Time-efficient A/B Testing for UX Projects

Not every user interaction should be treated equally, yet many traditional optimization methods assume they should be. A/B testing, the most commonly used approach for improving user experience, treats every variation as equal, showing them to users in fixed proportions regardless of performance. While this method has been widely used for conversion rate optimization, it is not the most efficient way to determine which design, feature, or interaction works best. A/B testing requires running experiments for a set period, collecting enough data before making a decision. During this time, many users are exposed to options that may not be effective, and teams must wait until statistical significance is reached before making any improvements. In fast-moving environments where user behavior shifts quickly, this delay can mean lost opportunities. What is needed is a more responsive approach, one that adapts as individuals utilize a product and adjusts the experience in real time. Multi-Armed Bandits does exactly that. Instead of waiting until a test is finished before making decisions, this method continuously tests user response and directs more people towards better-performing versions while still allowing exploration. Whether it's testing different UI elements, onboarding flows, or interaction patterns, this approach ensures that more users are exposed to the most optimal experience sooner. At the core of this method is Thompson Sampling, a Bayesian algorithm that helps balance exploration and exploitation. It ensures that while new variations are still tested, the system increasingly prioritizes what is already proving successful. This means conversion rates are optimized dynamically, without waiting for a fixed test period to end. With this approach, conversion optimization becomes a continuous process, not a one-time test. Instead of relying on rigid experiments that waste interactions on ineffective designs, Multi-Armed Bandits create an adaptive system that improves in real time. This makes them a more effective and efficient alternative to A/B testing for optimizing user experience across digital products, services, and interactions.

Really tired of posts saying that AB testing, and even worse Experimentation, is slow and even a waste of time. These folks really miss the forest for the trees. Please, we need to shift the conversation from “experimentation is slow” to “slow experimentation is a symptom of outdated processes.” Some thoughts: • 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 ≠ “𝗔/𝗕 𝘁𝗲𝘀𝘁𝗶𝗻𝗴”   > 𝘌𝘹𝘱𝘦𝘳𝘪𝘮𝘦𝘯𝘵𝘢𝘭 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 spans far more than a 50/50 split test on a live site. It includes pretotyping, fake-door MVPs, prototype usability sessions, synthetic‐data simulations, feature-flag rollouts, multi-armed bandits, and even counterfactual analysis on historical data.   > All of these methods share the same goal: 𝗿𝗲𝗱𝘂𝗰𝗲 𝘂𝗻𝗰𝗲𝗿𝘁𝗮𝗶𝗻𝘁𝘆 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗹𝗲𝗮𝘀𝘁 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲 𝗰𝗼𝘀𝘁 𝗶𝗻 𝘁𝗶𝗺𝗲, 𝘁𝗿𝗮𝗳𝗳𝗶𝗰, 𝗼𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝗳𝗳𝗼𝗿𝘁. When we treat “experimentation” as a single statistical ritual, we ignore dozens of faster ways to learn.    • 𝗧𝗵𝗲 𝘁𝗿𝘂𝗲 𝗞𝗣𝗜 𝗶𝘀 𝙩𝙞𝙢𝙚-𝙩𝙤-𝙞𝙣𝙨𝙞𝙜𝙝𝙩   > Businesses win by compressing the 𝗯𝘂𝗶𝗹𝗱 → 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 → 𝗹𝗲𝗮𝗿𝗻 loop, not by accumulating perfect p-values.   > Every day a hypothesis remains untested is an opportunity cost. The 𝘳𝘪𝘴𝘬 of moving slowly often dwarfs the 𝘳𝘪𝘴𝘬 of shipping an imperfect change.    • 𝗥𝗲-𝗳𝗿𝗮𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲   > 𝗔/𝗕 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗶𝘀 𝗷𝘂𝘀𝘁 𝙤𝙣𝙚 𝙜𝙚𝙖𝙧 𝗶𝗻 𝗮 𝗹𝗮𝗿𝗴𝗲𝗿 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗮𝗰𝗵𝗶𝗻𝗲. When it’s the bottleneck, upgrade the gear—don’t scrap the engine.   > The question isn’t “Should we A/B test?” but “What’s the 𝘧𝘢𝘴𝘵𝘦𝘴𝘵 𝘷𝘢𝘭𝘪𝘥 experiment we can run right now?” Sometimes that’s a quick usability test; other times it’s a sequential online test that can stop after two days.   > By 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆, teams like Amazon, Netflix, and Booking.com (and uncommonly thought of orgs like DrSquatch, Miro, Clickup, Gympass) run thousands of concurrent tests without slowing product velocity—proving that disciplined testing and speed are complements, not opposites.    • 𝗖𝗮𝗹𝗹 𝘁𝗼 𝗮𝗰𝘁𝗶𝗼𝗻   > Stop treating A/B testing as a speed bump. Treat it as a safety belt that lets you 𝘥𝘳𝘪𝘷𝘦 𝘧𝘢𝘴𝘵𝘦𝘳—because you can course-correct at any moment with data. Use this framing to shift the conversation from “experimentation is slow” to “slow experimentation is a symptom of outdated processes.” This table below took me 15min to create, it could be 10X better, perhaps a Speero blueprint here in the works? h/t to Ryan Levander for the nudge to post on this topic.

You spend weeks designing a test, running it, analyzing results... Only to realize the data is too weak to make any decisions. It’s a common (and painful) mistake (also completely avoidable). Poor experimentation hygiene damages even the best ideas. Let’s break it down: 1. Define Success Before You Begin Your test should start with two things: → A clear hypothesis grounded in data or rationale. (No guessing!) → A primary metric that tells you whether your test worked. But the metric has to matter to the business and be closely tied to the test change. This is where most teams get tripped up. Choosing the right metric is as much art as science, without it, you’re just throwing darts in the dark. 2. Plan for Every Outcome Don’t wait until the test is over to decide what it means. Create an action plan before you launch: → If the test wins, what will you implement? →If it loses, what’s your fallback? → If it’s inconclusive, how will you move forward? By setting these rules upfront, you avoid “decision paralysis” or trying to spin the results to fit a narrative later. 3. Avoid the #1 Testing Mistake: Underpowered tests are the ENEMY of good experimentation. Here’s how to avoid them: → Know your baseline traffic and conversion rates. → Don’t test tiny changes on low-traffic pages. → Tag and wait if needed. If you don’t know how many people interact with an element, tag it and gather data for a week before testing. 4. Set Stopping Conditions Every test needs clear rules for when to stop. Decide: → How much traffic you need. → Your baseline conversion rate. → Your confidence threshold (e.g., 95%). Skipping this step is the quickest way to draw false conclusions. This takes discipline, planning, and focus to make testing work for you. My upcoming newsletter breaks down everything you need to know about avoiding common A/B testing pitfalls, setting clear metrics, and making decisions that move the needle. Don’t let bad tests cost you time and money. Subscribe now and get the full breakdown: https://lnkd.in/gepg23Bs