{"id":1890,"date":"2026-09-07T05:00:47","date_gmt":"2026-09-07T05:00:47","guid":{"rendered":"https:\/\/listenlabs.com\/articles\/ai-conversational-brand-research\/"},"modified":"2026-09-07T05:00:47","modified_gmt":"2026-09-07T05:00:47","slug":"ai-conversational-brand-research","status":"publish","type":"post","link":"https:\/\/listenlabs.com\/articles\/ai-conversational-brand-research\/","title":{"rendered":"AI Conversational Brand Research: The Complete 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>Traditional brand trackers report KPI shifts but do not explain why metrics move, so teams commission separate qualitative studies that add weeks and cost.<\/li>\n<li>AI conversational brand research replaces static surveys with adaptive, AI-moderated conversations that capture the narratives and emotions behind brand metrics at quantitative scale and qualitative depth.<\/li>\n<li>The methodology reduces social desirability bias, delivers responses three times longer than surveys, and surfaces emerging themes before they show up as KPI declines.<\/li>\n<li>Teams use it for concept testing, sentiment tracking, customer experience feedback, and qual-at-scale, with Emotional Intelligence features that quantify tone, word choice, and micro-expressions.<\/li>\n<li>Listen Labs delivers the full research workflow, from study design to deliverables, in under 24 hours; <a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\" rel=\"noindex nofollow\">See how AI conversational brand research transforms brand studies<\/a>.<\/li>\n<\/ul>\n<h2>What Is AI Conversational Brand Research?<\/h2>\n<p>AI conversational brand research uses AI-moderated, chat-based or voice-based interviews to explore consumer perceptions, attitudes, and emotions about a brand. These conversations adapt in real time and probe deeper into responses to uncover the reasons behind brand metrics.<\/p>\n<p>In practice, teams replace a 30-question survey battery with a structured research outline of five to eight topics, each governed by probing logic. When a participant says they \u201clike\u201d a brand but hesitates, the AI moderator follows up with a specific prompt such as \u201cYou mentioned you like the brand. What specifically makes you say that?\u201d That adaptive follow-up is what surveys structurally cannot do. <a href=\"https:\/\/getperspective.ai\/blog\/brand-research-interviews-how-ai-captures-positioning-insights-surveys-can-t\" target=\"_blank\" rel=\"noindex nofollow\">Traditional brand perception surveys hit a structural ceiling: they can measure what people think but not how they think<\/a>, and they cannot reveal the experiences behind associations, whether \u201creliability\u201d means the same thing across segments, or whether perceptions actually influence purchase decisions.<\/p>\n<p>The methodology also addresses a persistent bias problem in conventional research. <a href=\"https:\/\/getperspective.ai\/blog\/brand-research-interviews-how-ai-captures-positioning-insights-surveys-can-t\" target=\"_blank\" rel=\"noindex nofollow\">Respondents frequently claim to recognize brands they have never encountered and express purchase intent for products they would never actually buy<\/a>, which inflates awareness and consideration scores. AI moderation reduces this effect. <a href=\"https:\/\/listenlabs.ai\/blog\/ai-moderation-improves-comfort-and-honesty\" target=\"_blank\">Thirty-two percent of participants explicitly stated they feel less judged with AI moderation, particularly on sensitive topics<\/a>, and <a href=\"https:\/\/getperspective.ai\/blog\/ai-vs-surveys-when-each-method-actually-wins-in-2026\" target=\"_blank\" rel=\"noindex nofollow\">respondents disclose more sensitive information to AI interviewers than to humans because they do not perceive social judgment<\/a>.<\/p>\n<p>Focus groups introduce a different set of distortions, including group dynamics, dominant voices, and conformity pressure that flatten individual perspectives. <a href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\" target=\"_blank\">AI-led one-on-one interviews deliver faster, more unbiased insights by avoiding social biases like groupthink and conformity<\/a>. At the same time, they enable hundreds of parallel conversations that no human moderation team could run.<\/p>\n<p><a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\" rel=\"noindex nofollow\">See how Listen Labs conducts AI conversational brand research end-to-end<\/a>.<\/p>\n<h2>Why Brands Are Moving from &#8220;What&#8221; to &#8220;Why&#8221;<\/h2>\n<p><a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">\u201cTraditional surveys may tell us what people do, but it takes a conversation to understand why.\u201d<\/a> That distinction captures the core value of AI conversational brand research. Traditional trackers measure awareness, consideration, preference, and loyalty, yet they do not capture the reasons those metrics move. By the time a KPI declines, the underlying shift has been building for months, and explaining it requires a separate qualitative study.<\/p>\n<p>Listen Labs\u2019 conversational tracker, Listen Pulse, illustrates this difference. One well-known clothing brand, famous for its bold logos, was quietly losing customers. Its traditional tracker caught the drop but could not explain it. A conversational study revealed the cause was style, not price. A growing segment of customers felt the logos were too loud for their changing lifestyles. The metric movement and the reason arrived in the same wave.<\/p>\n<p>This diagnostic capability surfaces emerging themes before they register as KPI declines. <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">\u201cThe why is what differentiates customer research that\u2019s alright from customer research that\u2019s outstanding.\u201d<\/a> For insights leaders managing growing backlogs and limited headcount, that difference determines whether the organization acts on intelligence or reacts to damage.<\/p>\n<h2>Key Applications of AI Conversational Brand Research<\/h2>\n<p>AI conversational brand research supports the full brand research lifecycle, from foundational perception work to continuous tracking and campaign measurement.<\/p>\n<p><strong>Concept and product testing<\/strong> benefits directly from adaptive questioning. Where a traditional concept score of 3.8 versus 3.6 provides no actionable signal, conversational probing reveals which specific words trigger positive versus negative reactions, which segments respond differently, and what would need to change for a concept to convert skeptics. <a href=\"https:\/\/getperspective.ai\/blog\/brand-research-interviews-how-ai-captures-positioning-insights-surveys-can-t\" target=\"_blank\" rel=\"noindex nofollow\">AI conversational positioning validation lets customers explain what a positioning statement makes them think, feel, and want to do, yielding concrete language suggestions rather than statistically meaningless concept scores.<\/a><\/p>\n<p><strong>Brand sentiment tracking<\/strong> through conversational methods keeps core questions constant to protect the trend line while timely questions cover new campaigns and competitors. Every metric movement arrives with its explanation attached, which removes the diagnostic gap that forces separate qualitative studies.<\/p>\n<p><strong>Customer experience feedback<\/strong> gains a dimension unavailable in traditional surveys. Listen Labs\u2019 Visual Insights feature allows the AI moderator to observe on-screen behavior during digital journey research and probe contradictions between what participants say and what they actually do. This approach catches the say-do gap that unmoderated testing misses entirely.<\/p>\n<p><strong>Qual-at-scale<\/strong> is a structurally significant application. <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">AI tools can engage hundreds or thousands of participants remotely and asynchronously<\/a>, which collapses the depth-versus-scale trade-off that has defined qualitative research for decades. <a href=\"https:\/\/getperspective.ai\/blog\/ai-moderated-interviews-how-they-work-when-to-use-them-and-what-they-replace\" target=\"_blank\" rel=\"noindex nofollow\">Completion rates for AI-moderated interviews routinely run three to four times higher than scheduled human interviews<\/a>. Conversations feel more engaging than matrix questions, and the responses generated are substantially richer. As noted earlier, Listen Labs\u2019 intelligent probing generates responses roughly three times longer than average surveys.<\/p>\n<p>Listen Labs\u2019 Emotional Intelligence feature adds another layer by analyzing tone of voice, word choice, and subconscious micro expressions to surface emotions that transcripts alone miss. <a href=\"https:\/\/listenlabs.ai\/blog\/emotional-intelligence\" target=\"_blank\">Teams are already using Emotional Intelligence for creative testing, concept comparison, brand research, and usability testing<\/a>. Every emotion is quantified per question and concept, with each label traceable to the exact timestamp and verbatim quote.<\/p>\n<h2>How AI Conversational Brand Research Works<\/h2>\n<p>The Listen Labs platform covers the entire research lifecycle and removes the need for separate vendors for recruitment, moderation, or analysis.<\/p>\n<p><strong>Study design<\/strong> begins with natural language. A researcher describes their goals, and the AI drafts structured objectives, questions, and probing context in seconds. Flexible study styles support free-flowing in-depth interviews, semi-structured conversations, or mixed-method designs that combine qualitative questions with Likert scales, NPS, and MaxDiff in a single instrument.<\/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>Participant recruitment<\/strong> draws from Listen Atlas, a global panel of more than 50 million verified respondents across 45+ countries and 120+ languages. An AI orchestration layer, Quality Guard, matches participants on behavioral and intent data, not just self-reported demographics. It monitors every interview in real time for fraud, low-effort responses, and repeat respondents. Participants are limited to three studies per month, which addresses the professional survey-taker problem that undermines commodity panels.<\/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>AI-moderated interviews<\/strong> run as personalized video, voice, or text conversations with dynamic follow-up questions. The AI moderator adapts based on what participants actually say and pursues the research objective rather than following a fixed script past unexpected responses.<\/p>\n<p><strong>Analysis<\/strong> is handled by the Research Agent, which identifies themes, emotions, and patterns across hundreds of responses objectively. <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>, and the Research Agent addresses that directly. Every emotion is quantified per question and concept, with each label traceable to the exact timestamp and verbatim quote.<\/p>\n<p><strong>Deliverables<\/strong> are generated automatically as consultant-quality slide decks, memo-style reports, video highlight reels, and statistical charts. These outputs are produced in under a minute. Every insight links back to the underlying response data, so stakeholders can drill into any finding and hear the original explanation in the participant\u2019s own words.<\/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><a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\" rel=\"noindex nofollow\">Watch the full Listen Labs research workflow from study design to deliverables<\/a>.<\/p>\n<h2>Human Perception and AI Brand Intelligence<\/h2>\n<p>AI conversational brand research now addresses two converging problems that sit inside a single discipline.<\/p>\n<p><strong>The first problem<\/strong> is the traditional domain of brand research: how consumers perceive a brand and why. The methodology described throughout this guide addresses that problem directly through adaptive conversations that uncover the narratives and emotions driving brand metrics.<\/p>\n<p><strong>The second problem<\/strong> focuses on how AI systems represent a brand. As consumers increasingly use AI assistants like ChatGPT and Gemini for product discovery and recommendations, what those systems say about a brand becomes as strategically significant as what consumers say. A consumer might ask ChatGPT, \u201cWhat are the best running shoes?\u201d and a brand either appears in that answer or remains absent.<\/p>\n<p>The scale of this shift is documented. <a href=\"https:\/\/klaviyo.com\/newsroom\/ai-persona-research\" target=\"_blank\" rel=\"noindex nofollow\">Forty-one percent of global shoppers have purchased a product recommended by AI in the past six months, and more than one in five now start with AI tools when making decisions.<\/a> <a href=\"https:\/\/klaviyo.com\/newsroom\/ai-persona-research\" target=\"_blank\" rel=\"noindex nofollow\">Traffic from AI-referred sources like ChatGPT and Gemini surged 1,936% year-over-year.<\/a> <a href=\"https:\/\/launchmetrics.com\/software\/ai-visibility\" target=\"_blank\" rel=\"noindex nofollow\">Thirty-four percent of consumers now start product research in AI rather than a search engine.<\/a><\/p>\n<p>This emerging discipline, often called AI brand intelligence, requires monitoring how AI systems describe a brand\u2019s products, whether they recommend it, and how it is positioned relative to competitors in AI-generated answers. It represents a research problem distinct from traditional brand tracking and calls for different methods and tools. Listen Labs and Profound published research of 100 CMOs finding that 90% use large language models daily and 22% now begin vendor research inside an LLM versus 16% using traditional search. These findings show that AI-mediated discovery is already reshaping how brands are evaluated at the enterprise level, and the same platforms used for conversational research with humans can extend to monitoring AI-mediated perception.<\/p>\n<p>For insights leaders, the implication is clear. Understanding brand perception now requires studying both human consumers and the AI systems those consumers increasingly consult.<\/p>\n<h2>Market Growth and Enterprise Adoption<\/h2>\n<p>The urgency of this shift appears clearly in market growth. <a href=\"https:\/\/trillet.ai\/blogs\/voice-ai-market-size-and-agency-opportunity-2026\" target=\"_blank\" rel=\"noindex nofollow\">The global conversational AI market is valued at $17.97 billion in 2026 and projected to reach $82.46 billion by 2034 at a 21% CAGR, according to Fortune Business Insights.<\/a> <a href=\"https:\/\/trillet.ai\/blogs\/voice-ai-market-size-and-agency-opportunity-2026\" target=\"_blank\" rel=\"noindex nofollow\">This growth trajectory is corroborated by five independent research firms, all converging on 20%+ annual growth for conversational AI through 2034.<\/a><\/p>\n<p>Enterprise adoption is accelerating alongside this expansion. <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> In January 2026, Listen Labs raised a $69 million Series B led by Ribbit Capital at a valuation above $500 million, bringing total funding to $100 million, after growing annualized revenue 15x to eight figures in nine months. <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\">Micky Malka, founder of Ribbit Capital, described the platform: \u201cListen is the best tool to understand the customer. Instead of having a bored person to ask the questions, this AI engine can engage with you, and modify the questions to go deeper.\u201d<\/a><\/p>\n<p>The platform now serves enterprises including Microsoft, Google, Anthropic, Sony, Procter &amp; Gamble, Skims, Levi\u2019s, Boston Consulting Group, and Nestl\u00e9, representing roughly 15% of the Fortune 100. This adoption signals that AI conversational brand research has moved from experimentation to enterprise standard.<\/p>\n<h2>How to Get Started with a Pilot<\/h2>\n<p>Insights leaders can evaluate AI conversational brand research with a structured pilot that reduces risk and builds internal confidence.<\/p>\n<p><strong>Define your research objectives<\/strong> by starting with the decisions that need to be made. Identify which metric movements currently lack explanations and which brand perceptions are assumed rather than evidenced. The most productive starting point is a specific \u201cwhy\u201d question tied to a business decision already in progress.<\/p>\n<p><strong>Choose an end-to-end platform<\/strong> that covers recruitment, moderation, and analysis without separate vendors. <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> The Listen Labs platform compresses research cycles from four to six weeks to under 24 hours. This speed is supported by access to the same global panel described earlier.<\/p>\n<p><strong>Run a pilot study<\/strong> by comparing conversational results to an existing tracker on the same brand metrics. Validate that the qualitative themes align with, and explain, the quantitative trends already being reported. This parallel-run approach builds the internal business case without requiring a full infrastructure change.<\/p>\n<p><strong>Integrate findings into existing reporting<\/strong> by charting emerging themes next to the KPIs already tracked. Listen Pulse, Listen Labs\u2019 conversational tracker, integrates with Qualtrics and Decipher, so teams keep the KPIs they already report while adding the narrative behind them. Every number should trace back to a real moment with a real person, including their words, the quote, and the clip.<\/p>\n<h2>Conclusion<\/h2>\n<p>AI conversational brand research represents a different research discipline that collapses the depth-versus-scale trade-off and delivers the \u201cwhy\u201d behind brand metrics in the same wave as the \u201cwhat.\u201d For insights leaders managing growing backlogs, limited headcount, and stakeholders frustrated by long wait times, the methodology offers a structural solution: qualitative depth at quantitative scale, in hours rather than weeks.<\/p>\n<p>As AI systems become primary discovery channels for consumers, understanding how those systems represent a brand converges with human perception research into a new discipline of AI brand intelligence. Organizations building that capability now gain a structural advantage as AI-mediated discovery continues to accelerate.<\/p>\n<p>The practical question for insights leaders is how quickly a pilot can be launched. <a href=\"https:\/\/listenlabs.com\/book-my-demo\" target=\"_blank\" rel=\"noindex nofollow\">Transform your next brand study with a Listen Labs demo<\/a>.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How Is AI Conversational Brand Research Different from a Traditional Brand Tracker?<\/h3>\n<p>Traditional brand trackers are quantitative instruments. They report that awareness moved from 34% to 41%, or that consideration declined by six points, but they carry no diagnostic for why. When a KPI declines, the underlying shift has typically been building for months, and explaining it requires a separate qualitative study that adds weeks and budget to a process that has already delivered a lagging indicator.<\/p>\n<p>AI conversational brand research addresses this gap by combining structured tracking questions with open-ended adaptive conversations in the same research wave. Core questions stay constant to protect the trend line, while the conversational layer surfaces the narratives, emotions, and experiences driving each metric movement. The result is that the metric change and its explanation arrive together, which removes the sequential qualitative-then-quantitative cycle that slows most insights teams. Listen Labs\u2019 Listen Pulse is designed specifically for this use case. It deploys alongside an existing tracker or as the primary tracking system, with integrations for Qualtrics and Decipher.<\/p>\n<h3>What Sample Sizes Are Appropriate for AI Conversational Brand Research?<\/h3>\n<p>Sample size requirements depend on the research objective. For directional pattern discovery, where the goal is to identify the themes and narratives driving brand perception, strong themes typically surface from 30 to 50 interviews per segment. For statistically powered work that quantifies how widespread a pattern is across a population, 200 to 400 participants provide both statistical reliability and qualitative depth, since AI conversations extract substantially more information per participant than traditional surveys.<\/p>\n<p>A 500-person AI conversational brand study can yield both statistical patterns, such as \u201c73% of participants mentioned price as a consideration,\u201d and strategic depth, such as price concerns clustering into distinct patterns like absolute price sensitivity, value-for-money comparisons, and frustration with unclear pricing pages. For continuous tracking, monthly pulses of 100 to 150 conversations on rotating brand topics maintain trend integrity while surfacing emerging themes before they register as KPI declines.<\/p>\n<h3>How Does Listen Labs Ensure Participant Quality in AI Conversational Brand Research?<\/h3>\n<p>Listen Labs uses three layers of quality control. First, the platform works exclusively with high-quality, non-commodity panel sources, avoiding professional survey-takers from incentive-driven commodity panels. Second, Quality Guard, an AI orchestration layer, matches participants on behavioral and intent data rather than self-reported demographics and monitors every interview in real time for fraud, low-effort responses, AI-generated scripts, and mismatched profiles. Participants are limited to three studies per month, which addresses the repeat-respondent problem that undermines traditional panels.<\/p>\n<p>Third, a dedicated recruitment operations team adds a human review layer and handles sourcing for hard-to-reach segments such as enterprise decision-makers, healthcare workers, engineers, and consumers below 1% incidence rate that commodity panels cannot reliably reach. Listen Labs claims a zero-fraud guarantee and describes a compounding quality advantage through reputation scoring.<\/p>\n<h3>Can AI Conversational Brand Research Replace Existing Quantitative Trackers?<\/h3>\n<p>AI conversational brand research is designed to complement existing trackers, and it can serve as the primary tracking system when teams are ready for that transition. For attribute-level quantitative tracking where the value lies in the trend line, such as awareness, consideration, preference, and NPS, traditional survey instruments remain efficient because the question battery is locked and methodology is identical wave over wave.<\/p>\n<p>The conversational layer adds the diagnostic capability that quantitative trackers structurally lack by explaining why perceptions are changing and what to do about it. The recommended approach is to keep core quantitative questions constant for trend continuity while adding open-ended conversational questions to every wave, so every metric movement arrives with its explanation. Listen Pulse is built for this hybrid model, and its integrations with Qualtrics and Decipher allow teams to preserve existing reporting infrastructure while adding conversational depth.<\/p>\n<h3>What Deliverables Does AI Conversational Brand Research Produce, and How Quickly?<\/h3>\n<p>Listen Labs compresses the entire research cycle, from study design through participant recruitment, AI-moderated interviews, analysis, and deliverables, to under 24 hours. As mentioned earlier, the entire research cycle completes within this window.<\/p>\n<p>The Research Agent automatically generates consultant-quality PowerPoint slide decks, memo-style reports, video highlight reels, statistical charts and comparisons, segmentation breakdowns, and custom reports based on any natural-language question. Every deliverable links back to the underlying response data, so stakeholders can drill into any finding and access the original verbatim quote, audio or video clip, and the reasoning behind each insight label.<\/p>\n<p>For teams managing executive reporting cycles, this means brand research findings can be presented with full source traceability, not just summary conclusions, within the same business day a study launches. Research Library extends this further by enabling cross-study queries across every study ever run on the platform, so institutional knowledge compounds rather than expiring with each project.<\/p>\n<section data-read-next=\"true\">\n<h2>Read Next<\/h2>\n<ul>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/perspective-ai-brand-perception-research\" target=\"_blank\">AI Brand Perception Research: A Step-by-Step Guide<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/ai-for-consumer-brand-insights\" target=\"_blank\">AI Consumer Insights: Top Tools for Brand Intelligence<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/brand-research-ai-vs-traditional\" target=\"_blank\">Brand Research AI vs Traditional Methods: 2026 Guide<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/ai-vs-traditional-customer-research\" target=\"_blank\">AI vs Traditional Customer Research: A Practical Guide<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/ai-improves-brand-perception-research\" target=\"_blank\">7 Ways AI Upgrades Your Brand Perception Research<\/a><\/li>\n<\/ul>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Discover how AI conversational brand research uncovers the &#8220;why&#8221; behind consumer behavior. Listen Labs helps brands act faster. Start your pilot.<\/p>\n","protected":false},"author":52,"featured_media":1889,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1890","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\/1890","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=1890"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/1890\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media\/1889"}],"wp:attachment":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media?parent=1890"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/categories?post=1890"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/tags?post=1890"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}