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

Lucas and Luna dig into one of the most misunderstood pitfalls in A-B testing: how your chosen success metric can make a perfectly run experiment give you the wrong answer. Using Booking.com's infamous failed test of a 'Book Now' button — where conversion rate went up but revenue per visitor went down — they explain the difference between a metric that's easy to measure and one that actually matte...

Most A-B tests ask 'which variant wins?' but the smartest CRO teams ask a different question first: 'what is the smallest change that could produce the largest effect?' In this episode, Lucas and Luna break down the 'minimum viable change' framework—why testing big redesigns is usually a waste of time, how Amazon's one-click button emerged from an M.V.C. mindset, and why Booking.com's famously rel...

Episode 30 of Conversion Rate Optimization with Fexingo. Lucas and Luna tackle one of the most frustrating problems in CRO: you run a textbook A-B test, get a statistically significant winner, implement it, and nothing happens to overall revenue. They break down the 'dilution effect' — a concept from multi-armed bandit theory — using Amazon's real-world experience with search result page tests. Yo...

Most marketers think bigger sample sizes always mean better A-B test results. In this episode, Lucas and Luna break down the sample size fallacy — why too large a sample can detect statistically significant but practically meaningless effects, and how Booking.com discovered this lesson the hard way when a test with 2 million users showed a 0.1 percent lift that vanished on rollout. They walk throu...

Lucas and Luna dig into the science of landing page headlines — why most fail, and what actually works. They break down a specific case: how a SaaS company tested 17 headline variations and found that their highest-converting version wasn't emotional, clever, or benefit-driven. It was a straightforward statement of what the product does. They walk through the psychology: the curse of knowledge, th...

In this episode of Conversion Rate Optimization with Fexingo, Lucas and Luna expose a hidden flaw in how most marketers set up A-B tests: they optimize for the wrong baseline. Using the example of a SaaS trial page that spent six months testing button colors — only to discover their real issue was page-load time — they explain why pre-test diagnostics like the 'minimum detectable effect' and 'base...

Lucas and Luna dissect a common CRO mistake: social proof that hurts conversion. They use a real Booking.com test where adding '1,200 people are viewing this property' decreased bookings. They explain the psychological mechanism (salience vs. scarcity), when social proof works (e.g., Basecamp's customer logo page), and how to test proof type, placement, and credibility threshold. Specific numbers:...

Episode 25 of Conversion Rate Optimization with Fexingo dives into one of the most controversial tactics in online commerce: artificial urgency. Lucas and Luna break down how Booking.com pushes users to book through countdown timers, social proof messages, and scarcity claims — all while staying on the right side of truth. They look at a 2017 study that found Booking.com runs over 1,000 A-B tests...

Netflix doesn't just recommend shows — it tests the artwork you see to maximize the chance you'll click play. In this episode, Lucas breaks down how Netflix runs massive A/B tests on thumbnail images, selecting from dozens of variants per title based on your viewing history. Luna asks whether this creates a filter bubble of imagery and whether smaller companies should copy the tactic. Specific num...

In this episode, Lucas and Luna dive into how Booking.com uses A-B testing to create a sense of urgency on its hotel listing pages without resorting to deceptive tactics. They examine specific experiments — like showing 'Only 1 room left!' messages, real-time booking notifications, and countdown timers — and discuss the ethical line between persuasion and manipulation. Lucas explains the statistic...

In this episode, Lucas and Luna dive into multivariate testing (MVT) and why it often outperforms simple A/B tests for complex pages. They use a concrete example from an e-commerce landing page with three elements — headline, image, and CTA color — to show how MVT can uncover interactions that A/B tests would miss entirely. They explain the sample size trade-off, when to use MVT versus A/B testing...

In this episode of Conversion Rate Optimization with Fexingo, Lucas and Luna dive into the surprising power of micro-copy changes — specifically, how one e-commerce company lifted revenue by over 10 percent just by rewording the text on their 'Add to Cart' button. They walk through the original A/B test, the psychology behind the winning variant, and why such tiny tweaks often outperform full-page...

In this episode of Conversion Rate Optimization with Fexingo, Lucas and Luna dive into how Airbnb continuously A/B tests its search ranking algorithm to balance user preferences with business goals. They break down the specific metrics Airbnb uses, like booking probability and guest satisfaction scores, and discuss the challenges of testing algorithmic changes that affect millions of listings. Lea...

In this episode of Conversion Rate Optimization with Fexingo, Lucas and Luna dive into Duolingo's rigorous A-B testing culture. They explore how the language app runs hundreds of concurrent experiments on everything from lesson structure to notification timing, using specific examples like the 'streak' mechanic and push notification tests. Learn how Duolingo leverages statistical significance at s...

In this episode of Conversion Rate Optimization with Fexingo, Lucas and Luna dive into a surprisingly powerful A/B test led by Apple's product marketing team. They tested whether adding the word 'New' to a product page description lifted conversions. The test ran for two weeks across three product categories, with a sample size of 750,000 visitors per variant. The 'New' variant increased click-thr...

Lucas and Luna dive into one of the most consequential A/B tests in travel: how Booking.com discovered that showing a strikethrough original price next to the discount dramatically increased conversions. They break down the psychology of the anchor effect, the specific 15-percent lift in bookings, and why most marketers get reference pricing wrong. Along the way, they discuss when to test price pr...

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 peeke...

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 Optimiz...

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...

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 Hi...

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 a...

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 mo...

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 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...

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 si...