{"id":1428,"date":"2026-08-05T05:02:37","date_gmt":"2026-08-05T05:02:37","guid":{"rendered":"https:\/\/listenlabs.com\/articles\/qualitative-research-telecom-companies\/"},"modified":"2026-08-05T05:02:37","modified_gmt":"2026-08-05T05:02:37","slug":"qualitative-research-telecom-companies","status":"publish","type":"post","link":"https:\/\/listenlabs.com\/articles\/qualitative-research-telecom-companies\/","title":{"rendered":"How To Run Qualitative Research for Telecom Companies"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for Telecom Teams<\/h2>\n<ul>\n<li>Traditional qualitative research takes 4\u20138 weeks, while telecom product cycles move in days, creating a costly gap that delays churn prevention and competitive decisions.<\/li>\n<li>AI-moderated interviews shrink this timeline to under 24 hours by running hundreds of simultaneous, adaptive conversations in 100+ languages while maintaining methodological rigor.<\/li>\n<li>Behavioral screeners, frequency caps, and real-time quality monitoring deliver high-quality participants from a 30M+ verified global network and remove professional survey-taker bias.<\/li>\n<li>Automated analysis with Emotional Intelligence surfaces not just what customers say, but the exact moments of frustration, confusion, or trust, and delivers consultant-grade outputs in hours.<\/li>\n<li>Listen Labs is the end-to-end AI research platform that makes this speed possible; <a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">see how it works for your team<\/a> and launch your first telecom study in under an hour.<\/li>\n<\/ul>\n<h2>Why Traditional Timelines No Longer Work in Telecom<\/h2>\n<p>Qualitative research such as in-depth interviews (IDIs), diary studies, and usability tests captures motivations, emotions, and contextual reasoning that quantitative surveys cannot reach. Quantitative methods scale but sacrifice depth, and no pre-set question can surface the specific moment a customer decided to switch because a bill jumped $40 unexpectedly. The core constraint is speed. A traditional IDI study is bottlenecked by a human moderator conducting 3\u20134 interviews per day, which makes it structurally impossible to keep pace with continuous product discovery.<\/p>\n<p>The costs of slow cycles compound across every telecom pain point. Billing is one of the top categories of telecom complaints, and repeat contact for the same problem is the strongest predictor of churn. Many customers still call the contact centre for tasks available in the app, which signals self-service friction that qualitative research can diagnose only when findings arrive before the next sprint cycle closes. On 5G, <a href=\"https:\/\/yougov.com\/en-us\/articles\/34868-americans-switch-phone-carriers\" target=\"_blank\" rel=\"noindex nofollow\">19% of Americans who switched carriers cite better internet and mobile data quality as a reason<\/a>, yet trust in 5G value propositions is rarely validated at the speed needed to inform packaging and pricing decisions.<\/p>\n<p>These timing gaps between insight and action point to a structural problem that traditional research methods cannot solve. The shift to continuous discovery and <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">qual-at-scale<\/a>, which runs hundreds of simultaneous AI-moderated interviews, collapses the depth-versus-scale trade-off that has constrained telecom research programs for decades. <a href=\"https:\/\/outset.ai\/almanac\/ai-moderated-research-tools-the-complete-guide-(2026)\" target=\"_blank\" rel=\"noindex nofollow\">Teams that once ran 5\u201310 interviews per week now run 50\u2013100 per week using AI-moderated interviews without sacrificing methodological rigor.<\/a><\/p>\n<h2>Step 1: Define Telecom-Specific Objectives With Clear Decisions<\/h2>\n<p>Every study that fails to deliver actionable findings usually traces back to an underspecified objective. The research team should align on the decision the study will inform, the audience segment most relevant to that decision, and the signal that will constitute a meaningful finding before recruiting a single participant.<\/p>\n<p>Common telecom research objectives map directly to commercial priorities:<\/p>\n<ul>\n<li><strong>Churn drivers:<\/strong> Identify the specific service failures, billing events, or competitive triggers that precede cancellation in postpaid, prepaid, or MVNO segments.<\/li>\n<li><strong>Bill-shock perception:<\/strong> Understand how customers interpret unexpected charges, which communication formats reduce confusion, and which moments in the billing cycle generate the most friction.<\/li>\n<li><strong>App and self-service friction:<\/strong> Pinpoint the exact steps in bill payment, plan changes, or support flows where customers abandon the digital channel and call instead.<\/li>\n<li><strong>5G trust and value perception:<\/strong> Surface the specific use cases, coverage concerns, and pricing signals that determine whether a customer upgrades, waits, or dismisses 5G entirely.<\/li>\n<li><strong>Plan migration barriers:<\/strong> Understand the cognitive and emotional obstacles that prevent customers from moving to a plan better suited to their actual usage.<\/li>\n<\/ul>\n<p>Each objective requires a defined screener with qualifying criteria that ensure participants have direct, recent experience with the behavior under study. Behavioral screeners that verify recent actions outperform demographic ones because actions are harder to fabricate than opinions. A churn study, for example, should require participants to have cancelled or seriously considered cancelling a mobile plan within the past six months, not simply to express dissatisfaction on a scale.<\/p>\n<p>Listen Labs&#8217; AI-assisted study co-design turns research goals described in natural language into structured objectives, screener criteria, and interview guides in seconds, which reduces the setup phase from days to minutes.<\/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><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Watch a live walkthrough<\/a> of how Listen Labs helps telecom teams define and launch studies in under an hour.<\/p>\n<h2>Step 2: Choose the Right Mix of Qualitative Methods<\/h2>\n<p>Three qualitative methods cover the majority of telecom research objectives, and the choice between them depends on the type of experience being studied.<\/p>\n<ul>\n<li><strong>In-depth interviews (IDIs)<\/strong> are the default method for churn root-cause research, bill-shock perception, and 5G value validation. A 30\u201345 minute adaptive conversation surfaces the specific sequence of events, emotional reactions, and decision logic that numeric scores cannot capture. Score-based NPS surveys often achieve response rates of 5\u201325% and can disproportionately miss disengaged subscribers most likely to churn, which is the exact population an IDI program is designed to reach.<\/li>\n<li><strong>Diary studies<\/strong> suit longitudinal experiences such as onboarding, the first 90 days of a 5G device, or the billing cycle itself. Participants log reactions at the moment they occur, which removes the recall degradation that affects retrospective interviews.<\/li>\n<li><strong>Usability tests<\/strong> with screen recording are the correct method for app friction research. Watching a participant attempt to change their plan or dispute a charge in the actual app, with the AI moderator probing at moments of hesitation, produces findings that no survey or focus group can replicate. <a href=\"https:\/\/listenlabs.ai\/blog\/ai-interviews-beat-focus-groups\" target=\"_blank\">AI-led one-on-one interviews avoid the social desirability bias and groupthink that distort focus group findings<\/a>, which makes them more reliable for sensitive topics like billing complaints and service dissatisfaction.<\/li>\n<\/ul>\n<p>Listen Labs supports all three formats within a single platform, including mobile screen recording on iOS, and supports mixed-method designs that combine qualitative probing with quantitative formats such as Likert scales, NPS, and MaxDiff in the same session.<\/p>\n<h2>Step 3: Source and Screen High-Quality Participants at Scale<\/h2>\n<p>Participant quality is the single largest variable in qualitative research reliability. A significant portion of data collected in traditional research studies can have quality issues, and commodity panels filled with professional survey-takers produce findings that cannot be trusted to inform product or retention decisions.<\/p>\n<p>A rigorous sourcing process for telecom research requires four controls that work together to eliminate the professional survey-taker dynamic while preserving behavioral validity:<\/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<ul>\n<li><strong>Non-commodity panels:<\/strong> Participants sourced from behavioral and intent data, not self-reported demographics. Listen Labs&#8217; Listen Atlas network covers 30M verified respondents across 45+ countries and uses AI orchestration to match participants across multiple panel partners.<\/li>\n<li><strong>Behavioral screeners:<\/strong> Qualifying criteria tied to recent, verifiable actions. For a churn study, this means confirming the participant cancelled or initiated a cancellation within a defined recency window, not simply asking whether they are &#8220;somewhat likely&#8221; to switch.<\/li>\n<li><strong>Frequency caps:<\/strong> Listen Labs limits participants to three studies per month, which removes the professional survey-taker dynamic that degrades commodity panel data.<\/li>\n<li><strong>Real-time quality monitoring:<\/strong> Quality Guard monitors every interview across video, voice, content, and device signals to detect fraud, low-effort responses, AI-generated scripts, and mismatched profiles before they enter the analysis dataset.<\/li>\n<\/ul>\n<p>For hard-to-reach telecom segments such as enterprise IT decision-makers, heavy data users below 1% incidence rate, or customers who have already churned, Listen Labs&#8217; dedicated recruitment operations team sources participants through niche communities and specialized networks that standard panels cannot access.<\/p>\n<h2>Step 4: Conduct Adaptive, AI-Moderated Interviews With Emotional Intelligence<\/h2>\n<p>The quality of a qualitative interview depends on the moderator&#8217;s ability to probe unexpected responses, follow emotional signals, and adapt the conversation in real time. <a href=\"https:\/\/listenlabs.ai\/blog\/ai-moderation-improves-comfort-and-honesty\" target=\"_blank\">92% of participants report top comfort levels in AI-moderated sessions, matching the comfort levels of human-moderated sessions<\/a>, and AI moderation is preferred for sensitive topics including personal finances, which directly relates to billing and plan research.<\/p>\n<p>Listen Labs&#8217; AI-moderated video interviews conduct personalized conversations with dynamic follow-up questions. When a participant gives a short or unexpected answer, the AI probes deeper in the same way a trained human interviewer would. Hundreds of interviews run simultaneously, asynchronously, in 100+ languages, which compresses fieldwork that would take a human moderator weeks into hours.<\/p>\n<p>Beyond speed and scale, AI moderation captures signals that human moderators often miss. Listen Labs&#8217; Emotional Intelligence layer adds a dimension that transcripts alone cannot capture. Built on Ekman&#8217;s universal emotions framework, it analyzes tone of voice, word choice, and subconscious micro-expressions to quantify emotions including frustration, confusion, trust, and surprise at the question level. For telecom research, this means identifying not just that customers say they find the bill confusing, but the precise moment in a billing walkthrough where confusion peaks, with a timestamp, verbatim quote, and the reasoning behind the emotional label. This capability is available across 50+ languages and connects directly with the Research Agent for natural-language queries and highlight reels of emotionally significant moments.<\/p>\n<h2>Step 5: Analyze, Synthesize, and Deliver Findings in Hours<\/h2>\n<p><a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">Researchers spend the bulk of their time in analysis: finding patterns, quantifying insights, testing significance, adding macro context, and formatting results for stakeholders who each need something different.<\/a> In a traditional 30-interview IDI study, <a href=\"https:\/\/koji.so\/docs\/complete-guide-ai-qualitative-research\" target=\"_blank\" rel=\"noindex nofollow\">synthesis takes 2\u20133 hours per interview hour, meaning a 10-interview study requires 30+ hours of manual coding, tagging, and theme identification.<\/a><\/p>\n<p>Listen Labs&#8217; <a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">Research Agent handles the full analysis workflow from raw data to final output<\/a> and automates every stage that traditionally requires manual coding and synthesis:<\/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<ul>\n<li>Automated theme extraction and key findings across all interviews<\/li>\n<li>Statistical comparisons and significance testing across segments such as postpaid versus prepaid, churned versus retained, or 5G adopters versus non-adopters<\/li>\n<li>Consultant-quality slide decks in branded templates and downloadable memo-style reports<\/li>\n<li>Video highlight reels automatically compiled from timestamped interview clips<\/li>\n<li>Natural-language chat queries that return charts, segmentations, and cross-study comparisons in seconds<\/li>\n<\/ul>\n<p><a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">Every insight links directly to the underlying response data<\/a>, which provides the traceability that enterprise compliance and stakeholder credibility require. Mission Control stores all findings as a persistent knowledge base and enables cross-study queries so teams can answer questions from past research without re-running studies.<\/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.ai\/book-my-demo\" target=\"_blank\">Request a demo<\/a> to see how the Research Agent delivers stakeholder-ready deliverables from hundreds of telecom interviews in under 24 hours.<\/p>\n<h2>Is 10 People Enough for Telecom Qualitative Research?<\/h2>\n<p>Research on qualitative sample sizes is frequently cited to justify small samples, and the guidance holds for exploratory studies with a tightly defined, homogeneous audience and a single focused research question.<\/p>\n<p>Telecom research rarely meets those conditions. Thematic saturation is segment-specific: each distinct group defined by dimensions such as customer tier, usage level, tenure, or demographics requires its own 15\u201320 interviews to reach saturation. A churn study comparing postpaid, prepaid, and MVNO segments requires 45\u201360 interviews at minimum to support reliable cross-segment claims. A multi-market 5G validation study across four countries requires proportionally more.<\/p>\n<p><a href=\"https:\/\/merren.io\/blog\/sample-size-qualitative-research\" target=\"_blank\" rel=\"noindex nofollow\">AI-moderated interviews allow typical ranges of 20 to 100+ total because lower per-interview cost enables larger samples for richer thematic coverage and more stable analysis than traditional human-moderated studies.<\/a> At 200+ interviews conducted with consistent AI-moderated probing, theme prevalence becomes measurable and margins of error for prevalence can be calculated. This combination delivers the statistical confidence of quantitative research with the depth of qualitative interviews, which is the core value proposition of qual-at-scale.<\/p>\n<h2>Telecom-Specific Research Questions for AI-Moderated IDIs<\/h2>\n<p>The following questions are designed for use in AI-moderated IDIs across the most common telecom research objectives. Each question is open-ended and structured to elicit specific, experience-based responses rather than general opinions.<\/p>\n<ul>\n<li><strong>Bill shock:<\/strong> &#8220;Walk me through the last time you looked at your bill and felt surprised by the amount. What did you do next?&#8221;<\/li>\n<li><strong>Plan migration:<\/strong> &#8220;When did you last think about changing your plan? What stopped you from making the switch?&#8221;<\/li>\n<li><strong>5G value perception:<\/strong> &#8220;What would need to be true about 5G for you to feel it was worth paying more for?&#8221;<\/li>\n<li><strong>In-app support friction:<\/strong> &#8220;Tell me about the last time you tried to resolve an issue using the app. Where did you get stuck?&#8221;<\/li>\n<li><strong>Churn trigger:<\/strong> &#8220;What was the moment you decided to leave your previous carrier? What had to happen before you actually made the call?&#8221;<\/li>\n<li><strong>Onboarding experience:<\/strong> &#8220;What was confusing or unexpected in the first week after you activated your new plan?&#8221;<\/li>\n<\/ul>\n<h2>Common Challenges and Realistic Fixes for Telecom Qual<\/h2>\n<p>Four failure modes account for the majority of telecom qualitative studies that produce inconclusive or unused findings.<\/p>\n<ul>\n<li><strong>Unclear objectives:<\/strong> Studies launched with a broad mandate such as &#8220;understand the customer experience&#8221; produce findings too diffuse to act on. The fix is to anchor every study to a specific decision with a named owner before the screener is written.<\/li>\n<li><strong>Low-quality respondents:<\/strong> Traditional manual screening often sees high failure rates because it relies on self-reported criteria that are easy to fabricate. Commodity panels compound this problem by introducing professional survey-takers whose answers optimize for incentives rather than accuracy. The fix is to prevent these issues at the sourcing stage through behavioral screeners, frequency caps, and real-time quality monitoring, not post-hoc data cleaning.<\/li>\n<li><strong>Analysis bottlenecks:<\/strong> When findings take longer to synthesize than the decision cycle allows, stakeholders move forward without research input. <a href=\"https:\/\/listenlabs.ai\/blog\/research-agent\" target=\"_blank\">AI-powered analysis that handles the full workflow from raw data to final output<\/a> removes this bottleneck entirely.<\/li>\n<li><strong>Stakeholder misalignment:<\/strong> Research findings that arrive without a clear connection to the business question they were designed to answer are deprioritized or ignored. The fix is to involve the decision-maker in objective-setting before fieldwork begins and to deliver findings in the format, such as slide deck, memo, or video clip, that matches how that stakeholder consumes information.<\/li>\n<\/ul>\n<h2>Measuring Success of a Telecom Qualitative Program<\/h2>\n<p>A telecom qualitative research program should be evaluated on operational and commercial metrics, reviewed quarterly. These five metrics capture both research velocity and business impact.<\/p>\n<ul>\n<li><strong>Study cycle time:<\/strong> Time from approved brief to delivered findings. The target for AI-moderated studies is under 24 hours, and the baseline for traditional studies is 4\u20138 weeks.<\/li>\n<li><strong>Completion rate:<\/strong> Percentage of recruited participants who complete the full interview. <a href=\"https:\/\/recruitingtechreviews.com\/articles\/ai-video-interview-candidate-experience-completion-rates\" target=\"_blank\" rel=\"noindex nofollow\">AI video interview completion rates average 65-82% across published vendor and independent research<\/a> because of the conversational format and scheduling flexibility.<\/li>\n<li><strong>Insight-to-decision rate:<\/strong> Percentage of completed studies that directly inform a product, pricing, or retention decision within 30 days of delivery. Studies that do not reach a decision-maker in time to influence a decision have zero commercial value regardless of methodological quality.<\/li>\n<li><strong>Quarter-over-quarter churn impact:<\/strong> Measurement of whether segments studied for churn root causes show measurable retention improvement in the quarters following intervention. <a href=\"https:\/\/customergauge.com\/blog\/reducing-customer-churn-in-telecommunications\" target=\"_blank\" rel=\"noindex nofollow\">Companies that close the loop with customers by responding to feedback and demonstrating changes can increase their retention rate by over 8%.<\/a><\/li>\n<li><strong>Backlog reduction:<\/strong> Number of research requests fulfilled per quarter relative to requests received. A growing backlog is the primary signal that the research program is not keeping pace with organizational demand.<\/li>\n<\/ul>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How long does a telecom qualitative study take with Listen Labs?<\/h3>\n<p>The full cycle from study design to delivered findings takes under 24 hours for most studies. AI-assisted study design reduces setup to minutes, Listen Atlas sources and recruits participants from a 30M+ verified network, AI-moderated interviews run simultaneously rather than sequentially, and the Research Agent generates slide decks, memos, and highlight reels automatically once fieldwork closes. Traditional IDI studies take 4\u20138 weeks because every stage is sequential and human-dependent, while Listen Labs runs the same stages in parallel.<\/p>\n<h3>How does Listen Labs handle data privacy and compliance for telecom enterprise requirements?<\/h3>\n<p>Listen Labs maintains SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications. Customer data is encrypted at 256-bit and is never used for AI model training. Enterprise SSO is supported. For telecom carriers operating across multiple jurisdictions, the platform&#8217;s compliance posture covers the Americas, Europe, APAC, and MEA, and the interview infrastructure supports localization and translation across 100+ languages without requiring separate regional vendors.<\/p>\n<h3>Can Listen Labs reach hard-to-find telecom audiences such as churned customers or enterprise IT decision-makers?<\/h3>\n<p>Yes. The dedicated recruitment operations team sources participants below 1% incidence rate through niche communities, micro-creators, and specialized networks that standard panels cannot access. For churned customer research, where the target audience has already left the carrier&#8217;s own CRM reach, Listen Labs sources from the broader panel network using behavioral matching criteria tied to recent cancellation events. Enterprise IT decision-makers and procurement leads are sourced through B2B panel partners including NewtonX.<\/p>\n<h3>How many interviews are needed for a reliable telecom churn study?<\/h3>\n<p>The answer depends on the number of segments being compared. A single-segment exploratory study on a focused churn question reaches thematic saturation at 15\u201325 interviews. A study comparing postpaid, prepaid, and MVNO churn drivers requires 15\u201320 interviews per segment, totaling 45\u201360 minimum. A multi-market study across four countries requires proportionally more. Because Listen Labs conducts hundreds of interviews simultaneously at a fraction of traditional per-interview cost, reaching the sample size required for cross-segment statistical confidence is operationally straightforward rather than a budget constraint.<\/p>\n<h3>What deliverables does a telecom team receive at the end of a Listen Labs study?<\/h3>\n<p>The Research Agent generates a full suite of stakeholder-ready outputs: automated key findings and theme analysis, a branded PowerPoint slide deck, a memo-style written report, video highlight reels compiled from timestamped interview clips, statistical charts and significance tests, and segmentation breakdowns by any demographic or behavioral variable. Every finding links back to the underlying response data, including the verbatim quote, timestamp, and emotional label where Emotional Intelligence is enabled. Teams can also query the full dataset in natural language after delivery to answer follow-up questions without commissioning a new study.<\/p>\n<h2>Conclusion: Start Your First Telecom Study<\/h2>\n<p>The five-step playbook above, which covers telecom-specific objectives, method mix, sourcing and screening, adaptive AI-moderated interviews, and automated synthesis, collapses the traditional research timeline into a 24-hour cycle. For telecom carriers where <a href=\"https:\/\/getperspective.ai\/blog\/telecom-customer-experience-2026-cutting-churn-hearing-the-why\" target=\"_blank\" rel=\"noindex nofollow\">a 20% annual churn rate on one million subscribers erases $120 million in annual revenue<\/a>, the speed of insight is not a convenience, it is a competitive requirement.<\/p>\n<p>Listen Labs handles the entire research lifecycle on a single platform: study design, global recruitment from the Listen Atlas network, AI-moderated video interviews with emotional intelligence capture, automated analysis, and consultant-grade deliverables. The same platform that Microsoft used to collect global customer stories within a day and that Anthropic used to surface churn drivers across 300+ interviews in 48 hours is purpose-built for the scale and compliance requirements of enterprise telecom.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Schedule your demo<\/a> and run your first telecom qualitative study in under 24 hours.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Run fast qualitative research for telecom in under 24 hours. Listen Labs delivers AI-moderated interviews, instant analysis, and churn insights.<\/p>\n","protected":false},"author":52,"featured_media":1427,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1428","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\/1428","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=1428"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/1428\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media\/1427"}],"wp:attachment":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media?parent=1428"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/categories?post=1428"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/tags?post=1428"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}