{"id":2514,"date":"2026-10-02T05:02:51","date_gmt":"2026-10-02T05:02:51","guid":{"rendered":"https:\/\/listenlabs.com\/articles\/ai-tools-for-pricing-research\/"},"modified":"2026-10-02T05:02:51","modified_gmt":"2026-10-02T05:02:51","slug":"ai-tools-for-pricing-research","status":"publish","type":"post","link":"https:\/\/listenlabs.com\/articles\/ai-tools-for-pricing-research\/","title":{"rendered":"AI Tools For Pricing Research: Four Key Tool Categories"},"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>AI pricing research tools fall into four categories: competitor monitoring, willingness-to-pay surveys, price optimization, and qualitative reasoning capture.<\/li>\n<li>Structured survey platforms (Conjointly, Conjoint Survey, quantilope, GWI Spark) automate Van Westendorp, Gabor-Granger, and conjoint studies to produce demand curves and revenue-maximizing price points.<\/li>\n<li>Free LLMs (ChatGPT, Claude, Gemini, Perplexity) help draft questions and summarize competitor pages, but they cannot recruit respondents, moderate interviews, or generate defensible pricing data.<\/li>\n<li>Listen Labs adds conversational AI interviews that connect demand curves to real buyer explanations, turning raw price data into a CFO-ready pricing strategy.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/listenlabs.com\/book-my-demo?utm_source=ai-growht-agent&amp;utm_term=ai-tools-for-pricing-research\" class=\"solid-button\" target=\"_blank\" rel=\"noindex nofollow\">Book a demo with Listen Labs<\/a><\/p>\n<h2>What Is AI Pricing Research?<\/h2>\n<p>AI pricing research uses AI-powered tools to monitor competitor prices, measure willingness-to-pay, set price points, and understand how buyers think about price. The four jobs are distinct:<\/p>\n<ul>\n<li>Competitor price monitoring<\/li>\n<li>Willingness-to-pay research<\/li>\n<li>Price optimization<\/li>\n<li>Qualitative understanding of the \u201cwhy\u201d behind a price ceiling<\/li>\n<\/ul>\n<p>AI pricing research tools span these jobs, but no single platform covers all of them equally well.<\/p>\n<h2>AI Tools For Competitor Price Monitoring<\/h2>\n<p>Competitor price monitoring tools answer one question: what are rivals charging right now? They do not answer what a company should charge or why customers accept or reject a given price.<\/p>\n<p><strong>Competera<\/strong> is an AI-driven pricing platform that <a href=\"https:\/\/competera.ai\/resources\/articles\/retail-competitive-intelligence\" target=\"_blank\" rel=\"noindex nofollow\">tracks competitor prices across 34 markets and delivers 119 million data points every month<\/a>. It covers price changes, promotional activity, assortment shifts, product availability, and marketplace seller behavior. Its AI assistant flags unusual pricing behavior and feeds competitive signals into a price optimization engine. <a href=\"https:\/\/competera.ai\/resources\/articles\/price-optimization-strategy\" target=\"_blank\" rel=\"noindex nofollow\">Competera does not publish list pricing; procurement requires a sales conversation.<\/a> Its main limitation is complexity. <a href=\"https:\/\/growbydata.com\/top-price-intelligence-tools\" target=\"_blank\" rel=\"noindex nofollow\">Smaller teams without a dedicated pricing analyst sometimes find the platform more sophisticated than they are ready to operationalize<\/a>. Competera tells you what competitors charge, but it cannot explain why customers accept or reject a price.<\/p>\n<p><strong>Competitaurus<\/strong> is a lighter-weight competitor tracking option starting at $29 per month for monitoring up to 15 competitors. This pricing makes it accessible for teams that need basic price surveillance without enterprise infrastructure. Like all monitoring tools, it tracks what competitors publish, not what customers will pay.<\/p>\n<p><strong>PRICE<\/strong> and <strong>Moatt<\/strong> occupy similar territory, offering competitor price tracking and alerting for ecommerce teams. Both tools answer the \u201cwhat are competitors charging?\u201d question. They share the same structural limitation: they observe market prices but cannot model demand, measure willingness-to-pay, or capture the reasoning behind a buyer\u2019s price threshold.<\/p>\n<p>Competitor monitoring tools work best when the pricing question is positional: where does this product sit relative to the market? For strategic questions about what to charge and why buyers will accept it, teams need additional tools.<\/p>\n<h2>AI Tools For Conjoint And Van Westendorp Analysis<\/h2>\n<p>Willingness-to-pay research tools use structured survey methodologies to measure what customers will pay. The four primary methodologies are Van Westendorp, Gabor-Granger, conjoint analysis, and MaxDiff. AI tools in this category automate study design, fielding, and analysis for one or more of these methods.<\/p>\n<p><strong>Van Westendorp Price Sensitivity Meter<\/strong> <a href=\"https:\/\/metricgate.com\/docs\/van-westendorp-price-sensitivity-meter\" target=\"_blank\" rel=\"noindex nofollow\">asks four open-ended price-perception questions about when a product feels too cheap, a bargain, expensive, and too expensive<\/a>. It maps <a href=\"https:\/\/knowledge.opinionx.co\/en\/articles\/15819736-van-westendorp\" target=\"_blank\" rel=\"noindex nofollow\">psychological boundaries of price acceptance rather than asking people directly what they would pay<\/a>. The output is an acceptable price range and an optimal price point. <a href=\"https:\/\/bms-net.de\/en\/what-is-the-difference-between-conjoint-analysis-and-van-westendorp-price-analysis\" target=\"_blank\" rel=\"noindex nofollow\">Van Westendorp answers \u201cwhat price do customers find acceptable?\u201d while conjoint analysis answers \u201cwhich price maximizes profit when customers are choosing between us and the competition?\u201d<\/a><\/p>\n<p><strong>Gabor-Granger<\/strong> presents respondents with an adaptive price ladder and records purchase intent at each price point. The method produces a demand curve and a revenue curve. <a href=\"https:\/\/solvimon.com\/glossary\/gabor-granger\" target=\"_blank\" rel=\"noindex nofollow\">The revenue-maximizing price sits at the peak of that curve. Raising it further loses more volume than the extra margin it earns. Lowering it gives away margin without enough added volume.<\/a> Gabor-Granger fits established categories and repricing decisions. Its structural limitation is that <a href=\"https:\/\/koji.so\/docs\/gabor-granger-pricing-method\" target=\"_blank\" rel=\"noindex nofollow\">it surfaces a revenue-maximizing price but tells you what price people accept rather than why they accept or reject it<\/a>.<\/p>\n<p><strong>Conjoint analysis<\/strong> <a href=\"https:\/\/koji.so\/docs\/gabor-granger-pricing-method\" target=\"_blank\" rel=\"noindex nofollow\">models how price trades off against features and brand in realistic purchase decisions<\/a>. <a href=\"https:\/\/bms-net.de\/en\/what-is-the-difference-between-conjoint-analysis-and-van-westendorp-price-analysis\" target=\"_blank\" rel=\"noindex nofollow\">Conjoint analysis always accounts for the competitive landscape, measuring willingness-to-pay in the context of alternatives rather than in isolation<\/a>. It delivers utility values, price elasticities, and market share projections. Teams use conjoint for price optimization, portfolio decisions, and competitive scenario modeling.<\/p>\n<p><strong>MaxDiff<\/strong> (maximum difference scaling) measures relative preferences by asking respondents to choose the best and worst items from small sets. MaxDiff\u2019s main decision contribution is feature and value-driver prioritization. It identifies which attributes matter most but does not directly estimate a price ceiling. It is most useful for shortlisting features before a conjoint study.<\/p>\n<p><strong>Conjointly<\/strong> supports conjoint analysis, Van Westendorp, and Gabor-Granger in a single platform. This breadth makes it a strong option for teams that need willingness-to-pay research across multiple methodologies. <strong>Conjoint Survey<\/strong> offers a <a href=\"https:\/\/www.itechguides.com\/best\/market-research-software\/conjoint-survey\/\" target=\"_blank\" rel=\"noindex nofollow\">$100 one-time payment plan (no subscription) that expands capacity to up to 1,000 saved surveys and 50,000 completed responses lifetime<\/a>. It also provides a free plan that includes one conjoint survey pilot with up to 20 completed responses. <strong>Quantilope<\/strong> and <strong>GWI Spark<\/strong> both automate survey-based pricing methodologies including Van Westendorp and conjoint, with GWI Spark adding audience intelligence from its consumer panel. All four platforms produce demand curves and price ranges, but none of them explain why a specific buyer\u2019s ceiling sits where it does.<\/p>\n<p>For a deeper look at how these tools compare in practice, see <a href=\"https:\/\/listenlabs.com\/articles\/best-ai-pricing-research-tools\/?utm_source=ai-growht-agent&amp;utm_term=ai-tools-for-pricing-research\" target=\"_blank\">The Best AI Tools For Pricing Research In 2026<\/a>.<\/p>\n<h2>Free AI Tools For Pricing Research<\/h2>\n<p>Free AI tools for pricing research, such as Perplexity, ChatGPT, Claude, and Gemini, are general-purpose large language models that assist with pricing tasks. They help summarize competitor pricing pages, draft Van Westendorp question sets, generate hypotheses about price anchors, and analyze open-ended survey responses at a surface level.<\/p>\n<p>Their limitations for pricing work are structural. <a href=\"https:\/\/sjofors.com\/blog\/why-ai-cannot-measure-willingness-to-pay-1\" target=\"_blank\" rel=\"noindex nofollow\">LLMs have zero access to proprietary willingness-to-pay data. Conjoint analyses, Van Westendorp studies, and Gabor-Granger tests are proprietary to the firms that commission them and never appear in LLM training corpora.<\/a> These tools cannot recruit participants, moderate interviews, or guarantee data quality. <a href=\"https:\/\/aitoolgiant.com\/reviews\/ai-chatbots-compared-2026.html\" target=\"_blank\" rel=\"noindex nofollow\">An independent six-week review testing ChatGPT, Claude, Gemini, and Perplexity across 200+ prompts found hallucination rates ranging from 2.1% (Claude) to 6.8% (Gemini)<\/a>. Those rates disqualify them for pricing decisions without a separate verification step.<\/p>\n<p>Conjoint Survey\u2019s free pilot is a low-cost option for teams that need a structured willingness-to-pay instrument rather than a general-purpose LLM. Free AI tools work well for drafting and hypothesis generation. For recruitment, moderation, or quality-controlled pricing data, teams need dedicated research platforms.<\/p>\n<h2>How To Use AI For Pricing Research<\/h2>\n<p>The most defensible pricing research workflow sequences quantitative measurement with qualitative understanding. Here are seven steps for teams using AI tools for pricing research.<\/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<ol>\n<li><strong>Define the pricing question precisely.<\/strong> Competitor positioning, willingness-to-pay, revenue optimization, and qualitative reasoning behind a price ceiling are four different questions that require different tools.<\/li>\n<li><strong>Run Van Westendorp to establish the acceptable price range.<\/strong> Use Conjointly, Conjoint Survey, quantilope, or GWI Spark to map psychological boundaries before testing specific price points.<\/li>\n<li><strong>Run a Gabor-Granger price ladder to find the revenue-maximizing price.<\/strong> This step identifies the peak of the revenue curve. Listen Labs adds a conversational price ladder that runs inside studies the team already fields.<\/li>\n<li><strong>Use Listen Labs to capture the qualitative \u201cwhy\u201d behind the demand curve.<\/strong> Each buyer sees a product description and decides whether they would buy at a given price. Their answer determines the next price they see, higher after a yes and lower after a no. The AI interviewer then asks contextual follow-up questions to separate \u201cI can\u2019t afford it\u201d from \u201cit isn\u2019t worth that much.\u201d These are two very different problems with very different fixes. Listen Labs codes those answers into themes, so individual anecdotes become data. Demand and revenue curves roll up across the study, with segment filters. Every point on the curve traces back to a real interview, so when someone challenges the results, the team can answer with clips of buyers explaining their choices in their own words.<\/li>\n<li><strong>Handle messy answers systematically.<\/strong> Listen Labs manages data quality issues that manual studies leave unresolved. Buyers who would not purchase at any price stay in the count so demand is not artificially inflated. Inconsistent ladders are counted separately. Half-finished ladders are dropped.<\/li>\n<li><strong>Cross-reference against competitor monitoring data.<\/strong> Use Competera or a comparable tool to confirm that the revenue-maximizing price sits in a defensible competitive position.<\/li>\n<li><strong>Engage white-glove support for complex projects.<\/strong> Listen Labs offers support from an insights team of career researchers for studies where the pricing decision carries significant revenue risk.<\/li>\n<\/ol>\n<p>For teams running monadic price tests alongside Gabor-Granger, see <a href=\"https:\/\/listenlabs.com\/articles\/best-monadic-price-testing-tools\/?utm_source=ai-growht-agent&amp;utm_term=ai-tools-for-pricing-research\" target=\"_blank\">Monadic Price Testing Tools: A Pricing Research Guide<\/a>.<\/p>\n<p><a href=\"https:\/\/listenlabs.com\/book-my-demo?utm_source=ai-growht-agent&amp;utm_term=ai-tools-for-pricing-research\" class=\"solid-button\" target=\"_blank\" rel=\"noindex nofollow\">Book a demo<\/a><\/p>\n<h2>Are Synthetic Audience Pricing Tools Reliable?<\/h2>\n<p>Before adopting any AI pricing tool, teams need to understand how much they can trust synthetic audiences. Skepticism about synthetic respondents in pricing research is well-founded and well-documented. Forum discussions about AI market research tools frequently surface concerns about uniform, unreliable answers, and the empirical literature supports those concerns in specific contexts.<\/p>\n<p><a href=\"https:\/\/marketresearchuk.co.uk\/synthetic-respondents-in-market-research\" target=\"_blank\" rel=\"noindex nofollow\">Research from insight and analytics group Strat7, comparing a nationally representative human sample of 3,000 respondents with synthetic-data providers, found that for willingness-to-pay questions, synthetic respondents generated prices generally around 16% higher than those given by human respondents. In one price-ordering exercise, purely synthetic respondents produced logically inconsistent price ordering 68% of the time.<\/a><\/p>\n<p><a href=\"https:\/\/entropik.io\/resources\/blog-articles\/synthetic-audiences-in-consumer-research\" target=\"_blank\" rel=\"noindex nofollow\">A Vanderbilt University study found that synthetic responses had a standard deviation of 16.1 versus 31.4 for real responses, meaning the synthetic distribution was roughly half as wide as reality.<\/a> Synthetic tools reproduce broad population averages but flatten segment-level differences. Pricing decisions often depend on those segment-level differences.<\/p>\n<p>Subconscious, a synthetic audience vendor, has acknowledged that synthetic outputs are directional rather than definitive. This candid position reflects the broader industry consensus. Synthetic tools support early hypothesis generation and question wording tests, but they do not replace final price setting.<\/p>\n<p>Use synthetic pricing data when the cost of being directionally wrong is low and reversible. Examples include screening a long list of price hypotheses, testing question wording before fielding a real study, or generating anchors for a Van Westendorp instrument. For final price setting, segment-level pricing architecture, regulated industries, or any output presented to a CFO or board, validate findings against real respondents.<\/p>\n<p><a href=\"https:\/\/getminds.ai\/blog\/synthetische-personas-marktforschung\" target=\"_blank\" rel=\"noindex nofollow\">Decisions involving capital allocation, formal pricing architecture changes, and major public brand repositioning require empirical data collected from verified, representative human samples.<\/a> A practical protocol uses synthetic-first hypothesis formation followed by recruited-human validation for the final decision.<\/p>\n<h2>How To Verify AI Pricing Research Outputs<\/h2>\n<p>A price recommendation is only as defensible as the evidence behind it. The following checklist applies whether the output came from a survey platform, a synthetic audience tool, or a general-purpose LLM.<\/p>\n<ol>\n<li><strong>Cross-reference AI-generated price recommendations against primary data.<\/strong> Conversion rates, margin data, and win\/loss analysis from real deals are the ground truth. AI outputs feed that analysis but do not replace it.<\/li>\n<li><strong>Validate synthetic or modeled outputs with real respondent interviews.<\/strong> <a href=\"https:\/\/surveymonkey.com\/learn\/market-research\/synthetic-respondents\" target=\"_blank\" rel=\"noindex nofollow\">The core validation test for synthetic respondent quality is whether synthetic answers hold up against actual human responses collected on the same questions, comparing distributions rather than just averages.<\/a><\/li>\n<li><strong>Confirm that every price point traces back to a source.<\/strong> In Listen Labs studies, every point on the demand curve links to a real interview. In survey-based studies, every price point should trace to a specific respondent cohort with documented sample quality.<\/li>\n<li><strong>Confirm sample quality and fraud controls.<\/strong> <a href=\"https:\/\/sjofors.com\/blog\/why-ai-cannot-measure-willingness-to-pay-1\" target=\"_blank\" rel=\"noindex nofollow\">A PNAS study found that AI agents can produce survey responses that pass standard attention checks at a 99.8% rate<\/a>. Traditional quality controls cannot distinguish AI-generated responses from human ones without additional verification layers.<\/li>\n<li><strong>Document methodology for stakeholder review.<\/strong> A CFO challenging a price recommendation needs to understand which methodology produced the demand curve, what sample it was drawn from, and what validation steps were applied. Methodology documentation functions as the defense.<\/li>\n<\/ol>\n<p>With these verification steps in mind, teams can address common questions about AI pricing research more confidently.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>Which AI Tool Is Best For Pricing?<\/h3>\n<p>The answer depends on the pricing job. Competitor monitoring tools such as Competera and Competitaurus answer what rivals charge. Willingness-to-pay tools such as Conjointly, quantilope, and GWI Spark measure demand curves using Van Westendorp, Gabor-Granger, or conjoint analysis. Price optimization platforms find the revenue-maximizing price point. Conversational AI research platforms like Listen Labs capture the qualitative reasoning behind a price ceiling and turn a price test into a pricing strategy.<\/p>\n<h3>How Do You Use AI For Pricing Research?<\/h3>\n<p>Start by defining the pricing question precisely. Use Van Westendorp to establish the acceptable price range and Gabor-Granger to find the revenue peak. Add conversational AI interviews to understand why buyers accept or reject specific prices. Cross-reference outputs against competitor monitoring data and validate any synthetic or modeled results against real respondent interviews before presenting to stakeholders.<\/p>\n<h3>Can AI Tools Replace Traditional Pricing Surveys?<\/h3>\n<p>AI tools do not replace traditional pricing surveys. They augment them, particularly by adding qualitative depth that structured surveys cannot capture. A demand curve shows where buyers drop off. Conversational AI interviews explain why. The combination produces a defensible pricing strategy rather than a chart that requires interpretation.<\/p>\n<h3>What Is The Difference Between Competitor Price Monitoring And Willingness-To-Pay Research?<\/h3>\n<p>Competitor price monitoring tracks what rivals charge across channels and markets. Willingness-to-pay research measures what customers will pay for a specific product using methodologies like Van Westendorp, Gabor-Granger, or conjoint analysis. Monitoring is observational. Willingness-to-pay research is predictive.<\/p>\n<h3>Are Free AI Tools Enough For Pricing Research?<\/h3>\n<p>Free AI tools such as ChatGPT, Claude, Gemini, and Perplexity are useful for drafting question sets, summarizing competitor pricing pages, and generating hypotheses. They are not sufficient for recruitment, interview moderation, quality control, or producing a demand curve that can be defended to a CFO. Use them as a starting point rather than a final source.<\/p>\n<h2>Conclusion: Matching Pricing Jobs To The Right AI Tools<\/h2>\n<p>AI pricing research tools are not interchangeable. The right tool depends on the specific pricing question.<\/p>\n<ul>\n<li><strong>Competitor price monitoring<\/strong> (Competera, Competitaurus, PRICE, Moatt) fits positional questions about where a product sits relative to the market.<\/li>\n<li><strong>Willingness-to-pay research<\/strong> (Conjointly, Conjoint Survey, quantilope, GWI Spark) fits quantitative questions about what customers will pay and where demand falls off.<\/li>\n<li><strong>Price optimization platforms<\/strong> fit operational questions about which price point maximizes revenue or contribution margin across the assortment.<\/li>\n<li><strong>Conversational AI research platforms like Listen Labs<\/strong> fit strategic questions about why the price ceiling sits where it does and what would move it.<\/li>\n<\/ul>\n<p>Listen Labs combines Gabor-Granger price ladders with AI-moderated follow-up questions and traceable evidence. Every theme in the analysis links to the buyers who expressed it, in their own words. When a CFO asks why the recommended price is $79 and not $99, the team can answer with clips of buyers explaining exactly what changed their mind at $89.<\/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>This connection between numbers and narratives turns a price test into a pricing strategy.<\/p>\n<p><a href=\"https:\/\/listenlabs.com\/book-my-demo?utm_source=ai-growht-agent&amp;utm_term=ai-tools-for-pricing-research\" class=\"solid-button\" target=\"_blank\" rel=\"noindex nofollow\">See How Listen Labs Turns A Price Test Into A Pricing Strategy<\/a><\/p>\n<section data-read-next=\"true\">\n<h2>Read Next<\/h2>\n<ul>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/best-ai-pricing-research-tools\/\" target=\"_blank\">The Best AI Tools for Pricing Research in 2026<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/ai-brand-tracking-pricing\/\" target=\"_blank\">AI Brand Tracking Pricing: Top Tools Compared<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/competitive-brand-analysis-ai-tools\/\" target=\"_blank\">Competitive Brand Analysis AI Tools: How to Choose<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/best-ai-market-research-tools\/\" target=\"_blank\">Best AI Market Research Tools in 2026: End-to-End Platforms<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/ai-pricing-interviews-at-scale\/\" target=\"_blank\">How To Run AI-Moderated Pricing Interviews at Scale<\/a><\/li>\n<\/ul>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Discover the best AI tools for pricing research. Listen Labs covers competitor monitoring, conjoint analysis, and more. Start smarter pricing today.<\/p>\n","protected":false},"author":52,"featured_media":2513,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2514","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\/2514","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=2514"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/2514\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media\/2513"}],"wp:attachment":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media?parent=2514"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/categories?post=2514"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/tags?post=2514"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}