{"id":9270,"date":"2026-03-27T15:27:26","date_gmt":"2026-03-27T15:27:26","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/influencer-experiment\/"},"modified":"2026-03-27T15:27:26","modified_gmt":"2026-03-27T15:27:26","slug":"influencer-experiment","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/influencer-experiment\/","title":{"rendered":"Influencer Experiment: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Influencer Marketing"},"content":{"rendered":"\n

An Influencer Experiment<\/strong> is a structured, measurable way to test how influencer-driven content affects outcomes like awareness, engagement, SEO visibility, and conversions\u2014without relying on guesswork. In Organic Marketing<\/strong>, where results are earned through trust, community, content, and relevance (not bought via ads), this kind of experimentation helps teams learn what truly moves audiences and what simply looks good on paper.<\/p>\n\n\n\n

The reason an Influencer Experiment<\/strong> matters today is that Influencer Marketing<\/strong> has matured. Brands can no longer treat influencer partnerships as purely creative collaborations or vanity exposure. They need repeatable learning: which creator profiles work, which messages resonate, which formats drive qualified traffic, and how influencer content contributes to long-term organic growth across search, social, and community channels.<\/p>\n\n\n\n

What Is Influencer Experiment?<\/h2>\n\n\n\n

An Influencer Experiment<\/strong> is a planned test that changes one or more variables in an influencer collaboration\u2014such as creator type, creative format, messaging angle, posting cadence, landing page, or offer\u2014and then measures the impact using defined success metrics.<\/p>\n\n\n\n

At its core, it applies the logic of experimentation (hypothesis \u2192 controlled change \u2192 measurement \u2192 learning) to Influencer Marketing<\/strong>. The business meaning is simple: instead of \u201cdoing influencer campaigns,\u201d you build a learning system that improves performance over time and reduces wasted spend.<\/p>\n\n\n\n

Within Organic Marketing<\/strong>, an Influencer Experiment<\/strong> typically focuses on outcomes that compound:<\/p>\n\n\n\n

    \n
  • Content discovery and sharing<\/li>\n
  • Brand search demand and direct traffic<\/li>\n
  • Community growth and repeat engagement<\/li>\n
  • Backlinks or mention signals that support SEO<\/li>\n
  • Trust-building and purchase intent over multiple touchpoints<\/li>\n<\/ul>\n\n\n\n

    In other words, the goal isn\u2019t only immediate conversions. It\u2019s to understand how influencer activity strengthens the organic engine\u2014content, brand, and audience\u2014so future efforts become more efficient and predictable.<\/p>\n\n\n\n

    Why Influencer Experiment Matters in Organic Marketing<\/h2>\n\n\n\n

    Organic Marketing<\/strong> is often harder to attribute than paid channels, yet it tends to create durable value. An Influencer Experiment<\/strong> brings clarity to that complexity by turning influencer activity into measurable, comparable learning.<\/p>\n\n\n\n

    Strategically, it helps teams:<\/p>\n\n\n\n

      \n
    • Reduce uncertainty<\/strong>: You can test assumptions about creators, audiences, and messages before scaling.<\/li>\n
    • Create competitive advantage<\/strong>: Over time, experimentation builds proprietary knowledge about what works for your niche.<\/li>\n
    • Improve creative and channel fit<\/strong>: You learn which influencer formats align with your brand voice and audience expectations.<\/li>\n
    • Strengthen organic lift<\/strong>: Well-designed tests can reveal how influencer content drives search demand, branded queries, and referral traffic.<\/li>\n<\/ul>\n\n\n\n

      From a business perspective, consistent Influencer Experiment<\/strong> cycles often lead to better resource allocation: you invest more in what works, eliminate what doesn\u2019t, and negotiate partnerships based on data rather than hype.<\/p>\n\n\n\n

      How Influencer Experiment Works<\/h2>\n\n\n\n

      In practice, an Influencer Experiment<\/strong> works like a lightweight research loop embedded in Influencer Marketing<\/strong> operations. A typical workflow looks like this:<\/p>\n\n\n\n

        \n
      1. \n

        Input \/ Trigger: Define the question<\/strong>\n – Example: \u201cDo micro-influencers drive higher-quality trial signups than mid-tier creators for our product?\u201d\n – Or: \u201cDoes a tutorial-style video outperform a testimonial post for organic conversions?\u201d<\/p>\n<\/li>\n

      2. \n

        Analysis \/ Design: Build the experiment plan<\/strong>\n – Choose the variable(s) to test and what stays constant.\n – Define the primary metric (the main success indicator) and supporting metrics.\n – Decide duration, sample size expectations, and tracking methods.<\/p>\n<\/li>\n

      3. \n

        Execution \/ Application: Run the collaboration<\/strong>\n – Brief creators with clear objectives and allowed creative freedom.\n – Use consistent tracking links, landing pages, and messaging guardrails.\n – Monitor in-flight performance without \u201cmoving the goalposts.\u201d<\/p>\n<\/li>\n

      4. \n

        Output \/ Outcome: Measure, interpret, and document<\/strong>\n – Compare results against the hypothesis and baseline.\n – Identify what likely caused the performance difference.\n – Turn findings into rules of thumb, playbooks, and next experiments.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        If you can\u2019t control enough variables to be \u201cscientific,\u201d that\u2019s normal. Most Influencer Experiment<\/strong> work is quasi-experimental. The value comes from tighter design, consistent measurement, and disciplined learning\u2014not from perfection.<\/p>\n\n\n\n

        Key Components of Influencer Experiment<\/h2>\n\n\n\n

        A strong Influencer Experiment<\/strong> blends creative execution with measurement discipline. Key components include:<\/p>\n\n\n\n

        1) Clear hypotheses and test variables<\/h3>\n\n\n\n

        Examples of variables you can test in Influencer Marketing<\/strong>:\n– Creator tier (nano vs micro vs macro)\n– Content format (short video, carousel, live stream, long-form post)\n– Narrative angle (problem\/solution, tutorial, \u201cday in the life,\u201d comparison)\n– Call-to-action (learn more vs sign up vs download)\n– Landing page approach (homepage vs dedicated influencer page)<\/p>\n\n\n\n

        2) Baselines and comparators<\/h3>\n\n\n\n

        In Organic Marketing<\/strong>, baselines matter because results fluctuate naturally. Useful baselines include:\n– Pre-campaign traffic and conversion averages\n– Prior influencer performance benchmarks\n– Comparable content posts without influencer amplification<\/p>\n\n\n\n

        3) Tracking and attribution approach<\/h3>\n\n\n\n

        Common data inputs:\n– UTM parameters and channel grouping\n– Unique discount codes (with caution\u2014codes can leak)\n– Dedicated landing pages and event tracking\n– Post-level engagement metrics from platform analytics<\/p>\n\n\n\n

        4) Governance and team responsibilities<\/h3>\n\n\n\n

        An Influencer Experiment<\/strong> fails when responsibilities are unclear. Define owners for:\n– Creator sourcing and contracts\n– Briefing and approvals (including compliance)\n– Analytics implementation and QA\n– Reporting and learnings documentation<\/p>\n\n\n\n

        5) Decision rules<\/h3>\n\n\n\n

        Pre-define what \u201csuccess\u201d means:\n– Statistical certainty is ideal, but directional confidence can be acceptable.\n– Set thresholds (e.g., \u201cIf cost per qualified lead improves by 20% or more, scale next cycle.\u201d)<\/p>\n\n\n\n

        Types of Influencer Experiment<\/h2>\n\n\n\n

        There aren\u2019t universally \u201cformal\u201d categories, but in real Influencer Marketing<\/strong> work, the most useful distinctions are:<\/p>\n\n\n\n

        A) Creative experiments<\/h3>\n\n\n\n

        Test how storytelling and format affect outcomes.\n– Tutorial vs review vs challenge format\n– Long-form video vs short clips\n– Hook variations and CTA placement<\/p>\n\n\n\n

        B) Audience and creator-fit experiments<\/h3>\n\n\n\n

        Test alignment between influencer audience and your ICP.\n– Niche creators vs general lifestyle creators\n– Regional creators vs national audience\n– Community credibility vs pure reach<\/p>\n\n\n\n

        C) Funnel-stage experiments<\/h3>\n\n\n\n

        Test influencer impact at different stages in Organic Marketing<\/strong>:\n– Top-of-funnel: reach, awareness lift, branded search\n– Mid-funnel: content engagement, email capture, webinar signups\n– Bottom-funnel: trials, demos, purchases<\/p>\n\n\n\n

        D) Distribution and sequencing experiments<\/h3>\n\n\n\n

        Test timing and combinations:\n– Single post vs multi-post series\n– Influencer post followed by brand repurposing\n– Staggered creators vs same-week \u201cburst\u201d<\/p>\n\n\n\n

        Real-World Examples of Influencer Experiment<\/h2>\n\n\n\n

        Example 1: SaaS brand testing creator type for qualified signups<\/h3>\n\n\n\n

        A B2B SaaS company runs an Influencer Experiment<\/strong> comparing:\n– 6 micro-influencers who create tutorial content for practitioners\n– 2 mid-tier influencers with broader industry reach<\/p>\n\n\n\n

        They keep the landing page and offer constant (free trial) and track:\n– Trial signups\n– Product activation events (e.g., first project created)\n– Branded search lift during the campaign window<\/p>\n\n\n\n

        Outcome: micro-influencers drive fewer signups but higher activation rate. In Organic Marketing<\/strong>, this informs a strategy to prioritize credibility-led creators and repurpose tutorial content into evergreen guides.<\/p>\n\n\n\n

        Example 2: E-commerce brand testing message angle for organic conversion<\/h3>\n\n\n\n

        A DTC skincare brand tests two creator briefs:\n– \u201cSensitive skin story + routine\u201d\n– \u201cIngredient breakdown + before\/after expectations\u201d<\/p>\n\n\n\n

        They run the Influencer Experiment<\/strong> across similar-sized creators and measure:\n– Saves and shares (as a proxy for intent)\n– Click-through rate to a dedicated product page\n– Add-to-cart and purchase rate<\/p>\n\n\n\n

        Outcome: ingredient breakdown content earns more saves and higher conversion. The brand uses the insight to reshape its Influencer Marketing<\/strong> brief templates and product education content in Organic Marketing<\/strong> channels.<\/p>\n\n\n\n

        Example 3: Local service business testing geo relevance<\/h3>\n\n\n\n

        A regional home services company runs an Influencer Experiment<\/strong>:\n– Local creators in each city with neighborhood credibility\n– One larger influencer in the state for broader exposure<\/p>\n\n\n\n

        They measure:\n– Calls and form fills by region\n– Google Business Profile actions (calls, directions)\n– Lift in \u201cbrand + service\u201d searches<\/p>\n\n\n\n

        Outcome: local creators deliver stronger lead quality and lower time-to-book. The company scales city-by-city, reinforcing organic presence and local trust.<\/p>\n\n\n\n

        Benefits of Using Influencer Experiment<\/h2>\n\n\n\n

        A disciplined Influencer Experiment<\/strong> approach can deliver:<\/p>\n\n\n\n

          \n
        • Performance improvements<\/strong>: Higher engagement, better conversion rates, and more consistent results as learnings compound.<\/li>\n
        • Cost efficiency<\/strong>: Fewer wasted partnerships, better negotiation leverage, and more confident scaling decisions.<\/li>\n
        • Faster iteration<\/strong>: Teams stop debating opinions and start iterating based on observed outcomes.<\/li>\n
        • Better audience experience<\/strong>: Experiments identify which creator-content combinations feel authentic and helpful rather than intrusive.<\/li>\n
        • Stronger Organic Marketing assets<\/strong>: Influencer content can be repurposed into FAQs, tutorials, product pages, and social proof libraries.<\/li>\n<\/ul>\n\n\n\n

          Challenges of Influencer Experiment<\/h2>\n\n\n\n

          Even well-designed Influencer Experiment<\/strong> programs face constraints:<\/p>\n\n\n\n

            \n
          • Attribution limitations<\/strong>: People may see influencer content and convert later through search or direct traffic. This is common in Organic Marketing<\/strong>.<\/li>\n
          • Small sample sizes<\/strong>: One creator\u2019s result can be noisy. Repetition is needed to avoid overfitting.<\/li>\n
          • Platform variability<\/strong>: Algorithm changes and posting time effects can distort comparisons.<\/li>\n
          • Creative control vs authenticity<\/strong>: Overly rigid briefs can reduce performance, but too much freedom can dilute the test variable.<\/li>\n
          • Compliance and disclosure<\/strong>: Missing or inconsistent disclosure can create legal and reputational risk.<\/li>\n
          • Selection bias<\/strong>: Choosing \u201cstar\u201d creators for one variant and average creators for another invalidates conclusions.<\/li>\n<\/ul>\n\n\n\n

            Best Practices for Influencer Experiment<\/h2>\n\n\n\n

            To make an Influencer Experiment<\/strong> reliable and scalable:<\/p>\n\n\n\n

              \n
            1. \n

              Start with one primary metric<\/strong>\n – Choose a single \u201cnorth star\u201d outcome (e.g., qualified leads, purchases, activations) and treat other metrics as diagnostic.<\/p>\n<\/li>\n

            2. \n

              Test one major variable at a time<\/strong>\n – Keep landing page, offer, and tracking consistent when possible.<\/p>\n<\/li>\n

            3. \n

              Standardize briefing templates<\/strong>\n – Provide consistent product facts, claims guidance, and CTA options.\n – Allow creators to adapt voice and storytelling to their audience.<\/p>\n<\/li>\n

            4. \n

              Use consistent tracking hygiene<\/strong>\n – Define UTM rules, link placement rules, and event definitions.\n – QA links and pixels before posts go live.<\/p>\n<\/li>\n

            5. \n

              Document learnings in a repeatable format<\/strong>\n – Hypothesis \u2192 setup \u2192 results \u2192 interpretation \u2192 next steps.\n – Store in a shared knowledge base for Influencer Marketing<\/strong> and Organic Marketing<\/strong> teams.<\/p>\n<\/li>\n

            6. \n

              Plan for repurposing from day one<\/strong>\n – Get usage rights where appropriate.\n – Capture raw assets when possible to extend organic value.<\/p>\n<\/li>\n

            7. \n

              Scale by repeating winners across multiple creators<\/strong>\n – A \u201cwin\u201d should be reproducible before it becomes a playbook.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

              Tools Used for Influencer Experiment<\/h2>\n\n\n\n

              An Influencer Experiment<\/strong> isn\u2019t about a single tool; it\u2019s about a workflow supported by systems commonly used in Organic Marketing<\/strong> and Influencer Marketing<\/strong>:<\/p>\n\n\n\n

                \n
              • Analytics tools<\/strong>: Web analytics for traffic sources, events, funnels, and cohort behavior.<\/li>\n
              • Tag management<\/strong>: To standardize event tracking and reduce engineering bottlenecks.<\/li>\n
              • CRM systems<\/strong>: To connect influencer-driven leads to pipeline stages and revenue outcomes.<\/li>\n
              • Influencer management workflows<\/strong>: Databases\/spreadsheets or dedicated platforms for outreach, contracts, content status, and payments.<\/li>\n
              • Social analytics<\/strong>: Native platform insights for reach, watch time, saves, shares, and audience demographics.<\/li>\n
              • SEO tools<\/strong>: To monitor branded query trends, content performance, and link\/mention discovery (where applicable).<\/li>\n
              • Reporting dashboards<\/strong>: To unify post-level metrics, web events, and CRM outcomes into one view.<\/li>\n<\/ul>\n\n\n\n

                If measurement maturity is low, a simple stack can still work: a spreadsheet for experiment design, web analytics for behavior, and a CRM for outcomes\u2014provided the definitions are consistent.<\/p>\n\n\n\n

                Metrics Related to Influencer Experiment<\/h2>\n\n\n\n

                The right metrics depend on goals, but common indicators include:<\/p>\n\n\n\n

                Performance and engagement metrics<\/h3>\n\n\n\n
                  \n
                • Reach and impressions (context, not the goal)<\/li>\n
                • Engagement rate (likes, comments, shares, saves)<\/li>\n
                • Video watch time and completion rate<\/li>\n
                • Click-through rate on tracked links<\/li>\n<\/ul>\n\n\n\n

                  Organic Marketing impact metrics<\/h3>\n\n\n\n
                    \n
                  • Direct traffic and referral traffic changes<\/li>\n
                  • Branded search volume trends (directional, not absolute truth)<\/li>\n
                  • Returning visitors and content consumption depth<\/li>\n
                  • Email subscribers or community joins driven by influencer content<\/li>\n<\/ul>\n\n\n\n

                    Conversion and ROI metrics<\/h3>\n\n\n\n
                      \n
                    • Cost per qualified lead (CPL) or cost per acquisition (CPA)<\/li>\n
                    • Trial-to-activation rate (for SaaS)<\/li>\n
                    • Add-to-cart rate and purchase conversion rate (for e-commerce)<\/li>\n
                    • Revenue per creator or per content asset (where attributable)<\/li>\n<\/ul>\n\n\n\n

                      Quality and brand metrics<\/h3>\n\n\n\n
                        \n
                      • Sentiment in comments and DMs (qualitative but valuable)<\/li>\n
                      • Brand lift surveys (when feasible)<\/li>\n
                      • Content save\/share rate (often correlates with genuine usefulness)<\/li>\n<\/ul>\n\n\n\n

                        A strong Influencer Experiment<\/strong> pairs \u201cfast\u201d metrics (engagement) with \u201cslow\u201d metrics (conversion, retention, search demand) to avoid optimizing for noise.<\/p>\n\n\n\n

                        Future Trends of Influencer Experiment<\/h2>\n\n\n\n

                        Several trends are reshaping how Influencer Experiment<\/strong> programs operate within Organic Marketing<\/strong>:<\/p>\n\n\n\n

                          \n
                        • AI-assisted creator discovery and content analysis<\/strong>: Expect better clustering of creator audiences, topic alignment, and brand safety checks\u2014useful for selecting test cohorts.<\/li>\n
                        • Automation of reporting and QA<\/strong>: More automated UTM validation, anomaly detection, and standardized post-to-dashboard ingestion will reduce manual effort.<\/li>\n
                        • Personalization and modular creative<\/strong>: Brands will test message modules (hooks, proof points, CTAs) that creators can adapt, enabling cleaner experiments without killing authenticity.<\/li>\n
                        • Privacy and measurement constraints<\/strong>: Tracking will remain imperfect. Teams will lean more on modeled insights, incrementality thinking, and blended metrics (web + CRM + surveys).<\/li>\n
                        • Shift toward \u201ccreator as distribution + creator as content\u201d<\/strong>: The experiment won\u2019t just test who posts; it will test how influencer content performs when repurposed across brand channels for ongoing organic lift.<\/li>\n<\/ul>\n\n\n\n

                          As Influencer Marketing<\/strong> becomes more operationally mature, the Influencer Experiment<\/strong> will look less like one-off campaigns and more like continuous optimization\u2014similar to editorial and SEO programs.<\/p>\n\n\n\n

                          Influencer Experiment vs Related Terms<\/h2>\n\n\n\n

                          Influencer Experiment vs Influencer Campaign<\/h3>\n\n\n\n

                          An influencer campaign is the execution: creators post content to achieve a goal. An Influencer Experiment<\/strong> is the learning design layered on top\u2014controlled variables, defined hypotheses, and measurement intended to inform future decisions. You can run campaigns without experiments; experiments make campaigns smarter.<\/p>\n\n\n\n

                          Influencer Experiment vs A\/B Testing<\/h3>\n\n\n\n

                          A\/B testing usually implies tighter control and randomization (common in landing pages or ads). An Influencer Experiment<\/strong> often can\u2019t fully randomize audiences or control algorithms, so it\u2019s usually a pragmatic, real-world test. The mindset is similar, but the environment is messier.<\/p>\n\n\n\n

                          Influencer Experiment vs Incrementality Testing<\/h3>\n\n\n\n

                          Incrementality testing tries to answer: \u201cWhat happened because of this activity that would not have happened otherwise?\u201d An Influencer Experiment<\/strong> may measure incremental lift, but many programs start with comparative performance learning (which creator\/message performs better). Incrementality is ideal when you can create credible holdouts or baselines.<\/p>\n\n\n\n

                          Who Should Learn Influencer Experiment<\/h2>\n\n\n\n
                            \n
                          • Marketers<\/strong>: To turn Influencer Marketing<\/strong> into a repeatable growth lever that supports Organic Marketing<\/strong> goals.<\/li>\n
                          • Analysts<\/strong>: To design measurement frameworks, improve attribution, and reduce bias in interpretation.<\/li>\n
                          • Agencies<\/strong>: To differentiate with rigorous strategy, clearer reporting, and scalable playbooks across clients.<\/li>\n
                          • Business owners and founders<\/strong>: To invest in influencer partnerships with confidence and avoid wasting budget on vanity metrics.<\/li>\n
                          • Developers and technical teams<\/strong>: To implement tracking, event definitions, and data pipelines that make experiments trustworthy.<\/li>\n<\/ul>\n\n\n\n

                            Summary of Influencer Experiment<\/h2>\n\n\n\n

                            An Influencer Experiment<\/strong> is a structured way to test influencer variables\u2014creator type, content format, message, CTA, and distribution\u2014and measure their impact using clear metrics. It matters because Organic Marketing<\/strong> depends on trust and compounding effects, and Influencer Marketing<\/strong> is too important (and too expensive) to run on intuition alone. When done well, an Influencer Experiment<\/strong> creates a learning loop that improves performance, strengthens brand credibility, and builds an evergreen content engine.<\/p>\n\n\n\n

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

                            1) What is an Influencer Experiment in simple terms?<\/h3>\n\n\n\n

                            An Influencer Experiment<\/strong> is a planned test where you change one thing about an influencer collaboration (like the content format or creator type) and measure how that change affects results such as engagement, traffic, or conversions.<\/p>\n\n\n\n

                            2) How does Influencer Marketing benefit from experimentation?<\/h3>\n\n\n\n

                            Influencer Marketing<\/strong> becomes more predictable when you run experiments because you learn which creators, messages, and formats drive meaningful outcomes\u2014rather than relying on follower counts or subjective creative preferences.<\/p>\n\n\n\n

                            3) Do I need a large budget to run an Influencer Experiment?<\/h3>\n\n\n\n

                            No. Many effective tests use nano or micro creators and focus on clean measurement. The key is a clear hypothesis, consistent tracking, and enough repetitions to avoid drawing conclusions from a single outlier.<\/p>\n\n\n\n

                            4) Which metrics should I prioritize in Organic Marketing experiments with influencers?<\/h3>\n\n\n\n

                            In Organic Marketing<\/strong>, prioritize metrics that indicate durable value\u2014qualified leads, activation, email signups, returning visitors, branded search trends\u2014along with engagement signals like saves and shares that correlate with genuine interest.<\/p>\n\n\n\n

                            5) How long should an Influencer Experiment run?<\/h3>\n\n\n\n

                            Long enough to capture typical audience behavior for the platform and purchase cycle. For fast-moving consumer products, this may be days to a couple of weeks; for B2B, it may require several weeks plus CRM follow-up to assess lead quality.<\/p>\n\n\n\n

                            6) What\u2019s the biggest mistake teams make with Influencer Experiment design?<\/h3>\n\n\n\n

                            Changing too many variables at once. If you change creator tier, message, landing page, and offer simultaneously, you won\u2019t know what caused the result. Start simpler, then expand.<\/p>\n\n\n\n

                            7) Can influencer experiments improve SEO?<\/h3>\n\n\n\n

                            They can contribute indirectly. Strong influencer content can drive brand awareness, increase branded searches, generate referral traffic, and sometimes earn mentions or links. The most consistent SEO benefit usually comes from repurposing influencer insights into high-quality content that supports Organic Marketing<\/strong> over time.<\/p>\n","protected":false},"excerpt":{"rendered":"

                            An **Influencer Experiment** is a structured, measurable way to test how influencer-driven content affects outcomes like awareness, engagement, SEO visibility, and conversions\u2014without relying on guesswork. In **Organic Marketing**, where results are earned through trust, community, content, and relevance (not bought via ads), this kind of experimentation helps teams learn what truly moves audiences and what simply looks good on paper.<\/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":[1903],"tags":[],"class_list":["post-9270","post","type-post","status-publish","format-standard","hentry","category-influencer-marketing"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/9270","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=9270"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/9270\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=9270"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=9270"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=9270"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}