{"id":11123,"date":"2026-04-01T10:04:00","date_gmt":"2026-04-01T10:04:00","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/n-gram-analysis\/"},"modified":"2026-04-01T10:04:00","modified_gmt":"2026-04-01T10:04:00","slug":"n-gram-analysis","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/n-gram-analysis\/","title":{"rendered":"N-gram Analysis: What It Is, Key Features, Benefits, Use Cases, and How It Fits in SEM \/ Paid Search"},"content":{"rendered":"\n

N-gram Analysis is a text-mining method that helps you understand which words and word sequences inside user queries are driving performance. In Paid Marketing<\/strong>, it\u2019s most often used to mine search query data so you can find profitable patterns, isolate waste, and turn messy long-tail searches into actionable insights.<\/p>\n\n\n\n

In SEM \/ Paid Search<\/strong>, marketers frequently face the same problem: thousands (or millions) of unique search terms, each with too little data to judge on its own. N-gram Analysis<\/strong> solves this by grouping queries into meaningful \u201cbuilding blocks\u201d (single words, two-word phrases, three-word phrases, and so on) so you can optimize at the pattern level instead of chasing individual queries.<\/p>\n\n\n\n

\n\n\n\n

What Is N-gram Analysis?<\/h2>\n\n\n\n

N-gram Analysis<\/strong> is the practice of breaking text into contiguous sequences of N<\/em> items (usually words) and analyzing those sequences for frequency and performance.\n– A 1-gram<\/strong> (unigram) is one word: \u201cpricing\u201d\n– A 2-gram<\/strong> (bigram) is two words: \u201centerprise pricing\u201d\n– A 3-gram<\/strong> (trigram) is three words: \u201centerprise pricing plan\u201d<\/p>\n\n\n\n

The core concept is simple: if certain words or phrases consistently appear in high-converting search terms, those n-grams can guide keyword expansion, ad messaging, and landing page alignment. If certain n-grams correlate with irrelevant intent, they can guide negative keywords and traffic quality controls.<\/p>\n\n\n\n

From a business perspective, N-gram Analysis<\/strong> translates raw query noise into decision-ready insights: what people really mean<\/em> when they search, and how those meanings map to cost, conversion value, and profitability.<\/p>\n\n\n\n

Within Paid Marketing<\/strong>, this is a structured way to scale learnings across campaigns and ad groups. Inside SEM \/ Paid Search<\/strong>, it\u2019s especially valuable because search intent is explicit\u2014but the volume and variety of queries can overwhelm manual analysis.<\/p>\n\n\n\n

\n\n\n\n

Why N-gram Analysis Matters in Paid Marketing<\/h2>\n\n\n\n

In modern Paid Marketing<\/strong>, efficiency is often won or lost in the details: small pockets of wasted spend, subtle intent mismatches, and under-captured demand. N-gram Analysis<\/strong> matters because it reveals those pockets quickly and at scale.<\/p>\n\n\n\n

Key strategic reasons it\u2019s valuable in SEM \/ Paid Search<\/strong>:<\/p>\n\n\n\n

    \n
  • Moves beyond last-click thinking.<\/strong> Instead of evaluating only keywords, you evaluate the language patterns that indicate intent.<\/li>\n
  • Finds scalable opportunities.<\/strong> One profitable bigram can represent hundreds of long-tail queries worth capturing.<\/li>\n
  • Reduces waste systematically.<\/strong> Negative keyword decisions become evidence-based and pattern-driven rather than anecdotal.<\/li>\n
  • Improves message match.<\/strong> N-grams can inform ad copy themes and landing page content so the user\u2019s wording matches your offer.<\/li>\n
  • Creates a competitive advantage.<\/strong> Many accounts still rely on top-level metrics; teams using N-gram Analysis<\/strong> often spot intent shifts earlier and react faster.<\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    How N-gram Analysis Works<\/h2>\n\n\n\n

    In practice, N-gram Analysis<\/strong> in SEM \/ Paid Search<\/strong> is a repeatable workflow that turns query logs into optimization actions.<\/p>\n\n\n\n

      \n
    1. \n

      Input (data capture)<\/strong>\n You start with search term data from ad platforms: queries, impressions, clicks, cost, conversions, and conversion value (or revenue proxy). You may also include device, location, match type, audience, and time period.<\/p>\n<\/li>\n

    2. \n

      Processing (text normalization + n-gram generation)<\/strong>\n You clean and standardize query text:\n – Lowercasing, trimming whitespace\n – Removing punctuation\n – Optional: removing stop words (e.g., \u201cthe\u201d, \u201cand\u201d) depending on your use case\n Then you split each query into unigrams, bigrams, trigrams, etc.<\/p>\n<\/li>\n

    3. \n

      Analysis (aggregation + performance weighting)<\/strong>\n You aggregate metrics by n-gram, not by query. Common rollups include:\n – Total cost and conversions where the n-gram appears\n – Conversion rate and cost per conversion\n – Revenue or value per click (if available)\n This step is where N-gram Analysis<\/strong> becomes a decision tool, not just a word count.<\/p>\n<\/li>\n

    4. \n

      Execution (campaign actions)<\/strong>\n You apply insights to account changes:\n – Add negatives for low-quality n-grams\n – Add new keywords\/ad groups for high-intent n-grams\n – Improve ads and landing pages aligned to converting language\n – Segment campaigns by intent themes revealed by n-grams<\/p>\n<\/li>\n

    5. \n

      Output (measurable outcomes)<\/strong>\n You track impact in Paid Marketing<\/strong> metrics: reduced wasted spend, improved ROAS, higher conversion volume, and clearer intent segmentation.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      \n\n\n\n

      Key Components of N-gram Analysis<\/h2>\n\n\n\n

      Strong N-gram Analysis<\/strong> depends on more than a script or spreadsheet. The main components include:<\/p>\n\n\n\n

      Data inputs<\/h3>\n\n\n\n
        \n
      • Search terms (queries)<\/li>\n
      • Impressions, clicks, cost<\/li>\n
      • Conversions and conversion value (or lead quality indicators)<\/li>\n
      • Time window (e.g., last 30\/60\/90 days)<\/li>\n
      • Optional segments: device, geo, audience, network, match type<\/li>\n<\/ul>\n\n\n\n

        Processing rules<\/h3>\n\n\n\n
          \n
        • Tokenization (how you split text)<\/li>\n
        • Normalization (case, punctuation, spelling variants)<\/li>\n
        • Inclusion\/exclusion logic (brand terms, competitor terms, stop words)<\/li>\n
        • N size selection (1\u20133 is common; 4+ can be useful but often sparse)<\/li>\n<\/ul>\n\n\n\n

          Metrics and thresholds<\/h3>\n\n\n\n
            \n
          • Minimum clicks or cost before acting<\/li>\n
          • Confidence rules (e.g., avoid negatives if conversions exist)<\/li>\n
          • Outlier handling (one large order can skew value-based metrics)<\/li>\n<\/ul>\n\n\n\n

            Governance and ownership<\/h3>\n\n\n\n
              \n
            • Who approves negatives and keyword additions<\/li>\n
            • How changes are tested (drafts\/experiments, staged rollouts)<\/li>\n
            • Documentation of why an n-gram was acted on (critical for agencies and teams)<\/li>\n<\/ul>\n\n\n\n
              \n\n\n\n

              Types of N-gram Analysis<\/h2>\n\n\n\n

              While the concept is consistent, N-gram Analysis<\/strong> is used in different ways in Paid Marketing<\/strong> depending on the question you\u2019re answering:<\/p>\n\n\n\n

              By n-gram length<\/h3>\n\n\n\n
                \n
              • Unigram analysis:<\/strong> Great for broad intent categories (\u201cfree\u201d, \u201cjobs\u201d, \u201cpricing\u201d). Can be noisy without context.<\/li>\n
              • Bigram analysis:<\/strong> Often the sweet spot for actionable intent (\u201cfree trial\u201d, \u201cnear me\u201d, \u201cpricing plan\u201d).<\/li>\n
              • Trigram analysis:<\/strong> More specific intent (\u201centerprise pricing plan\u201d, \u201csame day delivery\u201d). Lower volume but high precision.<\/li>\n<\/ul>\n\n\n\n

                By optimization goal<\/h3>\n\n\n\n
                  \n
                • Negative keyword mining:<\/strong> Identify n-grams that correlate with poor conversion rate or low-value leads.<\/li>\n
                • Keyword expansion:<\/strong> Find converting n-grams that aren\u2019t covered by your keyword set or ad group structure.<\/li>\n
                • Creative\/message insights:<\/strong> Extract language that can become ad headlines, descriptions, or sitelink themes.<\/li>\n
                • Landing page alignment:<\/strong> Detect mismatches where users search for a feature or use case your page doesn\u2019t address.<\/li>\n<\/ul>\n\n\n\n

                  By weighting method<\/h3>\n\n\n\n
                    \n
                  • Frequency-weighted:<\/strong> Prioritizes common n-grams; good for scale.<\/li>\n
                  • Cost-weighted:<\/strong> Prioritizes spend drivers; good for waste reduction.<\/li>\n
                  • Value-weighted:<\/strong> Prioritizes revenue\/lead quality; best when conversion value is reliable.<\/li>\n<\/ul>\n\n\n\n
                    \n\n\n\n

                    Real-World Examples of N-gram Analysis<\/h2>\n\n\n\n

                    Example 1: Reducing wasted spend with negative patterns<\/h3>\n\n\n\n

                    A B2B SaaS account in SEM \/ Paid Search<\/strong> sees rising spend but flat pipeline. N-gram Analysis<\/strong> shows \u201ctemplate\u201d, \u201cpdf\u201d, and \u201cexamples\u201d unigrams appear in many queries with high clicks and near-zero qualified leads.\nAction in Paid Marketing<\/strong>: Add negatives (or route to content campaigns if appropriate), tighten match strategy, and create a separate educational campaign if those users are still valuable at a different CPA.<\/p>\n\n\n\n

                    Example 2: Discovering new high-intent keyword themes<\/h3>\n\n\n\n

                    An ecommerce brand finds that \u201crefill packs\u201d and \u201csubscription refill\u201d bigrams have strong ROAS but appear mostly in search terms\u2014not as exact keywords.\nAction in SEM \/ Paid Search<\/strong>: Create a dedicated \u201cRefills\u201d ad group, add exact\/phrase variants, and build ad copy focused on refills and recurring savings.<\/p>\n\n\n\n

                    Example 3: Improving lead quality through intent segmentation<\/h3>\n\n\n\n

                    A services business notices that \u201cnear me\u201d and city-name bigrams convert well, while \u201csalary\u201d and \u201ccourse\u201d n-grams drive irrelevant traffic.\nAction in Paid Marketing<\/strong>: Split campaigns into local-intent and non-local. Use location-based landing pages for local n-grams, and add strong negatives for education\/employment intent.<\/p>\n\n\n\n

                    \n\n\n\n

                    Benefits of Using N-gram Analysis<\/h2>\n\n\n\n

                    Used consistently, N-gram Analysis<\/strong> can improve both performance and operational efficiency in SEM \/ Paid Search<\/strong>:<\/p>\n\n\n\n

                      \n
                    • Higher conversion efficiency:<\/strong> Better intent targeting improves conversion rate and reduces cost per conversion.<\/li>\n
                    • Cost savings:<\/strong> Pattern-based negatives reduce spend on irrelevant or low-value demand.<\/li>\n
                    • Faster optimization cycles:<\/strong> You don\u2019t need to review thousands of individual search terms one by one.<\/li>\n
                    • Better keyword coverage:<\/strong> Capture long-tail intent by turning recurring phrases into structured keyword\/ad group themes.<\/li>\n
                    • Improved customer experience:<\/strong> Users see ads and landing pages that match their wording and intent more closely.<\/li>\n
                    • Stronger learning loops:<\/strong> Insights from queries can influence creative, offers, and even product messaging across Paid Marketing<\/strong>.<\/li>\n<\/ul>\n\n\n\n
                      \n\n\n\n

                      Challenges of N-gram Analysis<\/h2>\n\n\n\n

                      Despite its power, N-gram Analysis<\/strong> has real limitations that matter in production Paid Marketing<\/strong> environments:<\/p>\n\n\n\n

                        \n
                      • Ambiguity and context loss:<\/strong> A unigram like \u201cfree\u201d can mean \u201cfree trial\u201d (good) or \u201cfree download\u201d (bad). Over-relying on short n-grams can cause mistakes.<\/li>\n
                      • Sparse data at higher N:<\/strong> Trigrams and 4-grams can be highly specific, which is useful, but may lack sufficient volume for confident decisions.<\/li>\n
                      • Attribution and conversion quality:<\/strong> If conversion tracking is incomplete or lead quality is delayed, n-gram decisions can optimize toward the wrong outcome.<\/li>\n
                      • Privacy and reporting constraints:<\/strong> Query visibility and data retention policies can limit how much search term data you can analyze.<\/li>\n
                      • Over-negative risk:<\/strong> Aggressive negatives based on limited data can accidentally block valuable traffic.<\/li>\n<\/ul>\n\n\n\n
                        \n\n\n\n

                        Best Practices for N-gram Analysis<\/h2>\n\n\n\n

                        To use N-gram Analysis<\/strong> safely and profitably in SEM \/ Paid Search<\/strong>, focus on disciplined execution:<\/p>\n\n\n\n

                          \n
                        1. Start with clear decision rules.<\/strong> For example: only add a negative if the n-gram has spent at least X and has zero conversions (or fails a lead-quality threshold).<\/li>\n
                        2. Prefer bigrams for actionability.<\/strong> Unigrams are useful for categorization; bigrams often drive the best balance of volume and intent clarity.<\/li>\n
                        3. Segment before you conclude.<\/strong> Run separate views by brand vs non-brand, device, geo, and campaign type. An n-gram can be good in one segment and bad in another.<\/li>\n
                        4. Use value-based metrics when possible.<\/strong> If you have reliable conversion value, prioritize n-grams that improve profit, not just CPA.<\/li>\n
                        5. Review false positives.<\/strong> Before adding negatives, inspect a sample of queries containing the n-gram to confirm intent.<\/li>\n
                        6. Turn findings into structure.<\/strong> When an n-gram consistently performs, reflect it in ad group themes, landing pages, and ad copy\u2014not just as a one-off keyword.<\/li>\n
                        7. Operationalize as a recurring process.<\/strong> Monthly or biweekly N-gram Analysis<\/strong> is often more useful than quarterly deep dives in fast-moving markets.<\/li>\n<\/ol>\n\n\n\n
                          \n\n\n\n

                          Tools Used for N-gram Analysis<\/h2>\n\n\n\n

                          N-gram Analysis<\/strong> can be done with many tool stacks; what matters is reliable query data and repeatable processing:<\/p>\n\n\n\n

                            \n
                          • Ad platforms:<\/strong> Search term reporting is the primary source for SEM \/ Paid Search<\/strong> query data and performance metrics.<\/li>\n
                          • Analytics tools:<\/strong> Used to validate on-site behavior, assisted conversions, and landing page engagement for n-gram-driven traffic.<\/li>\n
                          • Spreadsheets:<\/strong> Good for smaller accounts; pivot tables and formulas can generate unigrams\/bigrams with careful setup.<\/li>\n
                          • Databases and warehouses:<\/strong> Useful when query volume is large; enables scheduled processing, segmentation, and historical comparisons.<\/li>\n
                          • Scripting\/automation:<\/strong> Helps generate n-grams, refresh dashboards, and produce candidate negative\/keyword lists consistently.<\/li>\n
                          • Reporting dashboards:<\/strong> Turn n-gram rollups into shared, filterable views for Paid Marketing<\/strong> stakeholders.<\/li>\n
                          • CRM systems:<\/strong> Crucial for lead-quality feedback loops (e.g., qualified lead rate by n-gram theme).<\/li>\n<\/ul>\n\n\n\n
                            \n\n\n\n

                            Metrics Related to N-gram Analysis<\/h2>\n\n\n\n

                            The best metrics depend on your goal (waste reduction vs growth), but these are commonly tied to N-gram Analysis<\/strong> in Paid Marketing<\/strong>:<\/p>\n\n\n\n

                              \n
                            • Cost per conversion (CPA):<\/strong> Core efficiency metric for n-gram segments.<\/li>\n
                            • Conversion rate (CVR):<\/strong> Helps identify high-intent phrases.<\/li>\n
                            • ROAS or value per cost:<\/strong> Best for ecommerce or any program with meaningful conversion value.<\/li>\n
                            • Click-through rate (CTR):<\/strong> Can indicate message match; interpret carefully because CTR can rise on irrelevant curiosity clicks.<\/li>\n
                            • Search term coverage:<\/strong> Share of converting queries captured by your targeted keyword themes (a practical internal KPI).<\/li>\n
                            • Lead quality rate:<\/strong> Percent of leads that become qualified opportunities (ideal for B2B).<\/li>\n
                            • Wasted spend estimate:<\/strong> Spend associated with n-grams that consistently fail your outcome criteria.<\/li>\n<\/ul>\n\n\n\n
                              \n\n\n\n

                              Future Trends of N-gram Analysis<\/h2>\n\n\n\n

                              N-gram Analysis<\/strong> is evolving as automation and privacy reshape SEM \/ Paid Search<\/strong>:<\/p>\n\n\n\n

                                \n
                              • More automation, more need for diagnosis:<\/strong> As bidding and matching become more automated, n-grams remain a transparent way to understand why<\/em> performance changes.<\/li>\n
                              • Better intent modeling with AI:<\/strong> Teams increasingly combine n-grams with intent classification (grouping phrases into \u201cprice,\u201d \u201csupport,\u201d \u201ccompetitor,\u201d \u201chow-to,\u201d etc.).<\/li>\n
                              • First-party data integration:<\/strong> Connecting CRM outcomes back to n-gram themes will matter more as platforms optimize toward modeled conversions.<\/li>\n
                              • Creative personalization:<\/strong> N-gram insights can influence dynamic messaging frameworks\u2014without relying on one-to-one personalization that may be constrained by privacy.<\/li>\n
                              • Measurement resilience:<\/strong> Marketers will lean on aggregated, pattern-level insights (like n-grams) when user-level visibility is reduced.<\/li>\n<\/ul>\n\n\n\n

                                In Paid Marketing<\/strong>, the enduring value of N-gram Analysis<\/strong> is that it\u2019s interpretable: you can explain to stakeholders which language patterns drove better business outcomes.<\/p>\n\n\n\n

                                \n\n\n\n

                                N-gram Analysis vs Related Terms<\/h2>\n\n\n\n

                                N-gram Analysis vs Search Term Mining<\/h3>\n\n\n\n

                                Search term mining is the broader practice of reviewing queries for insights and actions. N-gram Analysis<\/strong> is a structured method within that practice that aggregates by word sequences, enabling scale and statistical confidence.<\/p>\n\n\n\n

                                N-gram Analysis vs Keyword Research<\/h3>\n\n\n\n

                                Keyword research is usually proactive (market demand discovery) and can be done without running ads. N-gram Analysis<\/strong> is reactive and evidence-based, using your actual SEM \/ Paid Search<\/strong> query and performance data to guide optimizations.<\/p>\n\n\n\n

                                N-gram Analysis vs Intent Analysis<\/h3>\n\n\n\n

                                Intent analysis aims to classify what users want (buy, compare, learn, navigate). N-gram Analysis<\/strong> is one technique that can support intent analysis by revealing recurring linguistic signals tied to outcomes.<\/p>\n\n\n\n

                                \n\n\n\n

                                Who Should Learn N-gram Analysis<\/h2>\n\n\n\n

                                N-gram Analysis<\/strong> is useful across roles because it bridges language, data, and optimization:<\/p>\n\n\n\n

                                  \n
                                • Marketers:<\/strong> Build better campaign structure, negatives, and messaging in Paid Marketing<\/strong>.<\/li>\n
                                • Analysts:<\/strong> Create repeatable models that connect query patterns to ROI and lead quality.<\/li>\n
                                • Agencies:<\/strong> Scale account reviews and deliver defensible optimization recommendations for SEM \/ Paid Search<\/strong> clients.<\/li>\n
                                • Business owners\/founders:<\/strong> Understand what customers are actually asking for\u2014and where spend is leaking.<\/li>\n
                                • Developers:<\/strong> Automate pipelines, integrate CRM outcomes, and build internal tools that operationalize n-gram insights.<\/li>\n<\/ul>\n\n\n\n
                                  \n\n\n\n

                                  Summary of N-gram Analysis<\/h2>\n\n\n\n

                                  N-gram Analysis<\/strong> breaks search queries into word sequences and evaluates those sequences against performance metrics. It matters because it turns overwhelming query volumes into clear, scalable optimization actions.<\/p>\n\n\n\n

                                  In Paid Marketing<\/strong>, it helps reduce waste, uncover new demand, and improve message match. In SEM \/ Paid Search<\/strong>, it\u2019s one of the most practical ways to translate real user language into better keywords, smarter negatives, and more relevant ads and landing pages.<\/p>\n\n\n\n

                                  \n\n\n\n

                                  Frequently Asked Questions (FAQ)<\/h2>\n\n\n\n

                                  1) What is N-gram Analysis used for in SEM?<\/h3>\n\n\n\n

                                  In SEM \/ Paid Search<\/strong>, N-gram Analysis<\/strong> is used to find word patterns in search terms that correlate with conversions, revenue, or wasted spend so you can expand keywords, add negatives, and improve relevance.<\/p>\n\n\n\n

                                  2) Which n-gram length is best for Paid Marketing optimization?<\/h3>\n\n\n\n

                                  Bigrams are often the most actionable because they carry more intent than single words while still having enough volume to be reliable. Trigrams can be excellent for high-intent niches but may be sparse.<\/p>\n\n\n\n

                                  3) How do I avoid blocking good traffic when adding negatives?<\/h3>\n\n\n\n

                                  Set minimum data thresholds (spend\/clicks), check whether conversions exist, and manually review a sample of queries containing the n-gram. Overly broad negatives are a common Paid Marketing<\/strong> mistake.<\/p>\n\n\n\n

                                  4) Can N-gram Analysis improve ad copy and landing pages?<\/h3>\n\n\n\n

                                  Yes. If certain phrases repeatedly appear in converting queries, you can mirror that language in headlines, descriptions, and page sections\u2014improving message match and conversion rate in SEM \/ Paid Search<\/strong>.<\/p>\n\n\n\n

                                  5) Do I need special software to run N-gram Analysis?<\/h3>\n\n\n\n

                                  Not necessarily. Smaller datasets can be handled in spreadsheets, while larger Paid Marketing<\/strong> programs often use databases, scripts, and dashboards for repeatability and segmentation.<\/p>\n\n\n\n

                                  6) How often should I run N-gram Analysis?<\/h3>\n\n\n\n

                                  For active SEM \/ Paid Search<\/strong> accounts, monthly is a common baseline. High-spend or fast-changing accounts may benefit from biweekly reviews, especially for negative mining and emerging intent trends.<\/p>\n\n\n\n

                                  7) What data is most important to include besides clicks and cost?<\/h3>\n\n\n\n

                                  Conversions are essential, and conversion value (or lead quality from a CRM) makes N-gram Analysis<\/strong> far more reliable because it optimizes toward business outcomes, not just surface-level efficiency.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                  N-gram Analysis is a text-mining method that helps you understand which words and word sequences inside user queries are driving performance. In **Paid Marketing**, it\u2019s most often used to mine search query data so you can find profitable patterns, isolate waste, and turn messy long-tail searches into actionable insights.<\/p>\n","protected":false},"author":10235,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1913],"tags":[],"class_list":["post-11123","post","type-post","status-publish","format-standard","hentry","category-sem-paid-search"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11123","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/users\/10235"}],"replies":[{"embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/comments?post=11123"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/11123\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=11123"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=11123"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=11123"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}