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

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

Episodes

May 27, 2026 8 mins
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. T...
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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. Th...
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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,...
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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 Opti...
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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 sta...
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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, avo...
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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 ra...
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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 ...
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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 b...
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Most marketers think A/B testing is low-risk. Run a test, pick the winner, move on. But a single flawed test can cost a company millions in lost revenue — not from a losing variant, but from a false positive that sends the business in the wrong direction for months. In this episode, Lucas and Luna break down the concept of opportunity cost in A/B testing. They walk through a real-world scenario: an e-commerce company testing a new ...
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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 compan...
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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...
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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...
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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 ...
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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...
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