{"id":7667,"date":"2026-03-24T21:55:44","date_gmt":"2026-03-24T21:55:44","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/affiliate-testing-framework\/"},"modified":"2026-03-24T21:55:44","modified_gmt":"2026-03-24T21:55:44","slug":"affiliate-testing-framework","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/affiliate-testing-framework\/","title":{"rendered":"Affiliate Testing Framework: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Affiliate Marketing"},"content":{"rendered":"\n

An Affiliate Testing Framework<\/strong> is a structured, repeatable way to plan, run, measure, and scale experiments across an affiliate program. In the context of Direct & Retention Marketing<\/strong>, it connects partner-driven acquisition with lifecycle outcomes like repeat purchases, email\/SMS engagement, and long-term customer value\u2014so you\u2019re not just \u201cbuying conversions,\u201d you\u2019re improving the quality and profitability of growth.<\/p>\n\n\n\n

Modern Affiliate Marketing<\/strong> is crowded, attribution is messy, and customer journeys span multiple sessions and devices. An Affiliate Testing Framework<\/strong> matters because it replaces guesswork with disciplined experimentation: you define hypotheses, control variables, validate incrementality, and turn learnings into operating standards. That\u2019s how affiliate becomes a durable channel inside Direct & Retention Marketing<\/strong>, not a short-term spike in last-click revenue.<\/p>\n\n\n\n

What Is Affiliate Testing Framework?<\/h2>\n\n\n\n

An Affiliate Testing Framework<\/strong> is a program-level system for testing affiliate strategies in a controlled, measurable way. It combines experimentation principles (hypotheses, controls, statistical thinking) with affiliate realities (partners, placements, tracking rules, payout structures, and compliance).<\/p>\n\n\n\n

At its core, the concept is simple: change one or more affiliate inputs on purpose, measure the impact, and decide whether to keep, iterate, or roll back<\/strong>. The business meaning is even more important\u2014this framework helps you answer questions like:<\/p>\n\n\n\n

    \n
  • Are certain partners truly incremental, or just capturing demand?<\/li>\n
  • Which commission changes improve profit without harming volume?<\/li>\n
  • Which landing pages convert best for affiliate traffic?<\/li>\n
  • Do coupon placements increase new customers or mainly discount existing ones?<\/li>\n<\/ul>\n\n\n\n

    Within Direct & Retention Marketing<\/strong>, an Affiliate Testing Framework<\/strong> helps align acquisition with retention by measuring downstream effects (repeat rate, LTV, refund rate) instead of stopping at the initial conversion. Inside Affiliate Marketing<\/strong>, it becomes the operating discipline that keeps partner growth profitable, compliant, and scalable.<\/p>\n\n\n\n

    Why Affiliate Testing Framework Matters in Direct & Retention Marketing<\/h2>\n\n\n\n

    Affiliate is often evaluated on short-term efficiency (CPA, ROAS) while Direct & Retention Marketing<\/strong> is responsible for long-term revenue (repeat purchase, lifecycle margins, customer equity). An Affiliate Testing Framework<\/strong> bridges that gap by forcing you to test for incrementality<\/em> and quality<\/em>, not just volume.<\/p>\n\n\n\n

    Strategically, the framework creates competitive advantage by making learning compounding. Teams that test systematically build a playbook for partner segmentation, commission models, and creative that competitors can\u2019t easily copy.<\/p>\n\n\n\n

    Business value typically shows up as:<\/p>\n\n\n\n

      \n
    • Higher contribution margin<\/strong> through smarter payouts and reduced discount leakage<\/li>\n
    • Better customer quality<\/strong> by identifying partners that drive new-to-file or high-LTV cohorts<\/li>\n
    • More predictable growth<\/strong> because you can forecast based on tested levers<\/li>\n
    • Lower risk<\/strong> by validating changes before rolling them out across the program<\/li>\n<\/ul>\n\n\n\n

      In Direct & Retention Marketing<\/strong>, this matters because affiliate-driven customers often enter your owned channels (email\/SMS, loyalty, account creation). Testing helps ensure those cohorts retain well and don\u2019t inflate churn, refunds, or support costs.<\/p>\n\n\n\n

      How Affiliate Testing Framework Works<\/h2>\n\n\n\n

      In practice, an Affiliate Testing Framework<\/strong> works as a loop that turns partner activity into measurable learning.<\/p>\n\n\n\n

        \n
      1. \n

        Input \/ Trigger (what you want to improve)<\/strong>\n – A performance issue (high CPA, low new customer rate)\n – A growth goal (expand content affiliates, international scale)\n – A risk concern (coupon abuse, brand bidding, policy compliance)\n – A lifecycle objective from Direct & Retention Marketing<\/strong> (increase second purchase rate)<\/p>\n<\/li>\n

      2. \n

        Analysis \/ Design (how you\u2019ll test)<\/strong>\n – Define a hypothesis (e.g., \u201ctiered commissions will increase incremental revenue without raising blended CPA\u201d)\n – Choose the test unit (partner, placement, landing page, audience cohort)\n – Set success metrics (primary + guardrails like refund rate)\n – Determine attribution rules and time windows you\u2019ll use consistently<\/p>\n<\/li>\n

      3. \n

        Execution \/ Application (run the experiment)<\/strong>\n – Implement tracking (tags, partner IDs, coupon codes, UTMs where appropriate)\n – Launch with a control group or phased rollout when possible\n – Document changes (commission terms, creative, placement requirements)<\/p>\n<\/li>\n

      4. \n

        Output \/ Outcome (decision and standardization)<\/strong>\n – Evaluate results, including statistical confidence where feasible\n – Decide: scale, iterate, or stop\n – Update program rules, partner playbooks, and reporting dashboards\n – Feed learnings into Affiliate Marketing<\/strong> recruiting and Direct & Retention Marketing<\/strong> lifecycle plans<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        The key is repeatability. A single \u201ctest\u201d is useful; an Affiliate Testing Framework<\/strong> creates an experimentation habit that improves program economics over time.<\/p>\n\n\n\n

        Key Components of Affiliate Testing Framework<\/h2>\n\n\n\n

        A strong Affiliate Testing Framework<\/strong> usually includes:<\/p>\n\n\n\n

        Testing strategy and governance<\/h3>\n\n\n\n
          \n
        • Clear ownership (affiliate manager + analytics partner + finance input)<\/li>\n
        • A test intake process (what qualifies as a test, how it\u2019s prioritized)<\/li>\n
        • Documentation standards (hypothesis, duration, changes, results)<\/li>\n
        • Compliance checks (disclosures, trademark rules, prohibited tactics)<\/li>\n<\/ul>\n\n\n\n

          Data inputs and tracking foundation<\/h3>\n\n\n\n
            \n
          • Partner\/placement identifiers and consistent naming conventions<\/li>\n
          • Order and customer data (new vs returning, margin, refunds)<\/li>\n
          • Attribution settings (click\/view windows, deduplication rules)<\/li>\n
          • Fraud and anomaly signals (sudden spikes, suspicious conversion paths)<\/li>\n<\/ul>\n\n\n\n

            Experiment design standards<\/h3>\n\n\n\n
              \n
            • Defined control approaches (holdouts, geo splits, partner cohorts)<\/li>\n
            • Minimum detectable effect thinking (what lift is worth acting on)<\/li>\n
            • Guardrail metrics (brand, profitability, lifecycle quality)<\/li>\n<\/ul>\n\n\n\n

              Metrics and reporting<\/h3>\n\n\n\n
                \n
              • Real-time monitoring for operational issues<\/li>\n
              • Post-test analysis templates<\/li>\n
              • Dashboards that tie Affiliate Marketing<\/strong> to Direct & Retention Marketing<\/strong> outcomes (LTV, repeat rate)<\/li>\n<\/ul>\n\n\n\n

                Types of Affiliate Testing Framework<\/h2>\n\n\n\n

                There aren\u2019t universally \u201cofficial\u201d types, but in real teams the most useful distinctions are based on what you\u2019re testing and how controlled the test can be:<\/p>\n\n\n\n

                1) Partner-level vs program-level testing<\/h3>\n\n\n\n
                  \n
                • Partner-level:<\/strong> Compare placements, creative, landing pages, or commission terms for a specific affiliate.<\/li>\n
                • Program-level:<\/strong> Changes that affect most or all partners (attribution policy, commission tiers, new customer bonuses).<\/li>\n<\/ul>\n\n\n\n

                  2) Commercial tests vs experience tests<\/h3>\n\n\n\n
                    \n
                  • Commercial tests:<\/strong> commission rates, bonus structures, payment terms, exposure buys.<\/li>\n
                  • Experience tests:<\/strong> landing pages, offer positioning, funnel steps, post-click content\u2014often shared with Direct & Retention Marketing<\/strong> and CRO teams.<\/li>\n<\/ul>\n\n\n\n

                    3) Attribution tests vs incrementality tests<\/h3>\n\n\n\n
                      \n
                    • Attribution tests<\/strong> change crediting rules (windows, dedupe).<\/li>\n
                    • Incrementality tests<\/strong> try to answer \u201cWould this sale happen anyway?\u201d using holdouts, geo experiments, or suppression methods.<\/li>\n<\/ul>\n\n\n\n

                      A mature Affiliate Testing Framework<\/strong> uses all three lenses depending on the decision at hand.<\/p>\n\n\n\n

                      Real-World Examples of Affiliate Testing Framework<\/h2>\n\n\n\n

                      Example 1: New-customer commission bonus for content partners<\/h3>\n\n\n\n

                      A subscription brand wants more first-time buyers and stronger retention. Using an Affiliate Testing Framework<\/strong>, they create a test where selected content affiliates earn a higher commission only for verified new customers, while a control group keeps the standard rate. Results are evaluated not only on CPA, but on 60\u201390 day retention and churn\u2014bringing Direct & Retention Marketing<\/strong> metrics into Affiliate Marketing<\/strong> decisions.<\/p>\n\n\n\n

                      Example 2: Coupon partner \u201cdiscount leakage\u201d audit and test<\/h3>\n\n\n\n

                      An ecommerce retailer suspects coupon sites are capturing existing customers who already intended to buy. The framework sets up a test that limits coupons to specific segments (e.g., new customers or carts above a threshold) and measures incremental revenue, AOV, and margin impact. Guardrails include customer support contacts and refund rate, ensuring Direct & Retention Marketing<\/strong> isn\u2019t harmed by aggressive discounting.<\/p>\n\n\n\n

                      Example 3: Landing page experimentation for affiliate traffic<\/h3>\n\n\n\n

                      A DTC brand finds affiliate traffic bounces more than paid search. They test two landing page variants tailored to partner intent (review-focused vs product-focused) and measure conversion rate, email opt-in rate, and second purchase rate. The Affiliate Testing Framework<\/strong> makes this a repeatable motion: partner segment \u2192 page variant \u2192 cohort analysis \u2192 scale.<\/p>\n\n\n\n

                      Benefits of Using Affiliate Testing Framework<\/h2>\n\n\n\n

                      An Affiliate Testing Framework<\/strong> drives improvements that are hard to get through ad hoc optimization:<\/p>\n\n\n\n

                        \n
                      • Performance lifts:<\/strong> higher conversion rate, better EPC, stronger partner productivity<\/li>\n
                      • Cost savings:<\/strong> reduced wasted commissions on non-incremental orders and fewer unprofitable discounts<\/li>\n
                      • Efficiency gains:<\/strong> faster decision-making through standardized analysis and consistent metrics<\/li>\n
                      • Better customer experience:<\/strong> fewer misleading offers, cleaner landing pages, more relevant partner messaging<\/li>\n
                      • Stronger lifecycle outcomes:<\/strong> by evaluating LTV, repeat rate, and churn, the affiliate channel supports Direct & Retention Marketing<\/strong> goals rather than conflicting with them<\/li>\n<\/ul>\n\n\n\n

                        Challenges of Affiliate Testing Framework<\/h2>\n\n\n\n

                        Affiliate experimentation has real constraints, and a good Affiliate Testing Framework<\/strong> plans for them.<\/p>\n\n\n\n

                          \n
                        • Attribution ambiguity:<\/strong> multiple channels touch a conversion; affiliates often appear late in the journey.<\/li>\n
                        • Data fragmentation:<\/strong> network reporting may not match internal analytics or CRM numbers.<\/li>\n
                        • Small sample sizes:<\/strong> many partners don\u2019t generate enough volume for fast, reliable tests.<\/li>\n
                        • Partner behavior changes:<\/strong> affiliates may adjust placements mid-test, adding noise.<\/li>\n
                        • Compliance and brand safety:<\/strong> testing incentives can unintentionally encourage policy violations.<\/li>\n
                        • Measurement limits with privacy:<\/strong> consent requirements, cookie loss, and server-side tracking transitions can disrupt comparability over time.<\/li>\n<\/ul>\n\n\n\n

                          Best Practices for Affiliate Testing Framework<\/h2>\n\n\n\n

                          To make an Affiliate Testing Framework<\/strong> work in real organizations, focus on operational discipline:<\/p>\n\n\n\n

                            \n
                          1. \n

                            Start with a test roadmap tied to business goals<\/strong>\n Prioritize tests that improve profit, incrementality, or new-customer share\u2014not just volume.<\/p>\n<\/li>\n

                          2. \n

                            Define primary metrics and guardrails upfront<\/strong>\n Pair a primary KPI (incremental revenue, contribution margin) with guardrails (refund rate, brand bidding violations, unsubscribe rate).<\/p>\n<\/li>\n

                          3. \n

                            Standardize segmentation<\/strong>\n Break partners into meaningful groups: content, loyalty, coupon, influencers, deal forums, sub-affiliate networks, B2B partners. Tests become more interpretable.<\/p>\n<\/li>\n

                          4. \n

                            Use the strongest control design you can afford<\/strong>\n If perfect holdouts aren\u2019t possible, use phased rollouts, matched partner cohorts, or geo splits.<\/p>\n<\/li>\n

                          5. \n

                            Measure beyond the first order<\/strong>\n In Direct & Retention Marketing<\/strong>, evaluate cohorts for 30\/60\/90-day LTV, repeat purchase rate, and churn. This is where many \u201ccheap\u201d affiliate sales become expensive.<\/p>\n<\/li>\n

                          6. \n

                            Document and operationalize learnings<\/strong>\n Turn winning tests into partner onboarding rules, commission tables, and creative guidelines so the program improves permanently.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                            Tools Used for Affiliate Testing Framework<\/h2>\n\n\n\n

                            An Affiliate Testing Framework<\/strong> is not a single tool; it\u2019s a system that typically combines:<\/p>\n\n\n\n

                              \n
                            • Affiliate platforms and tracking systems<\/strong> to manage partner relationships, track clicks\/conversions, handle payouts, and apply program rules.<\/li>\n
                            • Web analytics tools<\/strong> for on-site behavior, conversion paths, and landing page performance by partner\/placement.<\/li>\n
                            • Tag management and server-side tracking<\/strong> to improve data reliability and reduce attribution gaps.<\/li>\n
                            • Experimentation and CRO tooling<\/strong> for landing page and funnel tests tied to affiliate traffic segments.<\/li>\n
                            • CRM systems and customer data platforms<\/strong> to connect affiliate-acquired customers to lifecycle metrics central to Direct & Retention Marketing<\/strong>.<\/li>\n
                            • Reporting dashboards \/ BI<\/strong> to reconcile network data with internal revenue, margin, and cohort outcomes.<\/li>\n
                            • Fraud detection and compliance monitoring<\/strong> to identify suspicious traffic patterns, trademark bidding, or unauthorized placements.<\/li>\n<\/ul>\n\n\n\n

                              The best stack is the one that creates consistent identifiers and reconciles Affiliate Marketing<\/strong> reporting with internal finance and retention truth.<\/p>\n\n\n\n

                              Metrics Related to Affiliate Testing Framework<\/h2>\n\n\n\n

                              A practical Affiliate Testing Framework<\/strong> tracks a mix of acquisition, profitability, and lifecycle metrics:<\/p>\n\n\n\n

                              Core affiliate performance<\/h3>\n\n\n\n
                                \n
                              • Conversion rate (CVR)<\/li>\n
                              • Earnings per click (EPC) and revenue per click (RPC)<\/li>\n
                              • Average order value (AOV)<\/li>\n
                              • Click-to-purchase time lag<\/li>\n<\/ul>\n\n\n\n

                                Efficiency and profitability<\/h3>\n\n\n\n
                                  \n
                                • Cost per acquisition (CPA) or cost per order<\/li>\n
                                • Return on ad spend (ROAS) where applicable<\/li>\n
                                • Contribution margin per order (after commission, discounts, COGS where available)<\/li>\n
                                • Incremental revenue or incremental orders (measured via controls)<\/li>\n<\/ul>\n\n\n\n

                                  Customer quality (critical for Direct & Retention Marketing)<\/h3>\n\n\n\n
                                    \n
                                  • New-to-file rate \/ first-time customer share<\/li>\n
                                  • 30\/60\/90-day LTV by partner cohort<\/li>\n
                                  • Repeat purchase rate and time to second purchase<\/li>\n
                                  • Churn (for subscription) or reorder rate (for ecommerce)<\/li>\n<\/ul>\n\n\n\n

                                    Risk and brand metrics<\/h3>\n\n\n\n
                                      \n
                                    • Refund\/chargeback rate by partner<\/li>\n
                                    • Discount rate and promo code dependency<\/li>\n
                                    • Compliance violations (brand bidding, unauthorized claims)<\/li>\n
                                    • On-site engagement quality (bounce rate, pages per session)<\/li>\n<\/ul>\n\n\n\n

                                      Future Trends of Affiliate Testing Framework<\/h2>\n\n\n\n

                                      The Affiliate Testing Framework<\/strong> is evolving as measurement and privacy change:<\/p>\n\n\n\n

                                        \n
                                      • More incrementality emphasis:<\/strong> Brands increasingly demand proof of lift, not just attributed sales, pushing affiliate teams toward holdouts and experimentation design.<\/li>\n
                                      • AI-assisted analysis:<\/strong> Automated anomaly detection, partner clustering, and forecast models will speed up test interpretation, while humans still define hypotheses and guardrails.<\/li>\n
                                      • Server-side and consent-aware tracking:<\/strong> To cope with cookie loss and consent rules, affiliate measurement will rely more on first-party identifiers and clean data governance.<\/li>\n
                                      • Deeper personalization:<\/strong> Offers and landing experiences will be tailored by partner type and user segment, aligning affiliate acquisition with Direct & Retention Marketing<\/strong> personalization.<\/li>\n
                                      • Cross-channel experimentation:<\/strong> Testing will consider interactions with paid search, email, SMS, and referrals so Affiliate Marketing<\/strong> can be optimized as part of the full growth system.<\/li>\n<\/ul>\n\n\n\n

                                        Affiliate Testing Framework vs Related Terms<\/h2>\n\n\n\n

                                        Affiliate Testing Framework vs affiliate program optimization<\/h3>\n\n\n\n

                                        Affiliate program optimization is the broad practice of improving partners, payouts, and policies. An Affiliate Testing Framework<\/strong> is the method<\/em> that makes optimization reliable by using controlled experiments, consistent measurement, and documented decision rules.<\/p>\n\n\n\n

                                        Affiliate Testing Framework vs CRO (Conversion Rate Optimization)<\/h3>\n\n\n\n

                                        CRO focuses on on-site conversion improvements for any traffic source. An Affiliate Testing Framework<\/strong> may include CRO, but it also covers partner economics, attribution choices, compliance, and incrementality\u2014areas specific to Affiliate Marketing<\/strong> and tightly connected to Direct & Retention Marketing<\/strong> outcomes.<\/p>\n\n\n\n

                                        Affiliate Testing Framework vs attribution modeling<\/h3>\n\n\n\n

                                        Attribution modeling decides how credit is assigned across touchpoints. An Affiliate Testing Framework<\/strong> may test attribution rules, but it goes further by validating business impact (profit and LTV) and by standardizing how tests are run across partners and offers.<\/p>\n\n\n\n

                                        Who Should Learn Affiliate Testing Framework<\/h2>\n\n\n\n
                                          \n
                                        • Marketers and growth leads<\/strong> need it to scale affiliate without sacrificing profitability or brand integrity, especially when affiliate is part of Direct & Retention Marketing<\/strong> planning.<\/li>\n
                                        • Analysts<\/strong> benefit because affiliate data is noisy; a framework creates consistent methods for incrementality, cohort analysis, and decision confidence.<\/li>\n
                                        • Agencies<\/strong> can use an Affiliate Testing Framework<\/strong> to deliver repeatable wins and defend recommendations with evidence instead of opinions.<\/li>\n
                                        • Business owners and founders<\/strong> gain clarity on whether affiliate is truly incremental and how it impacts customer quality, not just top-line revenue.<\/li>\n
                                        • Developers and technical teams<\/strong> support tracking reliability, server-side measurement, and data pipelines that make Affiliate Marketing<\/strong> experimentation trustworthy.<\/li>\n<\/ul>\n\n\n\n

                                          Summary of Affiliate Testing Framework<\/h2>\n\n\n\n

                                          An Affiliate Testing Framework<\/strong> is a structured approach to experimenting within an affiliate program\u2014covering hypotheses, controls, tracking, analysis, and rollout decisions. It matters because it improves profitability, validates incrementality, and turns partner management into a measurable growth discipline. Within Direct & Retention Marketing<\/strong>, it ensures affiliate acquisition supports long-term outcomes like LTV and repeat purchase rate. Within Affiliate Marketing<\/strong>, it helps teams scale partner relationships with confidence, compliance, and compounding learnings.<\/p>\n\n\n\n

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

                                          1) What is an Affiliate Testing Framework?<\/h3>\n\n\n\n

                                          An Affiliate Testing Framework<\/strong> is a repeatable process for designing, running, and evaluating experiments in an affiliate program\u2014such as commission changes, partner segmentation, landing page variants, and incrementality tests\u2014using consistent tracking and decision rules.<\/p>\n\n\n\n

                                          2) How does this help Direct & Retention Marketing teams?<\/h3>\n\n\n\n

                                          It connects affiliate-driven acquisition to downstream lifecycle metrics like repeat purchase rate, churn, and LTV. That helps Direct & Retention Marketing<\/strong> teams avoid \u201ccheap first orders\u201d that become unprofitable customers later.<\/p>\n\n\n\n

                                          3) What should I test first in an affiliate program?<\/h3>\n\n\n\n

                                          Start with high-impact, high-uncertainty levers: new-customer bonuses, partner-type segmentation (content vs coupon), landing page alignment, and policies that reduce discount leakage. Use guardrails like margin and refund rate.<\/p>\n\n\n\n

                                          4) How do you test incrementality in Affiliate Marketing?<\/h3>\n\n\n\n

                                          Common methods include partner holdouts, geo splits, phased rollouts, and suppression tests (where a subset doesn\u2019t receive a placement or incentive). The goal is to estimate what would have happened without the affiliate influence.<\/p>\n\n\n\n

                                          5) What metrics matter most beyond CPA and ROAS?<\/h3>\n\n\n\n

                                          New-to-file rate, contribution margin, refund\/chargeback rate, and cohort LTV (30\/60\/90 days) are key. These align Affiliate Marketing<\/strong> optimization with Direct & Retention Marketing<\/strong> profitability.<\/p>\n\n\n\n

                                          6) How long should affiliate tests run?<\/h3>\n\n\n\n

                                          Long enough to capture typical buying cycles and reduce noise. Many tests need at least a few weeks for stable conversion patterns, and longer if you\u2019re evaluating retention outcomes like second purchase or churn.<\/p>\n\n\n\n

                                          7) What are common reasons affiliate tests fail?<\/h3>\n\n\n\n

                                          Inconsistent tracking, changing multiple variables at once, insufficient volume, partner behavior shifts mid-test, and unclear success criteria. A solid Affiliate Testing Framework<\/strong> reduces these risks through standardized design and governance.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                          An **Affiliate Testing Framework** is a structured, repeatable way to plan, run, measure, and scale experiments across an affiliate program. In the context of **Direct & Retention Marketing**, it connects partner-driven acquisition with lifecycle outcomes like repeat purchases, email\/SMS engagement, and long-term customer value\u2014so you\u2019re not just \u201cbuying conversions,\u201d you\u2019re improving the quality and profitability of growth.<\/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":[1892],"tags":[],"class_list":["post-7667","post","type-post","status-publish","format-standard","hentry","category-affiliate-marketing"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7667","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=7667"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/7667\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=7667"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=7667"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=7667"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}