{"id":1848,"date":"2026-09-04T05:03:10","date_gmt":"2026-09-04T05:03:10","guid":{"rendered":"https:\/\/listenlabs.com\/articles\/best-brand-tracking-data-quality\/"},"modified":"2026-09-04T05:03:10","modified_gmt":"2026-09-04T05:03:10","slug":"best-brand-tracking-data-quality","status":"publish","type":"post","link":"https:\/\/listenlabs.com\/articles\/best-brand-tracking-data-quality\/","title":{"rendered":"Brand Tracking Data Quality Best Practices 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>Brand tracking data quality issues often stay hidden until they have already distorted trend lines and misdirected budget decisions.<\/li>\n<li>Locked core questions, consistent samples, and bridged changes preserve trend comparability across waves.<\/li>\n<li>Respondent quality checks, including speeding detection, straightlining, and fraud monitoring, keep low-effort or fraudulent responses out of results.<\/li>\n<li>Systematic documentation through a methodology log and wave-level QA supports auditability and stakeholder trust in reported trends.<\/li>\n<li>Listen Pulse from Listen Labs combines quantitative KPIs with conversational depth to protect trend integrity while explaining the \u201cwhy\u201d behind every movement. <a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\" rel=\"noindex nofollow\">See how it safeguards your brand tracking in a live demo<\/a>.<\/li>\n<\/ul>\n<h2>Why Brand Tracking Data Quality Matters<\/h2>\n<p>Brand tracking is a longitudinal investment. Its value comes from the trend line the waves collectively produce, not any single wave\u2019s numbers. A disciplined tracker stays stable: the same instrument, fielded the same way, to a comparable population, with the same mode, at a regular cadence. Every change to any of those elements becomes a confound for the trend you want to read.<\/p>\n<p>The consequences of methodological instability are concrete. <a href=\"https:\/\/timelaps.io\/resources\/blogs\/brand-tracking-methodology-survey-panel-and-sample-size\" target=\"_blank\" rel=\"noindex nofollow\">A wording edit between waves can move a brand relevance score by more than six points<\/a>, which can create the appearance of a significant trend when the real change may be subtle. A single bad wave does more than report the wrong number. It bends the trend line and makes the next wave look like a swing when nothing about the brand actually changed.<\/p>\n<p>Poor data quality also erodes stakeholder trust. When leadership questions a trend, the research team must be able to show its work. Without documentation, auditability, and consistent methodology, that defense collapses. The <a href=\"https:\/\/aapor.org\" target=\"_blank\" rel=\"noindex nofollow\">American Association for Public Opinion Research (AAPOR)<\/a> sets standards for transparency and best practices in survey research that provide a foundation for defensible brand tracking methodology.<\/p>\n<h2>Brand Tracking Data Quality Best Practices<\/h2>\n<p>The best practices below span the full data quality lifecycle. They cover study design, questionnaire governance, wave-level QA, sample management, and methodology documentation. Each practice exists to protect the trend line.<\/p>\n<h3>Design Stable Questions and Scales<\/h3>\n<h3>1. Lock Core Measurement<\/h3>\n<p><strong>Why it matters:<\/strong> Questions that appear in multiple waves must stay identical in wording, response options, and presentation. Changing a question between waves, even to improve it, breaks comparability and introduces measurement error into the change estimate.<\/p>\n<p><strong>How to implement:<\/strong> Define a core question set of 6\u201310 questions asked word-for-word in every wave. Treat these as inviolable and use a separate flex section for timely topics. Record the exact question text, scale points, and order in your methodology log.<\/p>\n<h3>2. Bridge Periods for Questionnaire Changes<\/h3>\n<p><strong>Why it matters:<\/strong> Some changes are unavoidable. Running old and new versions side by side on comparable samples allows calculation of adjustment factors instead of guessing at the impact.<\/p>\n<p><strong>How to implement:<\/strong> Keep original wording running alongside refreshed wording for a short bridge period so you can compare old and new results directly. Maintain consistent scales, since even small adjustments to the number of points can disrupt established trends. When a question must change without a bridge, treat that wave as a reset for that metric and document it clearly.<\/p>\n<h3>Keep the Sample Truly Comparable<\/h3>\n<h3>3. Maintain Sample Consistency<\/h3>\n<p><strong>Why it matters:<\/strong> Sample comparability is the most common way brand trackers fail. Demographic quotas must be identical each wave, and sample size, source, and screening criteria must stay consistent so changes reflect real shifts in perception.<\/p>\n<p><strong>How to implement:<\/strong> Use consistent sampling methods, quotas, and sources. Set demographic quotas and enforce them identically each wave. Monitor sample demographics against targets before releasing data. <a href=\"https:\/\/timelaps.io\/resources\/blogs\/brand-tracking-methodology-survey-panel-and-sample-size\" target=\"_blank\" rel=\"noindex nofollow\">Panel-based data collection is the default for brand tracking because it is the only method that allows holding the demographic mix constant wave over wave<\/a>, which is a prerequisite for a credible trend line. For assessing panel quality, ESOMAR\u2019s \u201c37 Questions to Help Buyers of Online Samples\u201d is a gold-standard guide.<\/p>\n<h3>4. Blend Sample Sources Carefully<\/h3>\n<p><strong>Why it matters:<\/strong> <a href=\"https:\/\/smartinterview.ai\/blog\/brand-tracking-survey-tools\" target=\"_blank\" rel=\"noindex nofollow\">Topping up sample from a second supplier when a quota underfills causes more unexplained trend breaks than any other single factor<\/a>.<\/p>\n<p><strong>How to implement:<\/strong> Treat transitions to a new sample source as a gradual ramp, not a hard cut. <a href=\"https:\/\/pollfish.com\/resources\/blog\/market-research\/brand-health-tracking\" target=\"_blank\" rel=\"noindex nofollow\">Start with no more than 25% of traffic from the new source, evaluate results, then increase to 50%, and move to 100% only once stability is confirmed<\/a>. Monitor incidence, response rates, demographic balance, speed, and quality at each stage. Run A\/B source tests in parallel before making large moves.<\/p>\n<h3>Guard Respondent and Fraud Quality<\/h3>\n<h3>5. Implement Respondent Quality Checks<\/h3>\n<p><strong>Why it matters:<\/strong> Low-effort respondents add noise that obscures real movement in tracked metrics.<\/p>\n<p><strong>How to implement:<\/strong> Start with a baseline set of checks: attention, speeding, straightlining, and bots. Calibrate thresholds to your own sample. For speeding, flag survey completions faster than one-third to one-half of the median completion time in the actual sample as a defensive benchmark. <a href=\"https:\/\/casrai.org\/guides\/detecting-careless-responding-in-survey-data\" target=\"_blank\" rel=\"noindex nofollow\">For straightlining, common cutoffs include a longest identical-response run of 8\u201310 consecutive items, or a distribution-based cutoff such as the top 1% to 5% of long-string lengths in the dataset<\/a>. Exclude careless responders only when multiple indicators agree, combining long-string, response time, and attention checks instead of relying on a single flag.<\/p>\n<h3>6. Monitor and Manage Fraud<\/h3>\n<p><strong>Why it matters:<\/strong> Fraudulent responses contaminate waves and bend trend lines. As discussed earlier, a contaminated wave can distort the next wave\u2019s reading, so fraud control is essential.<\/p>\n<p><strong>How to implement:<\/strong> Use digital fingerprinting, device verification, and pattern detection to filter fraudulent responses. <a href=\"https:\/\/news.gallup.com\/opinion\/methodology\/708383\/monitoring-data-quality-probability-based-internet-panels.aspx\" target=\"_blank\" rel=\"noindex nofollow\">Speeding and straightlining are among the most broadly informative respondent-level quality indicators because they consistently identify respondents who also fail multiple other checks<\/a>. Limit participants to a maximum number of studies per month to prevent professional survey-takers from contaminating the panel.<\/p>\n<h3>Control Weighting, QA, and Documentation<\/h3>\n<h3>7. Apply Consistent Weighting<\/h3>\n<p><strong>Why it matters:<\/strong> Inconsistent weighting schemes make waves non-comparable and can mask real demographic shifts or create artificial ones.<\/p>\n<p><strong>How to implement:<\/strong> Weight to a fixed target profile from the first wave to keep waves comparable. Avoid retrofitting weighting after you notice sample composition drift. Document any weighting changes. When a subgroup changes dramatically, investigate before reweighting.<\/p>\n<h3>8. Conduct Wave-Level QA<\/h3>\n<p><strong>Why it matters:<\/strong> Releasing dirty data contaminates the trend line and erodes stakeholder trust. A structured QA process before release is essential.<\/p>\n<p><strong>How to implement:<\/strong> Run a pre-release QA checklist covering the following:<\/p>\n<ul>\n<li>Sample demographics vs. targets<\/li>\n<li>Open-end responses reviewed for gibberish or AI-generated content<\/li>\n<li>Completion rate, incidence rate, speeding rate, and straightlining rate<\/li>\n<li>Anomalies flagged and reviewed before analysis proceeds<\/li>\n<\/ul>\n<p>Combine automated and manual data quality checks at each wave. Include range checks, consistency checks, and cross-wave comparisons. Document all data cleaning decisions to support reproducibility.<\/p>\n<h3>9. Document Methodology Changes<\/h3>\n<p><strong>Why it matters:<\/strong> <a href=\"https:\/\/rwazi.com\/blog\/brand-tracking-studies\" target=\"_blank\" rel=\"noindex nofollow\">A methodology log is the documentation record that makes a brand tracking study auditable and defensible over time<\/a>. It preserves analysis definitions, such as whether \u201cconsideration\u201d uses a top-two-box or top-three-box rule, even after the original tracker builder leaves.<\/p>\n<p><strong>How to implement:<\/strong> Maintain a methodology log recording the date, change made, reason, and expected impact on comparability. Example entry: \u201c2026-08-15: Changed Q7 scale from 5-point to 7-point. Reason: align with new brand health framework. Impact: Q7 trend line broken; bridge study conducted Aug\u2013Sep; adjustment factor 1.12 applied.\u201d Record the exact wording of questions, since changing wording starts a new trend line and makes prior waves incomparable.<\/p>\n<h3>Add Diagnostic Depth with Conversation and AI<\/h3>\n<h3>10. Use a Conversational Approach to Capture the \u201cWhy\u201d<\/h3>\n<p><strong>Why it matters:<\/strong> Traditional trackers report numbers but do not explain why a metric moved. By the time a KPI declines, the underlying shift has often been building for months.<\/p>\n<p><strong>How to implement:<\/strong> Combine quantitative KPIs with open-ended conversation in the same instrument. Listen Pulse analyzes open-ended answers, sorts them into themes, quantifies them, and charts each theme next to the KPIs you already report. One well-known clothing brand was quietly losing customers. Its old tracker caught the drop but could not explain it. Pulse found the issue was style, not price. A growing group of customers felt the big logos were too loud for their changing lifestyles.<\/p>\n<h3>11. Ensure Data Governance and Transparency<\/h3>\n<p><strong>Why it matters:<\/strong> Auditability supports stakeholder trust and defensibility. When leadership questions a trend, the research team needs to show its work.<\/p>\n<p><strong>How to implement:<\/strong> Keep raw data, codebooks, and analysis scripts organized and accessible. Document variable naming conventions, question IDs, and wave markers. Store study designs for one-click relaunch to prevent methodology drift. Build a composite screening table before substantive analysis, with one row per respondent and one column per index, so exclusion decisions stay traceable and auditable instead of hidden inside an opaque data-cleaning step.<\/p>\n<h3>12. Use AI for Real-Time Quality Monitoring<\/h3>\n<p><strong>Why it matters:<\/strong> Catching low-quality responses after fielding is expensive and slow. Real-time monitoring prevents contamination before it enters the dataset.<\/p>\n<p><strong>How to implement:<\/strong> Use AI tools to monitor responses in real time and flag low-quality or fraudulent responses during data collection. AI can detect patterns humans miss, such as subtle speeding, AI-generated responses, and inconsistent answering across a battery. Use combinations of interpretable checks rather than any single rule, and track patterns across surveys over time.<\/p>\n<h2>How to Maintain Trend Comparability in Brand Tracking<\/h2>\n<p>Trend comparability is the discipline that separates genuine tracking from a sequence of unrelated surveys. The strategies that protect it work together: consistent measurement, bridging for unavoidable changes, rigorous sample management, and systematic documentation.<\/p>\n<p>Even with best practices in place, occasional methodology breaks will still occur. Handle them systematically. Document the change, run a bridge where possible, calculate adjustment factors, and annotate the trend line so future analysts understand what happened and when.<\/p>\n<p>When reading the trend line, keep two habits in mind:<\/p>\n<ul>\n<li>Read the direction rather than any single point. A one- or two-point move is usually noise. Look for sustained movement across multiple waves and confirm changes exceed sampling variation before building a story.<\/li>\n<li>Overlay marketing calendar events, product launches, and competitor activity onto the trend line to connect movements to actions.<\/li>\n<\/ul>\n<p>The model that protects trend integrity keeps core questions constant while timely add-on questions cover new campaigns, competitors, or news events. Listen Pulse follows this model. Core questions stay consistent wave over wave, while flex questions address what is happening now without breaking historical comparability.<\/p>\n<h2>The Role of Technology: How Listen Labs Ensures Data Quality<\/h2>\n<p>These practices become easier to enforce when the research platform automates them. Listen Labs built its technology to operationalize this discipline across every wave.<\/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<p>Listen Labs is an end-to-end AI research platform that sources the right participants inside its 50M+ verified respondent network across 45+ countries and 120+ languages. It conducts, analyzes, and summarizes thousands of in-depth customer interviews in hours, not weeks. Listen Pulse is its conversational tracker, purpose-built to address the data quality challenges described in this guide.<\/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<p>Listen Pulse keeps core questions constant to protect the trend line while allowing timely add-on questions each wave. It combines quantitative KPIs, such as awareness, consideration, preference, and NPS, with open-ended conversation in the same instrument. As a result, every metric movement arrives with the explanation behind it. It integrates with Qualtrics and Decipher, so teams keep the KPIs they already report while adding the narrative layer that explains them.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/listenlabs.ai\/\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1773099063654-7132de546a42.png\" alt=\"Listen Labs&apos; Research Agent quickly generates consultant-quality PowerPoint slide decks\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Listen Labs&#039; Research Agent quickly generates consultant-quality PowerPoint slide decks<\/em><\/figcaption><\/figure>\n<p>Quality Guard, Listen Labs\u2019 AI orchestration layer, addresses respondent quality at the source. It matches participants across behavioral and intent data in addition to self-reported demographics, and monitors every interview in real time for fraud, low-effort responses, and repeat respondents. Participants are limited to three studies per month, which removes professional survey-takers. A dedicated recruitment operations team adds a human review layer for hard-to-reach segments.<\/p>\n<p>Listen Pulse also surfaces shifts before they hit KPIs. It analyzes open-ended responses at scale and charts emerging themes next to tracked metrics. This gives research teams early warning of perception changes, something quantitative-only trackers miss until the damage is already reflected in the numbers. Every metric traces back to a real interview, a verbatim quote, and an audio or video clip.<\/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>Schedule a demo to see Listen Pulse in action and how a conversational tracker can protect your trend line while explaining the \u201cwhy\u201d behind every metric movement. <a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\" rel=\"noindex nofollow\">Schedule a demo<\/a>.<\/p>\n<h2>Conclusion<\/h2>\n<p>Brand tracking data quality is a continuous lifecycle discipline, not a one-time setup task. It spans study design, questionnaire governance, wave-level QA, sample management, and methodology documentation. Every practice in this guide exists to protect the trend line, the longitudinal asset that makes brand tracking worth the investment.<\/p>\n<p>The practices that matter most are the ones applied consistently. Lock core measurement, bridge unavoidable changes, maintain sample composition, monitor respondent quality with a convergent-evidence approach, and document every methodology decision so future teams can interpret the trend accurately. When technology enforces these practices automatically, through real-time quality monitoring, AI-moderated interviews, and conversational depth that explains metric movements, the trend line becomes a genuinely defensible strategic asset.<\/p>\n<p>Your trend line is your most valuable research asset. Protect it with Listen Pulse. <a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\" rel=\"noindex nofollow\">Request a demo to start protecting your trend line<\/a>.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is the most common cause of false trends in brand tracking studies?<\/h3>\n<p>The most common cause of false trends is uncontrolled methodology change between waves. This includes changes to question wording, scale formats, sample source composition, screening criteria, and weighting schemes. Any of these changes can produce apparent movement in a tracked metric that reflects the methodology change rather than a genuine shift in consumer perception. As noted earlier, even a small wording edit can shift a relevance score by several points, so treat any change as a methodological event. Protecting the trend line requires treating every element of the study design, including instrument, sample, mode, and cadence, as a potential confound if changed without documentation and bridging.<\/p>\n<h3>How should a brand tracking team handle a situation where a questionnaire change is unavoidable?<\/h3>\n<p>When a questionnaire change is unavoidable, <a href=\"https:\/\/pollfish.com\/resources\/blog\/market-research\/brand-health-tracking\" target=\"_blank\" rel=\"noindex nofollow\">the recommended approach is to run a bridge study: field the original wording alongside the new wording on comparable, parallel samples for at least one full wave<\/a>. This allows the team to calculate an adjustment factor, the quantified difference between old and new wording responses, instead of guessing at the impact. The adjustment factor is then documented in the methodology log and applied to maintain trend comparability going forward. When a bridge study is not feasible, treat the wave in which the change occurs as a reset for that specific metric, clearly annotate it in reporting, and restart the trend line for that metric from that wave. Every change should be treated as a methodological event with potential consequences for comparability.<\/p>\n<h3>What thresholds should be used for detecting speeders and straightliners in brand tracking data?<\/h3>\n<p>There is no single universal cutoff that applies across all studies. Set speeding thresholds relative to the study\u2019s own sample median completion time rather than importing them from a different survey. A common defensive benchmark is to flag completions faster than one-third to one-half of the median completion time in the actual sample. <a href=\"https:\/\/casrai.org\/guides\/detecting-careless-responding-in-survey-data\" target=\"_blank\" rel=\"noindex nofollow\">For straightlining, common cutoffs include a longest identical-response run of 8\u201310 consecutive items in a grid, or a distribution-based cutoff such as the top 1\u20135% of long-string lengths in the dataset<\/a>. <a href=\"https:\/\/casrai.org\/guides\/detecting-careless-responding-in-survey-data\" target=\"_blank\" rel=\"noindex nofollow\">The most defensible approach is a convergent-evidence method: combine multiple indicators, including speeding, straightlining, attention check failures, and open-end quality, rather than excluding respondents on any single flag alone<\/a>. This approach catches more careless responding than any single indicator and produces exclusion decisions that are traceable and auditable.<\/p>\n<h3>What should a methodology log for a brand tracking study include?<\/h3>\n<p><a href=\"https:\/\/rwazi.com\/blog\/brand-tracking-studies\" target=\"_blank\" rel=\"noindex nofollow\">A methodology log is the documentation record that makes a brand tracking study auditable and defensible over time<\/a>. At minimum, it should capture the following elements:<\/p>\n<ul>\n<li>Exact question wording, scale points, and question order for every wave<\/li>\n<li>Sample definition, demographic quotas, screening criteria, and sample source(s)<\/li>\n<li>Fielding dates, field period length, and cadence<\/li>\n<li>Weighting scheme and target profile<\/li>\n<li>Quality control procedures applied (speeding thresholds, straightlining cutoffs, attention checks, fraud detection methods)<\/li>\n<li>Any changes made between waves, with date, rationale, and expected impact on comparability<\/li>\n<li>Bridge study results and adjustment factors where applicable<\/li>\n<li>Analysis definitions, such as whether consideration is top-two-box or top-three-box<\/li>\n<li>Named decision or action owner for each wave<\/li>\n<\/ul>\n<p>Treat the methodology log as a living document updated after every wave. Store it alongside the raw data and codebooks, and keep it accessible to future team members who may not have been present at study inception.<\/p>\n<h3>How does Listen Pulse differ from a traditional brand tracker?<\/h3>\n<p>Traditional brand trackers are quantitative-only instruments. They report that awareness or consideration moved between waves, but they carry no diagnostic for why. By the time a KPI declines, the underlying shift in consumer perception has typically been building for months. Explaining it often requires commissioning a separate qualitative study, which adds time, cost, and the risk that the qualitative findings cannot be directly connected to the quantitative trend.<\/p>\n<p>Listen Pulse combines quantitative KPI tracking with open-ended conversational interviews in the same wave. It analyzes open-ended responses at scale, sorts them into themes, quantifies each theme, and charts them next to the KPIs teams already report. The metric movement and the reason behind it arrive together. Core questions stay constant wave over wave to protect the trend line, while timely flex questions address current campaigns, competitive events, or emerging topics. Listen Pulse deploys alongside an existing tracker or as the primary tracking system, and integrates with Qualtrics and Decipher so teams can keep their existing reporting infrastructure.<\/p>\n<section data-read-next=\"true\">\n<h2>Read Next<\/h2>\n<ul>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/brand-tracking-methodology-guide\" target=\"_blank\">Brand Tracking Methodology: The Complete 2026 Guide<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/maintain-consistent-brand-tracking-cadence\" target=\"_blank\">Brand Tracking Cadence: 7 Rules for Reliable Trend Data<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/qualitative-brand-tracking\" target=\"_blank\">Qualitative Brand Tracking at AI Scale<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/validate-brand-tracking-methodology\" target=\"_blank\">Validate Brand Tracking Methodology: A 7-Step Playbook<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/automating-brand-tracking-studies\" target=\"_blank\">How to Automate Brand Tracking Without Losing the Why<\/a><\/li>\n<\/ul>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Protect your brand trend line with proven data quality best practices. Listen Labs helps you track smarter \u2014 get reliable insights every wave.<\/p>\n","protected":false},"author":52,"featured_media":1847,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1848","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\/1848","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=1848"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/1848\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media\/1847"}],"wp:attachment":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media?parent=1848"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/categories?post=1848"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/tags?post=1848"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}