{"id":1563,"date":"2026-08-16T05:03:24","date_gmt":"2026-08-16T05:03:24","guid":{"rendered":"https:\/\/listenlabs.com\/articles\/brand-tracking-methodology-guide\/"},"modified":"2026-08-16T05:03:24","modified_gmt":"2026-08-16T05:03:24","slug":"brand-tracking-methodology-guide","status":"publish","type":"post","link":"https:\/\/listenlabs.com\/articles\/brand-tracking-methodology-guide\/","title":{"rendered":"Brand Tracking Methodology: The Complete 2026 Guide"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>Modern brand tracking in 2026 pairs fixed quantitative KPIs with AI-moderated conversations and emotional intelligence, so every metric shift includes a traceable qualitative explanation.<\/li>\n<li>Lock a stable core of five to eight metrics before wave one and never change their wording, scale anchors, or position to protect trend-line integrity.<\/li>\n<li>Continuous or hybrid cadence designs with weekly pulses, monthly depth, and quarterly diagnostics reduce blind spots and surface emerging themes before they show up as KPI declines.<\/li>\n<li>Emotional intelligence and behavioral observation layers close the say-do gap by capturing tone, micro-expressions, and real-time contradictions that self-reported ratings miss.<\/li>\n<li>Listen Labs integrates directly with Qualtrics and Decipher to add conversational depth and traceable diagnostics without disrupting existing trackers. <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>Book a demo<\/strong><\/a> to see how Listen Pulse delivers both the number and the explanation in the same wave.<\/li>\n<\/ul>\n<h2>Step 1: Define Objectives and Lock Your Fixed Core Metrics<\/h2>\n<p>Every defensible brand tracking program starts with a precise statement of the business decisions the data must inform. <a href=\"https:\/\/glowfeed.com\/2026\/04\/15\/most-brand-trackers-collect-data-few-actually-change-decisions\" target=\"_blank\" rel=\"noindex nofollow\">Enterprise brand health trackers should identify the specific business decisions the data must inform<\/a> such as whether to increase brand spend, evaluate campaign effectiveness, or monitor competitive threat before a single question is written.<\/p>\n<p>Once objectives are set, teams lock a stable core of five to eight metrics. <a href=\"https:\/\/glowfeed.com\/2026\/04\/15\/most-brand-trackers-collect-data-few-actually-change-decisions\" target=\"_blank\" rel=\"noindex nofollow\">A stable core of unprompted awareness, prompted awareness, consideration, and preference, plus three to five differentiating brand attributes<\/a> provides the foundation that supports modular additions without resetting trend integrity. <a href=\"https:\/\/formbricks.com\/blog\/brand-health-survey-questions\" target=\"_blank\" rel=\"noindex nofollow\">A permanent core tracker of six to eight questions should never change, while new questions are added only as a separate exploratory block.<\/a> This modular design enables program evolution while the core remains untouched, which sets up the mechanics of protecting trend-line integrity when the business demands new questions.<\/p>\n<h2>Step 2: Protect Trend Lines When You Add New Questions<\/h2>\n<p>Trend-line integrity is the most fragile asset in any longitudinal program. <a href=\"https:\/\/kicue.com\/en\/blog\/brand-tracking-survey-guide\" target=\"_blank\" rel=\"noindex nofollow\">Even a one-word change to question wording invalidates time-series comparisons because it becomes impossible to distinguish real brand movement from the effect of the edit.<\/a> When a change is unavoidable, <a href=\"https:\/\/kicue.com\/en\/blog\/brand-tracking-survey-guide\" target=\"_blank\" rel=\"noindex nofollow\">the old and new versions must be run in parallel on at least one wave to create a statistical bridge that preserves comparability.<\/a><\/p>\n<p><a href=\"https:\/\/boltinsight.com\/use-cases\/tracking-studies\" target=\"_blank\" rel=\"noindex nofollow\">A practical rule permits up to 20% of the questionnaire to be updated wave-by-wave for seasonal priorities, new launches, or emerging topics without breaking trend on core KPIs.<\/a> New questions must be piloted before addition to a live tracker. <a href=\"https:\/\/pulseairesearch.com\/pulse-shift\/blogs\/1375\/brand_tracking_surveys_how_to_measure_what_customers_really_think\" target=\"_blank\" rel=\"noindex nofollow\">Piloting new items before addition to a live brand tracker prevents confusing items from corrupting an entire wave.<\/a><\/p>\n<h2>Step 3: Balance Continuous and Wave-Based Tracking Cadence<\/h2>\n<p>The choice between continuous and wave-based tracking is a strategic decision tied to category velocity and budget. <a href=\"https:\/\/dynata.com\/why-dynata\/resources\/blog\/brand-tracking-journey\" target=\"_blank\" rel=\"noindex nofollow\">Fast-moving or highly competitive categories may require monthly or continuous tracking, while other categories can track quarterly to balance insight depth and cost.<\/a><\/p>\n<p>The strongest methodology combines cadences. Weekly pulse tracking detects shifts in near-real-time, monthly cycles provide segment-level dimensional depth, and quarterly studies deliver full diagnostic investigation, with each layer feeding the same longitudinal intelligence base. <a href=\"https:\/\/groupcaliber.com\/how-to-measure-brand-reputation\" target=\"_blank\" rel=\"noindex nofollow\">Periodic studies create blind spots because perceptions can shift due to campaigns, news cycles, or crises between waves, so the timing and cause of a metric change may remain unknown until the next wave.<\/a><\/p>\n<p>Listen Pulse addresses this trade-off by running always-on conversational tracking that analyzes tens of thousands of responses continuously. It surfaces emerging themes before they register as KPI declines and removes the need for a separate qualitative study to explain what happened. <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>Book a demo to see how Listen Pulse brand tracking methodology delivers continuous insight without breaking your trend line.<\/strong><\/a><\/p>\n<h2>Step 4: Add Conversational Depth Without Breaking Comparability<\/h2>\n<p><a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">Traditional surveys may tell us what people do, but it takes a conversation to understand why.<\/a> The integration challenge is adding that conversational depth without altering the fixed quantitative instrument.<\/p>\n<p><a href=\"https:\/\/conveo.ai\/insights\/best-brand-equity-analysis-tool\" target=\"_blank\" rel=\"noindex nofollow\">Rotating qualitative probes alongside fixed core tracker questions diagnoses metric movement, such as an eight-point drop in brand trust, without altering or contaminating the quantitative instrument.<\/a> This approach enables adaptive follow-ups that convert unexplained shifts into traceable explanations.<\/p>\n<p><a href=\"https:\/\/getperspective.ai\/blog\/how-to-use-ai-for-brand-perception-research\" target=\"_blank\" rel=\"noindex nofollow\">Mature brand programs run both structured brand tracking for longitudinal trend continuity and AI-driven conversational interviews for explanatory depth.<\/a> Listen Pulse operationalizes this by embedding open-ended AI-moderated conversation into every wave alongside the fixed quantitative core, so the metric change and its explanation arrive simultaneously. It also integrates directly with Qualtrics and Decipher, so teams keep the KPIs they already report while adding the narrative behind them.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098461736-796a7724447a.png\" alt=\"Screenshot of researcher creating a study by simply typing &quot;I want to interview Gen Z on how they use ChatGPT&quot;\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Our AI helps you go from idea to implemented discussion guide in seconds.<\/em><\/figcaption><\/figure>\n<h2>Step 5: Use Emotional Intelligence to Reveal Hidden Brand Perceptions<\/h2>\n<p>Self-reported ratings capture what respondents are willing to articulate, while emotional intelligence captures what they actually feel. <a href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\" target=\"_blank\">Emotional Intelligence analyzes three signals, tone of voice, word choice, and subconscious micro expressions, to surface nuanced emotions that transcripts alone miss.<\/a><\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\" target=\"_blank\">Every emotion is quantified per question and concept, with every label traceable to the exact timestamp, verbatim quote, and AI reasoning behind it.<\/a> This traceability separates emotional intelligence from black-box sentiment scoring. Insights leaders can see precisely why a particular emotion was identified and link it to the specific brand stimulus that triggered it.<\/p>\n<p>Quarterly emotional tracking using a consistent methodology across waves detects early shifts in emotional territory occupancy, connection depth, and competitive emotional movement before loyalty or business metrics decline. A 2025 Gartner Marketing Survey of 426 senior marketing leaders found that 84% of companies are stuck in a \u201cbrand doom loop\u201d that prevents marketing leaders from proving brand\u2019s impact on enterprise growth. Emotional intelligence metrics, when tracked consistently, provide the connective tissue between perception and commercial outcome.<\/p>\n<h2>Step 6: Capture Behavioral Signals to Close the Say-Do Gap<\/h2>\n<p><a href=\"https:\/\/theresearchagency.com\/insights\/comparing-brand-health-tracking-methods\" target=\"_blank\" rel=\"noindex nofollow\">Behavioral signals in brand-health tracking capture observable actions such as online conversation, engagement, or sentiment inferred from public content, providing timely visibility into real-time reactions.<\/a> However, <a href=\"https:\/\/theresearchagency.com\/insights\/comparing-brand-health-tracking-methods\" target=\"_blank\" rel=\"noindex nofollow\">behavioral signals alone rarely provide a complete picture of brand health because not all audiences express views publicly and silence does not indicate indifference.<\/a><\/p>\n<p>Listen Labs\u2019 Visual Insights adds a behavioral observation layer inside each interview. The AI Interviewer observes on-screen behavior during a session, detects contradictions between stated preference and observed behavior in real time, and probes the contradiction immediately rather than following a pre-written script past it. Every behavioral observation links to a timestamped moment with a verbatim quote, which turns the gap between what respondents say and what they do into a quantifiable, traceable data point rather than an inference.<\/p>\n<h2>Step 7: Keep Statistical Rigor and Sample Consistency Wave Over Wave<\/h2>\n<p>Statistical rigor in brand tracking depends on sample size, sample consistency, and confidence thresholds applied uniformly across waves. Larger samples per wave can help detect shifts in metrics and support segment-level analysis, which is why most programs establish minimum thresholds for statistical confidence and margin of error before wave one. These thresholds ensure that every wave has the statistical power to detect meaningful movement.<\/p>\n<p><a href=\"https:\/\/pulseairesearch.com\/pulse-shift\/blogs\/1375\/brand_tracking_surveys_how_to_measure_what_customers_really_think\" target=\"_blank\" rel=\"noindex nofollow\">Sample consistency across waves matters more than absolute size; a stable 400-respondent sample every wave produces more reliable wave-to-wave comparisons than fluctuating samples ranging from 200 to 800.<\/a> Sample composition consistency should be maintained within a 5% variance on key demographic and behavioral quotas across waves; any forced changes must be documented as methodological caveats.<\/p>\n<p>Screening criteria, including category usage requirements, brand awareness floor, demographic quotas, and exclusions for prior-wave participants, must be defined before wave one and held constant because changes introduce bias that can masquerade as brand movement. When criteria shift midstream, teams lose the ability to tell whether a metric moved due to the market or due to a new sample definition. Listen Labs\u2019 Quality Guard enforces screening rules through real-time fraud detection across video, voice, content, and device signals, with participants capped at three studies per month to eliminate professional survey-takers and panel fatigue.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098685817-eaceb6089d9a.png\" alt=\"Listen Labs finds participants and helps build screener questions\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs finds participants and helps build screener questions<\/em><\/figcaption><\/figure>\n<h2>Step 8: Turn KPI Movements into Traceable Diagnostics<\/h2>\n<p><a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">With AI-moderated interviews, talking to consumers at scale is no longer the hard part. The challenge is understanding what they mean.<\/a> Operationalization requires that every KPI movement arrive with a traceable diagnostic, not a separate qualitative study commissioned weeks later.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">\u201cThe why is what differentiates customer research that&#8217;s alright from customer research that&#8217;s outstanding.\u201d<\/a> Listen Pulse charts emerging themes directly next to the KPIs teams already report. This architecture, introduced in Step 3, means every KPI movement traces back to the interview, verbatim quote, and audio or video clip behind it. <a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">Every insight links directly to the underlying response data<\/a>, enabling insights leaders to drill into any metric movement and hear the original explanation from the respondent who produced it.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773098910279-d16bc544a32e.png\" alt=\"Listen Labs auto-generates research reports in under a minute\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs auto-generates research reports in under a minute<\/em><\/figcaption><\/figure>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>Book a demo to see Listen Pulse brand tracking methodology in action, from KPI dashboard to verbatim diagnostic in a single wave.<\/strong><\/a><\/p>\n<h2>Fixed-Core Questionnaire Template for Brand Tracking<\/h2>\n<p>This ready-to-use template shows the fixed core questions that should be locked before wave one and held constant across all subsequent waves. Unaided awareness must always precede any brand-name prompts.<\/p>\n<ol>\n<li><strong>Unaided awareness:<\/strong> \u201cWhen you think of [category], which brands come to mind?\u201d (Open text; no brand names shown)<\/li>\n<li><strong>Aided awareness:<\/strong> \u201cWhich of the following brands have you heard of?\u201d (Multi-select from randomized brand list)<\/li>\n<li><strong>Familiarity:<\/strong> \u201cHow familiar are you with [Brand]?\u201d (5-point scale: Not at all familiar \u2192 Extremely familiar)<\/li>\n<li><strong>Consideration:<\/strong> \u201cWould you consider purchasing [Brand] the next time you are in the market for [category]?\u201d (5-point scale: Definitely not \u2192 Definitely yes)<\/li>\n<li><strong>Preference:<\/strong> \u201cWhich brand do you prefer most for [category]?\u201d (Single select from randomized brand list)<\/li>\n<li><strong>Quality perception:<\/strong> \u201cHow would you rate the overall quality of [Brand]?\u201d (5-point scale: Very poor \u2192 Excellent)<\/li>\n<li><strong>Trust:<\/strong> \u201cHow much do you trust [Brand]?\u201d (5-point scale: Not at all \u2192 Completely)<\/li>\n<li><strong>Net Promoter Score:<\/strong> \u201cHow likely are you to recommend [Brand] to a friend or colleague?\u201d (0\u201310 scale)<\/li>\n<\/ol>\n<p>Three rotating modules address timely priorities without touching the fixed core. Each module is active for one to two waves and then retired or replaced.<\/p>\n<ol>\n<li><strong>Campaign exposure module:<\/strong> \u201cHave you seen or heard any advertising for [Brand] in the past 30 days?\u201d followed by channel attribution and message recall questions. Active during and immediately after media flights.<\/li>\n<li><strong>Competitive disruption module:<\/strong> \u201cWhich brand do you feel has improved the most in [category] over the past three months?\u201d with open-ended follow-up. Active when a competitor launches or a market event occurs.<\/li>\n<li><strong>Innovation perception module:<\/strong> \u201cHow innovative do you consider [Brand] compared to one year ago?\u201d (5-point scale) with AI-moderated open-end: \u201cWhat specifically gives you that impression?\u201d Active during product launch windows.<\/li>\n<\/ol>\n<p>Statistical significance thresholds: 95% confidence interval, minimum 300 completes per wave for top-line metrics, minimum 150 completes per subgroup for segment-level analysis, 5% margin of error for top-line metrics. For Qualtrics integration, embed the fixed core block as a locked survey flow element and append rotating modules as a separate, versioned block downstream. For Decipher integration, use the fixed core as a base template with rotating modules loaded via XML include files, preserving the base template\u2019s variable names across all waves.<\/p>\n<h2>Pre-Wave Methodology Checklist<\/h2>\n<p>Use this 12-item checklist before launching each wave to confirm methodological integrity.<\/p>\n<ol>\n<li>Screener criteria (category usage, awareness floor, demographic quotas, prior-wave exclusions) are identical to wave one.<\/li>\n<li>All eight fixed core questions retain original wording, scale anchors, and response options with zero edits.<\/li>\n<li>Unaided awareness question appears before any brand-name prompt in the survey flow.<\/li>\n<li>Question order within the fixed core block is unchanged from the previous wave.<\/li>\n<li>Rotating module questions are appended after the fixed core block and do not alter fixed-core variable names.<\/li>\n<li>Any new rotating module question has been piloted with 30\u201350 respondents before live deployment.<\/li>\n<li>Brand presentation order within aided awareness and preference questions is randomized using the same randomization seed rule applied in wave one.<\/li>\n<li>Emotional intelligence tagging is enabled for all open-ended and conversational questions, with Ekman emotion categories applied consistently across waves.<\/li>\n<li>Sample composition is within 5% variance on all demographic and behavioral quotas relative to the prior wave.<\/li>\n<li>Achieved base size meets the minimum 300 completes threshold for top-line metrics before data is released for analysis.<\/li>\n<li>Any methodological deviation (forced screener change, question bridge, sample source shift) is documented as a caveat in the wave report.<\/li>\n<li>Privacy compliance confirmed: respondent consent language is current, data handling meets GDPR requirements, and no customer data is used for AI model training.<\/li>\n<\/ol>\n<h2>Privacy, Compliance, and Governance for Enterprise Trackers<\/h2>\n<p><a href=\"https:\/\/alchemer.com\/resources\/blog\/what-cmos-need-to-know-for-their-next-brand-health-tracking-study\" target=\"_blank\" rel=\"noindex nofollow\">CMOs evaluating brand tracking providers should assess AI use and data governance practices, including whether vendors generate real-time survey follow-up questions or rely on synthetic data, and confirm that first-party customer data is not used to train models beyond the brand&#8217;s own use case.<\/a><\/p>\n<p>Listen Labs holds SOC 2 Type II, ISO 27001, ISO 27701, and ISO 42001 certifications, is GDPR compliant, and operates a strict policy that customer data is never used to train AI models. Enterprise SSO and 256-bit encryption are standard. Quality Guard applies real-time fraud detection across video, voice, content, and device signals, and participants are capped at three studies per month, which eliminates professional survey-takers and protects the integrity of the longitudinal sample.<\/p>\n<p>Governance best practice assigns a dedicated owner to audit sample composition, weighting, and methodological changes each wave. <a href=\"https:\/\/thelangstonco.com\/blog\/brand-health-tracking-program\" target=\"_blank\" rel=\"noindex nofollow\">Maintaining a glossary of every metric and question and assigning a dedicated owner to audit sample composition, weighting, and methodological changes protects data quality over time.<\/a><\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How many respondents do I need per wave to detect a 5-point shift?<\/h3>\n<p>The number of respondents needed per wave depends on the size of shift to be detected in top-line metrics, the confidence level, and the margin of error. Larger samples can help detect smaller shifts and support segment-level analysis by region, age cohort, or awareness state. Sample consistency across waves matters as much as absolute size, because maintaining a stable sample produces more reliable trend data than one that fluctuates. Listen Pulse enforces consistent screener criteria and demographic quotas wave over wave through Quality Guard, so the sample you designed in wave one is the sample you receive in wave ten.<\/p>\n<h3>Can I keep my existing Qualtrics tracker and still add conversational depth?<\/h3>\n<p>Yes. Listen Pulse integrates directly with Qualtrics and Decipher, so the fixed core questions and KPIs your team already reports remain in place. The integration appends AI-moderated open-ended conversation as a downstream module, preserving all existing variable names and trend lines. Teams receive the quantitative dashboard they are accustomed to, with theme charts and verbatim diagnostics added alongside each KPI, with no migration, no baseline reset, and no disruption to stakeholder reporting. Listen Pulse can deploy alongside an existing tracker or serve as the primary tracking system depending on program maturity.<\/p>\n<h3>What happens to trend lines if I change one core question?<\/h3>\n<p>Changing any core question, including wording, scale anchors, response options, or position in the survey flow, creates a methodological break that prevents valid longitudinal comparison from that point forward. The only way to preserve comparability is to run the old and new versions in parallel for at least one wave, creating a statistical bridge that quantifies the effect of the change. After the bridge wave, the new version becomes the locked baseline for all future comparisons, and the pre-bridge trend line must be reported as a separate series. This requirement makes question locking before wave one the single most important methodological decision in brand tracking program design. Listen Pulse enforces question locking at the platform level and flags any proposed edit to a core question before it can be deployed.<\/p>\n<h3>How does emotional intelligence improve brand perception measurement?<\/h3>\n<p>Standard brand perception scales capture what respondents are willing to report consciously, while emotional intelligence captures the signals respondents cannot fully articulate or may not be aware of, such as tone of voice, word choice patterns, and subconscious micro expressions. Built on Ekman&#8217;s universal emotions framework, the same standard used in clinical psychology, Listen Labs&#8217; Emotional Intelligence quantifies seven emotions per question and concept: anger, contempt, disgust, enjoyment, fear, sadness, and surprise. Every label is traceable to the exact timestamp and verbatim quote that produced it, so insights leaders can see not just that a concept triggered confusion but precisely when in the stimulus it occurred and what the respondent said at that moment. In brand tracking, this means a flat consideration score can be disaggregated into the emotional responses driving it, which distinguishes, for example, between respondents who are indifferent and those who are quietly frustrated and enables targeted intervention before the metric declines further.<\/p>\n<h2>Conclusion: Turn Every Metric Movement into Actionable Insight<\/h2>\n<p>A brand tracking methodology that reports only that a number moved acts as a lagging indicator with no diagnostic attached. The 2026 standard combines a fixed quantitative core, including locked question wording, consistent sample composition, and 95% confidence thresholds, with AI-moderated conversational interviews, emotional intelligence, and behavioral observation in the same wave. Every KPI movement arrives with the theme, the verbatim quote, and the clip that explains it.<\/p>\n<p>Listen Pulse is built on this architecture. Core questions stay constant to protect the trend line. Rotating modules address campaigns and competitive events without breaking historical comparability. Emotional intelligence surfaces what respondents feel, not just what they say. Every number traces back to a real person, in their own words, at a specific moment in time. The platform integrates with Qualtrics and Decipher so teams keep the infrastructure they already operate while adding the explanatory layer that traditional trackers have never provided.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>Book a demo of Listen Pulse brand tracking methodology today to get both the number and the explanation in the same wave.<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Master brand tracking methodology with Listen Labs \u2014 AI-moderated surveys, emotional intelligence, and traceable diagnostics. Book your demo now.<\/p>\n","protected":false},"author":52,"featured_media":1562,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1563","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/1563","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/comments?post=1563"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/1563\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media\/1562"}],"wp:attachment":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media?parent=1563"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/categories?post=1563"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/tags?post=1563"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}