Lucas and Luna dig into the 'peeking problem' in A-B testing: why checking your results early—even once—can inflate your false-positive rate from 5% to over 50%. They break down the math with a concrete example from a 10,000-visitor test, explain how sequential testing and fixed-horizon designs fix the issue, and share a real case where a marketing team launched a losing variant because they peeked at day three. By the end, you'll know exactly when to resist the urge to look at your test results.

#A-BTesting #PeekingProblem #StatisticalValidity #CRO #ConversionRateOptimization #DataScience #MarketingAnalytics #SequentialTesting #FalsePositives #HypothesisTesting #Marketing #Experimentation #Statistics #DigitalMarketing #FexingoBusiness #BusinessPodcast #LucasAndLuna #CROStrategy

Keep every episode free: buymeacoffee.com/fexingo

Episode 15 of Conversion Rate Optimization with Fexingo digs into the peeking problem — the most common practical mistake in A/B testing. Lucas and Luna use a concrete example from a mid-size e-commerce company to show exactly how peeking at results too early inflates false-positive rates and wastes months of testing effort. They explain the concept of sequential testing and why tools like Optimizely now warn you to stop peeking. The conversation covers the math behind false discovery rates, why 'p-hacking' by peeking is so tempting, and a simple rule of thumb: never check your test before it reaches the pre-determined sample size. Perfect for marketers and product managers who want trustworthy test results.

#ABTesting #PeekingProblem #StatisticalValidity #CRO #ConversionRateOptimization #FalsePositive #SequentialTesting #PValue #SampleSize #MarketingAnalytics #ExperimentDesign #DataScience #EcommerceTesting #Optimizely #P-Hacking #FexingoBusiness #BusinessPodcast #Marketing

Keep every episode free: buymeacoffee.com/fexingo

In this episode, Lucas and Luna look at how Etsy runs thousands of A/B tests per year without bogging down their engineering team. The secret? A self-serve experimentation platform with a 'feature flag' system that lets product managers launch variants without touching code. We walk through a specific 2023 case: Etsy tested whether showing 'Free shipping over $35' in search results vs. only on the product page lifted conversion. The winning variant added $8.2 million in annual revenue. But the real lesson is in the infrastructure — how Etsy handles statistical significance, prevents peeking, and kills losing variants in under 48 hours. We also discuss the trade-off: self-serve speed can lead to sloppy test design if guardrails aren't hard-coded. Tune in for a practical look at experimentation at scale.

#Etsy #ABTesting #Experimentation #FeatureFlags #SelfServePlatform #ConversionOptimization #Ecommerce #ProductManagement #StatisticalSignificance #PeekingProblem #RevenueGrowth #CROStrategy #DataDriven #Marketing #FexingoBusiness #BusinessPodcast #LucasAndLuna #May2026

Keep every episode free: buymeacoffee.com/fexingo

In this episode, Lucas and Luna dive into one of the most famous CRO case studies in the industry: how a single A-B test at Expedia uncovered a $12 million annual revenue leak — simply by removing a single optional field from a checkout form. They break down why the change worked, the psychology of forced choice, and why most companies overlook simple friction points. Drawing on principles from Hick's Law and the Paradox of Choice, they discuss when less really is more in conversion optimization. Tune in to learn how this one test reshaped best practices for checkout form optimization and what it means for your own landing pages.

#Expedia #A-BTesting #CRO #ConversionRateOptimization #CheckoutOptimization #FormOptimization #FrictionRemoval #HicksLaw #ParadoxOfChoice #UXDesign #LandingPage #MarketingPodcast #BusinessPodcast #FexingoBusiness #CROCaseStudy #UserExperience #OptionalFields #RevenueGrowth

Keep every episode free: buymeacoffee.com/fexingo

Most marketers peek at their A-B test results before the test is finished. Lucas and Luna explain why peeking invalidates your data — even if the sample size looks big enough — and how a simple rule called 'sequential testing' fixes it. They walk through a concrete example: a landing page test at a mid-size SaaS company that would have shipped a losing variant if the team had stopped early. They also discuss how platforms like Optimizely and VWO handle peeking, and why the Bayesian approach isn't a magic bullet. By the end, you'll know why your gut check during week two of an experiment is probably lying to you.

#ABTesting #Peeking #SequentialTesting #StatisticalSignificance #CRO #ConversionRateOptimization #DataDrivenMarketing #ExperimentDesign #BayesianInference #Optimizely #VWO #LandingPageOptimization #MarketingMetrics #FrequentistStatistics #SampleSize #FalsePositive #FexingoBusiness #BusinessPodcast

Keep every episode free: buymeacoffee.com/fexingo

Most A-B tests fail because teams choose 95 percent statistical significance and stop the test early. In episode 11 of Conversion Rate Optimization with Fexingo, Lucas and Luna dive into the concept of statistical power — the probability that a test will actually detect a real effect. Using a concrete example from a SaaS pricing-page test, Lucas shows how low power (often just 20 percent) means most tests are doomed before they start. He explains why a test with 80 percent power and 500 visitors per variant can yield trustworthy results, while the same test with 200 visitors per variant produces false negatives four times out of five. The hosts discuss practical sample-size calculators, how to avoid the 'peeking at results every day' trap, and why running a test to 95 percent significance with tiny sample sizes is worse than running no test at all.

#StatisticalPower #ABTesting #SampleSize #TypeIIError #FalseNegative #ConversionRateOptimization #CROStrategy #Marketing #DataDrivenMarketing #PricingPage #SaaSMarketing #FexingoBusiness #BusinessPodcast #MarketingPodcast #HypothesisTesting #BayesianStatistics #FrequentistStatistics #PowerAnalysis

Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna dive into a quietly powerful metric that most CRO teams overlook: the 'Average Value per Visitor' (AVPV) as a leading indicator of A-B test success. Instead of waiting days for statistical significance on conversion rate, teams at companies like ASOS and Wayfair use AVPV shifts to call winners — or kill losers — within hours. Lucas breaks down how ASOS caught a losing checkout flow in under 90 minutes using AVPV, avoiding weeks of bad data. Luna pushes back on the risk of false positives with early stopping, and they hash out when this shortcut works and when it doesn't. Episode also touches on the ad-free listener support model that keeps the show independent.

#ABTesting #CRO #ConversionRateOptimization #AverageValuePerVisitor #LeadingIndicators #ASOS #Wayfair #EcommerceTesting #StatisticalSignificance #FalsePositives #EarlyStopping #BayesianMethods #DataScience #MarketingMetrics #FexingoBusiness #BusinessPodcast #Marketing #DigitalExperimentation

Keep every episode free: buymeacoffee.com/fexingo

In this episode of Conversion Rate Optimization with Fexingo, Lucas and Luna dig into Spotify's approach to A/B testing for personalized playlists. They focus on a specific experiment from 2022 where Spotify tested two different ways to surface Discover Weekly tracks: one using a collaborative filtering algorithm and another using a neural network model. The test involved over 10 million users and measured not just click-through rate but also long-term retention and time spent listening. Lucas explains why most companies would have declared the neural network the winner after a few weeks, but Spotify waited three months to see if the novelty wore off. Luna pushes back on whether three months is enough for music fatigue. The episode reveals how Spotify balances short-term engagement metrics with true user satisfaction, and why their A/B testing framework explicitly includes a 'delight' metric alongside more traditional conversion goals. The hosts also briefly discuss how listeners can support the ad-free show via buy me a coffee dot com slash fexingo.

#Spotify #DiscoverWeekly #Personalization #ABTesting #Algorithm #NeuralNetwork #UserRetention #EngagementMetrics #DelightMetric #MusicStreaming #ConversionRateOptimization #CRO #Marketing #DataDriven #LongTermTesting #NoveltyEffect #FexingoBusiness #BusinessPodcast

Keep every episode free: buymeacoffee.com/fexingo

Lucas and Luna dig into the most overlooked failure in conversion rate optimization: optimizing for the wrong metric. They walk through a real case from a mid-market SaaS company that ran a perfect A/B test on its pricing page — statistically significant, properly powered, two-week duration — and got a clear winner. The winning variant boosted click-through to the signup form by 22 percent. But signup completion dropped 34 percent because the new button text attracted window-shoppers, not buyers. The company launched the change anyway and lost an estimated $180,000 in qualified leads over three months before reverting. Lucas explains the concept of 'surrogate metrics' — what you measure versus what you actually want — and why short-term engagement metrics often cannibalize long-term value. The hosts also discuss how to design tests around your north-star metric and why companies like HubSpot and Intercom explicitly train teams to distinguish between leading indicators and vanity metrics. If you've ever celebrated a test result that later hurt your business, this episode explains why and how to fix it.

#ConversionRateOptimization #ABTesting #CRO #MarketingMetrics #VanityMetrics #SurrogateMetrics #NorthStarMetric #SaaS #PricingPageTest #HubSpot #Intercom #DataDrivenMarketing #LeadQuality #Growth #Experimentation #Business #Marketing #FexingoBusiness

Keep every episode free: buymeacoffee.com/fexingo

Most marketers think A/B testing is the gold standard. But when you have multiple elements to optimize, traditional A/B tests can fail or take forever. In this episode, Lucas and Luna explore Multivariate Testing (MVT) — a method that tests combinations of changes at once on complex pages like ecommerce PDPs and SaaS pricing tables. They break down a real case from a mid-market DTC brand that used MVT to boost revenue per visitor by 14% using just one test, versus the 6 sequential A/B tests it would have required. Lucas explains sample size math, the curse of dimensionality, and when to stick with A/B instead. If you're a CRO practitioner tired of running endless sequential tests, this episode shows you a smarter path.

#MultivariateTesting #ABTesting #CRO #ConversionRateOptimization #Marketing #DTCBrand #Ecommerce #SaaS #PricingPage #LandingPage #SampleSize #CurseOfDimensionality #RevenuePerVisitor #DataDrivenMarketing #TestingStrategy #FexingoBusiness #BusinessPodcast #MarketingPodcast

Keep every episode free: buymeacoffee.com/fexingo