{"id":910,"date":"2026-02-18T13:00:46","date_gmt":"2026-02-18T13:00:46","guid":{"rendered":"https:\/\/www.authorityrank.app\/magazine\/6-zero-cost-keyword-research-frameworks-that-outperform-paid-tools\/"},"modified":"2026-05-17T15:58:09","modified_gmt":"2026-05-17T15:58:09","slug":"6-zero-cost-keyword-research-frameworks-that-outperform-paid-tools","status":"publish","type":"post","link":"https:\/\/www.authorityrank.app\/magazine\/6-zero-cost-keyword-research-frameworks-that-outperform-paid-tools\/","title":{"rendered":"6 Zero-Cost Keyword Research Frameworks That Outperform Paid Tools"},"content":{"rendered":"<blockquote><p> <strong>Key Strategic Insights:<\/strong> <\/p>\n<ul>\n<li>Google Ads Planner reveals bottom-funnel commercial intent patterns that paid tools often miss &#8211; enabling precise service page architecture without subscription costs<\/li>\n<li>Bing Webmaster Tools provides unfiltered search volume data with <strong>30-day recency filters<\/strong>, offering a predictive advantage for Google rankings before competition intensifies<\/li>\n<li>The Alphabet Soup Method (systematic A-Z autocomplete mining) uncovers <strong>26x more long-tail variations<\/strong> than single-query research, particularly effective for local service businesses<\/li>\n<\/ul>\n<\/blockquote>\n<p>Most SEO practitioners waste <strong>$200-500 monthly<\/strong> on keyword research subscriptions while leaving the most strategic data sources untapped. According to research by Kasra Dash, the convergence of Google&#8217;s native planning tools, competitor sitemap analysis, and systematic autocomplete mining delivers enterprise-level keyword intelligence at zero cost. The constraint isn&#8217;t budget &#8211; it&#8217;s methodology. Businesses operating on tight margins can architect comprehensive content strategies by exploiting six specific data extraction frameworks that paid platforms deliberately obscure to justify their pricing models.<\/p>\n<h2>\nGoogle Ads Planner: The Bottom-Funnel Intelligence Engine<br \/>\n<\/h2>\n<p>Google Ads Planner functions as the company&#8217;s native commercial intent database, originally designed for PPC campaigns but containing critical organic search architecture insights. The tool&#8217;s primary strategic value lies in its bias toward <strong>transactional queries<\/strong> &#8211; searches where users demonstrate purchase readiness or service evaluation behavior. Unlike paid tools that aggregate broad keyword universes, Ads Planner surfaces the exact terms Google associates with monetizable intent.<\/p>\n<p>The operational framework involves two distinct extraction modes. The first &#8211; direct keyword expansion &#8211; accepts a seed term like &#8220;leather boots&#8221; and returns commercial variations: <em>wide calf boots<\/em>, <em>black knee high boots<\/em>, <em>waterproof boots<\/em>. These represent product category segments rather than informational content opportunities. The second mode &#8211; competitor URL analysis &#8211; proves more strategically valuable. By inputting a competitor&#8217;s homepage or specific product page URL, the system reverse-engineers which bottom-funnel terms Google believes that domain targets.<\/p>\n<p>The critical limitation: Ads Planner systematically excludes long-tail informational queries like &#8220;how to clean leather cowboy boots.&#8221; This isn&#8217;t a bug &#8211; it&#8217;s algorithmic design. Google filters for terms with established ad inventory and commercial bidding activity. For service businesses and e-commerce operations, this constraint becomes an advantage. The tool functions as a <strong>service page blueprint generator<\/strong>, revealing which transactional pages competitors prioritize without requiring manual site audits.<\/p>\n<div>\n <\/p>\n<div>\n <\/p>\n<div>\n<br \/> <span>\u2605<\/span> <\/div>\n<p> <\/p>\n<p><strong>93% of AI Search sessions end without a visit to any website &#8211; if you&#8217;re not cited in the answer, you don&#8217;t exist.<\/strong> AuthorityRank turns top YouTube experts into your branded blog content &#8211; automatically.<\/p>\n<p> <\/div>\n<p> <a href=\"https:\/\/authorityrank.app\" target=\"_blank\" rel=\"noopener noreferrer\">Try Free \u2192<\/a> <\/div>\n<p>The advanced application involves exporting <strong>100+ competitor keywords<\/strong> and processing them through AI clustering tools like ChatGPT. The prompt structure: &#8220;Group these into SEO pages.&#8221; The AI identifies semantic overlap &#8211; terms like &#8220;leather boots men,&#8221; &#8220;men&#8217;s leather boots,&#8221; and &#8220;leather boots for men&#8221; consolidate into a single target page rather than three redundant URLs. This prevents keyword cannibalization while maximizing topical authority concentration. For a typical competitor analysis, <strong>100 raw keywords typically consolidate into 10-15 strategic pages<\/strong>, revealing the actual content architecture required to compete rather than an inflated keyword count that justifies tool subscriptions.<\/p>\n<p>Google Ads Planner eliminates guesswork in service page development by revealing which commercial terms Google&#8217;s own algorithm prioritizes for monetization, providing a zero-cost competitive intelligence layer that paid tools cannot replicate due to their informational keyword bias.<\/p>\n<h2>\nBing Webmaster Tools: The Predictive Volume Advantage<br \/>\n<\/h2>\n<p>Bing Webmaster Tools operates as Microsoft&#8217;s equivalent to Google Search Console, but with a critical architectural difference: <strong>unrestricted keyword research access without requiring domain ownership<\/strong>. While Google Search Console limits keyword visibility to properties you control, Bing&#8217;s platform functions as an open keyword database with granular filtering capabilities that surpass many paid alternatives.<\/p>\n<p>The core mechanism centers on Bing&#8217;s keyword research module, accessible under the &#8220;Diagnostic &#038; Tools&#8221; section. Users can query any seed term &#8211; &#8220;SEO,&#8221; &#8220;solar panels,&#8221; &#8220;legal services&#8221; &#8211; and receive impression data filtered by geography (United States, United Kingdom, etc.) and device type (web vs. mobile). The strategic insight: Bing&#8217;s lower market share creates a <strong>predictive multiplier effect<\/strong>. A term showing <strong>1,700 monthly searches in Bing<\/strong> typically translates to <strong>15,000-20,000 Google searches<\/strong>, given Bing&#8217;s approximate <strong>3-6% U.S. search market share<\/strong>.<\/p>\n<p>The platform includes three data views: primary keyword metrics, related keywords, and questions. The questions module remains inconsistent &#8211; Kasra Dash notes it frequently returns &#8220;no data available&#8221; messages, likely due to Bing&#8217;s ongoing platform updates. However, the related keywords function proves highly reliable, surfacing semantic variations and adjacent topics that indicate content expansion opportunities. For example, a &#8220;Super Bowl&#8221; query returns related terms like &#8220;Super Bowl live stream,&#8221; &#8220;Super Bowl live,&#8221; and &#8220;American football,&#8221; each with individual volume estimates.<\/p>\n<p>The temporal filtering capability &#8211; adjustable to <strong>last 30 days<\/strong> &#8211; provides recency advantages that annual or quarterly keyword tools cannot match. Seasonal trends, emerging topics, and breaking news queries surface in real-time, enabling content teams to capture traffic during the critical early-mover window before competition intensifies. This becomes particularly valuable for news-driven industries, event-based services, and trend-dependent e-commerce categories.<\/p>\n<p>Bing Webmaster Tools functions as a zero-cost predictive engine for Google search volume, with recency filters that enable first-mover content strategies before paid tool databases reflect emerging trends.<\/p>\n<h2>\nThe Alphabet Soup Method: Systematic Autocomplete Mining<br \/>\n<\/h2>\n<p>Google&#8217;s autocomplete algorithm represents one of the most underutilized keyword intelligence sources in SEO. The system aggregates real user search behavior, trending queries, and Google&#8217;s own query understanding models to predict what users intend to search. The Alphabet Soup Method &#8211; also called the Google Dropdown Method &#8211; systematically exploits this by cycling through every letter of the alphabet as a suffix to a base query.<\/p>\n<p>The operational framework: Start with a seed term relevant to your business vertical &#8211; &#8220;lawyers for,&#8221; &#8220;accountants for,&#8221; &#8220;solar panels for&#8221; &#8211; then append each letter A through Z. For &#8220;lawyers for a,&#8221; Google suggests: <em>lawyers for animals<\/em>, <em>lawyers for asylum seekers<\/em>, <em>lawyers for accidents<\/em>, <em>lawyers for apartment issues<\/em>, <em>lawyers for accident claims<\/em>, <em>lawyers for auto claims<\/em>, <em>lawyers for wills<\/em>. Progressing to &#8220;lawyers for b&#8221; yields: <em>lawyers for businesses<\/em>, <em>lawyers for buying a home<\/em>, <em>lawyers for breach of contract<\/em>, <em>lawyers for bank issues<\/em>, <em>lawyers for bullying<\/em>, <em>lawyers for bed bugs<\/em>.<\/p>\n<p>The method scales across <strong>26 letter variations<\/strong>, typically generating <strong>4-8 suggestions per letter<\/strong> for commercial queries, resulting in <strong>100-200 long-tail keywords per seed term<\/strong>. This volume surpasses what most practitioners extract from single-query paid tool searches. The quality advantage: These terms reflect actual user intent patterns rather than algorithmic keyword generation models that paid tools employ.<\/p>\n<p>Kasra Dash highlights a critical quality control mechanism: Some autocomplete results represent algorithmic &#8220;cheating&#8221; rather than genuine search volume. For example, &#8220;solar panels for a caravan&#8221; may appear not because of substantial search volume, but because Google&#8217;s language model recognizes grammatical validity. The validation step involves cross-referencing high-potential terms with Bing Webmaster Tools or Google Ads Planner to confirm actual search activity before committing content resources.<\/p>\n<p>The Alphabet Soup Method generates 26x keyword coverage compared to single-query research, with the added advantage of capturing user intent patterns that paid tools&#8217; algorithmic generation cannot replicate.<\/p>\n<h2>\nCompetitor Sitemap Reverse Engineering<br \/>\n<\/h2>\n<p>Every website&#8217;s robots.txt file functions as an unintentional competitive intelligence document. By appending <code>\/robots.txt<\/code> to any competitor&#8217;s root domain, SEO practitioners gain access to the sitemap URL &#8211; a structured index of every page the competitor considers valuable enough to submit to search engines. This transforms competitor analysis from manual site crawling into systematic data extraction.<\/p>\n<p>The process begins with identifying top-ranking competitors for your primary commercial keywords. For &#8220;solar panel company in Manchester,&#8221; the top three organic results represent businesses that have successfully solved Google&#8217;s ranking algorithm for that specific query. Accessing their robots.txt files (e.g., <code>competitor-domain.com\/robots.txt<\/code>) reveals sitemap locations, typically formatted as <code>sitemap.xml<\/code> or organized into category-specific sitemaps like <code>sitemap-products.xml<\/code> or <code>sitemap-services.xml<\/code>.<\/p>\n<p>The strategic application involves extracting all URLs from the competitor&#8217;s sitemap and processing them through AI analysis. The prompt structure Kasra Dash recommends: &#8220;This is my competitor&#8217;s sitemap. Can you extract the keywords they are going after?&#8221; The AI parses URL structures, page titles embedded in XML, and hierarchical organization to reverse-engineer the competitor&#8217;s content strategy. For a solar installation company example, the output reveals: <em>solar panel and PV installers<\/em>, <em>solar maintenance<\/em>, <em>battery storage<\/em>, <em>EV charging<\/em>, <em>voltage optimization<\/em>, <em>finance options<\/em>, and <em>lead capture<\/em> as core commercial pages, plus audience segmentation pages like <em>solar for developers<\/em>, <em>solar for builders<\/em>, <em>solar for farmers<\/em>, and <em>solar for landlords<\/em>.<\/p>\n<p>The critical strategic insight: Analyzing <strong>2-3 competitors simultaneously<\/strong> reveals content gaps and market consensus. If two out of three competitors maintain dedicated pages for &#8220;solar panels for farms,&#8221; this signals market validation &#8211; enough search volume and conversion potential exist to justify the content investment. Conversely, if only one competitor targets a specific keyword, it may represent a low-value experiment rather than a proven revenue driver.<\/p>\n<p>The risk mitigation protocol: Do not blindly replicate competitor service pages. Some businesses offer <strong>15+ services<\/strong> across diverse verticals, many of which may fall outside your operational capacity. The sitemap analysis identifies content opportunities, but business model alignment remains a manual validation step. A residential solar installer should not create commercial industrial solar content simply because a competitor does, if that competitor operates in both markets while you specialize in one.<\/p>\n<p>Competitor sitemap analysis reveals the exact content architecture that successfully ranks in your market, eliminating strategic guesswork while preventing the resource waste of targeting keywords with unproven commercial viability.<\/p>\n<h2>\nGoogle Search Console Regex Filtering for Long-Tail Discovery<br \/>\n<\/h2>\n<p>Google Search Console contains the most valuable keyword data for any established website: <strong>actual queries that triggered impressions<\/strong> for your domain. The limitation most practitioners encounter is data volume &#8211; sorting through thousands of query rows to identify content opportunities becomes prohibitively time-intensive. Regex (regular expression) filtering solves this by programmatically isolating specific query patterns, particularly question-based searches that indicate content gaps.<\/p>\n<p>The operational framework centers on a pre-built regex pattern designed to surface interrogative queries. Kasra Dash provides a specific regex formula (available via linked resources) that filters for questions containing &#8220;how,&#8221; &#8220;why,&#8221; &#8220;what,&#8221; &#8220;when,&#8221; &#8220;where,&#8221; &#8220;who,&#8221; &#8220;can,&#8221; &#8220;does,&#8221; &#8220;is,&#8221; &#8220;are,&#8221; and similar interrogative structures. The application process: Navigate to Google Search Console \u2192 Performance \u2192 Add Filter \u2192 Custom (regex) \u2192 Paste the question-detection pattern.<\/p>\n<p>The output transforms raw Search Console data into a curated list of <strong>long-tail question queries<\/strong> where your domain already generates impressions but lacks dedicated content. Examples from Kasra Dash&#8217;s own analysis include: &#8220;why don&#8217;t links from Crunchbase count,&#8221; &#8220;how to recover from a Google algorithm update,&#8221; and &#8220;what are the best off-page SEO techniques.&#8221; These represent high-intent informational queries where users actively seek expert guidance &#8211; precisely the content type that builds topical authority and generates qualified traffic.<\/p>\n<p>The strategic advantage over paid tools: Search Console data reflects <strong>your domain&#8217;s actual visibility patterns<\/strong> rather than generic keyword databases. A query showing <strong>500 impressions with 2% CTR<\/strong> indicates existing algorithmic recognition &#8211; Google already associates your domain with that topic. Creating dedicated content for that query doesn&#8217;t require building authority from zero; it optimizes an existing ranking signal. This reduces the time-to-rank compared to targeting entirely new keywords where your domain has no historical relevance.<\/p>\n<p>The filtering methodology extends beyond questions. Additional regex patterns can isolate comparison queries (&#8220;vs,&#8221; &#8220;versus,&#8221; &#8220;compared to&#8221;), location-based searches (&#8220;near me,&#8221; city names), or commercial intent modifiers (&#8220;best,&#8221; &#8220;top,&#8221; &#8220;review&#8221;). Each pattern reveals a different content opportunity category, enabling systematic gap analysis without manual query review.<\/p>\n<p>Regex-filtered Search Console data identifies long-tail content opportunities where your domain already possesses algorithmic recognition, dramatically reducing time-to-rank compared to targeting keywords with zero existing visibility.<\/p>\n<div style=\"background:linear-gradient(135deg,#667eea 0%,#764ba2 100%);padding:36px 28px;border-radius:14px;margin:36px 0;text-align:center;color:#fff;box-shadow:0 10px 30px rgba(102,126,234,0.25);\">\n<p style=\"font-size:13px;text-transform:uppercase;letter-spacing:2px;margin:0 0 10px;color:#ffd700;font-weight:600;\">\u2605 The Authority Revolution<\/p>\n<h3 style=\"color:#fff;font-size:28px;margin:0 0 14px;font-weight:700;\">Goodbye SEO. Hello AEO.<\/h3>\n<p style=\"font-size:16px;line-height:1.6;margin:0 auto 22px;color:#f0f0ff;max-width:620px;\">By mid-2025, zero-click searches hit 65% overall &#8211; for every 1,000 Google searches, only 360 clicks go to the open web. AuthorityRank makes sure that when AI picks an answer &#8211; that answer is <strong>you<\/strong>.<\/p>\n<p style=\"margin:0 0 16px;\"><a href=\"https:\/\/authorityrank.app\" target=\"_blank\" rel=\"noopener noreferrer\" style=\"display:inline-block;background:#fff;color:#667eea;padding:15px 38px;border-radius:50px;text-decoration:none;font-weight:700;font-size:16px;box-shadow:0 4px 14px rgba(0,0,0,0.15);\">Claim Your Authority \u2192<\/a><\/p>\n<p style=\"margin:14px 0 0;font-size:13px;color:#e0e0ff;\">\u2713 Free trial &nbsp; \u2713 No credit card &nbsp; \u2713 Cancel anytime<\/p>\n<\/div>\n<h2>\nReddit and Quora Mining: User Intent at Scale<br \/>\n<\/h2>\n<p>Social question platforms function as unfiltered user intent databases. Unlike keyword tools that aggregate search volume, Reddit and Quora capture the <strong>exact phrasing<\/strong> users employ when seeking expert guidance. The strategic value lies in discovering content opportunities that paid tools systematically miss due to low individual search volume but high collective engagement potential.<\/p>\n<p>The extraction methodology uses Google&#8217;s site search operator to filter these platforms by topic. The query structure: <code>site:quora.com [your keyword]<\/code> or <code>site:reddit.com [your keyword]<\/code>. For an SEO-focused example, <code>site:quora.com SEO<\/code> surfaces questions like: &#8220;Why is SEO hard?&#8221;, &#8220;What is SEO and how it works?&#8221;, &#8220;What are the best off-page SEO techniques?&#8221;, &#8220;What are the best strategies for using SEO to rank a website quickly in 2026?&#8221;, and &#8220;Technical SEO tips.&#8221; Each represents a potential article title or H2 section that directly addresses user pain points.<\/p>\n<p>The quality validation mechanism: Analyze comment counts and post age. A question with <strong>150 comments over 2 years<\/strong> indicates sustained interest &#8211; users continue engaging with the topic long after the initial post. Conversely, a question with zero replies after 12 months signals low market interest, regardless of how relevant it seems to your business. Kasra Dash emphasizes this engagement filter as critical: &#8220;If there&#8217;s been literally nobody that&#8217;s replied and the post has been up for like 2 years, it&#8217;s probably a bad indicator that you shouldn&#8217;t upload that.&#8221;<\/p>\n<p>The Reddit advantage over Quora: Subreddit-specific mining. Instead of broad site searches, targeting niche subreddits (e.g., <code>site:reddit.com\/r\/bigseo [keyword]<\/code>) surfaces expert-level discussions rather than beginner questions. This enables content differentiation &#8211; addressing advanced practitioner concerns rather than competing in the saturated &#8220;what is SEO&#8221; content space.<\/p>\n<p>The operational workflow involves exporting <strong>50-100 high-engagement questions<\/strong> from both platforms, then clustering them by topic similarity using AI tools. Questions like &#8220;How does SEO work?&#8221;, &#8220;How is SEO managed these days?&#8221;, and &#8220;What does SEO mean and does it matter?&#8221; consolidate into a single comprehensive guide rather than three shallow articles. This clustering prevents content fragmentation while maximizing topical depth &#8211; a critical factor in Google&#8217;s Helpful Content algorithm.<\/p>\n<p>Reddit and Quora mining reveals user intent patterns that keyword tools cannot capture, with engagement metrics providing built-in content validation that eliminates low-value topic selection.<\/p>\n<h2>\nIntegration Framework: The Six-Method Synthesis<br \/>\n<\/h2>\n<p>The strategic power of these six methods emerges through systematic integration rather than isolated application. Each framework addresses a specific keyword research blind spot: Google Ads Planner captures commercial intent, Bing provides predictive volume, Alphabet Soup surfaces long-tail variations, sitemap analysis reveals competitive content architecture, Search Console identifies existing visibility opportunities, and social mining uncovers authentic user questions.<\/p>\n<p>The recommended operational sequence begins with <strong>competitor sitemap analysis<\/strong> to establish baseline content requirements &#8211; the service pages and product categories necessary for market parity. This prevents the common mistake of pursuing informational content while lacking fundamental commercial pages. Next, apply <strong>Google Ads Planner<\/strong> to those competitor URLs to extract the specific transactional keywords Google associates with each page type.<\/p>\n<p>For content expansion beyond commercial pages, deploy the <strong>Alphabet Soup Method<\/strong> to generate long-tail variations of your core offerings. Cross-reference high-potential terms with <strong>Bing Webmaster Tools<\/strong> to validate actual search volume and identify emerging trends before competition intensifies. For established domains, layer in <strong>Search Console regex filtering<\/strong> to prioritize keywords where you already possess algorithmic recognition. Finally, supplement with <strong>Reddit\/Quora mining<\/strong> to identify content angles that address authentic user pain points rather than algorithmic keyword variations.<\/p>\n<p>The data consolidation step involves aggregating all extracted keywords into a master spreadsheet, then processing through AI clustering to eliminate redundancy. The output: a hierarchical content architecture organized by commercial intent (service pages), informational depth (pillar content), and long-tail specificity (supporting articles). This structure directly maps to Google&#8217;s topic clustering algorithm, which rewards sites that demonstrate comprehensive coverage of a subject area rather than scattered keyword targeting.<\/p>\n<p>The strategic insight Kasra Dash emphasizes: &#8220;The more data that we actually have, the better decisions that we can also make.&#8221; These six methods collectively generate <strong>500-1,000+ keyword opportunities<\/strong> without subscription costs, but raw volume isn&#8217;t the goal. The synthesis process filters for commercial viability, search volume validation, competitive gaps, and existing domain authority &#8211; producing a refined target list of <strong>50-100 strategic pages<\/strong> that balance traffic potential with ranking feasibility.<\/p>\n<p>Integrated application of all six frameworks creates a zero-cost keyword intelligence system that matches or exceeds paid tool capabilities by combining commercial intent data, predictive volume metrics, competitive analysis, existing visibility optimization, and authentic user intent capture into a unified content strategy.<\/p>\n<div style=\"background:#f8f9ff;border-left:4px solid #667eea;padding:18px 24px;margin:36px 0;border-radius:8px;text-align:center;box-shadow:0 2px 8px rgba(102,126,234,0.08);\">\n<p style=\"margin:0;font-size:15px;color:#333;\"><span style=\"color:#ffd700;font-size:18px;\">\u2605<\/span> Content powered by <a href=\"https:\/\/authorityrank.app\" target=\"_blank\" rel=\"noopener noreferrer\" style=\"color:#667eea;font-weight:700;text-decoration:none;\">AuthorityRank.app<\/a> &#8211; Build authority on autopilot<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Key Strategic Insights: Google Ads Planner reveals bottom-funnel commercial intent patterns that paid tools often miss &#8211; enabling precise service page architecture without subscription costs Bing Webmaster Tools provides unfiltered search volume data with 30-day recency filters, offering a predictive advantage for Google rankings before competition intensifies The Alphabet Soup Method (systematic A-Z autocomplete mining) [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":1871,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"tdm_status":"","tdm_grid_status":"","footnotes":""},"categories":[25],"tags":[],"class_list":{"0":"post-910","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-seo-aeo-strategy"},"_links":{"self":[{"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/posts\/910","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/comments?post=910"}],"version-history":[{"count":3,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/posts\/910\/revisions"}],"predecessor-version":[{"id":2551,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/posts\/910\/revisions\/2551"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/media\/1871"}],"wp:attachment":[{"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/media?parent=910"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/categories?post=910"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.authorityrank.app\/magazine\/wp-json\/wp\/v2\/tags?post=910"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}