Lucas and Luna dig into a surprising A/B test where changing the number of checkout form fields from eight to six actually decreased conversions. They explore why reducing friction isn't always the answer, how user intent reshapes form design, and what a notorious 2009 Expedia test reveals about the difference between perceived effort and real effort. Along the way, they discuss a 2.4 percent drop that taught a team more than any winning test ever did. This episode offers a concrete framework for deciding which fields matter and which ones just make you feel productive.

#ConversionRateOptimization #ABTesting #CheckoutForm #FormDesign #UserExperience #Expedia #FrictionReduction #Marketing #BusinessPodcast #FexingoBusiness #ConversionStrategy #LandingPage #DataDriven #UserIntent #TestingMindset #CRO #FormOptimization #BehavioralEconomics

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Episode 44 of Conversion Rate Optimization with Fexingo. Lucas and Luna explore the counterintuitive finding that deliberately slowing down a landing page can increase conversion rates. Using a real-world case from a mid-market SaaS company that A/B tested a 300-millisecond artificial delay, they unpack the psychology of perceived effort, the distinction between performance and patience, and when a 'wait' signals quality. They also discuss how luxury brands have long used this principle offline, and why most CRO teams overlook the emotional cost of speed. A fresh take on the tension between page-speed dogma and actual user behavior.

#ConversionRateOptimization #CRO #ABTesting #LandingPageOptimization #PageSpeed #UserExperience #PerceivedPerformance #PsychologyOfWaiting #SaaSMarketing #LuxuryBranding #DigitalMarketing #FrictionInDesign #MarketingStrategy #UXResearch #WebPerformance #BehavioralEconomics #FexingoBusiness #BusinessPodcast

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Episode 43 of Conversion Rate Optimization with Fexingo dives into one of the most common and costly mistakes in A-B testing: peeking at results before the experiment ends. Lucas explains the statistical problem of 'peeking bias' — how repeatedly checking significance inflates false positives — and illustrates it with a concrete example from a real e-commerce checkout flow. Luna pushes back on the temptation to stop early when a variant looks like a winner, and they walk through a practical framework: pre-register your test duration, set a minimum sample size, and resist the urge to peek. The episode also covers how to handle ethical dilemmas when a test seems to harm user experience mid-run. By the end, listeners will understand why 'just peeking' corrupts the very data they're relying on — and how to build discipline around it.

#ABTesting #PeekingBias #StatisticalSignificance #DataIntegrity #CROStrategy #ConversionRateOptimization #Experimentation #MarketingAnalytics #EcommerceTesting #TestDesign #FrequentistStatistics #FalsePositive #SampleSize #HypothesisTesting #CheckoutOptimization #FexingoBusiness #BusinessPodcast #Marketing

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Lucas and Luna tackle a subtle but dangerous threat to A-B test validity: the novelty effect. Listeners learn how early spikes in conversion often mask users' initial curiosity rather than true preference change. The hosts walk through a real case from a mid-market SaaS company that saw a 6% lift on the first day of a test only to watch it evaporate by day seven. They explain how to spot the pattern, when to run a two-week minimum, and why segmenting by new vs. returning users can save you from shipping a losing variant. Packed with practical guardrails for any marketer running tests in 2026.

#ABTesting #NoveltyEffect #ConversionRateOptimization #MarketingTips #DataDrivenMarketing #SaaS #LandingPage #UserBehavior #Statistics #TestValidity #FexingoBusiness #BusinessPodcast #MarketingPodcast #CROStrategy #ExperimentDesign #DigitalMarketing #LucasAndLuna #Fexingo

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Lucas and Luna explain 'peeking,' the practice of checking A-B test results early, which inflates false positive rates. They break down why stopping a test the moment it hits 95% confidence is a statistical trap, using examples from Optimizely and booking sites. The episode explores the concept of sequential testing and the 'always valid' p-value method, then discusses how persistent peeking led one travel company to implement stricter governance. They also share a simple rule: decide the sample size and duration before the test starts. A short donation segment highlights listener support via buymeacoffee.com/fexingo.

#Peeking #A-BTesting #StatisticalSignificance #FalsePositive #Optimizely #SequentialTesting #PValue #ConversionRateOptimization #CRO #Marketing #DataScience #Experimentation #BusinessPodcast #MarketingPodcast #FexingoBusiness #LucasAndLuna #SampleSize #ExperimentDesign

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Episode 40 digs into the 'winner's curse' of A-B testing: why a variant that crushes it in an experiment often fails when rolled out to all users. Lucas and Luna unpack the behavioral cause — novelty effects, seasonal confounds, and Simpson's Paradox — using real examples from ecommerce and SaaS. They explain how to spot a false positive before you ship, and why a holdout group is your only safety net. Plus a short, sincere listener-support moment tied to the show's marketing focus.

#A-BTesting #CRO #ConversionRateOptimization #DataScience #ExperimentDesign #NoveltyEffect #SimpsonsParadox #HoldoutGroup #FalsePositive #StatisticalSignificance #MarketingStrategy #DigitalMarketing #Ecommerce #SaaS #FexingoBusiness #BusinessPodcast #LucasAndLuna #ProductExperimentation

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When The Economist redesigned its checkout flow, something strange happened: conversions on the paid subscription page dropped by nearly a third. The culprit wasn't the price, the copy, or the call-to-action button. It was a single text input field — a 'Company Name' box that appeared only on the payment screen. In this episode, Lucas and Luna trace how a seemingly harmless form field added friction, increased cognitive load, and cost thousands of conversions. They walk through the A/B test that uncovered the problem, the surprising lift from removing it, and the broader lesson about 'just in case' fields that ask for information you don't actually need. If you've ever wondered why a small detail can tank a well-tested funnel, this one's for you.

#A/BTesting #ConversionRateOptimization #TheEconomist #CheckoutFlow #FormOptimization #CognitiveLoad #Friction #FieldRemoval #Subscription #Paywall #CRO #Marketing #UX #UserExperience #Funnel #LandingPage #FexingoBusiness #BusinessPodcast

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Most A-B tests are designed to detect a 10% or 20% lift — but what happens when the true effect is only 3%? In this episode, Lucas and Luna break down the concept of Minimum Detectable Effect (MDE) and why ignoring it leads to false negatives and wasted traffic. They walk through a real example from an e-commerce checkout flow where a properly sized test uncovered a 4% gain that an underpowered test would have missed. Learn how to set realistic MDEs, calculate sample sizes, and avoid the biggest trap in conversion rate optimization: testing for effects too small to measure with your current traffic. If you're running A-B tests without thinking about statistical power, this episode will change how you design every experiment.

#MinimumDetectableEffect #ABTesting #SampleSize #StatisticalPower #CRO #ConversionRateOptimization #Marketing #Experimentation #FalseNegative #Ecommerce #CheckoutOptimization #DataDriven #Business #FexingoBusiness #BusinessPodcast #LucasAndLuna #Podcast #MarketingScience

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Lucas and Luna dig into a subtle but catastrophic mistake in A-B testing: launching the winning variant to 100 percent of users without a holdout group. Using examples from Skyscanner, Etsy, and LinkedIn, they show how novelty effects, seasonal spikes, and metric degradation can make a test winner look real when it's not. They explain the holdout group method—keeping 5 percent of users on the old version to measure long-term lift—and why companies like Google and Booking.com treat it as non-negotiable. Two concrete rules for saving your next experiment from false positives.

#A-BTesting #ConversionRateOptimization #HoldoutGroup #NoveltyEffect #Skyscanner #Etsy #LinkedIn #Google #Bookingcom #FalsePositive #LongTermLift #CROStrategy #DataScience #ExperimentDesign #FexingoBusiness #BusinessPodcast #Marketing #StatisticalValidity

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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

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