{"id":640,"date":"2026-05-08T05:06:29","date_gmt":"2026-05-08T05:06:29","guid":{"rendered":"https:\/\/listenlabs.ai\/articles\/best-ai-market-intelligence-tools\/"},"modified":"2026-07-04T05:28:37","modified_gmt":"2026-07-04T05:28:37","slug":"best-ai-market-intelligence-tools","status":"publish","type":"post","link":"https:\/\/listenlabs.ai\/articles\/best-ai-market-intelligence-tools\/","title":{"rendered":"AI Market Intelligence Tools: 2026 Enterprise Buyer&#8217;s Guide"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs | Last updated: June 23, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>AI-powered market intelligence tools in 2026 automate the full research lifecycle, compressing weeks-long studies into under 24 hours.<\/li>\n<li>End-to-end platforms outperform fragmented solutions by removing handoff delays and quality gaps between separate vendors.<\/li>\n<li>Panel quality is critical because robust fraud detection, behavioral verification, and participation limits protect against professional respondents and AI-generated answers.<\/li>\n<li>Advanced platforms combine qualitative depth, quantitative methods, and emotional signal analysis to deliver richer insights than survey-only or AEO tools.<\/li>\n<li>Listen Labs unifies a 30M+ verified respondent network, AI-moderated interviews, Emotional Intelligence, and automated deliverables in one platform, so enterprise teams can experience research in under 24 hours. <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>Schedule a Listen Labs walkthrough<\/strong><\/a>.<\/li>\n<\/ul>\n<h2>Primary Research Tool Categories and Time-to-Insight<\/h2>\n<p>The market for primary research tools spans five distinct categories, and each category has a different time-to-insight profile. Traditional research agencies typically require 4\u20136 weeks from study brief to final report, and in enterprise settings internal prioritization can extend that to six months. Quantitative survey platforms such as SurveyMonkey or Qualtrics return data in days but capture only surface-level, pre-set responses with no adaptive follow-up. Point-solution panel and recruitment platforms such as Prolific, User Interviews, and Respondent solve sourcing but hand off moderation and analysis to other tools, which adds coordination time. Analysis and repository tools such as Dovetail organize completed research but do not conduct new studies. Human-moderated testing platforms like UserTesting rely on scheduled human moderators, which limits parallel capacity and extends turnaround. <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">End-to-end AI interview platforms collapse all five steps, from design through delivery, into a single workflow that returns results in under 24 hours<\/a>, making them the fastest category for primary qualitative research at scale.<\/p>\n<p>The critical distinction is end-to-end coverage. A platform that automates moderation but requires a separate recruitment vendor still introduces handoff delays and quality gaps. Evaluating tools on the full lifecycle, not just one step, is the most reliable way to assess true time-to-insight.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>See Listen Labs run a full study in under 24 hours<\/strong><\/a> across all five lifecycle stages.<\/p>\n<h2>Protecting Your Studies from Low-Quality Panels<\/h2>\n<p>Panel quality is the single most consequential variable in primary research because commodity panels carry well-documented risks: professional survey-takers optimizing for incentives, fraudulent or AI-generated responses, and repeat respondents who skew results. Evaluating any AI market intelligence tool against the following eleven dimensions before procurement reduces that risk materially.<\/p>\n<ol>\n<li><strong>Speed to insight:<\/strong> Measure time from study brief to final deliverable, including recruitment.<\/li>\n<li><strong>Qualitative depth:<\/strong> Confirm that the platform supports adaptive follow-up questions, not only pre-set items.<\/li>\n<li><strong>Sample quality controls:<\/strong> Review real-time fraud detection, behavioral verification, and participation frequency limits.<\/li>\n<li><strong>Global reach:<\/strong> Check how many countries and languages the platform supports natively.<\/li>\n<li><strong>Niche audience access:<\/strong> Verify that the platform can recruit below 1% incidence segments such as enterprise decision-makers or healthcare workers.<\/li>\n<li><strong>Emotional signal analysis:<\/strong> Look for capture of tone, micro-expressions, and word choice beyond transcript text.<\/li>\n<li><strong>Quantitative integration:<\/strong> Ensure qualitative interviews can include Likert scales, NPS, MaxDiff, and statistical testing in the same study.<\/li>\n<li><strong>Analysis automation:<\/strong> Confirm whether the platform generates themes, segments, and deliverables automatically or relies on manual analysis.<\/li>\n<li><strong>Cross-study knowledge management:<\/strong> Determine whether findings from past studies can be queried alongside new data.<\/li>\n<li><strong>Compliance and security:<\/strong> Validate SOC 2, GDPR, ISO 27001, and equivalent certifications.<\/li>\n<li><strong>Total cost of ownership:<\/strong> Compare all-in costs, including recruitment, moderation, analysis, and deliverable creation, against a fragmented multi-vendor stack.<\/li>\n<\/ol>\n<h2>Enterprise Competitive Intelligence Platforms<\/h2>\n<p><strong>Study setup:<\/strong> Enterprise competitive intelligence platforms typically require structured templates and manual configuration. Internal research staff or agency partners handle study design, and AI assistance for question drafting or logic setup is limited.<\/p>\n<p><strong>Recruitment:<\/strong> Most CI platforms rely on third-party panel integrations rather than proprietary networks. This dependency on external vendors adds sourcing time, especially for niche segments.<\/p>\n<p><strong>Moderation:<\/strong> Human moderators or scheduled sessions are standard. Parallel capacity depends on moderator availability, which limits the number of simultaneous interviews.<\/p>\n<p><strong>Data quality:<\/strong> Quality controls vary widely. Without behavioral verification or real-time fraud monitoring, commodity panel risk remains present.<\/p>\n<p><strong>Qualitative depth:<\/strong> Depth depends on moderator skill, and consistency across hundreds of interviews is difficult to maintain at scale.<\/p>\n<p><strong>Quantitative support:<\/strong> Survey modules are often available but remain siloed from qualitative workflows, which requires separate analysis passes.<\/p>\n<p><strong>Analysis workflow:<\/strong> Analysis is largely manual, and analysts code transcripts and build themes over days or weeks.<\/p>\n<p><strong>Deliverable creation:<\/strong> Reports are written by human analysts, which introduces subjectivity in interpretation and extends turnaround time compared with automated generation.<\/p>\n<p><strong>Cross-study knowledge management:<\/strong> Findings typically live in static reports with no queryable knowledge base across studies.<\/p>\n<p><strong>Best-fit use cases:<\/strong> These platforms fit organizations with large internal research teams, long planning horizons, and studies where human moderator relationships are a strategic requirement.<\/p>\n<h2>AI Research and Insight Platforms for Enterprise Teams<\/h2>\n<p><strong>Study setup:<\/strong> <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">AI can schedule and conduct interviews, analyze transcripts for themes, and generate quantitative insights from qualitative data<\/a>, but the degree of automation varies significantly by platform. Listen Labs uses AI-assisted co-design. Researchers describe goals in natural language, and the platform drafts structured objectives, questions, and probing context in seconds.<\/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><strong>Recruitment:<\/strong> Listen Labs operates a proprietary network of <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\">30M+ verified respondents across 45+ countries<\/a>, with an AI orchestration layer (Listen Atlas) that matches participants on behavioral and intent data rather than self-reported demographics alone. A dedicated recruitment operations team manages segments below 1% incidence rate.<\/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><strong>Moderation:<\/strong> <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 move from question to findings in hours, not weeks<\/a>. AI-led video interviews conduct personalized conversations with dynamic follow-up questions and can run thousands of sessions simultaneously.<\/p>\n<p><strong>Data quality:<\/strong> Quality Guard monitors every interview in real time across video, voice, content, and device signals. Participants are limited to three studies per month, which eliminates professional survey-takers. The platform does not use commodity quantitative panels.<\/p>\n<p><strong>Qualitative depth:<\/strong> <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">With qual-at-scale, the old trade-off between depth and scale is no longer a barrier<\/a>. Adaptive follow-up questions surface unexpected findings that pre-set survey items miss.<\/p>\n<p><strong>Quantitative support:<\/strong> Mixed-method studies combine qualitative interviews with Likert scales, NPS, sliders, grids, and MaxDiff in a single session. Statistical significance testing is available in the analysis layer.<\/p>\n<p><strong>Analysis workflow:<\/strong> <a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">Research Agent handles the full analysis workflow from raw data to final output<\/a>. It generates themes, personas, segmentations, and answers to natural-language queries without manual coding.<\/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><strong>Deliverable creation:<\/strong> <a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">Research Agent generates a slide deck in a company\u2019s branded template and a downloadable report<\/a>. It also creates video highlight reels, memos, and charts in under a minute.<\/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><strong>Cross-study knowledge management:<\/strong> Mission Control serves as a queryable source of truth across all completed studies. Teams can track cross-study trends and build institutional knowledge without digging through static reports.<\/p>\n<p><strong>Best-fit use cases:<\/strong> These platforms serve enterprise insights teams running high-volume research programs, UX researchers needing rapid usability feedback, product managers without dedicated research staff, and consultancies operating under tight client timelines.<\/p>\n<h2>AI Visibility and AEO Tools in Your Stack<\/h2>\n<p><strong>Study setup:<\/strong> AI visibility and Answer Engine Optimization tools monitor how brands appear in AI-generated responses across platforms such as ChatGPT, Perplexity, and Google AI Overviews. Study setup usually involves defining brand and competitor tracking parameters rather than designing participant-facing research instruments.<\/p>\n<p><strong>Recruitment:<\/strong> AEO tools do not recruit human participants. They query AI engines programmatically and analyze the outputs, so they complement rather than replace primary research platforms.<\/p>\n<p><strong>Moderation:<\/strong> No human or AI moderation of participant interviews is involved. Monitoring is automated and continuous.<\/p>\n<p><strong>Data quality:<\/strong> Output quality depends on the breadth of AI engine coverage and the frequency of query cycles. Gaps in engine coverage can create blind spots in brand visibility tracking.<\/p>\n<p><strong>Qualitative depth:<\/strong> AEO tools surface what AI engines say about a brand, but they do not capture why customers hold those perceptions. Pairing AEO monitoring with primary qualitative research closes that gap.<\/p>\n<p><strong>Quantitative support:<\/strong> Share-of-voice metrics and citation frequency provide quantitative benchmarks for AI visibility. These metrics are not directly comparable to primary research sample sizes or statistical significance thresholds.<\/p>\n<p><strong>Analysis workflow:<\/strong> Dashboards surface visibility trends over time. Manual interpretation is still required to translate visibility data into strategic action.<\/p>\n<p><strong>Deliverable creation:<\/strong> Standard outputs include dashboards and trend reports. Integration with primary research deliverables requires manual synthesis.<\/p>\n<p><strong>Cross-study knowledge management:<\/strong> AEO tools maintain historical visibility data but do not connect to customer interview repositories.<\/p>\n<p><strong>2026 context on emotional signal analysis:<\/strong> A meaningful gap in AEO tools is the absence of emotional signal data. Knowing that a brand appears in an AI-generated answer does not reveal whether the associated sentiment is trust, confusion, or indifference. <a href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\" target=\"_blank\">Listen Labs\u2019 Emotional Intelligence analyzes three layers of signal, including tone of voice, word choice, and subconscious micro expressions, to surface nuanced emotions that transcripts alone miss<\/a>, which provides the emotional context that AEO dashboards cannot generate. <a href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\" target=\"_blank\">Built on Ekman\u2019s universal emotions framework, the same standard used in clinical psychology and UX research, every emotion is quantified per question and concept and traceable to the exact timestamp and verbatim quote<\/a>.<\/p>\n<p><strong>Best-fit use cases:<\/strong> These tools support brand and marketing teams tracking AI search presence, SEO and content teams improving answer engine placement, and organizations that need to complement primary research with continuous brand monitoring.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>Explore how Emotional Intelligence and qual-at-scale work together in Listen Labs<\/strong><\/a>.<\/p>\n<h2>Operational and Long-Term Considerations<\/h2>\n<p>Understanding platform capabilities is only half of the procurement equation. Successful deployment of any enterprise research tool also depends on operational realities that extend beyond feature comparisons.<\/p>\n<p><strong>Stakeholder alignment:<\/strong> Enterprise research platforms require buy-in from legal, IT, procurement, and research leadership before deployment. Platforms with established enterprise security certifications such as SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 reduce procurement friction significantly.<\/p>\n<p><strong>Change management:<\/strong> Moving from a fragmented multi-vendor stack to an end-to-end platform requires workflow redesign. Teams accustomed to separate tools for recruitment, moderation, transcription, and analysis need structured onboarding to realize the full speed advantage.<\/p>\n<p><strong>Compliance:<\/strong> Global research programs must account for data residency requirements, participant consent frameworks, and regional privacy regulations. Platforms with built-in compliance infrastructure reduce the legal review burden per study.<\/p>\n<p><strong>Repeatability:<\/strong> Platforms that support study cloning, templated designs, and version control help research teams build repeatable methodologies instead of rebuilding from scratch for each project. This capability is particularly valuable for tracking studies run quarterly or annually.<\/p>\n<p><strong>Global programs:<\/strong> Multilingual research at scale requires native language support in both moderation and analysis. Listen Labs supports 100+ languages for interview moderation with automatic translation and transcription, and Emotional Intelligence is available across 50+ languages, which enables consistent methodology across markets without separate localization vendors.<\/p>\n<h2>Risks and Limitations of AI Research Tools<\/h2>\n<p><strong>Shallow data from survey-only tools:<\/strong> Quantitative surveys return structured data quickly but cannot probe unexpected responses or capture the reasoning behind an answer. Decisions made on survey data alone risk missing the motivations that drive behavior.<\/p>\n<p><strong>Slow manual workflows:<\/strong> Platforms that automate moderation but leave analysis to human coders do not eliminate the bottleneck. They simply move it. Full-cycle automation from interview to deliverable is the only reliable path to sub-24-hour turnaround.<\/p>\n<p><strong>Hidden recruitment complexity:<\/strong> Platforms that advertise large panel sizes without disclosing quality controls, fraud detection methods, or participation frequency limits may be sourcing from commodity panels. Evaluating the specific mechanisms behind a panel claim, not just the headline number, is essential.<\/p>\n<p><strong>Fraud risk:<\/strong> AI-generated interview responses, mismatched participant profiles, and incentive-optimizing behavior are active risks in any panel-based research. Real-time monitoring across video, voice, content, and device signals is the current standard for mitigation.<\/p>\n<p><strong>Overestimating automation:<\/strong> General-purpose LLMs can assist with study design or summarization, but they lack the proprietary methodology data, built from tens of thousands of completed studies, that determines which question types produce analyzable responses and which methodologies match specific research objectives. This is why automation built on that foundation performs differently from automation built on generic language models, because the underlying training data directly shapes output quality.<\/p>\n<h2>Decision Framework and Checklist for Buyers<\/h2>\n<p>Matching a tool category to a specific research need requires an honest assessment of four variables. Teams must consider the time available before a decision, the depth of insight required, the audience\u2019s accessibility, and the internal capacity to manage research logistics.<\/p>\n<p>For teams running more than a handful of studies per quarter with a fixed headcount, the total cost of ownership calculation usually favors an end-to-end platform over a fragmented stack. <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>, which demonstrates enterprise-scale reliability across diverse research programs.<\/p>\n<p>Use the following checklist before finalizing a platform decision:<\/p>\n<ul>\n<li>Does the platform cover the full research lifecycle, including design, recruitment, moderation, analysis, and delivery, or does it require external vendors for any step?<\/li>\n<li>What specific fraud detection mechanisms are in place, and are participation frequency limits enforced?<\/li>\n<li>Can the platform recruit the specific audience segments required, including niche or low-incidence populations?<\/li>\n<li>Does the analysis layer support both qualitative theme extraction and quantitative statistical testing in the same workflow?<\/li>\n<li>Are deliverables generated automatically, or does a human analyst need to write the final report?<\/li>\n<li>Does the platform maintain a queryable knowledge base across all completed studies?<\/li>\n<li>What compliance certifications does the platform hold, and are they current?<\/li>\n<li>What is the realistic time from study brief to final deliverable, including recruitment?<\/li>\n<\/ul>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How long does it actually take to get results from an AI research platform?<\/h3>\n<p>End-to-end AI platforms like Listen Labs compress the full research cycle, from study design through recruitment, moderation, analysis, and deliverable generation, to under 24 hours for most studies. Traditional agencies require 4\u20136 weeks for comparable studies, the timeline discussed earlier in this guide, while Listen Labs delivers results in under 24 hours. The 24-hour benchmark applies to studies using Listen Labs\u2019 panel network. Studies using a client\u2019s own participant base or requiring niche recruitment for very low-incidence audiences may take slightly longer but still operate in hours rather than weeks.<\/p>\n<h3>How does Listen Labs ensure participant quality and prevent fraudulent responses?<\/h3>\n<p>Listen Labs uses three reinforcing layers of quality control. First, the platform works exclusively with high-quality, non-commodity panel sources and does not use professional survey-taker pools. Second, Quality Guard monitors every interview in real time across video, voice, content, and device signals, which detects AI-generated scripts, mismatched profiles, and low-effort responses before they enter the dataset. Third, participants are limited to three studies per month across the platform, which eliminates the incentive-optimization behavior common in commodity panels. A dedicated recruitment operations team adds a human review layer for hard-to-reach segments. Listen Labs offers a zero-fraud guarantee on verified data.<\/p>\n<h3>What is the difference between AI-moderated interviews and traditional survey tools?<\/h3>\n<p>Survey tools deliver structured, quantitative data through pre-set questions, and they provide no mechanism for follow-up, probing, or adapting to an unexpected response. AI-moderated interviews conduct real-time conversations where the AI asks follow-up questions based on what the participant actually says, which mirrors the adaptive behavior a trained human interviewer would use. This approach produces qualitative depth, emotional nuance, and unexpected findings that pre-set survey items structurally cannot capture. Listen Labs combines both modalities in a single study. Qualitative interview questions and quantitative formats such as Likert scales, NPS, and MaxDiff can run in the same session, with statistical significance testing applied in the analysis layer.<\/p>\n<h3>Can Listen Labs support multilingual and multi-market research programs?<\/h3>\n<p>Yes. As noted in the platform overview, Listen Labs supports 100+ languages for moderation and 50+ for Emotional Intelligence, covering 45+ countries across all major regions. A single research program can run simultaneously across multiple markets with consistent methodology, without separate localization vendors or manual translation workflows. Findings from all markets are analyzed and delivered in a unified output, with segmentation by country, language, or custom demographic available in the Research Agent.<\/p>\n<h3>Does implementing Listen Labs require replacing an existing research team?<\/h3>\n<p>No. Listen Labs functions as a force multiplier for existing research teams, not a replacement. The platform handles logistics-intensive steps such as recruitment, scheduling, moderation, transcription, and initial analysis, which frees researchers to focus on strategic interpretation and stakeholder communication. Teams that previously ran a limited number of studies per quarter due to capacity constraints can run significantly more studies with the same headcount. The platform\u2019s AI-assisted study design, automated deliverable generation, and cross-study knowledge base, Mission Control, reduce the time researchers spend on operational tasks without removing human judgment from the research process.<\/p>\n<h2>Conclusion<\/h2>\n<p>Evaluating AI market intelligence tools in 2026 requires a systematic assessment of the full research lifecycle, including how a platform handles study design, recruitment quality, moderation depth, analysis automation, deliverable generation, and long-term knowledge management. <a href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\" target=\"_blank\">Traditional focus groups cost $4,000\u2013$12,000 per 90-minute session and take 3\u20135 weeks<\/a>, a pace that cannot support the continuous insight cycles modern enterprises require. End-to-end AI platforms that integrate all five research lifecycle stages, enforce rigorous sample quality controls, and deliver emotional signal analysis alongside traditional qualitative and quantitative data represent the most complete solution available for enterprise insights teams in 2026.<\/p>\n<p>Listen Labs is the only platform that combines its verified participant network, referenced earlier, with AI-moderated interviews that use adaptive follow-up, Emotional Intelligence built on Ekman\u2019s universal emotions framework, automated deliverable generation, and a cross-study knowledge base. The platform operates as a single end-to-end system trusted by Microsoft, Google, Sony, Anthropic, Procter &amp; Gamble, and Nestl\u00e9.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\"><strong>Run a pilot in Listen Labs and see sub-24-hour qualitative insights for your team<\/strong><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Discover the best AI market intelligence tools for enterprise teams. Listen Labs delivers insights in under 24 hours. Start your research today.<\/p>\n","protected":false},"author":52,"featured_media":639,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-640","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\/640","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"}],"replies":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/comments?post=640"}],"version-history":[{"count":1,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/640\/revisions"}],"predecessor-version":[{"id":1004,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/640\/revisions\/1004"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media\/639"}],"wp:attachment":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media?parent=640"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/categories?post=640"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/tags?post=640"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}