{"id":1328,"date":"2026-07-27T05:09:07","date_gmt":"2026-07-27T05:09:07","guid":{"rendered":"https:\/\/listenlabs.ai\/articles\/financial-services-market-research\/"},"modified":"2026-07-27T05:09:07","modified_gmt":"2026-07-27T05:09:07","slug":"financial-services-market-research","status":"publish","type":"post","link":"https:\/\/listenlabs.ai\/articles\/financial-services-market-research\/","title":{"rendered":"How to Do Financial Services Market Research: A Guide"},"content":{"rendered":"<p><em>Written by: Anish Rao, Head of Growth, Listen Labs<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for Financial Services Research Teams<\/h2>\n<ul>\n<li>Traditional financial services research cycles take 8\u201312 weeks, often delivering insights after decisions are already made.<\/li>\n<li>A structured seven-step approach covering scope, secondary data, AI interviews, recruitment, compliance, synthesis, and stakeholder communication enables faster, high-quality insights.<\/li>\n<li>AI-moderated interviews reduce timelines to under 24 hours while improving data quality on sensitive financial topics through reduced participant judgment.<\/li>\n<li>Quality recruitment, fraud prevention, and mixed-methods synthesis are critical for reliable findings in regulated financial services audiences.<\/li>\n<li><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">See how Listen Labs compresses a full financial services research cycle into a single day<\/a> and supports your next study.<\/li>\n<\/ul>\n<h2>Why Financial Services Research Needs a Clear Process<\/h2>\n<p>Several terms appear throughout this guide and carry precise meanings in a research context.<\/p>\n<ul>\n<li><strong>Qualitative research<\/strong> captures depth, including motivations, emotions, and decision logic, through open-ended interviews or discussions. <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">Qualitative methods make up for smaller sample sizes tenfold in their ability to uncover nuance and complexity in human decision-making.<\/a><\/li>\n<li><strong>Quantitative research<\/strong> captures breadth through structured surveys and scales. Statistical reliability often requires hundreds of respondents.<\/li>\n<li><strong>Sample frame<\/strong> is the defined population from which participants are drawn, for example, small business checking account holders at regional banks.<\/li>\n<li><strong>Incidence rate<\/strong> is the proportion of the general population that qualifies for a study. Low-incidence audiences under 5% significantly increase recruitment cost and time.<\/li>\n<li><strong>Panel<\/strong> refers to a managed pool of pre-recruited respondents available for research studies.<\/li>\n<li><strong>Screener<\/strong> is the qualification questionnaire used to verify that a participant meets study criteria before the interview begins.<\/li>\n<li><strong>Moderation<\/strong> is the process of guiding an interview, asking follow-up questions, probing short answers, and keeping the conversation on topic.<\/li>\n<li><strong>Analysis frameworks<\/strong> are structured methods for organizing and interpreting qualitative data, such as thematic coding, affinity mapping, or jobs-to-be-done.<\/li>\n<\/ul>\n<p>Financial services organizations face compounding pressures from long timelines and rising expectations. Research cycles can delay market entry by several weeks for financial institutions. At the same time, <a href=\"https:\/\/enumerate.ai\/blog\/industry-trends\/ai-in-qualitative-research-trends-2026\" target=\"_blank\" rel=\"noindex nofollow\">AI-moderated voice interviews are becoming the default method for qualitative depth at scale<\/a> because speech models now deliver low-latency probing that approximates a real conversation. Internal stakeholders expect faster answers, and the structured approach below meets that expectation while maintaining rigor.<\/p>\n<h2>Step 1: Define Scope and Objectives for the Study<\/h2>\n<p>Every research study starts with a written brief that answers four questions: What decision will this research inform, who is the target participant, what does success look like, and what are the time and budget constraints?<\/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>In banking, a typical objective might be: \u201cUnderstand why small business owners switch checking accounts within the first 90 days.\u201d In fintech, it might be: \u201cIdentify friction points in the onboarding flow for first-time investors aged 25\u201340.\u201d In insurance, it might be: \u201cDetermine which claim communication formats reduce inbound call volume.\u201d<\/p>\n<p>The scope brief should specify whether the study requires qualitative depth, quantitative breadth, or a mixed-methods design. Qualitative depth often involves 20\u201340 interviews for thematic saturation on a single question. Quantitative breadth often requires 200\u2013500 or more respondents for statistical reliability. Budget and timeline constrain this choice. A 30-interview product concept test costs $15,000\u2013$24,000 with traditional agencies versus $750\u2013$2,100 on AI-moderated platforms, with findings delivered in 24 hours rather than weeks.<\/p>\n<p>Because financial services research involves regulated data and often recorded conversations, coordination at this stage must include legal or compliance review of the research design. This review becomes especially important when the study collects account-related data or engages regulated professionals.<\/p>\n<h2>Step 2: Turn Secondary Data into a Gap Map<\/h2>\n<p>Secondary research can answer a substantial portion of a research brief before any primary respondents are contacted. For financial services, the most reliable secondary sources include:<\/p>\n<ul>\n<li>Regulatory filings and annual reports from public financial institutions, including SEC EDGAR and FDIC call reports<\/li>\n<li>Federal Reserve and CFPB consumer finance surveys<\/li>\n<li>Industry analyst reports from Forrester, Celent, and Javelin Strategy<\/li>\n<li>Trade association publications from ABA, LIMRA, and NAFCU<\/li>\n<li>Academic and government datasets from the Bureau of Labor Statistics and Census Bureau<\/li>\n<\/ul>\n<p>Secondary synthesis should produce a gap map, which is a structured list of questions the existing data cannot answer. Those gaps become the primary research agenda. <a href=\"https:\/\/pulseairesearch.com\/pulse-shift\/blogs\/257\/secondaryresearchthebrandteamsguidetousingexistingdataintelligently\" target=\"_blank\" rel=\"noindex nofollow\">Mature research teams treat secondary research as a trigger for primary research rather than a substitute<\/a>. They maintain a clear distinction between what the data shows and what it implies for a specific decision.<\/p>\n<h2>Step 3: Choose and Run Primary Research with AI Interviews<\/h2>\n<p>Primary research methods for financial services fall into three categories: traditional moderated interviews, surveys, and AI-moderated interviews. For organizations balancing depth requirements with timeline constraints, AI-moderated in-depth interviews have emerged as the most efficient method for closing the gap between depth and scale. <a href=\"https:\/\/listenlabs.ai\/blog\/what-is-qual-at-scale\" target=\"_blank\">AI can schedule and conduct the interview, analyze transcripts for themes, and generate quantitative insights from qualitative responses<\/a>, collapsing a process that previously required separate vendors for recruitment, moderation, transcription, and analysis.<\/p>\n<p>The practical advantages are measurable. AI-powered conversational research platforms can significantly reduce research timelines in financial services contexts. <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, enabling AI tools to engage hundreds or thousands of participants remotely and asynchronously.<\/a><\/p>\n<p>AI moderation also improves data quality on sensitive topics. <a href=\"https:\/\/listenlabs.ai\/blog\/ai-moderation-improves-comfort-and-honesty\" target=\"_blank\">Thirty-two percent of participants explicitly state they feel less judged with AI moderation<\/a>, and participants disclose more to AI interviewers than to human ones on topics including personal finances. The perceived absence of judgment reduces concerns about disclosure. For financial services research, where participants discuss debt, account balances, and financial anxiety, this comfort level creates a material quality advantage.<\/p>\n<p>Platforms built specifically for financial services research operationalize these benefits at scale. Listen Labs conducts AI-moderated video interviews with dynamic follow-up questions, mixed-method question formats such as Likert scales, NPS, and MaxDiff, and support for more than 100 languages. The Research Agent then generates automated key findings, slide decks, highlight reels, and segmentation breakdowns in under a minute.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">See how Listen Labs runs AI-moderated financial services interviews at scale<\/a> and delivers results on a one-day timeline.<\/p>\n<h2>Step 4: Recruit and Protect Quality in Financial Audiences<\/h2>\n<p>Recruitment often becomes the largest time variable in research. Regulated populations such as financial services customers can take 14\u201328 days to recruit through traditional channels because of consent processes and compliance review.<\/p>\n<p>Screener design for financial audiences must verify product usage, account tenure, and any professional credentials relevant to the study. A screener might ask, \u201cDo you currently hold a brokerage account with assets over $50,000?\u201d Incidence rates for niche financial segments, including wealth management clients, small business owners with specific revenue thresholds, or recent mortgage applicants, can fall below 5%. These segments require access to large, verified 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>Fraud prevention is a distinct requirement in financial services research. Commodity panels carry risk of professional survey-takers and incentive-driven responses that undermine data quality. Listen Labs\u2019 Quality Guard uses real-time AI monitoring across video, voice, content, and device signals to detect fraud, low-effort responses, and mismatched profiles. Participants are limited to three studies per month, which reduces panel fatigue and repeat-respondent bias.<\/p>\n<p>Incentive benchmarks for financial services participants vary by audience, with higher amounts for more specialized or higher-net-worth individuals. Clear incentive structures support faster recruitment and better engagement.<\/p>\n<h2>Step 5: Turn Raw Interviews into Clear Recommendations<\/h2>\n<p>Raw interview data, even from more than 100 participants, does not automatically produce decisions. Synthesis requires a structured workflow that moves from themes to implications to recommendations.<\/p>\n<p>A practical mixed-methods synthesis process follows this sequence:<\/p>\n<ol>\n<li>Run automated thematic analysis across all interview transcripts to identify recurring patterns and outliers.<\/li>\n<li>Quantify theme frequency across segments by product type, customer tier, or geography to identify which findings are broadly held versus segment-specific.<\/li>\n<li>Map themes to the original research objectives and the decision the study was designed to inform.<\/li>\n<li>Draft recommendations as action statements tied to specific evidence, including verbatim quotes, emotional signal data, and frequency counts.<\/li>\n<li>Prioritize recommendations by business impact and implementation feasibility.<\/li>\n<\/ol>\n<p><a href=\"https:\/\/veridatainsights.com\/data-collection-guide-for-financial-research-in-2026\" target=\"_blank\" rel=\"noindex nofollow\">Mixed-methods research approaches that combine qualitative depth with quantitative breadth at the design stage produce more defensible outputs than bolting methods together after data collection.<\/a> Listen Labs\u2019 Research Agent executes the first three steps automatically, generating segmentation breakdowns, statistical tests, and natural-language answers to follow-up questions. This automation compresses analysis from days to minutes.<\/p>\n<p>Listen Labs\u2019 Emotional Intelligence feature adds a layer that transcripts alone cannot provide. It delivers multimodal analysis of tone of voice, word choice, and micro-expressions, built on Ekman\u2019s universal emotions framework. In financial services research, where participants may verbally endorse a product while displaying confusion or hesitation, this signal separation directly informs product and messaging decisions.<\/p>\n<h2>Step 6: Ensure Compliance and Data Governance<\/h2>\n<p>Compliance and data governance keep financial services research defensible with regulators and internal risk teams. These controls must span study design, data collection, storage, and deletion.<\/p>\n<p>Effective programs start with a clear privacy notice that explains data collection purpose, sharing practices, retention periods, and participant rights. Participants review this notice and provide consent before the interview begins. Data minimization applies throughout the workflow, so teams collect only the data necessary for the stated research purpose.<\/p>\n<p>Strong governance separates personally identifiable information from research content. Participant contact information is stored in a different system or database from anonymized transcripts and video files. Access controls limit who can view raw recordings, and retention schedules define when data is deleted or archived.<\/p>\n<p>Vendor selection also plays a central role. Listen Labs holds SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications, and customer data is never used for AI model training. For studies involving EU residents, GDPR consent requirements apply regardless of where the research organization is headquartered, so teams should confirm cross-border data transfer mechanisms before launch.<\/p>\n<h2>Step 7: Deliver Findings that Stakeholders Actually Use<\/h2>\n<p>Research findings only create value when they reach decision-makers in a format they can act on. Stakeholder communication works best when formats match audience type and decision timeline.<\/p>\n<p>Executive sponsors usually benefit from a two-page memo with three to five prioritized recommendations and supporting evidence. Product teams often need a structured findings document with verbatim quotes, theme frequency data, and a prioritized \u201cmust-fix\u201d list that feeds directly into sprint planning. Board-level presentations gain credibility from video highlight reels of participant responses, which convey urgency and emotion that summary statistics cannot match.<\/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>Listen Labs\u2019 Research Agent generates consultant-quality PowerPoint slide decks, memo-style reports, video highlight reels, and custom segmentation breakdowns in under a minute. This automation removes the report-writing bottleneck that typically adds one to two weeks to a traditional research cycle.<\/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><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">See automated report generation in action<\/a> and watch how Listen Labs delivers stakeholder-ready outputs from financial services interviews in hours, not weeks.<\/p>\n<h2>Common Pitfalls in Financial Services Research<\/h2>\n<ul>\n<li><strong>Unclear objectives:<\/strong> Studies without a specific decision to inform produce findings no one acts on. Fix: require a written brief that names the decision, the decision-maker, and the deadline before fieldwork begins.<\/li>\n<li><strong>Poor recruitment fit:<\/strong> Participants who do not match the target profile produce misleading data. Fix: invest in screener design and use panels with behavioral verification, not self-reported demographics alone.<\/li>\n<li><strong>Low response quality:<\/strong> Incentive-driven or fraudulent respondents inflate sample size while degrading data. Fix: use real-time quality monitoring and participant frequency limits.<\/li>\n<li><strong>Analysis bottlenecks:<\/strong> Manual thematic coding of more than 50 interviews takes days and introduces analyst bias. Fix: use AI-automated analysis as the first pass, with human review focused on interpretation and strategic implication.<\/li>\n<li><strong>Stakeholder misalignment:<\/strong> Research delivered after a decision has been made is ignored. Fix: align on the decision timeline at scope definition and design the study to deliver findings before the decision point.<\/li>\n<\/ul>\n<h2>Measuring Success with Concrete Metrics<\/h2>\n<p>Research program quality is measurable, and consistent tracking builds credibility with stakeholders. Track these indicators across studies:<\/p>\n<ul>\n<li><strong>Study cycle time:<\/strong> Days from brief approval to final deliverable. Benchmark: under five days for AI-moderated studies and under three days for repeat study designs.<\/li>\n<li><strong>Participation rate:<\/strong> Percentage of invited participants who complete the interview. Benchmark: 70% or higher for well-screened panels with appropriate incentives.<\/li>\n<li><strong>Finding consistency:<\/strong> Degree to which themes from a 30-interview study replicate in a follow-up 30-interview study on the same question. High consistency indicates thematic saturation.<\/li>\n<li><strong>Stakeholder usage rate:<\/strong> Percentage of research deliverables cited in product, marketing, or strategy documents within 90 days. Low usage usually indicates a communication or timing problem rather than a research quality problem.<\/li>\n<li><strong>Decision influence rate:<\/strong> Percentage of research studies that directly inform a documented product, pricing, or messaging decision. Tracking this quarterly demonstrates research ROI.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">See how Mission Control tracks these metrics<\/a> and builds institutional knowledge across your financial services studies.<\/p>\n<h2>Advanced Strategies for Continuous Financial Insights<\/h2>\n<p>Continuous research programs create compounding organizational intelligence rather than isolated answers. Financial services firms running always-on research gain the ability to detect customer sentiment shifts before they appear in churn data, NPS scores, or regulatory complaints.<\/p>\n<p><a href=\"https:\/\/enumerate.ai\/blog\/industry-trends\/ai-in-qualitative-research-trends-2026\" target=\"_blank\" rel=\"noindex nofollow\">Real-time AI translation across more than 40 languages allows global qualitative studies to be fielded with one discussion guide while respondents speak their native language and analysts work from a single clean transcript<\/a>. This capability enables multi-market programs that were previously cost-prohibitive.<\/p>\n<p>Readiness criteria for an always-on program include a defined research calendar tied to product and campaign milestones, a standardized brief template, an approved vendor list with compliance sign-off, and a repository for cross-study queries. Listen Labs\u2019 Mission Control serves as that repository and enables teams to query findings from past studies in seconds rather than re-running research on questions already answered.<\/p>\n<p>Organizations new to AI-moderated research can start with a single 30-interview study on a well-understood research question, run in parallel with a traditional method. Comparing outputs, timelines, and stakeholder reception builds internal confidence before scaling to a continuous program.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Explore how Listen Labs supports always-on financial services research<\/a> across more than 45 countries.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<p><strong>How long does a financial services consumer insights study take with AI-moderated interviews?<\/strong><\/p>\n<p>A 30\u201350 interview study on Listen Labs completes in less than 24 hours from launch to final deliverables, including automated thematic analysis, slide decks, and video highlight reels. Traditional agencies require 6\u201312 weeks for comparable financial services studies, including legal and compliance review. Compliance review for a new AI research vendor typically takes 1\u20133 weeks as a one-time step, and subsequent studies with an approved vendor run on standard timelines.<\/p>\n<p><strong>What does financial services consumer insights cost on AI platforms versus traditional agencies?<\/strong><\/p>\n<p>Traditional research agencies, boutique consultancies, and major consulting firms charge different rates per interview for financial services studies. AI-moderated platforms conduct comparable depth interviews at a lower cost. Churn diagnosis studies, for example, cost more with traditional agencies than on AI-moderated platforms, and AI platforms deliver findings on a one-day schedule instead of over several weeks.<\/p>\n<p><strong>How do you handle GDPR, GLBA, and other regulatory requirements in financial services research?<\/strong><\/p>\n<p>Compliance begins at study design. Participants receive a privacy notice covering data collection purpose, sharing practices, retention periods, and their rights before the interview begins. Participant contact information is stored separately from anonymized transcripts. Data minimization applies throughout, so teams collect only data necessary for the stated research purpose. Listen Labs holds SOC 2 Type II, GDPR, ISO 27001, ISO 27701, and ISO 42001 certifications, and customer data is never used for AI model training. For studies involving EU residents, GDPR consent requirements apply regardless of where the research organization is headquartered.<\/p>\n<p><strong>How do you recruit hard-to-reach financial services audiences such as high-net-worth individuals or small business owners?<\/strong><\/p>\n<p>Hard-to-reach financial audiences require behavioral screening, including verified account types, transaction history, or business revenue, rather than self-reported demographics. Listen Labs\u2019 dedicated recruitment operations team partners with niche communities and specialized networks to source audiences below 1% incidence rate, including wealth management clients, enterprise treasury decision-makers, and recent mortgage applicants. Listen Atlas, the AI orchestration layer, matches across behavioral and intent data within a global network of 30 million verified respondents in more than 45 countries.<\/p>\n<p><strong>When should a financial services organization repeat or expand a research study?<\/strong><\/p>\n<p>Teams should repeat a study when a significant product, pricing, or regulatory change has occurred since the last study, when stakeholders question whether findings still reflect current customer behavior, or when a follow-up study is needed to validate that an implemented change produced the expected customer response. Teams should expand a study when initial findings reveal a segment with unexpectedly different behavior that warrants deeper investigation, or when a single-market study needs validation across additional geographies before a global product decision.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">Discuss your financial services research objectives with the Listen Labs team<\/a> and identify a suitable pilot.<\/p>\n<h2>Conclusion<\/h2>\n<p>Fast, high-quality consumer insights work in financial services requires a structured sequence. Effective programs move through defined scope, secondary synthesis, AI-moderated primary interviews, quality recruitment, regulatory compliance, rigorous analysis, and stakeholder-ready communication. Each step has measurable quality indicators and clear decision points.<\/p>\n<p>The historic depth-versus-scale trade-off that forced financial services researchers to choose between 15 rich interviews and 500 shallow survey responses no longer applies. <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> AI-moderated interviews deliver the statistical confidence of large samples and the motivational depth of one-on-one conversations within a compliance-ready, end-to-end platform built for regulated industries.<\/p>\n<p>Listen Labs runs the entire research lifecycle, including study design, global recruitment from 30 million verified respondents, AI-moderated interviews, automated analysis, and consultant-quality deliverables, on a one-day timeline. Trusted by Microsoft, Robinhood, and Anthropic, the platform supports the speed and quality standards that financial services organizations require.<\/p>\n<p><a href=\"https:\/\/listenlabs.ai\/book-my-demo\" target=\"_blank\">See how Listen Labs can transform your financial services consumer insights program<\/a> and support your next round of decisions.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Learn how to run financial services market research faster. Listen Labs delivers AI-moderated consumer insights in under 24 hours. Book a demo today.<\/p>\n","protected":false},"author":52,"featured_media":1327,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1328","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\/1328","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=1328"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/posts\/1328\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media\/1327"}],"wp:attachment":[{"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/media?parent=1328"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/categories?post=1328"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.ai\/articles\/wp-json\/wp\/v2\/tags?post=1328"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}