User Experience Testing with A/B Variants

💡A/B Testing: 8 Essential Tips A/B testing is a powerful method for comparing two versions of a design against each other to determine which one performs better. Here are the top 8 tips for conducting effective A/B tests: 1️⃣ Define clear goals: Know what you want to achieve with your test. Whether it's increasing conversions, click-through rates, or user engagement, having clear goals is crucial. 2️⃣ Test one variable at a time: To understand the effect of a change, test only one variable at a time (i.e., color of a primary call to action button). Multiple changes can confound results. 3️⃣ Randomize your sample: Ensure your sample is randomly selected to avoid biases and ensure the test results are reliable. 4️⃣ Ensure sufficient sample size: Make sure your test runs long enough to gather a statistically significant sample size to make confident decisions. Use sample size calculator: https://lnkd.in/dCXpgv2Z 5️⃣ Segment your audience: Consider segmenting your audience to understand how different groups respond to the change. 6️⃣ Monitor metrics beyond primary goal: Track secondary metrics to ensure that the changes do not negatively impact other important aspects of user experience (i.e., you have a higher conversion rate but a lower user retention rate). 7️⃣ Check for statistical significance—you need to ensure that the data you collect cannot be attributed to pure chance. Use the calculator to check significance: https://lnkd.in/d5jcWa7N 8️⃣ Consider long-term effects: Assess whether the changes have a lasting positive impact or if they might lead to long-term negative consequences (this can happen if you use dark patterns: https://lnkd.in/dtztGgFW) 📕 Introduction to A/B testing for product designers (YouTube): https://lnkd.in/dxuW8-hq #testing #design #research #productdesign #design #abtesting

A 6% revenue lift. 99% statistical significance. Ship it. It couldn't go wrong, could it? 🫣 In 2016, I was leading a product analytics team at Credit Karma. We ran an A/B test for a personal loans redesign. The results looked fantastic: - 𝗔𝗽𝗽𝗿𝗼𝘃𝗮𝗹𝘀 𝘄𝗲𝗿𝗲 𝘂𝗽 (good for users). - 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝘄𝗮𝘀 𝘂𝗽 𝟲% (good for business). - 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝘀𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝗰𝗲: 𝟵𝟵%. We should have ramped it up to 100% of users and closed out the test. However, we couldn't roll it out immediately due to other constraints. Over the next few weeks, I watched that 6% revenue lift drift down to 3%. It was still positive. It was still 99% significant. But the downward trend didn't sit right with me. I dug into the segments and found the reality: 𝗨𝘀𝗲𝗿𝘀 𝗻𝗲𝘄 𝘁𝗼 𝘁𝗵𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: +10% revenue. 𝗨𝘀𝗲𝗿𝘀 𝗿𝗲𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝘁𝗼 𝘁𝗵𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: -5% revenue. The aggregate number was positive only because the traffic was initially heavy with people seeing the design for the first time. Over time, as those people returned to the page, they fell into the negative bucket. 𝗜𝗳 𝘄𝗲 𝗵𝗮𝗱 𝘀𝗵𝗶𝗽𝗽𝗲𝗱 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝘁𝗵𝗲 𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲, 𝘄𝗲 𝘄𝗼𝘂𝗹𝗱 𝗵𝗮𝘃𝗲 𝗲𝘃𝗲𝗻𝘁𝘂𝗮𝗹𝗹𝘆 𝗹𝗼𝘀𝘁 𝗺𝗼𝗻𝗲𝘆. We wouldn't have even known that it was due to a negative A/B test. Because we caught this, we redesigned the experience to address the issues for the returning users before rolling it out. Don't just blindly follow A/B tests and their implied results. While I love A/B testing, you need to be very careful to understand what you are truly measuring. (we did end up fixing the experience for returning users and deploying a win-win)

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.

Data Science Interview Question: We are rolling out an e-commerce homepage banner personalization feature. How do you measure its impact? First, let's ask questions to better understand the problem. What is the feature optimizing for? Are we trying to increase banner click-throughs or improve downstream conversions, or enhance overall shopping engagement? Is it for all visitors or only logged-in users? Once the goal is clear, I would organize the evaluation across four dimensions: engagement, conversion, retention, and system integrity. For each dimension, I would define both success metrics and guardrail metrics to ensure that we drive positive impact without creating unintended side effects. The first dimension is 𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭, which captures immediate interaction with the personalized banner. Success metrics include click-through rate, hover or dwell time on the banner etc. These indicate whether personalization increases visibility and relevance. Guardrail metrics include bounce rate and session abandonment, which can reveal if the banner distracts or overwhelms users instead of helping them explore. The second dimension is 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧, which measures the business value generated by the personalization. Here, I would track add-to-cart rate, conversion rate, and average order value among exposed users. I would also look at assisted conversions, such as cases where the banner leads a user to other valuable pages. As guardrails, I would monitor for revenue cannibalization, or overuse of promotions that inflate short-term performance but harm profitability. The third dimension is 𝐫𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. A strong personalization system should build long-term relationships, not just single-session engagement. Success here includes improved return visit rate, repeat purchase rate, etc. This would take a long window, though. Guardrails include lower satisfaction ratings or negative feedback, which could indicate that the personalization feels intrusive, repetitive, or irrelevant. The fourth dimension is 𝐬𝐲𝐬𝐭𝐞𝐦 𝐚𝐧𝐝 𝐦𝐨𝐝𝐞𝐥 𝐡𝐞𝐚𝐥𝐭𝐡. From an operational perspective, I would expect stable latency, consistent banner coverage across user segments, etc. Guardrail metrics help detect regressions such as overexposure to a small set of items, or degradation in serving performance under load. I would measure these outcomes through a well-designed A/B test. The experiment would define one or two primary success metrics—typically banner click-through rate and conversion rate—and several guardrails drawn from the other dimensions. Based on what the interviewer shows interest in, we can dive into those more. For detailed breakdowns, subscribe at https://lnkd.in/g5YDsjex For ML interview crash course, check out Decoding ML Interviews https://lnkd.in/gc76-4eP For interview prep, check out BuildML services https://lnkd.in/gBBygPex

Most A/B tests leave value on the table We run an experiment, pick the winning variant, and ship it to everyone. But the “average winner” is not always the best choice for every user. A/B tests are not just a way to pick one variant. They’re an opportunity to learn which experience works best for which users. That’s where uplift modeling comes in. As part of the causal inference world, uplift modeling uses experiment data to build a policy at the user or segment level. The model can automatically identify groups that respond differently and recommend: Treatment 1 for some users Treatment 2 for others Control when neither treatment is expected to help You can then estimate the expected lift of that policy compared with deploying any single variant to everyone, or simply keeping the control. This is becoming even more relevant in the agentic world. It is now much easier to create and orchestrate different experiences for different users or segments. Experiments no longer have to end with “ship variant B.” They can become the foundation for continuously improving how decisions are personalized. Every test becomes part of a learning loop: Run an experiment → learn what works best for whom → deploy a smarter policy → measure the results → improve again That’s how experimentation becomes an ongoing improvement process, with learnings that compound over time. The example below shows this in practice. Rather than choosing one winner, the model creates a deployable policy and estimates its expected lift compared with every individual variant and the control.

A/B testing is not a new method at all, in behavioral science, this logic has been around for a very long time, even if we do not usually call it A/B testing. We call it experiments, condition comparisons, randomized studies, or controlled designs. The basic idea is the same: compare versions, observe responses, and learn from the difference. But in behavioral science, that comparison was never restricted to metrics alone. Depending on the question, researchers have long used quantitative data, qualitative data, or a combination of both to understand how people respond to different conditions. That broader view makes a lot of sense to me, especially because human behavior is rarely just a number. In UX, though, A/B testing is still usually framed in a much narrower way. Most of the time, it is treated as a purely quantitative exercise: which version got more clicks, which flow converted better, which option increased engagement. That is valuable, and I am not arguing against it. I use behavioral metrics too, and they matter. But across projects, one thing has become very clear to me: a metric can tell you that something changed without telling you what that change actually meant to users. I have seen cases where a version performed better, but the real insight was not the lift itself. The real insight was that users felt less hesitation, understood the next step faster, or trusted the experience more. Without talking to them, observing them, or giving them space to explain their thinking, that deeper layer would have remained invisible. That is why I think qualitative methods can make A/B testing far more insightful. Interviews, think aloud sessions, usability observation, and even short open ended follow ups can reveal the mechanism behind the outcome. Instead of stopping at Version B won, we can start understanding whether it reduced confusion, lowered cognitive effort, aligned better with expectations, or made the interface feel more credible. To me, that is where the real value is. A/B testing should not only help us choose between options. It should help us learn something meaningful about perception, attention, trust, decision making, and friction. Otherwise, we risk becoming very good at measuring outcomes while staying relatively shallow in how we interpret them. I also think qualitative methods are useful at more than one stage of the process. Before a test, they help generate stronger variants because the changes are grounded in actual user problems rather than assumptions. During a test, they can capture reactions that behavioral logs cannot fully explain. After a test, they help interpret both positive and null results. Sometimes a version does not win because the change was weak, because users did not notice it, or because the thing the team cared about was not what users cared about. Those are important lessons, and they rarely come from the dashboard alone. Perceptual User Experience Lab

How to Approach A/B Testing as a Data Analyst A/B testing is a great way to help make data driven decisions on whatever project or product you may be working on.  Here’s a step by step setup guide for how you can go about creating and analyzing A/B tests. This example is mainly focused on doing an A/B test in an ecomm site, but the general principles apply regardless. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗚𝗢𝗔𝗟 𝗮𝗻𝗱 𝗮 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁: Before doing any tech work, you need to clearly understand what you’re trying to accomplish from the test. Make a document outlining the test and set a clear objective in a doc that exactly states what the goal of the A/B test is - are you trying to increase CVR from testing a new feature, encourage repeat rates, etc. What ever the objective is - make a doc outlining the test and start at the top with clearly writing down the goal, then write down your whole testing plan. 𝗠𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀: In the same doc you state the GOAL - right after it, write down what your test hypothesis is. This really just is, what change do you expect or think you will see fro your test. Here’s an example: Changing the color of the add-to-cart button from green to red, will increase ATC rate by 10%. 𝗦𝗲𝗴𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗔𝘂𝗱𝗶𝗲𝗻𝗰𝗲: Divide your test population into smaller groups, for an A/B usually 50,50 but if your testing 2 variables could be 33/33/33%. Each sub group you make assign in the Testing doc, which variation of the test will the group get, either control or variant. 𝗗𝗼 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵 𝘄𝗼𝗿𝗸 𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝘁𝗵𝗲 𝘃𝗮𝗿𝗶𝗮𝗻𝘁𝘀: Now you actually have to hookup in the backend to direct your site traffic to receive either the control group or test group that you’ve defined in the Testing doc. Usually you’re going to work with a frontend engineer to make sure all the code is hooked up and ready to go. 𝗥𝘂𝗻 𝘁𝗵𝗲 𝗧𝗲𝘀𝘁: Kick off the test. Make sure you let the test run long enough for statistical significance to be reached. 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: Before kicking off the test, at least make sure you have all you need to collect the data to measure the results on the test. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: Do a through analysis of all the data that answers the question. Did the change in the variant group lead to a statistically significant improvement over the control? Make sure to validate with stat tests. 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱 𝗮 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻: Make a recommendation and document it in your testing doc, using data as evidence to support if you should implement the change in your Variant group or stay using the tech in the control group. And in a nutshell, that’s how you do an A/B test, this is just a high level overview of it. Overall patience in data collection and precision in the GOAL of the test are key for a successful A/B test.

Day 6 - CRO series Strategy development ➡  A/B Testing (Part 3) Common Pitfalls in A/B Testing (And How to Avoid Them) A/B testing can unlock powerful insights—but only if done right. Many businesses make critical mistakes that lead to misleading results and wasted effort. Here’s what to watch out for: 1. Testing Multiple Variables at Once If you change both a headline and a CTA button color, how do you know which caused the impact? Always test one variable at a time to isolate its true effect. 2. Using an Inadequate Sample Size Small sample sizes lead to random fluctuations instead of reliable trends. ◾ Use statistical significance calculators to determine the right sample size. ◾ Ensure your audience size is large enough to draw meaningful conclusions. 3. Ending Tests Too Early It’s tempting to stop a test the moment one variation seems to be winning. But early spikes in performance may not hold. ◾ Set a minimum duration for each test. ◾ Let it run until you reach statistical confidence. 4. Ignoring External Factors A/B test results can be influenced by: ◾ Seasonality (holiday traffic may differ from normal traffic). ◾ Active marketing campaigns. ◾ Industry trends or unexpected events. Always analyze results in context before making decisions. 5. Not Randomly Assigning Users If users aren’t randomly split between Version A and B, results may be biased. Most A/B testing tools handle randomization—use them properly. 6. Focusing Only on Short-Term Metrics Click-through rates might rise, but what about conversion rates or long-term engagement? Always consider: ◾ Immediate impact (CTR, sign-ups). ◾ Long-term effects (retention, revenue, lifetime value). 7. Running Tests Without a Clear Hypothesis A vague goal like “Let’s see what happens” won’t help. Instead, start with: ◾ A clear hypothesis (“Changing the CTA button color will increase sign-ups by 15%”). ◾ A measurable outcome to validate the test. 8. Overlooking User Experience Optimizing for conversions shouldn’t come at the cost of usability. ◾ Does a pop-up increase sign-ups but frustrate users? ◾ Does a new layout improve engagement but slow down the page? Balance performance with user satisfaction. 9. Misusing A/B Testing Tools If tracking isn’t set up correctly, your data will be flawed. ◾ Double-check that all elements are being tracked properly. ◾ Use A/B testing tools like Google Optimize, Optimizely, or VWO correctly. 10. Forgetting About Mobile Users What works on desktop may fail on mobile. ◾ Test separately for different devices. ◾ Optimize for mobile responsiveness, speed, and usability. Why This Matters ✔ More Accurate Insights → Reliable data leads to better decisions. ✔ Higher Conversions → Avoiding mistakes ensures real improvements. ✔ Better User Experience → Testing shouldn’t come at the expense of usability. ✔ Stronger Strategy → A/B testing is only valuable if done correctly. See you tomorrow!

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