{"id":902,"date":"2026-06-16T05:14:53","date_gmt":"2026-06-16T05:14:53","guid":{"rendered":"https:\/\/listenlabs.ai\/articles\/scale-user-interviews-large-panels\/"},"modified":"2026-06-16T05:14:53","modified_gmt":"2026-06-16T05:14:53","slug":"scale-user-interviews-large-panels","status":"publish","type":"post","link":"https:\/\/listenlabs.ai\/articles\/scale-user-interviews-large-panels\/","title":{"rendered":"Scale User Interviews to Large Panels: 7-Step Playbook"},"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>Traditional small-sample research with 5\u201315 interviews cannot meet modern enterprise demands for 100\u20131,000+ sessions across multiple segments and global markets.<\/li>\n<li>A 4-phase scaling model (Discovery, Structured, Large-Panel, Continuous) plus a 7-step workflow preserves qualitative depth while compressing cycle times from weeks to hours.<\/li>\n<li>High-quality, behaviorally screened panels with frequency caps, session-length discipline, and reputation scoring protect against fatigue and keep data quality high at scale.<\/li>\n<li>AI-moderated interviews with emotional-signal capture, traceable codebooks, and hybrid qual-quant validation deliver reliable insights from large samples in under 24 hours.<\/li>\n<li>Listen Labs provides an end-to-end platform that automates recruitment, AI moderation, analysis, and deliverables at this scale. <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Book a demo<\/a> to see how it works.<\/li>\n<\/ul>\n<h2>7-Step Scaling Checklist<\/h2>\n<p>The following seven steps give you a tactical roadmap for putting the 4-phase scaling model into practice. Each step builds on the previous one so you can increase volume while protecting quality.<\/p>\n<ul>\n<li><strong>Step 1:<\/strong> Clarify scale goals, success metrics, and stakeholder alignment (Phase 1 \u2013 Discovery)<\/li>\n<li><strong>Step 2:<\/strong> Build a behaviorally screened, segmented research panel<\/li>\n<li><strong>Step 3:<\/strong> Design adaptive, branching interview guides (Phase 2 \u2013 Structured)<\/li>\n<li><strong>Step 4:<\/strong> Moderate at scale with AI, capturing video, audio, and emotional signals (Phase 3 \u2013 Large-Panel)<\/li>\n<li><strong>Step 5:<\/strong> Apply a traceable codebook and inter-rater calibration system<\/li>\n<li><strong>Step 6:<\/strong> Run a hybrid qual-quant validation pipeline<\/li>\n<li><strong>Step 7:<\/strong> Generate and socialize deliverables in hours (Phase 4 \u2013 Continuous)<\/li>\n<\/ul>\n<h2>Phase 1 \u2013 Discovery: Set Scale Goals and Sample Targets<\/h2>\n<p><strong>Step 1<\/strong> establishes the research contract before a single participant is recruited. Define the business decision the research must inform, the stakeholders who will act on findings, and the segments that must be represented. Decide whether this is a one-off study or the first wave of a continuous program. Decide whether the population is homogeneous enough that 6\u201310 interviews per segment will reach saturation, or heterogeneous enough to require 15\u201325.<\/p>\n<p><a href=\"https:\/\/skimle.com\/blog\/qualitative-research-sample-size\" target=\"_blank\" rel=\"noindex nofollow\">Hagaman and Wutich (2017)<\/a> show that identifying meta-themes across cultures requires a larger number of interviews per site, while single-site homogeneous studies can saturate with fewer. This evidence supports sizing multi-segment comparisons per segment rather than as a single blended sample. <a href=\"https:\/\/skimle.com\/blog\/qualitative-research-sample-size\" target=\"_blank\" rel=\"noindex nofollow\">If each of three market segments requires 8 interviews to reach saturation, roughly 24 interviews are needed in total.<\/a> Document these targets, assign a study owner, and set a cycle-time SLA before moving forward.<\/p>\n<h2>How to Build a High-Quality Research Panel<\/h2>\n<p><strong>Step 2<\/strong> focuses on panel construction because panel quality drives every downstream insight. <a href=\"https:\/\/greatquestion.co\/blog\/panel-management-guide\" target=\"_blank\" rel=\"noindex nofollow\">High-quality recruitment starts with explicit consent and matching the real customer matrix<\/a>. For B2B audiences, that matrix includes company size, role, industry, and use case. For B2C audiences, it includes behavior, geography, and device preference.<\/p>\n<p>Behavioral screening outperforms demographic screening. <a href=\"https:\/\/cleverx.com\/blog\/what-is-a-research-panel\" target=\"_blank\" rel=\"noindex nofollow\">High-quality research panels verify participant profile information such as role, background, and usage behavior during enrollment<\/a> rather than relying solely on self-report. Add articulation questions, which are short open-ended prompts that reveal whether a candidate can provide substantive responses, to the screener. <a href=\"https:\/\/userinterviews.com\/blog\/best-online-survey-panel-companies\" target=\"_blank\" rel=\"noindex nofollow\">Keep screeners to 10 questions or fewer to preserve response quality and rates.<\/a><\/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>For enterprise-scale programs, <a href=\"https:\/\/greatquestion.co\/blog\/panel-management-guide\" target=\"_blank\" rel=\"noindex nofollow\">a practical sizing rule is to aim for about 10 times the target study sample size<\/a>. This buffer absorbs screener attrition, availability conflicts, and participation frequency limits. Listen Labs\u2019 Listen Atlas AI orchestration layer matches and sources participants across its 30M verified respondent network, with a dedicated recruitment ops team handling audiences below 1% incidence rate, including enterprise decision-makers, healthcare workers, and highly specialized consumer segments.<\/p>\n<h2>Avoiding Panel Fatigue at Scale<\/h2>\n<p>Even a perfectly recruited panel will degrade if participants are over-contacted or burned out. A cross-sectional study found an overall research fatigue prevalence of 56.3%, with participation in two or more prior studies associated with higher odds of fatigue. At enterprise scale, this becomes a systemic risk that degrades data quality across the entire program.<\/p>\n<p>Practical controls work together as a fatigue-prevention system:<\/p>\n<ul>\n<li><strong>Frequency caps:<\/strong> <a href=\"https:\/\/greatquestion.co\/blog\/panel-management-guide\" target=\"_blank\" rel=\"noindex nofollow\">System-enforced contact limits of 1\u20132 attempts per participant per month<\/a> prevent over-contact, which is the primary driver of fatigue. Listen Labs enforces a hard limit of 3 studies per month per participant, which also eliminates professional survey-takers.<\/li>\n<li><strong>Session length discipline:<\/strong> Even with appropriate contact frequency, long sessions burn out participants. <a href=\"https:\/\/merren.io\/survey-fatigue\" target=\"_blank\" rel=\"noindex nofollow\">Keeping sessions under 20 questions or 15 minutes<\/a> and using branching logic to skip irrelevant sections reduces mid-session dropout and preserves attention.<\/li>\n<li><strong>Reputation scoring:<\/strong> Frequency and length controls reduce fatigue but do not reward quality. Listen Labs\u2019 Quality Guard builds a reputation score across every interview. As participants complete more studies, the platform accumulates richer behavioral signals, which creates a compounding quality flywheel.<\/li>\n<li><strong>Panel health monitoring:<\/strong> These controls only work when their impact is measured. <a href=\"https:\/\/greatquestion.co\/blog\/panel-management-guide\" target=\"_blank\" rel=\"noindex nofollow\">Track participation rate (target 10\u201325% monthly), opt-out rate (below 5%), and time-to-recruit (under 24 hours)<\/a> as leading indicators of panel health and fatigue risk.<\/li>\n<\/ul>\n<h2>Phase 2 \u2013 Structured: Design Adaptive Interview Guides<\/h2>\n<p><strong>Step 3<\/strong> translates research objectives into a guide that scales without losing conversational depth. Static question lists fail at volume because no single path fits every participant. Branching logic solves this by defining core questions that every participant answers, then triggering conditional follow-up paths based on response content.<\/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>Stimuli logic, such as showing images, video, PDFs, or live prototypes at specific points, should be randomized monadically or sequentially to control order effects. <a href=\"https:\/\/cleverx.com\/blog\/mixed-methods-research-complete-guide-to-integrating-qualitative-and-quantitative-methods\" target=\"_blank\" rel=\"noindex nofollow\">Exploratory sequential mixed-methods design begins with qualitative data collection to identify themes, followed by quantitative surveys to validate prevalence across a larger population<\/a>. To support this, embed quantitative anchors such as Likert scales, NPS, or MaxDiff alongside open-ended questions from the start. This design choice enables the hybrid pipeline in Step 6. Version-control every guide iteration so findings remain traceable to the exact instrument version used.<\/p>\n<h2>Phase 3 \u2013 Large-Panel: Shift from Human to AI Moderation<\/h2>\n<p>Phase 3 introduces AI moderation as the primary lever for scale. <strong>Step 4<\/strong> is where the 4-phase model diverges most sharply from traditional practice. Human moderators can conduct 5\u201310 interviews per week. <a href=\"https:\/\/outset.ai\/almanac\/ai-moderated-research-tools-the-complete-guide-(2026)\" target=\"_blank\" rel=\"noindex nofollow\">AI moderated research tools enable teams to run 50\u2013100+ interviews in parallel, compressing research cycles from weeks to days.<\/a><\/p>\n<p>Listen Labs\u2019 AI moderator conducts personalized video interviews with dynamic follow-up questions. It probes deeper on short or ambiguous answers in the same way a trained human interviewer would. <a href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\" target=\"_blank\">Platforms like Listen Labs layer on auto-recruiting, transcription, sentiment tagging, and insight summarization so teams jump from question to findings in hours, not weeks.<\/a> Rich response capture includes video, audio, text, and screen recordings. Emotional Intelligence analyzes tone of voice, word choice, and subconscious micro-expressions, based on Ekman\u2019s universal emotions framework, to surface signals that transcripts alone miss. Every emotion label is traceable to the exact timestamp and verbatim quote.<\/p>\n<p><a href=\"https:\/\/www.forbes.com\/sites\/iainmartin\/2026\/01\/14\/this-500-million-ai-startup-runs-customer-interviews-for-microsoft-and-sweetgreen\/\" target=\"_blank\">Listen Labs has run over 1 million AI-powered customer interviews for companies including Microsoft, Perplexity, and Sweetgreen.<\/a> As CEO Alfred Wahlforss states, <a href=\"https:\/\/www.forbes.com\/sites\/iainmartin\/2026\/01\/14\/this-500-million-ai-startup-runs-customer-interviews-for-microsoft-and-sweetgreen\/\" target=\"_blank\">\u201cCompanies use it for all kinds of large decisions. This AI interviewer means that you can have hundreds of one-on-one interviews run at scale.\u201d<\/a><\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Book a demo to see AI moderation running 100+ parallel interviews with full emotional signal capture.<\/a><\/p>\n<h2>Interview Coding Systems for 100+ Interviews<\/h2>\n<p><strong>Step 5<\/strong> removes the analysis bottleneck that appears when interview volume scales. <a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">With AI-moderated interviews, talking to users at scale is no longer the hard part. The challenge is understanding what they mean.<\/a><\/p>\n<p>A traceable codebook assigns each theme a unique identifier, a definition, inclusion and exclusion criteria, and anchor examples drawn from the first wave of interviews. Inter-rater calibration, where two analysts independently code a 10% sample and reconcile disagreements, establishes a reliability baseline before full coding begins. No 2025 analysis in the International Journal of Qualitative Methods reported those results. <a href=\"https:\/\/digitalcommons.usu.edu\/cgi\/viewcontent.cgi?article=1919&amp;context=itls_facpub\" target=\"_blank\" rel=\"noindex nofollow\">Studies such as the 2025 DSI conference paper<\/a> found that LLMs exhibit higher consistency and faster processing than humans on deductive coding tasks.<\/p>\n<p>Listen Labs\u2019 Research Agent automates theme identification and codebook generation across all interview data, with every finding linked back to the underlying response. <a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">One researcher ran a full buying intent analysis across three user segments in under a minute.<\/a> Codebooks are versioned and exportable, which maintains traceability from raw verbatim to final deliverable.<\/p>\n<h2>Hybrid Qual-Quant Pipeline for Large Samples<\/h2>\n<p><strong>Step 6<\/strong> converts qualitative themes into statistically defensible findings. <a href=\"https:\/\/cleverx.com\/blog\/mixed-methods-research-complete-guide-to-integrating-qualitative-and-quantitative-methods\" target=\"_blank\" rel=\"noindex nofollow\">In mixed-methods studies, qualitative samples typically require 10\u201330 participants to reach thematic saturation while quantitative components need 100+ respondents for statistical validity.<\/a> At 100\u20131,000+ interviews, both thresholds are met within a single study.<\/p>\n<p>The pipeline runs in three stages. First, qualitative coding identifies themes and segments. Next, quantitative anchors embedded in the guide in Step 3 are extracted and aggregated. Finally, statistical significance testing validates which theme prevalence differences across segments are reliable rather than noise. <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">AI can schedule and conduct the interview, analyze the transcripts for themes, and generate quantitative insights from those interviews<\/a>, which collapses what was previously a multi-vendor, multi-week process into a single automated pipeline. The Research Agent supports natural-language queries, segment comparisons with significance testing, and side-by-side emotional breakdowns across stimuli and markets.<\/p>\n<h2>Phase 4 \u2013 Continuous: Turn Analysis into Actionable Deliverables<\/h2>\n<p><strong>Step 7<\/strong> closes the cycle by converting analysis into stakeholder-ready outputs before the business context changes. <a href=\"https:\/\/getperspective.ai\/blog\/ai-focus-group-software-12-platforms-ranked-by-research-depth-in-2026\" target=\"_blank\" rel=\"noindex nofollow\">AI-supported studies compress the research cycle from 4\u20138 weeks for traditional studies to 3\u20137 days total<\/a>. Listen Labs compresses this further to under 24 hours.<\/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>The Research Agent generates consultant-quality PowerPoint slide decks, memo-style reports, video highlight reels of emotionally significant moments, and statistical charts in under a minute. Every deliverable links back to the underlying data so stakeholders can interrogate findings rather than accept them on faith. For continuous programs, Mission Control serves as the organization\u2019s source of truth. It enables cross-study queries, trend tracking, and institutional knowledge building so teams avoid re-researching the same question.<\/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<h2>Common Challenges and Early-Warning Signals<\/h2>\n<ul>\n<li><strong>Unclear objectives:<\/strong> Studies that begin without a documented decision trigger produce findings no one acts on. Fix: require a one-sentence \u201cdecision this research will inform\u201d before study launch.<\/li>\n<li><strong>Low-effort responses:<\/strong> <a href=\"https:\/\/unimrkt.com\/blog\/gamified-surveys-combating-respondent-fatigue.php\" target=\"_blank\" rel=\"noindex nofollow\">Respondent fatigue manifests as straight-lining, random responses, reduced time per question, and higher incidence of \u201cdon\u2019t know\u201d selections.<\/a> Fix: monitor response length and session duration in real time. Quality Guard flags these automatically.<\/li>\n<li><strong>Traceability loss:<\/strong> When themes cannot be traced back to specific verbatims and timestamps, findings lose credibility with skeptical stakeholders. Fix: enforce codebook versioning and link every insight to its source response.<\/li>\n<li><strong>Stakeholder misalignment:<\/strong> Research delivered without prior stakeholder involvement is frequently ignored. Fix: include key stakeholders in Step 1 objective-setting and share a topline within 24 hours of field close.<\/li>\n<\/ul>\n<h2>Objective Success Metrics and Tracking<\/h2>\n<p>Four metrics define a healthy large-panel research program:<\/p>\n<ul>\n<li><strong>Cycle time:<\/strong> Study brief to final deliverable. Target: under 24 hours with Listen Labs.<\/li>\n<li><strong>Completion rate:<\/strong> Percentage of recruited participants who complete the full interview. Target: above 80%. A rate below 70% signals screener or guide issues.<\/li>\n<li><strong>Inter-rater agreement:<\/strong> Cohen\u2019s kappa on the calibration sample. Target: above 0.70 before full coding begins.<\/li>\n<li><strong>Insight adoption rate:<\/strong> Percentage of research recommendations that result in a documented business decision. Track this quarterly via retrospectives with stakeholder teams.<\/li>\n<\/ul>\n<p>Dashboard these metrics in Mission Control alongside panel health indicators. Run a post-study retrospective within one week of delivery to capture process improvements before the next wave.<\/p>\n<h2>Advanced Scaling Considerations for Enterprise Teams<\/h2>\n<p><strong>Always-on programs<\/strong> replace one-off studies with continuous fielding. A standing panel receives new interview waves as product or market conditions change. Recent industry reports indicate that a growing share of insights teams use AI in qualitative research, which reflects a structural shift toward continuous intelligence programs.<\/p>\n<p><strong>Global multi-market studies<\/strong> require localized guides, native-language moderation, and market-specific segment sizing. Listen Labs supports 100+ languages for interview moderation with automatic translation and transcription, covering 45+ countries across the Americas, Europe, APAC, and MEA.<\/p>\n<p><strong>Emotion and signal analysis<\/strong> adds a layer of data that verbal transcripts cannot provide. Listen Labs\u2019 Emotional Intelligence quantifies anger, anticipation, disgust, fear, joy, sadness, trust, and surprise per question and concept, with timestamp-level precision across 50+ languages.<\/p>\n<p><strong>Continuous panel reputation scoring<\/strong> through Quality Guard means panel quality compounds over time. This creates a structural advantage that point-in-time panel providers cannot replicate.<\/p>\n<p><strong>Readiness criteria for scaling:<\/strong> Before moving from Phase 2 to Phase 3, confirm that the guide has passed auto-QA, the codebook has been calibrated on a pilot wave of 10\u201315 interviews, and stakeholder alignment on success metrics is documented. Safe pilots of 25\u201350 interviews before full-scale launch catch instrument and recruitment issues at low cost.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<p><strong>How many interviews are needed to reach thematic saturation in a large-panel study?<\/strong><\/p>\n<p>Saturation depends on population homogeneity and research scope. For homogeneous single-segment studies, 6\u201312 interviews typically surface the majority of themes. For heterogeneous or multi-segment studies, 15\u201325 interviews per segment is the practical baseline. Large-panel studies running 100\u20131,000+ sessions are not primarily designed to reach saturation faster. They are designed to validate theme prevalence statistically across multiple segments simultaneously, which produces both qualitative depth and quantitative confidence in a single study.<\/p>\n<p><strong>How do you prevent panel fatigue when running repeated waves of interviews?<\/strong><\/p>\n<p>Three controls work in combination. Frequency caps ensure no participant is contacted more than 1\u20132 times per month, and no more than 3 studies per month on the Listen Labs platform. Session length discipline keeps interviews under 15 minutes with branching logic to skip irrelevant sections. Reputation scoring tracks participant engagement quality over time. Panel health metrics such as participation rate, opt-out rate, and time-to-recruit should be reviewed monthly and treated as leading indicators of data quality risk.<\/p>\n<p><strong>How does Listen Labs handle data privacy and compliance for large-panel studies?<\/strong><\/p>\n<p>Listen Labs maintains enterprise-grade security with 256-bit encryption and holds SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications. Customer data is never used for AI model training. Participant consent is captured at enrollment with granular consent by research type, right to erasure by default, and consent refresh cadences. For regulated industries, the dedicated recruitment ops team applies additional verification and compliance layers appropriate to the audience.<\/p>\n<p><strong>Can Listen Labs reach hard-to-find or niche audiences at large-panel scale?<\/strong><\/p>\n<p>Yes. The Listen Atlas AI orchestration layer matches across behavioral and intent data, not just demographics, across a 30M verified respondent network. For audiences below 1% incidence rate, including those described in Step 2 plus additional specialized segments like engineers and technical practitioners, a dedicated recruitment ops team partners with niche communities, micro-creators, and specialized networks. Anthropic used Listen Labs to conduct 300+ user interviews in 48 hours to surface churn drivers, and P&amp;G delivered 250+ interviews with quantified themes shaping product and brand strategy in hours.<\/p>\n<p><strong>When should a large-panel study be retired or repeated?<\/strong><\/p>\n<p>Retire a study when the business decision it was designed to inform has been made and documented. Repeat a study when market conditions, product changes, or competitive dynamics have shifted enough to invalidate prior findings. These shifts are typically signaled by a meaningful change in quantitative tracking metrics or a new strategic question that the existing codebook cannot answer. For continuous programs, Mission Control\u2019s cross-study query capability allows teams to check whether a question has already been answered before commissioning a new wave, which prevents redundant research spend.<\/p>\n<h2>Conclusion: Move from 10 Interviews to 1,000 with Confidence<\/h2>\n<p>The 4-phase model (Discovery, Structured, Large-Panel, Continuous) and the 7-step workflow give research ops leads, consumer insights directors, and UX teams a repeatable system for scaling from 5\u201315 interviews to 100\u20131,000+ sessions without sacrificing depth or extending cycle time. The critical enabler is an end-to-end platform that handles recruitment, AI moderation, emotional signal capture, traceable coding, hybrid qual-quant validation, and deliverable generation in a single workflow.<\/p>\n<p>Listen Labs delivers on the speed promise outlined above. Microsoft, P&amp;G, Anthropic, Skims, and Robinhood have already made this shift from multi-week cycles to same-day insights.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Book a demo and see how Listen Labs scales your next study to 100\u20131,000+ interviews in under 24 hours.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Scale user interviews to 1,000+ sessions without losing quality. Listen Labs automates recruiting, AI moderation &amp; analysis. Start scaling today.<\/p>\n","protected":false},"author":52,"featured_media":901,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-902","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/902","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/users\/52"}],"replies":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/comments?post=902"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/902\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media\/901"}],"wp:attachment":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media?parent=902"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/categories?post=902"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/tags?post=902"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}