{"id":12451,"date":"2026-07-26T04:02:45","date_gmt":"2026-07-25T20:02:45","guid":{"rendered":"http:\/\/longzhuplatform.com\/?p=12451"},"modified":"2026-07-26T04:02:45","modified_gmt":"2026-07-25T20:02:45","slug":"how-semantics-and-topical-authority-improve-local-seo","status":"publish","type":"post","link":"http:\/\/longzhuplatform.com\/?p=12451","title":{"rendered":"How semantics and topical authority improve local SEO"},"content":{"rendered":"

<\/p>

Publishing more pages doesn\u2019t automatically build topical authority. In many local SEO projects, the bigger challenge is deciding which queries deserve their own pages. Getting that decision right can strengthen relevance, reduce content overlap, and improve search performance.<\/p>

The \u2018Query Deserves a Page\u2019 framework<\/h2>

\u201cProcess all the attributes of an entity\u201d and \u201cprocess all the variations of a query template\u201d are the two simplest ways to explain how topical authority works for local or location-agnostic SEO projects.<\/p>

If a project is primarily about \u201crehab,\u201d one way is to process each type of addiction along with every relevant attribute. Another is to identify a query template, such as \u201cCan X cause addiction?\u201d and process all its variations.\u00a0<\/p>

In both cases, we rely on a core metric called Query Deserves a Page (QDP). QDP is inspired by Query Deserves Freshness (QDF), a concept introduced by former Google engineer Amit Singhal.<\/p>

Below is an example site from this case study and its results. The project operates in the luxury rehab industry and is based in Southeast Asia. It needs to rank for the \u201crehab [country name]\u201d query template to generate leads while also ranking for variations such as \u201cbest rehab,\u201d \u201cx addiction,\u201d \u201cx addiction treatment,\u201d \u201cx rehab,\u201d and \u201crehab [country name].\u201d<\/p>

\"Image\"Image<\/figure> <\/div>

The project successfully uses both approaches for topical authority, as it processes all the possible attributes of an entity and all the variations of certain query templates. Google\u2019s ranking decision tree uses basic machine learning algorithms and works as follows.<\/p>

  • If a site satisfies the \u201cCan X cause [Y] addiction\u201d query, it can possibly satisfy the \u201cCan [C] cause [D] addiction\u201d query too. Google performs a small click test, and if the click test result is positive, it makes the website more rankable for the specific query template based on query similarity.<\/li>
  • If a site satisfies the queries from a certain entity-context pair, with certain attribute combinations and verbalizations, it can do the same for other entities from the same class. This helps the website rank better both initially and during the re-ranking phases.<\/li>
  • Google tends to trust a website through satisfied clicks or historical data. If the site exploits this search engine trust by performing parasite SEO, going after irrelevant topics, or publishing lower-quality content, the earlier decision tree can be altered to a lower initial and re-ranking scale during a re-ranking, and the positive ranking state can be erased.<\/li> <\/ul>

    This brings a question to the center of local and location-agnostic SEO campaigns: Which query template variations and which entity-attribute pairs deserve a page?<\/p>

    I explained the QDP concept during an iGaming SEO conference using the slide below. If we create a query search taxonomy, from \u201cholster\u201d to \u201cNylon OWB Glock 19 Gen 4 5.2 Inch Holster,\u201d there are 17 steps between the canonical and represented queries. Do we need to open 17 different pages?<\/p>

    \"Image\"Image<\/figure>

    The same question applies to local SEO projects. If you\u2019re a car accident attorney in California, do you need to open a web page for every city, district, and type of accident? That would mean at least 300 pages.\u00a0<\/p>

    If you open all of them, can you use the same templated, similar, or repeated sentences, paragraphs, lists, tables, images, and structured data samples, or do you need to make them more unique?<\/p>

    Be the brand customers find first<\/span>. <\/p>

    Track, grow, and measure your visibility across Google, AI search, social, local, and every channel that influences buying decisions. <\/p> <\/p><\/div>

    Start your free trial<\/span> <\/p> <\/p><\/div>

    Core concepts for building topical authority<\/h2>

    Let\u2019s explain some concepts first. Then we\u2019ll examine more than seven different websites by sharing their transparent data and, in most cases, their names.<\/p>

    Query Deserves a Page<\/h3>

    Topical authority isn\u2019t only about writing simple blog articles or for programmatic and templatic projects that cover more topics.\u00a0<\/p>

    It\u2019s a \u201ccost-of-retrieval\u201d optimization for search engines: satisfying the needs behind a query with click satisfaction while spending less computational effort.<\/p>

    Some queries deserve representation at the page level. Others deserve it at the heading, sentence, table, list, form, calculator, converter, product filter-like visual annotation, or many other possible levels.\u00a0<\/p>

    The most important representation decision comes from the \u201cPage or Not\u201d question because every unnecessary page increases the cost of retrieval.<\/p>

    Topical authority is defined as \u201chistorical data multiplied by topical coverage,\u201d divided by \u201ccost of retrieval,\u201d and finally \u201cinterpreted with visual semantics.\u201d<\/p>

    Detecting near-duplicate pages according to search queries<\/h3>

    The patent, \u201cDetecting query-specific duplicate documents,\u201d explains how Google can sometimes see two documents as \u201cexact duplicates,\u201d sometimes as \u201cnear-duplicates,\u201d and sometimes as \u201cfully unique,\u201d because every page becomes repetitive or unique according to the query that makes it necessary in Google\u2019s index.<\/p>

    Having a level of \u201coverlap\u201d helps relate documents to each other, especially for justifying internal links and \u201clink descriptions,\u201d meaning anchor text. But if the overlap exceeds the threshold, documents become near or exact duplicates.<\/p>

    \"Image\"Image<\/figure>

    Google constructs an index. SEO creates a page.<\/h3>

    From \u201cGlock Holster\u201d to \u201cGlock 18 Holster\u201d and other variations, there\u2019s always a level of duplication. Whether these variations deserve separate pages depends on four metrics:<\/p>

    • The query has high search demand.<\/li>
    • The query has different entities.<\/li>
    • The query has low similarity.<\/li>
    • The query has a pattern.<\/li> <\/ul>

      If certain metrics are met, these variations can become separate pages in the topical map. Otherwise, they should exist as different sections on the same page, represented through visual semantics such as headings, sentences, product cards, or information cards.<\/p>

      These metrics help an SEO process a query the way a search engine does because search engines essentially ask: \u201cShould we create an index for this query?\u201d<\/p>

      An SEO asks a different question: \u201cShould we create a page for this query?\u201d<\/p>

      Whenever Google creates an index, we create a page for indexing. If you parse the query incorrectly, you\u2019ll dilute your ranking signals by causing micro-cannibalization.<\/p>

      To better understand Google\u2019s indexing tiers and decision trees, read the \u201cIndex server architecture using tiered and sharded phrase posting lists\u201d patent. Also, review what Gary Illyes explained about indexing tiers and shards during the Search Off the Record podcast.\u00a0<\/p>

      You can\u2019t fully grasp Google\u2019s ranking decision tree for local or location-agnostic SEO without understanding how Google parses queries and constructs indexes.<\/p>

      \"Image\"Image<\/figure>

      Query Deserves a Page in practice<\/h2>

      Below are exercises for deciding whether a query deserves its own page, progressing from easier to more difficult.<\/p>

      \u201cGlock Holster\u201d and \u201cNylon Glock Holster\u201d have the same entities. Only \u201cGlock Holster\u201d is highlighted with yellow corners to indicate that it has a unique entity, while the second query doesn\u2019t.\u00a0<\/p>

      If \u201cNylon Glock Holster\u201d doesn\u2019t have enough search demand, it isn\u2019t highlighted. \u201cNylon Glock Holster\u201d can exist as a subsection under the \u201cGlock Holster\u201d page rather than deserving its own page.\u00a0<\/p>

      Here\u2019s a similar comparison between the \u201cIWB Kimber Holster\u201d and \u201cKimber Holster\u201d queries.<\/p>

      \"Image\"Image<\/figure>

      Below, the query networks for \u201cGlock 19,\u201d \u201cGlock 20,\u201d and \u201cGlock 21\u201d are processed under the same web page, while \u201cGlock 18\u201d and \u201cGlock 18X\u201d are processed as separate pages.\u00a0<\/p>

      The highlighted corners indicate why. \u201cGlock 19,\u201d \u201cGlock 20,\u201d and \u201cGlock 21\u201d don\u2019t have enough search demand, and they\u2019re excessively similar queries.<\/p>

      This raises a question. If \u201cGlock 18\u201d and \u201cGlock 18X\u201d aren\u2019t considered similar, why are \u201cGlock 19,\u201d \u201cGlock 20,\u201d and \u201cGlock 21\u201d considered similar?\u00a0<\/p>

      Query similarity isn\u2019t calculated using basic string similarity. It\u2019s a weighted similarity based on the importance of the entity and search demand.<\/p>

      \"Image\"Image<\/figure>

      Another research paper, \u201cEnd-to-end query term weighting\u201d from Michael Bendersky and Marc Najork\u2019s team, shows how BERT weights the \u201cNike running shoes\u201d query, treating \u201cNike\u201d as more important for relevance than \u201crunning\u201d or \u201cshoes.\u201d<\/p>

      In this case, similarity between queries is calculated based on term weights, informed by natural language processing and how Google-like search engines construct indices.<\/p>

      \"Image\"Image<\/figure>

      If \u201cNike\u201d is the heaviest term in this query, the created web document should also weight \u201cNike\u201d more heavily in its vocabulary, triples, and annotations. For example, the subject of the triples should contain \u201cNike.\u201d\u00a0<\/p>

      If \u201crunning\u201d were the heaviest term, it would change the entire topical map, sentence structure, and triples. Similarly, without understanding the weight of \u201cLos Angeles\u201d in the query \u201cLos Angeles Car Accident Attorney,\u201d we can\u2019t process the query and decide whether it deserves its own page.<\/p>

      Below is another example involving cross-brand products. Here, we multiply the product query by the brand entity because search demand is high enough, while the \u201cquery has different entities,\u201d \u201cquery has low similarity,\u201d and \u201cquery has a pattern\u201d metrics all exceed their thresholds. Since three of the four metrics are satisfied, these queries can become new pages.<\/p>

      \"Image\"Image<\/figure>

      The \u201cquery has low similarity\u201d metric isn\u2019t approved because \u201c1911\u201d is the highest-weighted query term, and it exists in every query. As a result, these queries don\u2019t pass the threshold for that metric.\u00a0<\/p>

      The \u201cquery has a pattern\u201d metric is always marked approved because these products all have attribute queries such as \u201cprice,\u201d \u201creview,\u201d \u201cproblems,\u201d \u201csolutions,\u201d \u201crepair,\u201d and \u201cmaintenance,\u201d either as interrogative queries or incomplete query phrases.<\/p>

      \"Image\"Image<\/figure>

      Not all these metrics are based primarily on the query. Google sometimes constructs a new index because of the nature of the document clusters. For example, the PageRank of the ranking documents or the size of the document cluster can cause a search engine to construct a new index.\u00a0<\/p>

      In both cases, a high-PageRank document cluster with a large number of URLs should have a uniform vocabulary. If the vocabularies differ too much, Google might distribute the documents to other existing indices for different queries.<\/p>

      \"Image\"Image<\/figure>

      If Google created an index based solely on search demand-related or derived metrics, it would miss a significant amount of information and opportunities to satisfy queries driven by sudden needs or less popular topics. Based on index size, index PageRank, and index vocabulary, it can still construct new indices.<\/p>

      Now, before moving to the other case study projects, let\u2019s apply this logic to our \u201cRehab [Country]\u201d example.<\/p>

      \"Image\"Image<\/figure>

      Why should the homepage target the most important location-service pair?<\/h2>

      In my local SEO projects, I follow one rule: The most important location-service pair should be targeted from the most important page, usually the homepage. This can be amplified by a partial- or exact-match domain or site name.<\/p>

      The site name isn\u2019t the same as the domain name. Google treats it as part of your brand name, and it doesn\u2019t have to match your domain.\u00a0<\/p>

      \"Image\"Image<\/figure>

      Most of the time, the homepage is the strongest page because it has the highest PageRank, and after your robots.txt file, it\u2019s usually the most crawled page by search engine crawlers.\u00a0<\/p>

      For the \u201crehab Thailand\u201d query network, the queries have high similarity, and the variations don\u2019t have enough search demand. Thus, we\u2019re opening a single page, and since this is the most important query network, it should be targeted from the homepage.<\/p>

      In many local SEO projects, the homepage, About page, or main service pages cannibalize each other because PageRank, query relevance, click data (historical data), and the Google Business Profile website URL are some of the main contributors to rankings, and they\u2019re shared by multiple pages.<\/p>

      Here are the third-party results from Ahrefs and Semrush. Semrush shows higher rankings because it includes Google Business Profile rankings, while Ahrefs doesn\u2019t.<\/p>

      \"Image\"Image<\/figure>

      The last two years of Semrush results are below.<\/p>

      \"Image\"Image<\/figure>

      Before moving into the page design and page content necessities, to better explain the Query Deserves a Page principle for forming a topical map, check another live example below.\u00a0<\/p>

      An incident and injury attorney website\/brand from Melbourne previously had around 19 different location-specific pages, and these pages were opened for very similar locations, such as \u201cMelbourne Beach\u201d and \u201cMelbourne Center,\u201d with certain service terms.\u00a0<\/p>

      The query similarity and query search demand criteria aren\u2019t met for any subdistrict or neighborhood of Melbourne.<\/p>

      \"Image\"Image<\/figure>

      After we merged these unnecessary [service] + [location] landing pages, the initial results for both the Google Business Profile and the website improved greatly.\u00a0<\/p>

      More than 232 new queries came in, and 60 queries gained better rankings. If the website used a homepage with an exact- or partial-match domain name or site name, it could amplify these improvements even further.<\/p>

      \"Image\"Image<\/figure>

      Here are some of these monetary query terms and their values.<\/p>

      \"Image\"Image<\/figure>

      How visual semantics play a role in local SEO and topical authority<\/h2>

      Visual semantics are based on Google\u2019s visual annotations, such as the \u201ccenter-piece annotation.\u201d They help identify the main function and purpose of a web document. A web document doesn\u2019t consist of text alone. It\u2019s defined by how that text is presented and how the presentation satisfies query needs.\u00a0<\/p>

      In local SEO projects, processing a query with its location and service attributes, then distributing those attributes across the web document, carries great importance.\u00a0<\/p>

      Below are the micro- and macro-context segments of a homepage in which every subsegment carries a function and matches an augmented query.<\/p>

      Query augmentation is a system for processing a query and representing it within a bigger context. What Google publicly calls \u201cquery fan-out\u201d is technically query augmentation in its patents.\u00a0<\/p>

      Query augmentation is also a patent name that comes directly from Anand Shukla, who also worked on \u201cSearch with stateful chat,\u201d the patent behind Google\u2019s AI Overviews and AI Mode.<\/p>

      In other words, if you understand how Google augments a query, you can build a better contextual vector for the page and optimize its layout accordingly.<\/p>

      \"Image\"Image<\/figure> <\/div>

      \u201cA representation of the Query Augmentation from Anand Shukla of Google\u201d.<\/em><\/p>

      The table below shows how the canonical query \u201crehab thailand\u201d can be augmented.<\/p>

      Query augmentation<\/strong><\/td> Visual semantics (Figure Component)<\/strong><\/td> Triples (Subject \u2013 Predicate \u2013 Object)<\/strong><\/td> Named entities<\/strong><\/td> <\/tr>
      find a rehab thailand, book rehab thailand<\/td> 2 \u2013 Hero with H1 \u201cDrug and Alcohol Rehab in Thailand\u201d + CTA \u201cTalk to us \u2013 free and confidential\u201d<\/td> ({Rehab Name}, is a, Rehab Centre), ({Rehab Name}, located in, Thailand), (User, contacts, {Rehab Name})<\/td> {Rehab Name}, Thailand<\/td> <\/tr>
      rehab thailand for foreigners, rehab in thailand from uk<\/td> 1 \u2013 Utility bar with regional phone chips (UK, US, AU, HK, TH)<\/td> ({Rehab Name}, serves, United Kingdom), ({Rehab Name}, serves, United States), ({Rehab Name}, serves, Australia)<\/td> United Kingdom, United States, Australia, Hong Kong, Thailand<\/td> <\/tr>
      licensed rehab thailand, accredited rehab thailand<\/td> 3 \u2013 Evidence strips: \u201c{Accreditation Body} + licensed by {Health Authority}\u201d and logo pills {Prof. Association}, {Addiction Assoc.}, {Accreditation}, {Cert. Body}<\/td> ({Rehab Name}, licensed by, {Health Authority}), ({Rehab Name}, certified under, {Accreditation Body}), (Staff, member of, {Prof. Association})<\/td> {Health Authority}, {Accreditation Body}, {Prof. Association}, {Cert. Body}, Thailand<\/td> <\/tr>
      reviews for rehab thailand, is rehab in thailand safe<\/td> 4 \u2013 Review carousel \u201cH2: Rehab in Thailand \u2013 {Rehab Name} Reviews\u201d with {Reviewer 1}, {Reviewer 2}, {Reviewer 3}, star ratings, Google badge<\/td> ({Reviewer 1}, rates, {Rehab Name}), ({Rehab Name}, has rating, 5 Stars), (Review, verified by, Google)<\/td> {Rehab Name}, {Reviewer 1}, {Reviewer 2}, {Reviewer 3}, Google<\/td> <\/tr>
      rehab chiang mai, rehab resort thailand<\/td> 5 \u2013 Definitional block: \u201c{Rehab Name} is a luxury rehab centre in {City}, Thailand\u2026\u201d + proof line \u201c7000+ successful recoveries, {Landmark} location\u201d<\/td> ({Rehab Name}, is a, Luxury Rehab Centre), ({Rehab Name}, located in, {City}), ({Rehab Name}, situated on, {Landmark}), ({Rehab Name}, has recovery count, 7000+)<\/td> {Rehab Name}, {City}, {Landmark}, Thailand<\/td> <\/tr>
      best rehab therapist thailand, rehab counsellor thailand<\/td> 6 \u2013 Therapist carousel with named practitioner entities (Dr. A. Example, B. Example MSc\u2026)<\/td> ({Therapist}, works at, {Rehab Name}), ({Therapist}, has credential, MSc), ({Therapist}, specializes in, CBT)<\/td> {Rehab Name}, therapist person entities<\/td> <\/tr>
      cocaine rehab thailand, ketamine rehab thailand, alcohol rehab thailand<\/td> 7 \u2013 Tabbed micro context selector \u201cWhat We Treat\u201d with active Ketamine tab and definitional snippet<\/td> ({Rehab Name}, treats, Ketamine Addiction), (Ketamine Abuse, may lead to, Cognitive Decline), (Tab, links to, \/ketamine-addiction-treatment\/)<\/td> {Rehab Name}, Alcohol, Cocaine, Heroin, GHB, Ketamine<\/td> <\/tr>
      rehab thailand cost, how much is rehab in thailand<\/td> 8 \u2013 Mid-page conversion band with form \u201cI\u2019m ready for a change\u201d + \u201cPrivacy protected, 24h reply\u201d<\/td> (Enquiry, protected by, Privacy Policy), ({Rehab Name}, responds within, 24 Hours), (User, submits, Enquiry)<\/td> {Rehab Name}<\/td> <\/tr>
      meth rehab thailand, gambling addiction thailand, trauma treatment thailand<\/td> 9 \u2013 Programme card grid, 8 contextual bridges with anchor texts matching child page H1s<\/td> ({Rehab Name}, offers, Meth Addiction Programme), ({Rehab Name}, offers, Gambling Programme), (Card, links to, Child Page)<\/td> {Rehab Name}, Meth, Gambling, Trauma, PTSD<\/td> <\/tr>
      medical detox thailand, aftercare rehab thailand<\/td> 10 \u2013 Process accordions in treatment order: Medical Withdrawal, Residential Programme, Aftercare<\/td> (Programme, starts with, Medical Withdrawal), (Programme, followed by, Residential Treatment), (Programme, ends with, Aftercare)<\/td> {Rehab Name}, Medical Withdrawal, Aftercare<\/td> <\/tr>
      luxury rehab thailand, best rehab thailand<\/td> 11 \u2013 Vertical tab attribute verbalizer: Luxury Rehab Care, Asia\u2019s Rehab Hub, Premium Facilities, Integrated Team<\/td> ({Rehab Name}, has care type, Luxury Rehab Care), ({Rehab Name}, positioned as, Asia\u2019s Rehab Hub), ({Rehab Name}, has, Premium Facilities)<\/td> {Rehab Name}, Asia<\/td> <\/tr>
      how long is rehab in thailand, rehab thailand what to expect<\/td> 12 \u2013 Tabbed FAQ accordion: \u201cQ: How long does the alcohol rehab program last at {Rehab Name}?\u201d<\/td> ({Programme Name}, has duration, 4 to 12 Weeks), (Duration, depends on, Individual Needs), (Answer, aligned with, PAA)<\/td> {Rehab Name}, Alcohol<\/td> <\/tr> <\/tbody> <\/table> <\/figure>

      After augmenting the query, we distribute the resulting entity-attribute-context triples across the subsections of the web document, expressing each one through a specific visual component or engagement element.\u00a0<\/p>

      The example below shows a live website whose layout is modeled on this query augmentation logic.<\/p>

      \"Image\"Image<\/figure>

      Since this case study mainly focuses on the intersection of local SEO and topical authority, I\u2019ll leave this detailed infographic for your examination and move on to the next points.<\/p>

      In my web documents, the content is divided into macro context and micro context, a distinction that parallels Google\u2019s Quality Rater Guidelines, which separate content into \u201cmain content\u201d and \u201csupplementary content.\u201d\u00a0<\/p>

      Most of the time, internal links are distributed within the micro context, while the macro context carries the \u201ccenter-piece annotation\u201d that represents the purpose and primary engagement point of the web document.<\/p>

      The page here curates opinionated, factual, structured, and unstructured content together. For example, users can read and submit reviews, send questions directly to the rehab specialists, or complete a poll to learn the likely treatment duration and requirements for their situation.<\/p>

      Once we\u2019re certain that a specific topical map, its content briefs, and the layout modeling work, we can scale them to secondary websites.\u00a0<\/p>

      Here\u2019s a secondary website with the same homepage design, which continues to generate click growth even though its older informational sections are outdated and have lost their initial rankings.<\/p>

      \"Image\"Image<\/figure>

      Topical authority for location-specific queries without physical offices<\/h2>

      Some local SEO projects are performed mainly for SaaS, real estate projects, cruise ships, train itineraries, hospitals, allergists, and similar businesses. Rather than ranking as a brick-and-mortar business, we sometimes rank as a software or aggregator brand across various locations. The example below is a project I haven\u2019t published before: Doktorsitesi.com, an aggregator for doctors and hospitals.<\/p>

      The problem with the website is a classic one: It opened millions of unnecessary pages for every city, district, hospital, hospital department, doctor, and doctor\u2019s clinic. Let\u2019s say you search for a doctor named \u201cAhmet Y\u0131lmaz.\u201d There are more than 2,000 doctors with the same name. When you search for \u201ccancer treatment,\u201d the company has more than 40,000 pages that can target the query.<\/p>

      That\u2019s why I mentioned Google\u2019s SERP diversification and query-specific duplicate documents patent. The website initially had 4,000,000 documents that weren\u2019t indexed by Google, and the indexed ones were also mostly duplicated. This decreases the ranking signal per document while increasing the computational cost of understanding the website, creating millions of micro-cannibalizations.<\/p>

      \"Image\"Image<\/figure>

      Here are the initial results just after deleting unnecessary documents. It brought 600,000 additional clicks while keeping impressions the same, based mainly on the technical changes.<\/p>

      \"Image\"Image<\/figure>

      Many local SEO projects don\u2019t calculate the relevance confusion between web documents when Google can\u2019t find enough reasons to index a specific location-based page.\u00a0<\/p>

      For example, Doktorsitesi.com has more than 40,000 pages related to \u201ccancer,\u201d but which one is the most relevant? Which one is most relevant to \u201ccancer + symptoms\u201d or \u201ccancer + treatments\u201d? Which one is most relevant to \u201ccancer + treatments + Istanbul\u201d?<\/p>

      To get a snapshot of relevance-based listings from your site, use a Programmable Search Engine created only for your website. You can also use it to compare your pages against each other, or add three or more competitor websites and see how Google prioritizes them for relevance because the website that performs better in Google\u2019s evaluation will provide most of the first 20 results.<\/p>

      Here\u2019s how pages from different periods repeat each other because of the different doctors\u2019 cancer-related articles. In addition, there are many repeated \u201ccancer hospital in [city]\u201d pages related to the root query \u201ccancer.\u201d<\/p>

      \"Image\"Image<\/figure>

      Some of the cancer-related, city-based hospital and doctor search pages are also represented below as the result of the \u201csite:[sitename] intitle:[disease name]\u201d search template.<\/p>

      \"Image\"Image<\/figure>

      These two screenshots demonstrate that more than 40,000 documents are near-duplicates based solely on the query \u201ccancer.\u201d\u00a0<\/p>

      Below is a snapshot of the Google Search Console Pages report from before we started pruning the website\u2019s unnecessary segments. It shows large \u201cCrawled \u2013 currently not indexed\u201d and \u201cDiscovered \u2013 currently not indexed\u201d segments, which are clear signs of a lack of quality and unique, original main content for queries that deserve an index from Google Search.<\/p>

      \"Image\"Image<\/figure>

      The \u201cDiscovered \u2013 currently not indexed\u201d segment might be the most important sign of insufficient quality for any type of project, local or location-agnostic, in Google Search Console.<\/p>

      \"Image\"Image<\/figure>

      Below is the internal link distribution of a programmatic local SEO project, which is mostly wrong. The only accurate section here is the homepage because it\u2019s the most important page and should target the most important query, as I explained earlier.\u00a0<\/p>

      However, most of the other highly linked pages are completely irrelevant to the other important query networks, which hurts PageRank distribution and signals the importance of a web document to Google in the wrong way.\u00a0<\/p>

      \"Image\"Image<\/figure>

      A local SEO project should target its most important query from the homepage while channeling most ranking signals and importance qualifiers to it. The homepage should then link back to the most important quality pages, which I call \u201cquality nodes,\u201d because Google predicts a website\u2019s quality from the internal links on the homepage.\u00a0<\/p>

      As Martin Splitt explains, when Google\u2019s crawler reaches more unique, original, human-effort documents from the homepage, it\u2019s convinced to crawl deeper and evaluate the website more favorably.\u00a0<\/p>

      Google\u2019s page quality prediction system relies on internal links, which is why we link our most important quality pages directly from the homepage. In the homepage mock-up design from the rehab example earlier, the contextual bridges are adjusted solely for this purpose.<\/p>

      For local or location-agnostic projects, track these four core KPI metrics:<\/p>

      • Maintain an HTML crawl rate of at least 99%.<\/li>
      • Keep the combined crawl rate of 200 and 304 status codes at 99% or higher.<\/li>
      • Keep the discovery rate above 20%.<\/li>
      • Direct 100% of HTML crawls exclusively to indexable URLs that return a 200 status code and appear in both the sitemap and internal links.<\/li> <\/ul>
        \"Image\"Image<\/figure>

        I always compare the crawl rate per HTML document to see how many days need to pass for Google to crawl my entire website end to end. As an important note, check your raw log files to see your full crawl data because Google Search Console provides only around 30% of the actual crawl log data.\u00a0<\/p>

        Also, Google has different crawling purposes, meaning not every crawl is equal. Google can sometimes crawl a site only to check status codes, refresh caches, or recognize entities. The higher the crawl rate, the more Google cares about the site.\u00a0<\/p>

        For example, below you can see a section from my CMSEO speech featuring a quote from Pandu Nayak, Google\u2019s chief of ranking, and his statement from the Google antitrust lawsuit.\u00a0<\/p>

        He directly states that if a document doesn\u2019t deserve it, they don\u2019t run the expensive algorithms for these websites. Instead, they check core topicality and locality signals.<\/p>

        \"Image\"Image<\/figure>

        Here\u2019s a better example. RankBrain is an expensive algorithm. Thus, if a website isn\u2019t worth it, this expensive algorithm doesn\u2019t run for it.<\/p>

        \"Image\"Image<\/figure>

        That\u2019s why I suggest targeting the most important query network from the most important page with an exact-match or partial-match domain. This way, you earn click signals earlier, and if you satisfy those clicks, Google runs its expensive algorithms for your website sooner.\u00a0<\/p>

        In my previous case study, I gave audiototext.com as an example. If you examine the graphic below carefully, you can recognize one absolute fact.<\/p>

        Our page, content, design, and everything else remain 100% identical whether we receive five clicks a day or 8,000 clicks a day. Sometimes the website loses 60% of its clicks, but after every broad core algorithm update, it comes back stronger.\u00a0<\/p>

        Google performs click tests, and if the click signals demonstrate satisfaction while visual and textual semantics are implemented properly, the website can outrank even the biggest enterprises like Veed.io, Cleedo, and ElevenLabs, all without a single backlink.<\/p>

        \"Image\"Image<\/figure>

        Google\u2019s Content Warehouse API leak module \u201cRankLabTitle\u201d has attributes like \u201cbaseRank\u201d and \u201ctestRank,\u201d which annotate how Google slowly extends its trust in a website by performing re-rankings across different broad core algorithm updates.<\/p>

        \"Image\"Image<\/figure>

        To prevent ranking signal dilution across all the pages targeting the canonical query \u201ccancer,\u201d there should be one comprehensively optimized web document, both textually and visually, that ranks for the concept and transfers its relevance to the location-specific pages.\u00a0<\/p>

        In other words, a location-agnostic page for the concept can lift the rankings of the location-specific pages. The visual semantics implemented after the query augmentation are shown below.<\/p>

        \"Image\"Image<\/figure>

        Many local SEO projects forget the subdomains or microservices they leave open, and Google keeps crawling these unnecessary subdomains, whose content quality also impacts Google\u2019s judgment of your website.\u00a0<\/p>

        Thus, if a subdomain isn\u2019t necessary, remove it and redirect it to the main domain using Google\u2019s Change of Address tool. If it is necessary, disallow it for Googlebot, but keep it functioning behind the scenes.<\/p>

        \"Image\"Image<\/figure>

        Below, you\u2019ll find the results for the extension domain of the same project for Azerbaijan with the same implementation from a content pruning, ranking signal consolidation, and content (visual and textual semantics) perspective.<\/p>

        \"Image\"Image<\/figure>

        Here\u2019s the six-month difference during this process.<\/p>

        \"Image\"Image<\/figure>

        Here\u2019s the Query Deserves a Page exercise for the cancer-related queries and their augmented variations.<\/p>

        \"Image\"Image<\/figure>

        The exact same approach can be seen in another location-specific programmatic ranking project, this time for yacht chartering, a non-brick-and-mortar business that needs to rank for location-specific queries.\u00a0<\/p>

        We decreased the number of pages by resolving many unnecessary query-specific duplicates and removing pages that don\u2019t have a query to trigger an index on Google\u2019s end.\u00a0<\/p>

        In this cleanup, we removed pages targeting queries like \u201c[model] yacht charter [city] [district]\u201d that lack a sufficient level of query search demand, entity prominence, and uniqueness.<\/p>

        \"Image\"Image<\/figure>

        Here are the results during this process: a 248% click increase, a 37% impression increase, and a 29% average position improvement.<\/p>

        \"Image\"Image<\/figure>

        The direct results from the last six months, taken from a first-party data source, are shown below. Feel free to examine them together with the possible spam, core, or unannounced quality updates.<\/p>

        \"Image\"Image<\/figure>

        The Query Deserves a Page exercise for the same context is below.<\/p>

        \"Image\"Image<\/figure>