『Conversion Rate Optimization with Fexingo: A/B Testing, Landing Pages, and CRO Strategy』のカバーアート

Conversion Rate Optimization with Fexingo: A/B Testing, Landing Pages, and CRO Strategy

Conversion Rate Optimization with Fexingo: A/B Testing, Landing Pages, and CRO Strategy

著者： Fexingo
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Lucas and Luna sit at a CRO-team desk, two tablets between them showing side-by-side landing page variants. They don't just talk about A/B testing—they walk through actual experiments: the e-commerce checkout flow that lifted conversions by 12% when the CTA button changed from 'Buy Now' to 'Add to Cart', the SaaS pricing page where removing a form field reduced abandonment by 8%. This show is for marketers who want to know not just that a test worked, but why: statistical significance, sample size calculations, the difference between a winning variant and a false positive. Lucas pushes on the numbers—what's a 95% confidence interval really telling you? Luna challenges assumptions about visitor intent, heatmaps, and the psychology of micro-conversions. They dissect landing page structure, form length, headline A/B tests, and the interplay between CRO and SEO. Each episode is a tight, data-dense conversation that leaves you with a specific framework you can apply tomorrow. No fluff, no 'growth hacks'—just the mechanics of turning traffic into revenue, one test at a time. What happens when a test that 'should' win fails—and what that failure teaches you about your audience? #ConversionRateOptimization #ABTesting #LandingPages #CROStrategy #MarketingExperiments #DataDrivenMarketing #ConversionFunnel #Optimization #WebAnalytics #UXDesign #UserExperience #GrowthMarketing #DigitalMarketing #Marketing #FexingoBusiness #BusinessPodcast #MarketingPodcast #CRO Keep every episode free: buymeacoffee.com/fexingo© 2026 Fexingo. All rights reserved. 経済学
エピソード
  • How Tiny Button Effects Kill Your A-B Test Results

    How Tiny Button Effects Kill Your A-B Test Results
    2026/06/07
    Lucas and Luna dig into the 'button effect' — the subtle, often invisible ways that changing one element in an A/B test (like a button's size, color, or wording) can ripple through user behavior and distort results. They use a real example from a SaaS company that tested a green versus red CTA button: the red button won by 12%, but when they dug deeper, they found it was actually driving fewer high-value signups. They explain the concept of 'interaction effects' where a button change affects other metrics like time-on-page and scroll depth, and how to isolate those effects with multivariate testing and guardrail metrics. The episode offers a practical framework: always measure secondary metrics, run five-minute user tests before launching a full A/B test, and use a 'button effect audit' checklist to avoid misleading conclusions. #ABTesting #ConversionRateOptimization #ButtonEffect #UserExperience #SaaS #CTAButton #MultivariateTesting #GuardrailMetrics #InteractionEffects #LandingPage #CROStrategy #DigitalMarketing #UserBehavior #FexingoBusiness #BusinessPodcast #MarketingPodcast #DataDrivenMarketing #ConversionOptimization Keep every episode free: buymeacoffee.com/fexingo
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    6 分
  • How Your A-B Test Results Disappear After Ship

    How Your A-B Test Results Disappear After Ship
    2026/06/06
    In this episode of CRO with Fexingo, Lucas and Luna dig into the phenomenon of vanishing lift: why an A-B test that shows a clear winner in the experiment often fails to move the needle after the winning variant goes live. Drawing on a case study from Etsy, where a button redesign showed a 5 percent relative lift in the lab but zero impact in production, the hosts explore three root causes: novelty bias, audience mismatch, and the Hawthorne effect. They walk through specific fixes, including holdout groups, staggered rollouts, and pre-registering secondary metrics. If you've ever shipped a winner only to see flat conversion rates, this episode explains why and what to do about it. #A-BTesting #CRO #Marketing #Etsy #ConversionRateOptimization #NoveltyBias #HawthorneEffect #AudienceMismatch #HoldoutGroups #StaggeredRollout #VanishingLift #ExperimentDesign #DataDriven #StatisticalValidity #FexingoBusiness #BusinessPodcast #MarketingPodcast #CROStrategy Keep every episode free: buymeacoffee.com/fexingo
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    9 分
  • Why Your A-B Test Results Disappear After You Ship

    Why Your A-B Test Results Disappear After You Ship
    2026/06/06
    Episode 34 of Conversion Rate Optimization with Fexingo tackles a painful, rarely discussed failure mode: the winner that vanishes the moment you deploy it. Lucas and Luna dig into the 'novelty effect' and the 'Hawthorne effect' — two behavioural phenomena that inflate A-B test results during the experiment but evaporate once the change becomes permanent. They walk through a real case study from an e-commerce company that saw a 15 percent lift for a redesigned checkout flow during the test, only to see conversion revert to baseline within two weeks of launch. The hosts break down the diagnostic steps CRO teams can take: running longer tests to measure decay rates, building a 'persistence metric' into your testing framework, and differentiating between treatments that work because they're genuinely better versus treatments that work because users are paying attention to something new. If you've ever shipped a winning variant that quietly stopped winning, this episode explains why — and how to stop wasting resources on illusions of improvement. #NoveltyEffect #HawthorneEffect #ABTesting #CRO #ConversionRateOptimization #StatisticalValidity #BehavioralEconomics #ExperimentDesign #PersistenceMetric #DecayRate #Ecommerce #CheckoutOptimization #LandingPageTest #DataReliability #MarketingScience #FexingoBusiness #BusinessPodcast #Business Keep every episode free: buymeacoffee.com/fexingo
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    12 分
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