A/B Testing Psychology

(3/3) The most useful research I run isn't a study. It's a question bolted onto someone else's A/B test... Your product team runs experiments constantly. Almost none of that behavioral data gets paired with a single word about why, so every test answers "what happened" and nearly none of them answer "what were people actually thinking." That gap is the cheapest, most overlooked research most teams have sitting right in front of them. The move is simple. When a variant goes live, you target a short intercept survey at the users inside that variant. Behavioral data from the experiment, attitudinal data from the survey, same users, same moment. When the test wins, you know what changed in their heads. When it loses, you still walk away with the why. The part I value most: it preserves the learning even when an experiment gets killed early. A test shut off on day three normally teaches you nothing. Pair it with a survey and even a dead experiment leaves you smarter than you were. This is exactly what we built experiment-paired surveys for at Sprig. You point a study at a specific variant, by URL or by a list of user IDs, and capture the attitude alongside the behavior without standing up a full research cycle. And the survey isn't a static form. It can ask an AI-driven follow-up based on what someone just told you, so when a user says the new layout "felt cluttered," the next question digs into which part, in their own words. You get the reason, not just the checkbox. It's the third column of the score card, made practical. A score tells you the room got hotter. Behavior tells you where the heat is. Attitude tells you why someone lit the match. You want all three, and you can have them on the experiments you're already running. That's the series: stop letting a single number have the last word. Read the signals underneath it. Curious which metric your team over-trusts the most?

Last week, I was showing Sameer Munshi (EY's Head of Behavioral Science) an emotional trading preventor app I vibecoded in an hour. I'd vibecoded the entire A/B test on Lovable - not just two versions of the app, but the actual A/B test logic built right into it with random user assignment. Everyone who visited got randomly assigned to: • either the experimental condition (with intervention) • or control condition (without intervention). While the design of the prototype simulated that of actual trading platforms, Sameer said something that I hadn't thought of: "It's a little hard, out of context, to say 'here, buy or sell' and then expect the intervention to work." I'd built this A/B testing setup but hadn't included the most basic thing yet: you can't test *emotional* trading without inducing the *emotions* that drive it. Sameer suggested creating scenarios like showing a screen that said: "Tesla just jumped 15% after Elon tweeted about record sales. You can buy before it rises more..." Suddenly it's not just clicking buttons - it's FOMO. That sick feeling you're missing easy money. Exponentially more visceral. If you're testing any behavior change intervention: • Identify the emotional triggers that drive the behavior • Engineer those moments in your test environment so it's as close to the real world as possible • Then test your intervention Whether it's impulse purchases, doomscrolling, or overtrading - you need to recreate the psychological context first. We don't make decisions in vacuums. Our decisions are driven by interactions between multiple situational and internal (psychological, biological, demographic) factors. Try to engineer as many of them as possible while testing your intervention. P.S. if you're trying to engineer delight for your ideal buyers via your marketing (without any dark patterns or salesy tactics) 📙 Here’s how to do that in 5 behavioral science-based upgrades (free!) → https://lnkd.in/g54xD9pY

Publisher experiments fail when they start with tactics, not hypotheses. A/B testing has become a staple in digital publishing, but for many publishers, it’s little more than tinkering with headlines, button colours, or send times. The problem is that these tests often start with what to change rather than why to change it. Without a clear, measurable hypothesis, most experiments end up producing inconclusive results or chasing vanity wins that don’t move the business forward. Top-performing publishers approach testing like scientists: They identify a friction point, build a hypothesis around audience behaviour, and run the experiment long enough to gather statistically valid results. They don’t test for the sake of testing; they test to solve specific problems that impact retention, conversions, or revenue. 3 experiments that worked, and why 1. Content depth vs. breadth: Instead of spreading efforts across many topics, one publisher focused on fewer topics in greater depth. This depth-driven strategy boosted engagement and conversions because it directly supported the business goal of increasing loyal readership, and the test ran long enough to remove seasonal or one-off anomalies. 2. Paywall trigger psychology: Rather than limiting readers to a fixed number of free articles, an engagement-triggered paywall is activated after 45 seconds of reading. This targeted high-intent users, converting 38% compared to just 8% for a monthly article meter, resulting in 3x subscription revenue. 3. Newsletter timing by content type: A straight “send time” test (9 AM vs. 5 PM) produced negligible differences. The breakthrough came from matching content type to reader routines: morning briefings for early risers, deep-dive reads for the afternoon. Open rates increased by 22%, resulting in downstream gains in on-site engagement. Why most tests fail • No behavioural hypothesis, e.g., “testing headlines” without asking why a reader would care • No segmentation - treating all users as if they behave the same • Vanity metrics over meaningful metrics - clicks instead of conversions or LTV • Short timelines - stopping before 95% statistical confidence or a full behaviour cycle What top performers do differently ✅ Start with a measurable hypothesis tied to business outcomes ✅ Isolate one behavioural variable at a time ✅ Segment audiences by actions (new vs. returning, skimmers vs. engaged) ✅ Measure real results - retention, conversions, revenue ✅ Run tests for at least 14 days or until reaching statistical significance ✅ Document learnings to inform the next test When experiments are designed with intention, they stop being random guesswork and start becoming a repeatable growth engine. What’s the most valuable experimental hypothesis you’re testing this quarter? Share with me in the comment section. #Digitalpublishing #Abtesting #Audienceengagement #Contentstrategy #Publishergrowth

A Fortune 500 brand ran 127 A/B tests last year. Guess how many actually improved their bottom line? Just 3. Here's why most optimization programs fail... I see it constantly: companies trapped in an endless cycle of A/B testing without meaningful results. They're obsessed with testing button colors while ignoring the psychological principles driving user decisions. This approach is like trying to assemble IKEA furniture without the instruction manual. You might eventually succeed, but at what cost? The problem isn't testing itself. It's testing without strategy. After optimizing digital experiences for companies like Adobe, Nike, and Xerox for over a decade, I've learned that successful optimization starts with understanding how people actually make decisions online. When our team at The Good tackles optimization, we first evaluate: ↳ Which psychological trigger points are missing from your current experience? ↳ Where are users encountering choice overload or decision fatigue? ↳ What specific information gaps exist that prevent conversion? This framework consistently delivers tests with 5-10x greater impact than random tactical changes. One enterprise client was running 3-4 tests weekly with minimal results. After refocusing around psychological principles from our framework, their very next test delivered a 34% conversion lift. Are you running tests that matter? Or just testing for the sake of testing? The difference is understanding not just what users do, but *why* they do it.