Episode 5 of Conversion Rate Optimization with Fexingo tackles one of the most overlooked pitfalls in A/B testing: peeking at results. Lucas and Luna walk through a real-world case where a $500,000 checkout-flow redesign looked like a clear winner after one weekend, only to flip negative by the time the trial reached statistical significance. They explain the math behind sequential testing, why Bayesian methods help, and how companies like Netflix and Booking.com guard against the 'peeking problem.' If you run tests or rely on data-driven decisions, this episode gives you one concrete fix you can apply today.

#ABTesting #CRO #StatisticalSignificance #PeekingProblem #SequentialTesting #BayesianMethods #ConversionRate #MarketingAnalytics #DataDriven #Netflix #BookingCom #HypothesisTesting #Luna #Lucas #Fexingo #BusinessPodcast #Marketing #ExperimentDesign

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Episode 4 of Conversion Rate Optimization with Fexingo dives into the most overlooked element of A/B testing: sample size. Lucas and Luna break down why most marketers run tests that are too small to detect real improvements, using a concrete example from a mid-sized e-commerce brand that wasted six months on inconclusive results. They explain statistical significance, power analysis, and minimum detectable effect in plain language, and share a simple rule of thumb for planning tests so you don't waste traffic on noise. If you've ever run an A/B test that 'didn't find anything,' this episode is for you.

#ABTesting #SampleSize #StatisticalSignificance #PowerAnalysis #MinimumDetectableEffect #CRO #ConversionRateOptimization #MarketingAnalytics #DataDrivenMarketing #Experimentation #BusinessPodcast #FexingoBusiness #Marketing #Ecommerce #Optimization #TestingMethodology #LucasAndLuna #Podcast

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Ever wonder how Amazon decides which new feature to roll out and which to kill? In this episode of Conversion Rate Optimization with Fexingo, Lucas and Luna dig into Amazon's internal culture of 'failing fast' through rigorous A/B testing. They explore the famous 'two-pizza team' structure, the statistical thresholds Amazon uses to avoid false positives, and a real example of a feature that was scrapped after testing showed it hurt conversion by 0.3%. Along the way, they discuss why most companies test the wrong things and how Amazon's approach to experimentation differs from traditional CRO. By the end, you'll understand why Amazon doesn't just test to improve — it tests to learn what to abandon.

#Amazon #ABTesting #ConversionRateOptimization #FailFast #Experimentation #Marketing #DataDriven #TwoPizzaTeams #StatisticalSignificance #CRO #ProductDevelopment #TechCulture #LucasAndLuna #FexingoBusiness #BusinessPodcast #MarketingStrategy #DigitalMarketing #Innovation

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In this episode, Lucas and Luna dive into Netflix's A/B testing culture, exploring how the streaming giant runs experiments on everything from artwork thumbnails to the exact shade of a play button. They break down Netflix's 'micro-experiment' philosophy, why they test one variable at a time even at massive scale, and how even tiny lift percentages translate into millions of additional viewing hours. Lucas explains the statistical rigor behind their approach, including how they determine sample sizes and avoid false positives. Luna challenges whether over-testing can lead to optimization paralysis. Specific examples include the 2023 'play button color' test, the personalized artwork algorithm, and how Netflix's testing infrastructure differs from mainstream tools like Optimizely or Google Optimize. Perfect for marketers, product managers, and anyone curious about how data drives creative decisions in streaming.

#Netflix #ABTesting #ConversionRateOptimization #CRO #Marketing #Streaming #DataDriven #Experimentation #UXDesign #Personalization #NetflixArtwork #MicroExperiments #StatisticalSignificance #OptimizationParalysis #PlayButton #FexingoBusiness #BusinessPodcast #MarketingStrategy

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In this debut episode of Conversion Rate Optimization with Fexingo, Lucas and Luna dive into Booking.com's legendary testing culture. With over 1,000 concurrent A/B tests running at any time, the travel giant treats every pixel as a hypothesis. We break down their 'test everything' philosophy — from button colors to price-display formatting — and explore how they avoid common pitfalls like false positives and novelty effects. Lucas shares the story of a single test that boosted bookings by 3% by changing the call-to-action label from 'Book now' to 'Check availability'. Luna questions whether this extreme testing approach works for smaller businesses. The hosts also discuss the concept of 'minimum detectable effect' and why Booking.com prioritizes tiny, cumulative wins over big swings. A practical primer for anyone who wants to move from guesswork to data-driven design.

#ConversionRateOptimization #ABTesting #Bookingcom #LandingPages #CROStrategy #Marketing #DataDriven #ExperimentDesign #StatisticalSignificance #UXOptimization #DigitalMarketing #Ecommerce #TravelTech #CallToAction #HypothesisTesting #FexingoBusiness #BusinessPodcast #MarketingPodcast

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