR pitches

    Journalists receive an enormous number of PR pitches, and those messages can either support or obstruct their work. According to a 2026 Muck Rack study, nearly nine in 10 journalists said at least some of their stories originated with PR pitches.

    The same survey found that 54% of journalists seldom or never responded to most pitches. Relevance was a central problem: nearly half said a genuinely relevant pitch was rare.

    If we send a journalist at an economics publication a pitch about music-listening habits, we should expect a rejection because the subject may matter to only a small part of that publication’s audience. We do not take that response personally. Journalists build their careers around particular topics and beats, and our job is to support that work rather than distract from it.

    We therefore approach outreach as relationship building: a two-way exchange that should benefit everyone involved. Above all, we remember that there is a real person on the other side of every email.

    • We personalize our emails and explain why a story fits the journalist’s audience.
    • We respond graciously when a journalist says no because our next idea may be a better fit.
    • We share relevant work from journalists and publications through social media.
    • We contribute thoughtful comments when we have something useful to add.
    • We cite journalists’ reporting in future content when it genuinely supports our work.

    As we strengthen these relationships, journalists become more likely to consider future opportunities. A thoughtful follow-up or second pitch can receive a warmer response when a reporter already knows that we provide reliable data and useful commentary.

    PR relationships grow over time. Even when our first pitch does not fit a journalist’s beat, we remain willing to return with a better story or a new set of relevant data.

    How we measure real brand authority

    We recognize that authority, trust, and legitimacy feel less concrete than traffic or keyword position. Yet they have become more important. A traffic surge may look encouraging while reflecting temporary attention, weak intent, or an advantage that disappears after an algorithm update.

    Authority and legitimacy are more durable. We can also measure the impact of credibility-focused work through several meaningful indicators.

    Infographic showing EZ Contacts’ digital PR results: 1,000+ media placements, a Domain Rating of 43, and doubled visibility in ChatGPT and AI Overviews.
    EZ Contacts’ six-month digital PR campaign delivered 1,000+ media placements, raised Domain Rating from 40 to 43, and doubled visibility across ChatGPT and Google AI Overviews.
    • Earned media placements: We track the publications that cover our brand, including coverage containing an unlinked mention. These placements help us assess brand credibility.
    • Branded search volume: We monitor whether more people search for our company or products after discovering us through media coverage.
    • Industry coverage: We look for the point at which publications we have not contacted begin citing our work. That organic pickup is a valuable sign that our authority is spreading.
    • Conversions: We measure whether greater credibility leads more people to trust our organization, products, or services and ultimately take meaningful action.
    • Organic ranking improvements for target keywords: We still review rankings, but we treat them as one indicator within a broader picture. Sustained movement can show that search engines increasingly view us as a credible result relative to competing pages.
    Metrics for measuring brand authority and credibility

    We do not expect these indicators to appear overnight.

    • We invest real effort in creating proprietary data.
    • We build trust with journalists through repeated, useful interactions.
    • We grow authority through sustained work over time.

    Our advice is simple: we stay patient, keep improving, and allow credible results to compound.

    How we build a credibility-focused link strategy

    Knowing the principles of SEO authority is one thing; building an entire campaign around them is another. We use the following five-step process to turn those principles into consistent action.

    1. Step 1 — We define our target publications: We identify five to 10 publications that our audience trusts and that search engines are likely to recognize as authoritative within our field. These become our priority coverage targets.
    2. Step 2 — We develop linkable assets: We create at least two content or media assets designed around the interests of those publications. We may use original survey data, visual guides, proprietary analysis, or expert thought leadership.
    3. Step 3 — We launch a digital PR campaign: We proactively pitch our assets to relevant publications. We can also use platforms such as Connectively or Muck Rack to identify ongoing opportunities with writers covering subjects related to our research.
    4. Step 4 — We nurture relationships: We treat every positive media interaction as the beginning of a longer relationship. We follow up with useful information, engage with published coverage, and build the kind of rapport a journalist can rely on.
    5. Step 5 — We measure and iterate: We review our authority indicators each quarter, learn from the response to our campaigns, and adjust our content and outreach accordingly.
    Resolve credibility-focused link-building process

    We know this process can consume a team’s time, particularly when resources or specialized expertise are limited.

    In those situations, we may benefit from working with a link-building and digital PR specialist who can expand our capacity and keep pace with search changes. The right support can help us establish sustainable visibility without allowing every minor ranking fluctuation to pull us off course.

    How we build authority that lasts at Resolve

    We know that quality usually stands the test of time better than quantity. The difficult part is maintaining that focus when competitors appear to be winning with sudden traffic spikes or eye-catching vanity metrics.

    We do not let temporary numbers distract us from the larger goal. We focus on lasting authority and legitimacy earned through sustained content creation, thoughtful PR outreach, and genuine relationship building.

    When an internal team lacks the time or patience required to maintain that effort, we can step in.

    At Resolve, we work with brands to build credibility-focused SEO campaigns through linkable content, data-led digital PR, and hands-on link building. Our goal is sustainable organic growth, not a burst of visibility that disappears after the next algorithm update.

    Resolve results from credibility-focused digital PR

    We have seen this approach pay off. In a recent data-led campaign for EZ Contacts, we earned more than 1,000 placements in outlets including the New York Post and Yahoo. As the coverage grew, the brand’s visibility in ChatGPT and Google’s AI Overviews doubled. That is the kind of durable growth we want to build — growth that extends beyond the next algorithm update.

    Large Google logo over colorful stacks of digital pages and folders, symbolizing search advertising, web content, and online marketing updates.
    A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.

    When we are ready to build links that last, we can visit growresolve.com to learn more.

    We see considerable overlap between link building and digital PR, but we do not treat them as identical. Link building is the broader practice of acquiring backlinks from other websites to improve search authority. Digital PR is a particular approach within that practice, focused on earning links through media coverage, journalist relationships, and placements in credible publications rather than relying on directory submissions, guest-post exchanges, or other lower-authority tactics.

    We often use digital PR to pursue the strongest editorial backlinks because reputable outlets have real audiences and established review standards. At the same time, this work builds brand visibility and consumer trust in ways that many conventional link-building methods do not.

    How long do we wait for meaningful results?

    We do not expect authoritative backlinks or earned media coverage to produce results overnight. That is an honest trade-off when we choose a credibility-focused approach instead of more aggressive tactics. Most brands can begin seeing meaningful domain-authority gains and early ranking movement after three to six months of consistent execution, while highly competitive keywords and top-tier placements may require more time.

    The advantage is that our results can compound. Links from credible publications tend to endure, strong journalist relationships can create repeat opportunities, and the authority generated through consistent coverage can keep delivering value long after the initial campaign.

    We define an authority backlink as a link from a source that search engines and its audience regard as credible and trustworthy. These sources typically have genuine readers, clear editorial processes, established authority, and topical relevance to our industry.

    A regular backlink can come from any website willing to link to us, regardless of its relevance, quality, or editorial standards. That distinction matters because search engines do not evaluate every link equally. One editorial link from a respected industry publication can be more valuable than dozens of links from low-authority sites, while also supporting the kind of E-E-A-T credibility that bulk link acquisition cannot reproduce.

    Do we value brand mentions without hyperlinks?

    Yes. We recognize that Google can associate brand mentions with entities even when a publication does not include a hyperlink. Relevant, unlinked mentions in credible coverage can still contribute to the wider authority signals surrounding our brand.

    That is why we consider digital PR valuable even when every placement does not produce a direct link. A credibility-focused off-page strategy should not be reduced to backlink acquisition alone. Our larger objective is to build a brand that respected publications genuinely want to mention, cite, and cover.

    We see risks ranging from wasted effort to serious search penalties. Link buying, reciprocal-link schemes, private blog networks, and manipulative anchor-text optimization can violate Google’s spam policies. These tactics may trigger manual actions or algorithmic suppression that substantially reduces our search visibility.

    Even when outdated tactics do not produce an immediate penalty, they can lose their value as search systems become better at identifying manufactured signals. Recovering from a link-related penalty can be slow and expensive. We would rather invest in credible link building from the beginning than repair the damage caused by shortcuts later.


    Inspired by this post on Search Engine Land.


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  • Why My Original Data Gets Cited Only as Benchmarks

    Why My Original Data Gets Cited Only as Benchmarks

    In Part 1, I looked at the third-party citation signals that matter so much for AI visibility. In Part 2, I made the case for publishing original data, because it is the strongest single predictor of page originality, and the barrier to earning visibility and authority through this approach is still surprisingly low.

    Now I have more evidence for why proprietary data should be part of content creation.

    Publishing a number matters, but the number itself is not always what gets cited. I looked at Gauge’s citation data to understand what AI systems actually reward when brands publish first-party data. The answer is narrower, sharper, and more useful than simply saying, “original data wins.” Original data does win, but only when it is packaged in the right way.

    The format AI rewards most is the benchmark that answers a clear commercial question: which option is best?

    First-party research is scarce and punches above its weight

    I worked from Gauge’s cited-URL set: 301 live pages cited by AI systems across 316 unique prompts and 7 verticals. Together, those pages carried 1,075 citations.

    After auditing the URLs, I found that only 8 of the 301 pages qualified as primary research. To count, the page had to include the original source of the data and its methodology, rather than simply writing about someone else’s numbers.

    That means primary research made up just 2.7% of the cited set. But those same 8 pages earned 90 of the 1,075 citations, or 8.4% of the total citation volume. In other words, first-party research appeared rarely, but when it appeared, it over-indexed by roughly 3x on citation share.

    The cleaner way I see this is citation density.

    Primary research averaged 11.3 citations per page. Everything else averaged 3.4 citations per page. A primary-research page was 3.3x as citation-dense as a non-primary page.

    Bar chart showing primary research earns 11.3 citations per cited page versus 3.4 for other pages, a 3.3x citation advantage.
    Primary research is rare, but this Gauge analysis shows it punches above its weight: cited pages with original research averaged 3.3x more citations than everything else.

    That is the compounding effect of primary research.

    This mirrors the information gain finding I discussed in Part 2, but from the AI citation side rather than the classic 10 blue links side.

    There, original data correlated with page originality more strongly than any other trait. Here, original data correlates with citation density. Both findings point in the same direction: the number only you can produce is the lever.

    Original research wins when the question has a benchmark

    This is where the “original data wins” idea needs more precision.

    The 90 primary-research citations were not distributed evenly across the 8 pages. They were not distributed evenly across topics either.

    Of those 90 citations, 75 came from one cluster: cloud data warehouse benchmarks. Fivetran’s warehouse benchmark alone earned 44 citations, which was just under half of every primary-research citation in the set.

    Once I strip out the benchmark cluster, first-party research barely registers in the citation set. The win is not simply, “I published original data.”

    The real win is, “I published a benchmark that answers a buying comparison,” and almost nobody builds those well. By benchmark, I mean a page that measures a set of named things against each other on a specific yardstick and publishes the results as numbers.

    Bar chart showing data warehouse benchmark pages earned 75 of 90 primary research citations, led by Fivetran benchmark with 44 citations.
    A striking citation split: cloud data warehouse benchmarks dominated AI-cited primary research, with Fivetran’s benchmark alone pulling 44 citations from the 90-citation set.

    Original research is most powerful when it directly answers commercial comparison queries.

    This is also what Google is pushing toward with non-commodity content: new, helpful information that is hard to get elsewhere.

    The primary-research citations clustered where prompts asked AI to compare options on measurable specs such as speed, cost, latency, yield, or performance.

    That explains the warehouse benchmark spike. The “HR Tech / Compensation” label was noisy, but the citations inside that bucket mostly came from cloud data warehouse benchmark prompts. Fivetran, Estuary, and ClickHouse had numbers AI could use.

    Crypto / Solana showed the same pattern at a smaller scale. Marinade and Helius earned citations because staking and MEV questions need firsthand ecosystem data, not generic explainers.

    The pattern disappeared in topics without a clear benchmark. B2B SaaS / CRM, Education / TEFL, and Product Analytics returned listicles, product pages, explainers, and case studies. After cleaning the data, I found no cited primary-research page in those topics.

    A closer look at the content that held 44 citations

    Fivetran’s warehouse benchmark took 44 citations from this dataset on its own. Fivetran’s 2 benchmark pages together took 58 of the 90 primary-research citations. So I wanted to understand why.

    The page was published in 2022, but when I examine it, it is easy to see why LLMs still prefer it.

    Bar chart showing primary-research citation share by topic, led by HR Tech/Comp Mgmt at 24.1% and Crypto/Solana at 10.7%.
    Primary-source visibility is highly concentrated: benchmark-driven topics like HR tech and crypto attract far more AI citations than explainers or listicles.

    It answers a measurable comparison head-on. The page names BigQuery, Redshift, Snowflake, and Databricks, then ranks them on speed and cost. It is entity-rich and willing to name the major players directly.

    It runs on real first-party data. Fivetran tested against actual customer usage rather than relying on synthetic assumptions, and the page calls that choice out clearly.

    It shows the method step by step. The page walks through what data was queried, which queries were used, and how each warehouse was configured and tuned. A reader, or a model, can see how the numbers were produced.

    The structure is easy to lift. Descriptive headings such as “Results,” “How much did performance improve?,” and “Why are our results different from previous benchmarks?” help AI map a question to the exact passage that answers it.

    It links to raw data and sources. The page footnotes references, including the C-Store paper, and points to the underlying data. That makes the claims verifiable. Few brands put that much work into a data-backed content piece, and even fewer share the full dataset for transparency.

    It shows its limits. Dated correction notes from December 2022, named qualitative limitations, and an honest “performance floor” caveat make the claims more credible, not less. The corrections also show care.

    The URL never moved. A page from 2022 is still earning citations in 2026 because it stayed live at one canonical address.

    The data behind a page like this is easier to pull and analyze than it has ever been. The hard part is everything around the data: the clean method, linked sources, corrections, navigable structure, and willingness to say what the numbers do not prove. That is the craft, and that is the moat.

    Screenshot of Fivetran's Cloud Data Warehouse Benchmark article with author George Fraser and data warehouse graphic.
    Fivetran's 2022 benchmark page shows why clear, comparison-led research can become a lasting citation source for AI and search visibility.

    This kind of first-party data content is not a thin press release with a few loosely pulled numbers. It requires real work, and it can hold authority for years. My takeaway is simple: AI does not reward “original data” by default. It rewards first-party research when the page gives a clear answer to a measurable comparison and signals depth, expertise, and trust.

    The opportunity is to publish a retrievable dataset for a buyer question where AI does not yet have a clean benchmark source. That connects directly to the unanswered-questions finding from Part 2. The opening exists, but in many verticals, nobody has walked through it with a real dataset.

    Original data needs a citation-ready package

    Original data gives a page something AI cannot get from another explainer. But AI still has to retrieve it, parse it, and map it to the user’s question.

    That is where many brands lose the citation. They publish proprietary numbers, but bury them in narrative, gate them behind forms, move the URL, or skip the methodology. The data exists, but the citation never happens.

    The pages that won in this dataset had both ingredients: original numbers and a clean citation shape. They had stable URLs, clear methods, named comparisons, and results that answered buyer questions directly.

    Who wins: brands with proprietary product, usage, or pricing data that package it into a comparison a buyer can act on, especially one that can inform LLM-generated recommendations.

    Who loses: brands that publish original numbers inside dense narratives, on slow or unstable pages, with no clear comparison frame for AI to retrieve and reuse.

    When I think about a citation-ready research page, I look for four parts.

    Futuristic data archive with glowing server-like filing cabinets, stacked documents, and network lights symbolizing AI marketing data infrastructure.
    Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

    Lead with the comparison result. The headline finding, such as “X is fastest” or “Y is cheapest at scale,” should appear in the first 30% of the page. Lead with the result, then explain the method and nuance.

    Box the methodology. Show the sample, time window, what was measured, and how the measurement happened. Attribution confidence is part of what makes a number citable, so the method needs to be clear on the page.

    Frame the comparison explicitly. AI reaches for benchmarks when prompts ask “which is best.” A table comparing named options on named specs is the format most likely to be lifted.

    Keep the URL stable. Use one canonical page and keep it live. Do not migrate it or rename it every redesign. The citation earned this quarter only compounds if the page is still there next quarter. In this dataset, 64 of 365 cited URLs were dead, redirected, or otherwise broken, taking 203 citations down with them.

    This is the work behind a citable benchmark, and it is more involved than it looks.

    HockeyStack documented its own version in a playbook on launching research reports. The company published 18 original reports built entirely on anonymized first-party customer data, the kind of data no competitor could replicate.

    Its process includes the same steps the Fivetran page demonstrates: list the data points needed, have a teammate pull them with SQL, define and document the method so the numbers can withstand scrutiny, and structure the report around a real ICP question. HockeyStack calls methodology non-negotiable because without it, someone will always dispute the data.

    With AI analysis, pulling the data is often the easier part now. Building the content into something citable, trustworthy, and durable enough to keep earning visibility for commercial queries years later is where the harder work sits.

    What sites are already trusted for your topic? When a benchmark you did not publish is earning the citations in your category, the Citation Source Mapper can map that trusted set into a ranked, pitchable target list. It is available in the premium library.

    This post first appeared on the author’s website and is republished here with permission.


    Inspired by this post on Search Engine Land.


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  • How I Turn Proprietary Data Into AI Citations

    How I Turn Proprietary Data Into AI Citations

    Why proprietary data is your most defensible AI citation asset - featured-image

    When I want a page to feel genuinely original, I start with original numbers. They are still one of the most reliable ways to make content stand apart, especially when those numbers come from the business itself instead of a one-off study created just to fill a content calendar.

    The old approach was to pay a PR or research firm for a loosely related survey, like a car insurance FinTech commissioning road-trip research to earn a mention in Yahoo. I see that play as increasingly outdated. Almost every product now creates data worth publishing, and extracting that data is easier than it has ever been.

    I do not need a full research department to compete here. The bar for standing out is lower than many teams assume.

    View embedded content

    First-party data: The strongest correlation of originality

    On-Page.ai’s recent information gain study scored 150 top-3 Google pages across 50 keywords and 10 verticals. The study looked at how much each page added beyond the rest of its ranking cohort, grading contribution from 0 to 100 by meaning rather than wording.

    The median page scored 52. More importantly, original data correlated with that score more strongly than any other page-level trait, including content length.

    Pages with at most 1 unique figure averaged an information gain score of 40.2. Pages with 15 or more unique figures averaged 62.1, and the score increased steadily at every step in between.

    Image

    The good news is that the bar is not especially high. The study found that top organic results usually include only 4 unique data points on average. If I publish a page with more than 4 real original claims, figures, or answers, I create another lever for earning visibility in increasingly competitive organic search.

    The analysis also found that almost every search leaves adjacent questions unanswered. On-Page used synthetic reader questions, meaning plausible related questions generated for the study, and found room for new pages to answer those questions more completely. That immediately reminds me of query fan-out.

    I saw a similar pattern in an analysis of ChatGPT citations.

    “A single evergreen page covering 10+ query intents is worth more in AI citation reach than 10 single-intent pages. The ROI of comprehensive content is front-loaded: one well-built page compounds citation reach over time. The long tail exists, but the top 5% of pages capture a disproportionate share of ongoing citation activity.” – The science of how AI picks its sources

    That is why I believe high-intent prompts should be monitored across the full buyer journey. I would map them across the five stages from Reasoning Lift: Problem, Exploration, Comparison, Validation, and Selection. I would also use more accurate AI prompt tracking to understand where those questions emerge, then answer them with the kind of knowledge only the brand can provide.

    My main takeaway is simple: most pages are only middling on originality, genuinely original pages are still a minority, and scoring high enough to stand out is achievable without an extraordinary lift.

    Image

    The limitation is just as important. This study focuses on classic search visibility and rankings, which makes sense because the SEO concept of information gain comes from Google patent language. It does not analyze AI citations or mentions, and it does not appear to include AI Mode or AI Overviews.

    Caveat: Being the primary source may not win the citation

    This is the part of proprietary data advice I think gets skipped too often. Everyone says to publish original research. Far fewer people test whether AI rewards the brand that created the number or the page that presents it in the clearest, most extractable way.

    More data analysis is still coming, but based on analyses completed at Growth Memo over the last year, I already see two patterns worth paying attention to.

    • The entity types that predict ChatGPT citations the most are DATE and NUMBER (from The science of what AI actually rewards). Highly cited pages tend to be dense with specific entities, such as a particular methodology, a precise statistic, or a named comparison. Even when another source picks up my proprietary findings and gets cited instead, those external third-party authority signals can still build over time.
    • Entity-richness and balanced sentiment matter (from The science of how AI pays attention). Generic advice is vague and risky. Specific entities are grounded and verifiable. Proprietary data can produce, verify, validate, and create entity-rich content at the same time. I can explain why a feature saves a certain percentage of dollars, how many hours clients save, or how performance compares with previous vendors. When I add balanced sentiment to the analysis and explanation, I get a stronger tactic from the same asset.

    If the hypothesis holds that first-party data is crucial in the era of AI search, then publishing proprietary data is necessary, but it is not enough. LLM extraction structure, along with the sites AI search engines already trust for a topic, helps decide who actually earns the citation, even when the brand owns the data.

    That is the frustrating part: an aggregator can repackage my benchmark into a cleaner, answer-ready page and collect the citation my research earned.

    • Who wins: Brands that already have proprietary product, usage, or pricing data and also structure that data for extraction while continuing to build organic brand authority. This connects directly to How to build an AI SEO strategy that outlasts tactics.
    • Who loses: Brands publishing opinion content that any tool can replicate, brands ignoring off-site authority, and primary sources that bury their own numbers inside narrative instead of surfacing them clearly.

    I do not yet know whether some verticals reward data content more than others. The science series found that citation signals vary sharply by vertical, so I would be surprised by a uniform payoff. Still, I would not claim a pattern without data.

    Image

    How to structure data for extraction

    Owning the data gets me into the visibility race. How I structure that data may decide whether I win the citation.

    In an analysis of 18,012 verified ChatGPT citations, we found a ski-ramp distribution: 44.2% of all citations came from the first 30% of a page. The middle 30-70% earned 31.1%, and content buried deep in a long post was roughly 2.5x less likely to be cited.

    The follow-up analysis across 7 verticals made the target even clearer. The 10-20% band of a page is where AI reads hardest in every vertical, while the first 10% is usually navigation and intro filler that AI skips. The bottom 10% of any page earns only 2.4-4.4% of citations regardless of vertical.

    When I apply that to a data study, the structure becomes straightforward.

    • I lead with the headline statistic. My strongest number belongs in the first 30% of the page, ideally right after the title block where the 10-20% band begins. I want the number, the comparison, and the implication visible quickly.
    • I define the metric immediately. I include one sentence explaining what the number measures and which population it covers. An undefined statistic is harder to extract with confidence.
    • I box the methodology. I make the sample size, time window, and collection method easy to find in a short labeled block. Attribution confidence is part of what makes a number citable.
    • I front-load every secondary finding. I rank findings by strength, with the strongest first. A 20-paragraph narrative buildup may help human suspense, but it can cost machine citations.
    • I skip the suspense close. AI reads more like a busy editor than a patient student. The payoff-at-the-end structure that worked for ultimate guides often works against extraction.

    This post first appeared on the author’s website and is republished here with permission.


    Inspired by this post on Search Engine Land.


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  • SEO in 2026: Expert Predictions for a Transforming Landscape

    SEO in 2026: Expert Predictions for a Transforming Landscape

    I’ve been thinking a lot about how the search landscape is evolving. It’s not just a shift; it’s a complete reimagining of the digital roadmaps we’re used to. To dig deeper, I reached out to six trailblazers in the SEO world to get their insights on where we’ll be by 2026. Here’s what they shared.

    Our interactions with AI are going beyond simple Q&A scenarios. Enter the era of AI acting as your executive aide, seamlessly handling everything from finding the right product to processing your purchase. This shift demands that we optimize not just for clicks, but for machine readability and compatibility with AI protocols.

    Jim Yu, CEO of BrightEdge, emphasized the need for preparation as AI takes on a more agentic role. According to him, the brands that embrace structured data and machine-readability will stand out as AI-driven commerce becomes mainstream.

    Samanyou Garg, CEO of Writesonic, predicted a future where AI will take users straight from discovery to transaction within a single conversation. Meanwhile, Crystal Carter from Wix warned that simply being discoverable isn’t enough if you’re ignoring the agentic potential.

    Key takeaway: Your product data needs to be machine-readable. Without it, AI agents may overlook your brand in favor of more compliant competitors.

    As AI matures, advertising will become more integrated, moving away from traditional placements to conversational approaches. Jim Yu suggested that AI responses embedded throughout search result pages will become routine, reinforcing the importance of broad optimization strategies.

    ```json
{
  "alt": "Google search result page with extended 'o's in logo indicating multiple pages.",
  "caption": "Discovering deeper into Google's search results with a twist in the logo as it humorously stretches across multiple pages.",
  "description": "This image features a Google search results page in dark mode with an amusing twist. The Google logo humorously extends with multiple 'o's, symbolizing additional result pages numbered 1 to 10. This visual emphasizes exploring the depths of Google's search results. The bottom text indicates results personalization with an option to try without personalization, adding a layer of user control over the search experience."
}
```

    By 2026, we’ll see SEO professionals functioning more like engineers, using natural language tools to create marketing solutions. According to Garg, this approach allows for a significant increase in productivity, reducing manual labor and cutting costs.

    Key takeaway: Automation is the future. Teams that embrace tool-building over task-completing will speed up their progress significantly.

    The concept of singular search rankings is becoming obsolete as search results become personalized in real-time. Mike King views this as an opportunity to tailor content to specific audiences, enhancing relevance and engagement.

    Key takeaway: Generic content risks invisibility. Tailor your SEO strategy to focus on specific audience segments.

    We are witnessing a divergence in SEO roles: one focusing on traditional human users and the other on AI agents. Understanding both audiences will be crucial for future SEO success, as traditional metrics like rankings and clicks may no longer measure true impact.

    ```json
{
  "alt": "Venn diagram showing the overlap between SEO and AI Search.",
  "caption": "Exploring the intersection of SEO and AI Search—where digital marketing meets advanced technology for optimized results.",
  "description": "This image is a Venn diagram illustrating the overlap between SEO (Search Engine Optimization) and AI Search. Two peach-colored circles intersect, with 'SEO' labeled on one side and 'AI Search' on the other, highlighting their intersection in digital marketing strategies. This visual representation emphasizes the synergy between traditional SEO techniques and modern AI-driven search capabilities, crucial for enhancing web visibility and search accuracy."
}
```

    Key takeaway: Optimize for human interactions and AI processes separately to ensure you’re not missing hidden opportunities for engagement.

    Proprietary data and unique, authentic content are becoming increasingly valuable as AI-generated content proliferates. Brands that own distinctive datasets will stand out, as their information becomes essential for AI models to cite.

    Key takeaway: Develop proprietary data and unique content to maintain an edge in an AI-saturated landscape.

    AI literacy is essential. In 2026, the ability to effectively integrate AI into processes will differentiate market leaders from the rest. Neil Patel stresses the importance of linking AI usage to measurable business outcomes.

    Key takeaway: Equip your team with the right AI tools and training to translate AI initiatives into tangible results and growth.

    Ultimately, achieving search visibility in 2026 will involve being more than just relevant in rankings. It means becoming a reliable resource for both human users and AI systems. Investing in the right data and AI strategies now will secure your success in the upcoming year.


    Inspired by this post on Search Engine Land.


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