{"id":2640,"date":"2026-10-03T06:54:14","date_gmt":"2026-10-03T06:54:14","guid":{"rendered":"https:\/\/listenlabs.com\/articles\/ai-moderated-interviews-price-testing\/"},"modified":"2026-10-03T06:54:14","modified_gmt":"2026-10-03T06:54:14","slug":"ai-moderated-interviews-price-testing","status":"publish","type":"post","link":"https:\/\/listenlabs.com\/articles\/ai-moderated-interviews-price-testing\/","title":{"rendered":"AI Moderated Interviews for Price Testing: Gabor-Granger"},"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-moderated interviews run Gabor-Granger price ladders conversationally and capture the reasoning behind every price decision. The output includes both a demand curve and the \u201cwhy\u201d behind each point.<\/li>\n<li>Follow-up logic separates affordability objections from perceived-value objections. Individual anecdotes turn into measurable, coded themes that guide go-to-market and product decisions.<\/li>\n<li>Teams can run both Van Westendorp and Gabor-Granger in the same AI-moderated study. This sequence establishes an acceptable price range and then pinpoints the revenue-maximizing price without fielding a second project.<\/li>\n<li>AI-moderated price testing fits most consumer and straightforward B2B categories. Highly emotional categories, B2B negotiated pricing, and genuinely novel products without reference prices still call for human moderation or alternative methods.<\/li>\n<li>Listen Labs compresses the entire pricing-research cycle, from study design through AI-moderated interviews to demand and revenue curves, into under 24 hours.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/listenlabs.com\/book-my-demo?utm_source=ai-growth-agent&amp;utm_term=ai-moderated-interviews-price-testing\" class=\"solid-button\" target=\"_blank\" rel=\"noindex nofollow\">Book a demo with Listen Labs<\/a><\/p>\n<h2>How AI-Moderated Price Testing Works Step By Step<\/h2>\n<p>The Gabor-Granger method presents a single offer at a series of price points and records purchase intent at each. The result is a demand curve and a revenue curve that identify the revenue-maximizing price. Here is how it runs inside an AI-moderated interview.<\/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>Show the offer and a starting price.<\/strong> The buyer sees a standardized product description and a starting price drawn from a predefined ladder. <a href=\"https:\/\/surveymonkey.com\/learn\/gabor-granger\" target=\"_blank\" rel=\"noindex nofollow\">Every respondent must see the same clear, factual product description before any price is shown<\/a>. Overselling the product at this stage inflates willingness to pay and produces a demand curve that will not hold after launch.<\/li>\n<li><strong>Ask a binary purchase question.<\/strong> The buyer answers yes or no to whether they would purchase at that price. <a href=\"https:\/\/solvimon.com\/glossary\/gabor-granger\" target=\"_blank\" rel=\"noindex nofollow\">Randomizing the starting price point across respondents is a required design step, not optional<\/a>. Anchoring on the first price shown is the method\u2019s defining weakness, and rotating the entry point spreads that bias across the sample instead of concentrating it.<\/li>\n<li><strong>Adjust the next price based on the answer.<\/strong> A yes raises the floor. A no lowers the ceiling. The previous answer determines the next price shown.<\/li>\n<li><strong>Continue the ladder until it finds the ceiling.<\/strong> The ladder continues until it identifies the most that person would pay. <a href=\"https:\/\/vistaar.com\/glossary\/gabor-granger-method\" target=\"_blank\" rel=\"noindex nofollow\">Practitioners typically test five to seven evenly spaced price points within a defined range<\/a>. Too narrow a range risks missing the revenue peak. Too wide a range reduces the curve\u2019s useful resolution.<\/li>\n<li><strong>Fire contextual follow-up questions.<\/strong> Once the buyer lands on a price, the AI moderator asks why that price felt worth it or why it did not. This step goes beyond what a static survey can do and makes the output defensible.<\/li>\n<li><strong>Roll individual ladders into curves.<\/strong> Individual results aggregate into a demand curve and a revenue curve across the whole study. <a href=\"https:\/\/isoglu.com\/insights\/concepts\/gabor-granger\" target=\"_blank\" rel=\"noindex nofollow\">The revenue-maximizing price P* is estimated by maximizing P \u00d7 Q(P)<\/a>. Here, Q(P) is the proportion of surveyed respondents who indicate purchase intent at or above price P.<\/li>\n<\/ol>\n<p><a href=\"https:\/\/research.contrary.com\/company\/listen-labs\" target=\"_blank\" rel=\"noindex nofollow\">Listen Labs compresses the research cycle, from study design and participant recruitment through interviews to synthesized reports, highlight reels, and slide decks, to under 24 hours from study close, replacing traditional four-to-eight-week research timelines.<\/a> Traditional Gabor-Granger studies typically took 2\u20136 weeks from briefing to analysis. With AI-modelled shoppers or AI-moderated interviews that attach the buyer\u2019s reasoning to each price point, the same study can run in minutes to under 30 minutes.<\/p>\n<p><a href=\"https:\/\/listenlabs.com\/book-my-demo?utm_source=ai-growth-agent&amp;utm_term=ai-moderated-interviews-price-testing\" class=\"solid-button\" target=\"_blank\" rel=\"noindex nofollow\">See how AI-moderated pricing works<\/a><\/p>\n<h2>The Follow-Up Logic For Separating Price Sensitivity From Affordability<\/h2>\n<p>AI moderation adds value over a static survey because the AI interviewer keeps probing after the price decision. When a buyer rejects a price, an AI-moderated interview captures whether the objection is about affordability or perceived value. Those are two different problems with two different fixes.<\/p>\n<p><a href=\"https:\/\/cleverx.com\/blog\/ai-moderated-interviews-pricing-research-response-quality\" target=\"_blank\" rel=\"noindex nofollow\">Willingness-to-pay laddering can be programmed into an AI discussion guide as a conditional logic sequence, and transcript analysis shows AI-facilitated laddering produces pricing dialogue comparable to skilled human moderation of the same script.<\/a> The AI agent anchors on a midpoint price, observes the participant\u2019s reaction, adjusts in the programmed direction, and probes the reasoning behind each reaction.<\/p>\n<p>Once a buyer lands on a price, the AI moderator asks follow-up questions designed to separate affordability from perceived value. Examples include:<\/p>\n<ul>\n<li>\u201cYou said $80 felt too high. Is that because it is more than you would budget for this, or because the product does not deliver enough value at that price?\u201d<\/li>\n<li>\u201cWhat would need to be true for $80 to feel like a fair price?\u201d<\/li>\n<li>\u201cWhat are you comparing this to when you say it feels expensive?\u201d<\/li>\n<\/ul>\n<p>The answers get coded into recurring themes, so individual anecdotes become measurable data. <a href=\"https:\/\/getperspective.ai\/blog\/how-to-do-pricing-research-2026-willingness-to-pay-interviews-at-scale\" target=\"_blank\" rel=\"noindex nofollow\">The critical difference between an AI-moderated pricing interview and a pricing survey is the follow-up: when someone says \u201cthat feels expensive,\u201d the AI asks compared to what, and that answer, the anchor, is usually the finding.<\/a><\/p>\n<p>This follow-up logic produces the \u201cwhy\u201d that a demand curve alone cannot carry. The reasoning feeds directly into go-to-market approach and product roadmap. Listen Labs runs Gabor-Granger price ladders with AI follow-ups that code buyer reasoning into themes, so the demand curve arrives with the story driving it.<\/p>\n<p><a href=\"https:\/\/listenlabs.com\/articles\/ai-moderated-vs-human-interviews\/?utm_source=ai-growth-agent&amp;utm_term=ai-moderated-interviews-price-testing\" target=\"_blank\">AI-Moderated vs Human Interviews: Enterprise Guide<\/a> covers the broader tradeoffs between moderation approaches. For pricing specifically, <a href=\"https:\/\/cleverx.com\/blog\/ai-moderated-interviews-pricing-research-response-quality\" target=\"_blank\" rel=\"noindex nofollow\">AI-moderated sessions produce more consistent data than human moderators when the same discussion guide runs across 20 to 40 sessions without moderator drift<\/a>. Human moderators naturally vary phrasing, emphasis, and follow-up depth across sessions, and that variability introduces noise in quantified pricing outputs.<\/p>\n<p><a href=\"https:\/\/listenlabs.com\/book-my-demo?utm_source=ai-growth-agent&amp;utm_term=ai-moderated-interviews-price-testing\" class=\"solid-button\" target=\"_blank\" rel=\"noindex nofollow\">Explore AI-moderated pricing interviews<\/a><\/p>\n<h2>How Van Westendorp And Gabor-Granger Work In AI-Moderated Studies<\/h2>\n<p>Van Westendorp and Gabor-Granger answer different pricing questions. <a href=\"https:\/\/quali-fi.com\/learn\/price-sensitivity-meter\" target=\"_blank\" rel=\"noindex nofollow\">Van Westendorp is the right choice for establishing an acceptable price range and understanding perceived value thresholds across a category.<\/a> It asks four open-ended questions about price perception, covering too cheap, bargain, expensive, and too expensive. It then plots cumulative curves whose intersections yield the acceptable price range, the Optimal Price Point (OPP), and the Indifference Price Point (IPP).<\/p>\n<p><a href=\"https:\/\/humes.pl\/en\/glossary\/gabor-granger-method\" target=\"_blank\" rel=\"noindex nofollow\">Gabor-Granger is the right choice for finding the revenue-maximizing price point and modeling demand at specific price levels.<\/a> It presents specific prices and asks a binary purchase question at each one. <a href=\"https:\/\/solvimon.com\/glossary\/gabor-granger\" target=\"_blank\" rel=\"noindex nofollow\">The recommended sequence is Van Westendorp first to establish a credible range, then Gabor-Granger inside that range to pick the level.<\/a><\/p>\n<p>AI moderation changes the tradeoff between these methods. Because the AI moderator can run follow-ups on every price decision, the method choice is no longer constrained by moderator time or cost. Both can run conversationally at scale in a single study.<\/p>\n<p>A team can run Van Westendorp first to establish a credible range, then run Gabor-Granger inside that range to pick the level, all with the same participants. This approach removes the need to field a second project and keeps the sample consistent across both outputs.<\/p>\n<p><a href=\"https:\/\/qualitati.com\/blog\/what-are-conversational-surveys\" target=\"_blank\" rel=\"noindex nofollow\">Pricing and willingness-to-pay research is a strong fit for conversational surveys, with the recommendation to pair a Van Westendorp or Gabor-Granger structure with conversational follow-ups on rationale.<\/a> That pairing is now executable at scale inside a single AI-moderated study.<\/p>\n<h2>Where AI-Moderated Price Testing Does Not Fit<\/h2>\n<p>AI moderation is the wrong tool for pricing in three situations.<\/p>\n<ul>\n<li><strong>Highly emotional or identity-laden categories.<\/strong> If the purchase is tied to self-image, status, or a deeply personal decision, a human moderator reads the room better. AI moderation is a strong fit for pricing reactions as long as consent and data handling are clear, but a trained human moderator is usually the better choice for highly sensitive topics. The signal you are in this situation: buyers hesitate, deflect, or give socially desirable answers that do not match their behavior. The practical move is to run a small number of human-moderated interviews alongside the AI study.<\/li>\n<li><strong>B2B negotiated pricing.<\/strong> If the real price is set in a procurement conversation rather than a stated willingness to pay, an AI-moderated interview measures the wrong thing. <a href=\"https:\/\/softwarepricing.com\/blog\/why-willingness-to-pay-surveys-fail-b2b-software\" target=\"_blank\" rel=\"noindex nofollow\">B2B software purchases are typically collective decisions, so individual WTP elicitation methods measure one person\u2019s willingness to pay while the number that matters belongs to the buying group.<\/a> The signal: your buyer is not the final decision-maker, or the price is subject to approval chains and contract terms. The better path is to map the buying process with human-moderated interviews before running a price ladder.<\/li>\n<li><strong>Genuinely novel products with no reference price.<\/strong> <a href=\"https:\/\/surveymonkey.com\/learn\/gabor-granger\" target=\"_blank\" rel=\"noindex nofollow\">Gabor-Granger assumes respondents already understand the category and value, and works better for familiar product types.<\/a> If buyers have nothing to anchor against, a Gabor-Granger ladder produces noise. The signal: respondents ask what the product is comparable to, or their answers swing wildly across price points. In that case, run Van Westendorp first to establish a range, or use conjoint analysis if feature trade-offs matter.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/listenlabs.com\/articles\/run-ai-moderated-interviews-2026\/?utm_source=ai-growth-agent&amp;utm_term=ai-moderated-interviews-price-testing\" target=\"_blank\">How to Run AI-Moderated Interviews: A 7-Step Playbook<\/a> covers study design decisions that apply across research types, including how to scope a study guide for a focused pricing objective. Once you have confirmed the method fits your category, the next step is understanding what the output looks like and how to use it.<\/p>\n<h2>How To Read AI-Moderated Pricing Output<\/h2>\n<p>A well-run AI-moderated price test produces two core deliverables and one key analysis feature.<\/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><strong>A demand curve<\/strong> shows how many buyers you keep at each price. <a href=\"https:\/\/intotheminds.com\/blog\/en\/gabor-granger\" target=\"_blank\" rel=\"noindex nofollow\">A very steep curve indicates high price sensitivity, or elastic demand.<\/a> A flatter curve suggests inelastic demand, where prices can be increased without losing a large number of customers.<\/li>\n<li><strong>A revenue curve<\/strong> shows the price that earns the most. The revenue curve multiplies each price by its acceptance rate. The peak identifies the revenue-maximizing tested price. <a href=\"https:\/\/entropik.io\/resources\/blog-articles\/gabor-granger-pricing-method\" target=\"_blank\" rel=\"noindex nofollow\">The revenue-maximizing tested price is not automatically the profit-maximizing price, because the basic revenue index calculation ignores costs.<\/a> Once unit costs are included, the best price for profit is often higher than the best price for revenue.<\/li>\n<li><strong>Segment filters<\/strong> act as an analysis layer that lets you see how key audiences behave on their own without fielding a new study.<\/li>\n<\/ul>\n<p>Every point on the curve traces back to a real interview. When leadership challenges the number, you can respond with clips of buyers explaining their choices in their own words instead of a defense of your methodology.<\/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>Messy real-world answers need to be handled correctly so demand is not artificially inflated. These include buyers who would not purchase at any price, buyers who say yes to a high price but no to a lower one, and half-finished ladders. Listen Labs cleans these automatically. Buyers who would not purchase at any price stay in the count because excluding them would overstate demand. Inconsistent responses are counted separately so they do not distort the curve. Half-finished ladders are dropped entirely.<\/p>\n<p><a href=\"https:\/\/listenlabs.com\/book-my-demo?utm_source=ai-growth-agent&amp;utm_term=ai-moderated-interviews-price-testing\" class=\"solid-button\" target=\"_blank\" rel=\"noindex nofollow\">Review a sample pricing output<\/a><\/p>\n<h2>AI-Moderated Price Testing Vs. Traditional Pricing Surveys<\/h2>\n<p>A traditional pricing survey presents pre-set price questions with no follow-up. It returns a chart with no diagnostic. <a href=\"https:\/\/getperspective.ai\/blog\/ai-survey-tools-in-2026-when-a-survey-should-be-a-conversation\" target=\"_blank\" rel=\"noindex nofollow\">Static surveys fall short because they fix the entire conversation in advance, meaning they can only collect answers to questions you already knew to ask, with no real-time probing and shallow open-text responses.<\/a><\/p>\n<p>AI-moderated interviews for price testing adapt in real time and capture the reasoning behind each choice. The real difference is diagnostic: a survey tells you a price was rejected, while an AI-moderated interview tells you why and what to change. <a href=\"https:\/\/verasight.io\/reports\/ai-moderated-interviews-vs-survey-open-ends\" target=\"_blank\" rel=\"noindex nofollow\">A randomized controlled experiment found that AI-moderated interviews produced 4.8 times as many respondent words per assigned respondent as written survey open-ends, with follow-up probing accounting for roughly 80% of that cross-mode gap.<\/a><\/p>\n<p><a href=\"https:\/\/cleverx.com\/blog\/ai-moderated-interviews-pricing-research-response-quality\" target=\"_blank\" rel=\"noindex nofollow\">For pricing research specifically, AI-moderated interviews produce response quality equivalent to live moderated sessions on most dimensions and measurably better results on social desirability bias<\/a>. Participants disclose lower price thresholds, surface more explicit objections, and volunteer stronger no-anchor reactions when no human moderator is present. That candor matters because live moderated sessions may systematically overestimate the price a typical buyer will actually accept at the point of purchase.<\/p>\n<h2>Decision Framework For Choosing AI-Moderated Pricing<\/h2>\n<p>Teams can use four questions to decide whether AI-moderated pricing is the right fit.<\/p>\n<ol>\n<li>Do you need to know why a price is rejected, or only whether it is?<\/li>\n<li>Is your category one where buyers can articulate a reference price, and not one of the three exclusions covered earlier?<\/li>\n<li>Do you need segment-level reads without fielding a second study?<\/li>\n<li>Will you have to defend the number to leadership?<\/li>\n<\/ol>\n<p>If you answered yes to the first, third, and fourth questions, and your category is not one of the three exclusions covered earlier, AI-moderated price testing is the right instrument. <a href=\"https:\/\/getperspective.ai\/blog\/how-to-do-pricing-research-2026-willingness-to-pay-interviews-at-scale\" target=\"_blank\" rel=\"noindex nofollow\">Quantifying themes across a full interview set produces a price corridor, a floor below which margin is left unclaimed and a ceiling above which specific objections appear, with quotable transcript evidence for each boundary, which survives executive scrutiny in a way a bare survey chart does not.<\/a><\/p>\n<p><a href=\"https:\/\/listenlabs.com\/book-my-demo?utm_source=ai-growth-agent&amp;utm_term=ai-moderated-interviews-price-testing\" class=\"solid-button\" target=\"_blank\" rel=\"noindex nofollow\">Discuss your pricing use case<\/a><\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How Long Does An AI-Moderated Price Test Take From Brief To Demand Curve?<\/h3>\n<p><a href=\"https:\/\/research.contrary.com\/company\/listen-labs\" target=\"_blank\" rel=\"noindex nofollow\">As noted earlier, Listen Labs compresses the research cycle to under 24 hours from study close.<\/a> The platform handles study design, global participant recruitment from its network of 50M+ verified respondents, AI-moderated interviews, automated analysis, and delivery of the demand curve, revenue curve, and segment breakdowns, all within that window.<\/p>\n<h3>Does The AI Moderator Follow A Script Or Adapt?<\/h3>\n<p>The AI moderator adapts within a structured guide. It runs the price ladder and then fires contextual follow-up questions based on what the buyer says. It probes vague answers, asks for comparisons, and separates affordability from perceived value. <a href=\"https:\/\/questionpro.com\/blog\/ai-moderated-interviews\" target=\"_blank\" rel=\"noindex nofollow\">The follow-up logic is configured before the study launches, defining what counts as a weak answer and what the AI should probe, but the actual probes respond to what each participant says rather than following a fixed sequence.<\/a> The result is consistent probing logic applied to every participant, without the moderator drift that affects human-led sessions over a long fielding period.<\/p>\n<h3>How Does The AI Separate Affordability From Perceived Value?<\/h3>\n<p><a href=\"https:\/\/questionpro.com\/blog\/ai-moderated-interviews\" target=\"_blank\" rel=\"noindex nofollow\">The AI asks follow-up questions after the price decision.<\/a> It asks what the buyer is comparing the price to, what would need to be true for the price to feel fair, and whether the objection is about budget or about value. These probes connect directly to what the participant said at the price decision point. Answers are coded into themes across the full study, so the distinction between \u201cI can\u2019t afford it\u201d and \u201cit isn\u2019t worth that much\u201d becomes a measurable data point rather than an anecdote.<\/p>\n<h3>Can You Run Gabor-Granger And Van Westendorp In The Same Study?<\/h3>\n<p>Yes. Because the AI moderator can run follow-ups on every price decision, both methods can run conversationally in the same study without additional moderator time or cost. <a href=\"https:\/\/solvimon.com\/glossary\/gabor-granger\" target=\"_blank\" rel=\"noindex nofollow\">The recommended sequence, Van Westendorp first, then Gabor-Granger inside that range, is covered above.<\/a> Running both in the same study with the same participants eliminates the need to field a second project and keeps the sample consistent across both outputs.<\/p>\n<h3>How Are Segment-Level Results Produced Without A New Study?<\/h3>\n<p>Segment filters let you see how key audiences behave on their own within the same study. You do not need to field a second project to get a read on a specific segment. The demand curve and revenue curve can be filtered by segments (e.g., OTA, Corporate, Groups, Direct) and other attributes such as nationality, booking source, property, and rate, allowing demand and revenue trends to be analyzed at the segment level from a single dataset.<\/p>\n<h3>How Do You Trace A Curve Point Back To The Interview Behind It?<\/h3>\n<p>Every point on the demand and revenue curve traces back to a real interview. Listen Labs provides clips of buyers explaining their choices in their own words, so you can answer challenges with evidence rather than methodology. When a stakeholder questions why demand drops at a specific price point, you can play the clips of buyers who rejected that price and explain what they said, whether affordability, competitive comparison, missing feature, or perceived value gap. The curve becomes a navigable body of evidence rather than a static chart.<\/p>\n<section data-read-next=\"true\">\n<h2>Read Next<\/h2>\n<ul>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/ai-pricing-interviews-at-scale\/?utm_source=ai-growth-agent&amp;utm_term=ai-moderated-interviews-price-testing\" target=\"_blank\">How To Run AI-Moderated Pricing Interviews at Scale<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/best-ai-pricing-research-tools\/?utm_source=ai-growth-agent&amp;utm_term=ai-moderated-interviews-price-testing\" target=\"_blank\">The Best AI Tools for Pricing Research in 2026<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/running-ai-moderated-interviews-guide\/?utm_source=ai-growth-agent&amp;utm_term=ai-moderated-interviews-price-testing\" target=\"_blank\">How to Run AI Moderated Interviews: Complete 2026 Guide<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/ai-moderated-tests-large-panels\/?utm_source=ai-growth-agent&amp;utm_term=ai-moderated-interviews-price-testing\" target=\"_blank\">AI-Moderated Interviews vs Traditional Methods at Scale<\/a><\/li>\n<li><a href=\"https:\/\/listenlabs.com\/articles\/how-ai-moderates-research-interviews\/?utm_source=ai-growth-agent&amp;utm_term=ai-moderated-interviews-price-testing\" target=\"_blank\">How AI Moderates Research Interviews: A Complete Guide<\/a><\/li>\n<\/ul>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Run defensible Gabor-Granger &amp; Van Westendorp price tests with AI-moderated interviews. Get faster, richer pricing insights with Listen Labs.<\/p>\n","protected":false},"author":52,"featured_media":2639,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2640","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\/2640","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=2640"}],"version-history":[{"count":0,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/posts\/2640\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media\/2639"}],"wp:attachment":[{"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/media?parent=2640"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/categories?post=2640"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/listenlabs.com\/articles\/wp-json\/wp\/v2\/tags?post=2640"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}