{"id":1325,"date":"2026-07-26T05:04:55","date_gmt":"2026-07-26T05:04:55","guid":{"rendered":"https:\/\/listenlabs.ai\/articles\/cpg-consumer-insights-market-research\/"},"modified":"2026-07-26T05:04:55","modified_gmt":"2026-07-26T05:04:55","slug":"cpg-consumer-insights-market-research","status":"publish","type":"post","link":"https:\/\/listenlabs.ai\/articles\/cpg-consumer-insights-market-research\/","title":{"rendered":"CPG Consumer Insights vs Market Research: AI Closes the Gap"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for CPG Research Leaders<\/h2>\n<ul>\n<li>Traditional CPG research creates a critical gap between when decisions must be made and when evidence can be delivered, often taking 4-8 weeks while product windows close and innovation stalls.<\/li>\n<li>Listen Labs closes the depth-versus-scale gap by combining AI-moderated interviews, emotional intelligence analysis, and mixed-method data collection to deliver consultant-quality insights in under a day.<\/li>\n<li>Quality Guard and Listen Atlas ensure verified participants from a 30M+ global network while preventing fraud, panel fatigue, and professional respondents that undermine traditional commodity panels.<\/li>\n<li>Automated analysis, one-click deliverables, and Mission Control\u2019s cross-study knowledge base remove the reporting bottleneck and build institutional memory that compounds with each study.<\/li>\n<li>Listen Labs serves as the force multiplier for CPG research teams. <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Book a demo<\/a> to see how the platform performs against your current research stack.<\/li>\n<\/ul>\n<h2>Faster Study Setup and Practical Design Support<\/h2>\n<p>Traditional CPG research turns a simple business question into a long, manual sequence. A research brief moves from stakeholder to internal researcher to external agency, with instrument design, internal review, and agency revision cycles consuming multiple weeks before a single participant is contacted. Internal stakeholder review cycles add even more time to qualitative timelines, especially when multiple markets or methods are involved.<\/p>\n<p>Listen Labs replaces that sequence with AI-assisted co-design. A researcher describes objectives in natural language, and the platform drafts structured study objectives, interview questions, and probing context in seconds. Auto-QA flags instrument issues before launch. Advanced stimuli support such as images, video, PDFs, prototypes, live URLs, and logic including monadic randomization, branching, skip logic, and piping are configured within the same interface. For CPG teams running concept tests, packaging research, or claims validation without a dedicated researcher on every project, this shift compresses the time from brief to fieldable guide from weeks to hours.<\/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>This change reshapes how teams manage research backlogs. A tiered research model lets AI-moderated platforms handle high-frequency, moderate-complexity work such as concept screens and claims validation in 24 hours, reserving senior staff and agencies for complex custom studies. Study design that previously required a senior researcher\u2019s full attention for days becomes a collaborative starting point that the platform generates and the researcher refines.<\/p>\n<h2>Recruitment, Sampling, and Participant Quality at Scale<\/h2>\n<p>Participant quality directly determines whether a CPG study produces defensible intelligence or misleading noise. Survey platforms can carry substantial fraud rates in raw responses, and commodity quantitative panels are populated with professional survey-takers focused on incentives instead of genuine category experience. Traditional qualitative recruitment through agencies often takes multiple weeks for small numbers of participants and incurs notable per-recruit costs before moderation or analysis fees.<\/p>\n<p>Listen Labs operates Listen Atlas, an AI orchestration layer that matches and bids across multiple consumer and B2B panel partners alongside a proprietary database of 30M verified respondents spanning 45+ countries and 100+ languages. Behavioral matching operates on intent and past actions rather than self-reported demographics. Quality Guard monitors every interview in real time across video, voice, content, and device signals to detect fraud, low-effort responses, AI-generated scripts, and mismatched profiles. Participants are capped at three studies per month, which reduces panel fatigue and the professional respondent 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>For sub-1% incidence audiences such as enterprise decision-makers, healthcare workers, or highly specific consumer segments, a dedicated recruitment operations team partners with niche communities and specialized networks to source participants that standard panels cannot reach. Testing with verified category purchasers rather than general population samples produces more valid launch research by capturing real reference prices, brand preferences, and competitive dynamics. Listen Labs applies that standard at scale across global markets, without the weeks of sourcing overhead that niche recruitment traditionally requires.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Book a demo<\/a> to see how Listen Atlas sources verified participants for your specific CPG audience.<\/p>\n<h2>AI Moderation and Emotional Intelligence for CPG<\/h2>\n<p>Human moderation in traditional qualitative research faces a hard throughput ceiling. A skilled human moderator can conduct only four to six depth interviews per day before fatigue degrades probe quality, making 200 interviews take a full quarter. Focus groups introduce additional distortion through group dynamics, dominant voices, and social desirability bias. <a href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\" target=\"_blank\">Traditional focus groups cost $4,000 to $12,000 per 90-minute session and take three to five weeks<\/a>, producing insights shaped as much by room dynamics as by genuine consumer sentiment.<\/p>\n<p>Listen Labs conducts AI-led video interviews that run hundreds of personalized, adaptive conversations simultaneously. The AI probes deeper on interesting or short answers, maintains consistent five-to-seven level laddering across every interview, and adapts follow-up questions based on each participant\u2019s specific responses. This approach replicates the behavior of a trained human interviewer without the throughput ceiling. Interviews are available in 100+ languages with automatic transcription and translation, enabling multi-market CPG studies across regions without separate agency engagements per market.<\/p>\n<p>The platform\u2019s Emotional Intelligence layer adds a dimension that transcripts alone cannot provide. Built on Ekman\u2019s universal emotions framework, the same standard used in clinical psychology, it analyzes three simultaneous signal layers: tone of voice, word choice, and subconscious micro-expressions. Every emotion label is quantified per question and concept, and every label is traceable to the exact timestamp, verbatim quote, and reasoning behind it. For CPG concept testing, this reveals the gap between what a consumer says about a product claim and what they actually feel when they encounter it. Two concepts may both receive positive ratings while one triggers genuine anticipation and the other produces confusion or flat affect, which changes launch decisions.<\/p>\n<h2>Data Quality Controls and Integrated Quantitative Support<\/h2>\n<p>Emotional depth is one dimension of confidence, and statistical backing is another. Traditional CPG research workflows separate qualitative and quantitative methods across different vendors, timelines, and instruments. A churn diagnosis that traditionally requires an extended sequence of qualitative interviews followed by a quantitative survey can cost tens of thousands of dollars and produces findings that may be stale by the time they reach leadership. The sequential structure means that quantitative validation cannot begin until qualitative fieldwork and analysis are complete.<\/p>\n<p>Listen Labs combines qualitative and quantitative methods within a single interview instrument. Likert scales, NPS, sliders, grids, and MaxDiff questions integrate directly alongside open-ended conversational probing, with branching and skip logic controlling the flow. This mixed-method architecture allows a single study to deliver the statistical confidence of large samples alongside the motivational depth of in-depth interviews, without a second study phase or a second vendor.<\/p>\n<p>Quality Guard operates throughout fieldwork rather than as a post-hoc cleaning step. Real-time monitoring across video, voice, content, and device signals flags and removes fraudulent or low-effort responses before they enter the analysis dataset. Every emotion label, theme, and finding traces back to a specific timestamp and verbatim quote, giving CPG research leaders the source transparency needed to defend conclusions to leadership. <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">Qual-at-scale removes the old depth-versus-scale barrier for CPG teams<\/a>, and Listen Labs\u2019 traceability infrastructure ensures that expanded scale still supports auditability.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Book a demo<\/a> to see how Listen Labs\u2019 mixed-method interviews and real-time quality controls perform on a CPG study brief.<\/p>\n<h2>Analysis Workflow, Deliverables, and Knowledge Reuse<\/h2>\n<p>Manual qualitative analysis is often the longest single phase in CPG research and the hardest to compress. Traditional qualitative studies demand substantial internal researcher time for vendor management, observation, analysis, and presentation, adding thousands of dollars in unbudgeted internal costs at fully loaded rates. Human coding of open-ended responses is subjective, prone to confirmation bias, and inconsistent across analysts. A typical agency project allocates a large share of budget to analysis, synthesis, reporting, and presentation, and those costs scale with study size.<\/p>\n<p>Listen Labs\u2019 Research Agent processes all interview data automatically, identifying patterns, themes, and insights across hundreds of responses without human bias. Natural-language queries allow researchers to ask any question of the dataset and receive answers, charts, statistical comparisons, and segmentation breakdowns in real time. One-click deliverables such as consultant-quality PowerPoint slide decks, memo-style reports, video highlight reels, and custom charts generate in under a minute. For CPG teams presenting to leadership on tight timelines, this removes the reporting bottleneck that traditionally adds three to five days after analysis is complete.<\/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>Mission Control serves as the organization\u2019s persistent source of truth across all studies. Every completed study grows the institutional knowledge base, enabling cross-study queries, trend tracking, and retrieval of past consumer language for current decisions. <a href=\"https:\/\/conveo.ai\/insights\/cpg-market-research\" target=\"_blank\" rel=\"noindex nofollow\">A searchable insight library connects findings across CPG studies by theme, product category, or consumer segment so teams can query prior research before fielding new studies, turning episodic insights into cumulative institutional memory<\/a>. For ongoing CPG programs such as brand tracking, innovation pipelines, and continuous shopper learning, this compounding knowledge base becomes a structural advantage that strengthens with every study run on the platform.<\/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>Best-Fit Use Cases for Enterprise and Product Teams<\/h2>\n<p>The capabilities described above map directly to specific organizational pressures. Consumer insights leaders at Fortune 500 CPG companies managing teams of five to thirty researchers face a persistent constraint: research backlogs grow faster than the team can clear them, and each traditional study cycle consumes four to six weeks of capacity. Leading CPG companies are shifting internal versus external research spend from a traditional 30\/70 split toward 50\/50 or 60\/40, driven by in-housing and AI-moderated platforms that reduce agency dependence for high-frequency studies. Listen Labs fits this shift by handling high-frequency, moderate-complexity work such as concept screens, claims validation, packaging tests, and ad message testing at roughly 24-hour turnaround, which frees senior researchers for complex custom studies and strategic synthesis.<\/p>\n<p>UX research leads at digital product companies need faster feedback loops than traditional recruitment and scheduling allow. Listen Labs\u2019 screen-sharing and task-based testing capabilities, combined with 50+ to 100+ participant samples instead of the five to ten typical of human-moderated sessions, provide the statistical grounding that sprint-cycle decisions require while avoiding heavy scheduling overhead.<\/p>\n<p>Product managers and brand managers without dedicated research support can describe objectives in natural language and receive a structured study guide, recruited participants, moderated interviews, and automated analysis without research methodology expertise. Agencies and consultancies operating on client timelines measured in days use Listen Labs\u2019 global reach and niche recruitment capabilities to deliver bespoke consumer intelligence within engagement windows that traditional agency workflows cannot accommodate.<\/p>\n<h2>Operational, Compliance, and Long-Term Program Fit<\/h2>\n<p>Enterprise research programs need governance infrastructure that scales with study volume and geographic scope. <a href=\"https:\/\/www.databricks.com\/customers\/reckitt\" target=\"_blank\" rel=\"noindex nofollow\">Reckitt reported up to 60% faster concept development, 40% time saved in campaign analytics, and a 60% efficiency boost in marketing from GenAI pilots; Kraft Heinz reported reducing new product content development from 8 weeks to 8 hours<\/a>. Achieving similar outcomes requires a platform that meets enterprise security and compliance standards without creating new procurement or legal risk.<\/p>\n<p>Listen Labs holds SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications, which address the security and privacy standards enterprise procurement requires. Beyond certifications, the platform enforces data isolation, so customer data is never used for AI model training, and supports enterprise SSO for centralized access control. For CPG companies operating across the EU, APAC, and the Americas, this combination satisfies regional hosting, data residency, and access requirements that must be cleared before adoption.<\/p>\n<p>Participant trust functions as a long-term asset for any research program. Quality Guard\u2019s reputation scoring builds across every interview conducted on the platform, so the more studies a team runs, the stronger the audience quality becomes. This flywheel compounds over time in a way that commodity panel sourcing cannot replicate. For CPG teams running continuous programs such as quarterly brand tracking or rolling innovation screens, the participant network improves with use rather than degrading through panel fatigue.<\/p>\n<p>Change management for research teams adopting Listen Labs focuses on role evolution, not replacement. The platform is designed as a force multiplier: it handles logistics, moderation, and initial analysis so that researchers focus on strategic interpretation and stakeholder communication. <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 one million AI-powered customer interviews for companies including Microsoft, Perplexity, and Sweetgreen<\/a>, demonstrating enterprise-scale reliability across diverse research programs.<\/p>\n<h2>Risks and Limitations of AI-Moderated Research<\/h2>\n<p>Traditional customer research workflows carry well-documented risks for CPG teams. Rigid survey instruments cannot probe unexpected responses, producing shallow data that misses the motivational layer behind behavioral patterns. Sequential study phases, with qualitative followed by quantitative, stretch timelines even though <a href=\"https:\/\/conveo.ai\/insights\/cpg-market-research\" target=\"_blank\" rel=\"noindex nofollow\">most in-flight CPG decisions require consumer input within two to four weeks<\/a>. Recruitment complexity for niche audiences is routinely underestimated, with overruns adding weeks to fieldwork timelines. Commodity panel fraud contaminates datasets before analysis begins, and manual analysis introduces confirmation bias that undermines the objectivity of findings.<\/p>\n<p>AI-moderated research introduces its own boundaries that teams must understand. Automation does not eliminate the need for clear research objectives, and a poorly designed study guide produces poor findings regardless of how efficiently it is fielded. Even with strong objectives, the platform\u2019s emotional intelligence analysis requires video-enabled interviews, and text-only or audio-only responses limit the signal available for micro-expression analysis. For highly sensitive research topics or populations requiring specialized clinical or legal expertise, human moderator judgment may remain necessary for specific study components despite the platform\u2019s adaptive probing capabilities. Mission Control builds institutional knowledge over time, yet that compounding benefit appears only when teams use the platform consistently across the research program instead of treating it as a one-off tool.<\/p>\n<p>The most significant risk in any research program is overestimating what automation delivers without human strategic oversight. Listen Labs is designed to amplify research team capacity, not replace the judgment that connects consumer evidence to business decisions.<\/p>\n<h2>Decision Framework and Nine-Point Checklist<\/h2>\n<p>CPG research leaders can evaluate their approach using nine practical criteria that align with the sections above. The checklist below moves from immediate constraints to long-term program design so teams can match options to goals, timelines, and internal capabilities.<\/p>\n<ul>\n<li><strong>Speed requirement:<\/strong> If the decision must be made within one to two weeks, traditional agency qualitative workflows cannot deliver. AI-moderated platforms with day-scale turnaround become the only viable option for in-flight decisions.<\/li>\n<li><strong>Sample size and depth:<\/strong> Once the timeline is clear, assess whether the study requires both statistical confidence, such as 100+ participants, and motivational depth with adaptive probing and follow-up questions. Mixed-method AI-moderated interviews remove the need to choose between these requirements.<\/li>\n<li><strong>Participant specificity:<\/strong> After defining depth and scale, consider audience incidence. If the target audience is below 1% incidence, such as enterprise buyers, healthcare workers, or specific category purchasers, verify that the platform has dedicated recruitment operations, not just a self-serve panel.<\/li>\n<li><strong>Geographic and language scope:<\/strong> When studies span multiple markets, confirm that moderation, transcription, and translation are handled within a single platform rather than across separate regional vendors, which simplifies governance and consistency.<\/li>\n<li><strong>Emotional signal requirement:<\/strong> For creative testing, concept comparison, or brand perception work where stated responses may diverge from felt responses, emotional intelligence analysis becomes necessary to capture the full picture.<\/li>\n<li><strong>Deliverable format:<\/strong> If findings must reach leadership in slide deck or highlight reel format within a day of fieldwork completion, automated deliverable generation shifts from a convenience to a hard requirement.<\/li>\n<li><strong>Institutional knowledge:<\/strong> For ongoing programs such as brand tracking, innovation pipelines, or continuous shopper learning, evaluate whether the platform builds a searchable cross-study knowledge base or produces isolated reports that cannot compound.<\/li>\n<li><strong>Compliance:<\/strong> Before procurement, confirm SOC 2, GDPR, and ISO certifications for any enterprise deployment involving consumer video data, and ensure that data usage and access controls align with internal policies.<\/li>\n<li><strong>Team capacity:<\/strong> Finally, consider internal bandwidth. If the research team is already at capacity, the platform must reduce operational burden on researchers by automating logistics and first-pass analysis, not simply add another tool to manage.<\/li>\n<\/ul>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is the actual turnaround time for a CPG consumer insights study on Listen Labs?<\/h3>\n<p>Listen Labs compresses the entire research lifecycle, including study design, participant recruitment, AI-moderated interviews, automated analysis, and deliverable generation, to under 24 hours for most study types. Traditional qualitative research takes four to eight weeks end-to-end, with recruitment alone consuming one to two weeks. The 24-hour benchmark applies to studies drawing from the 30M+ verified participant network. Niche or sub-1% incidence audiences may require additional sourcing time from the dedicated recruitment operations team, although this still remains substantially faster than traditional agency recruitment timelines.<\/p>\n<h3>How does Listen Labs source participants, and what prevents fraud?<\/h3>\n<p>Listen Labs uses Listen Atlas, an AI orchestration layer that matches participants across its proprietary 30M+ respondent database and multiple vetted panel partners. Behavioral matching relies on intent and past actions rather than self-reported demographics. Quality Guard monitors every interview in real time across video, voice, content, and device signals to detect and remove fraudulent responses, AI-generated scripts, and mismatched profiles. Participants are limited to three studies per month to reduce professional survey-takers, and a dedicated recruitment operations team adds a human review layer for hard-to-reach segments. Listen Labs avoids commodity quantitative panels to maintain data integrity.<\/p>\n<h3>How is AI moderation different from a human moderator for CPG research?<\/h3>\n<p>AI moderation runs hundreds of personalized, adaptive conversations simultaneously without the throughput ceiling that limits human moderators to a small number of interviews per day. The AI probes deeper on interesting or short answers, maintains consistent laddering methodology across every interview, and adapts follow-up questions based on each participant\u2019s specific responses. Emotional Intelligence analysis adds a layer that human moderation cannot systematically deliver at scale, providing real-time analysis of tone of voice, word choice, and micro-expressions, quantified per question and traceable to exact timestamps. Human moderators still bring contextual judgment that remains valuable for highly sensitive or clinically specialized research, while AI moderation covers the majority of CPG use cases such as concept testing, brand research, packaging, ad testing, and innovation screening with comparable depth at far greater speed and scale.<\/p>\n<h3>What deliverables does Listen Labs produce, and how quickly?<\/h3>\n<p>The Research Agent generates automated key findings and theme analysis, consultant-quality PowerPoint slide decks, memo-style reports, video highlight reels, statistical charts and comparisons, segmentation breakdowns by demographics or custom cohorts, and custom reports based on natural-language queries. All deliverables generate in under a minute after fieldwork is complete. Emotional Intelligence outputs include per-question emotion quantification, side-by-side concept comparisons, and highlight reels of emotionally significant moments. Mission Control stores all findings in a searchable cross-study knowledge base so past research remains retrievable for future decisions.<\/p>\n<h3>Is Listen Labs suitable for ongoing CPG research programs, not just one-off studies?<\/h3>\n<p>Listen Labs supports both one-off projects and continuous programs. Mission Control functions as the organization\u2019s persistent source of truth, with each study growing the institutional knowledge base and enabling cross-study queries and trend tracking over time. For continuous programs such as rolling brand tracking, quarterly innovation screens, and post-launch monitoring at 30, 60, and 90 days, the compounding knowledge base and Quality Guard\u2019s reputation scoring both improve with use. CPG teams running high-frequency research programs can conduct significantly more studies per quarter on the same budget compared to traditional agency-supported workflows, which creates a learning advantage that one-off deployments cannot match.<\/p>\n<h2>Conclusion: Making CPG Insight Cycles Match 2026 Timelines<\/h2>\n<p>The distinction between customer research data collection and strategic consumer insights generation is highly practical for CPG teams in 2026. <a href=\"https:\/\/bcg.com\/publications\/2026\/the-ai-forward-cpg-marketing-organization\" target=\"_blank\" rel=\"noindex nofollow\">70% of CPG marketing leaders expect GenAI to help marketing functions work faster and more efficiently, yet only 13% report the technology is currently in widespread use or fully integrated into marketing workflows<\/a>. The gap between expectation and adoption exists because most available tools address only one part of the research lifecycle, such as recruitment, moderation, or analysis, without removing the trade-offs that slow the full cycle.<\/p>\n<p>Listen Labs addresses the full lifecycle from brief to decision. <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">Qual-at-scale is ideal when research requires large sample sizes or broad geographic reach, with AI tools engaging hundreds or thousands of participants remotely and asynchronously<\/a>, and Listen Labs delivers that capability with participant quality controls, emotional intelligence analysis, and institutional knowledge infrastructure that enterprise CPG programs require. P&amp;G uses it to evaluate product claims before market. Microsoft used it to collect global customer stories within a day. Skims used it to validate campaign direction with thousands of premium consumers overnight. Robinhood used it to surface experience patterns and user segments driving 2.4x higher weekly re-engagement.<\/p>\n<p>For CPG consumer insights leaders managing growing backlogs, compressed decision timelines, and the persistent depth-versus-scale trade-off, Listen Labs functions as the force multiplier that existing research teams need. The platform does not replace researcher judgment. It provides the infrastructure that makes that judgment possible at the speed and scale 2026 requires. <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Book a demo<\/a> to see how Listen Labs performs against your current research workflow.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Learn how CPG consumer insights and market research differ\u2014and how Listen Labs uses AI to deliver both at speed and scale. Get answers in under a day.<\/p>\n","protected":false},"author":52,"featured_media":1324,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1325","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\/1325","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=1325"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/1325\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media\/1324"}],"wp:attachment":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media?parent=1325"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/categories?post=1325"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/tags?post=1325"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}