Evaluating Test Outcomes in E-commerce

If an A/B test is 'inconclusive', it does not necessarily mean that the change does not work. It rather just means that you have not been able to prove whether it works or not. It is entirely possible that the change does have an impact (positive or negative), but that it is just too subtle for you to detect with the volumes of traffic you have. Mostly though, subtle (if you could detect it) would still be meaningful in terms of revenue. If you discard everything which is inconclusive, how do you know you are not throwing away things which would be worth implementing? So what to do? Well, experimentation is really about degrees of risk management. If you cannot prove the positive benefit of a change, then the first thing is to accept that the risk surrounding that decision is greater. BUT, you can understand the parameters of that risk. The image is from the awesome sequential testing calculator in Analytics Toolkit, created by Georgi Georgiev. This is the analysis of an inconclusive test, which is nevertheless able to show, based on what was determined by the observation, that there is a 70% likelihood of the effect falling between around -8.5% and +5%. This particular case is vague, but at least you know the boundaries of the risk you're playing with. In some cases the picture is more heavily skewed in one direction. An A/B test is a way of making a decision, and the outcome of that test is always simply an expression of the degrees of confidence you can have in making that decision. How you make the decision is always still up to you. #cro #experimentation #ecommerce #digitalmarketing #ux #userexperience

We thought overlaying USPs would boost sales. The opposite happened. Here's why. We tested adding USPs and breadcrumbs to product images, hoping they'd highlight product value and increase sales. But the results told a different story. The ARPU decreased by 2%. 🤔 Here's what we discovered: ❌ Cognitive Overload: Users faced information overload with both visuals and text. They couldn't easily process USPs. A separate area might help them digest info at their own pace. ❌ Hidden Benefits: USPs were tucked away behind info icons. Users couldn't see them immediately. When benefits aren't clear, users hesitate. They need to know why they should choose your product right away. ❌ Importance of Presentation: Our test showed that how we present information affects user decisions. In some cases, users prioritize how things look over details. Especially in women's fashion, visuals can make or break a sale. The key lesson? Testing isn't just about confirming our ideas; it's about learning and refining our approach. Each test brings valuable insights, even if things don't go as planned. 💡 Have you ever tried different ways to show USPs? What was your experience? #cro #abtesting #ecommerce

Click-based attribution had its run. Clicks aren’t proof. Tests are. But only if the tests are run correctly, start to finish. Here's a 9-step walkthrough of what that looks like, using a real example: 1. Context 👇️ This is a commerce brand. Multi-channel sales: D2C, Amazon, retail Real media mix: Meta, Google/YouTube, Email, CTV, Podcasts Growth objective: Within ROAS targets Status: No prior MMM or formal testing 2. Customer goal 👇️ "We’re trialing LiveIntent and TikTok and want to prove incrementality before scaling." 3. Hypothesis 👇️ Each new channel can hit ~1× ROAS at ~$1K/day across D2C and Amazon. 4. Next action (this part is often missed, but it's key!) 👇️ If it works: scale and aim for 2× ROAS. If not: iterate on creative/targeting and try again. 5. Test design 👇️ Run both in parallel (one platform has an easy holdout system and the other can be tested through a geo test) TikTok → Geo test, sized by: Geo sales trend + target ROAS + minimum detectable lift LiveIntent → Holdout test, sized by: Target ROAS + minimum lift + holdout % 6. Volume and velocity of testing 👇️ Test velocity improves with Variance × Cells × Target Lift × Length. If the variance of your metric or number of cells increase, you need bigger budgets or longer test periods to get high confidence results. 7. Results (that execs care about) 👇️ Report three things: Incremental Lift, incremental sales/conversions, and iROAS. 8. Things to look out for/testing traps 👇️ Ask yourself: Is the lift real or noise? Was the test powered well enough to draw a conclusion? “No lift” can be also be conclusive if your test is powerful enough. Underpowered tests = inconclusive. Testing traps: Stopping early (less than 4 weeks), ignoring seasonality/external shocks, confusing correlation with causation, cherry-picking. Complications to plan for: Lift in repeat but not new (should paid dollars chase repeats?), lift in Amazon total but not D2C (what levers create D2C lift?), lift in orders but not revenue (push AOV or rebundle SKUs). 9. From insight to action (monthly cadence) 👇️ Identify high-incremental channels → Shift budget → Form new hypotheses → Repeat Make the case for an incrementality platform powering ongoing tests + MMM, and keep a lightweight system of record (Channel, Spend, iROAS, Date) to see diminishing returns as you scale. 10. Bonus tip? I always tell customers to set aside at least 10% of their budget for testing. Yes, this depends/has caveats, but you need enough of your budget allocated to experimentation to see real movement. Testing at that scale allows you to take bigger swings, try new creative, and, most importantly, drive growth. P.S. If this helps - or if you want to do deeper on experimentation - we have a full testing guide we just published last month. DM me for the link!

Every test – whether it’s a new product bundle, pricing change, or checkout tweak – impacts your KPIs. But if you’re not tracking those changes properly, you risk missing out on both expected wins and unexpected insights. Here’s how to stay disciplined and make experiments work for you: ➝ Test one change at a time. Too many experiments at once? You’ll never know what actually moved the needle. ➝ Keep detailed records. For every test, document: - What you changed - Timeline of the experiment - KPI data before & after - Lessons learned (including surprises!) ➝ Watch for unexpected outcomes. Sometimes, a test affects metrics you didn’t anticipate. Those insights can be game-changers. ➝ Build a knowledge repository. A well-kept experiment log helps refine strategies, speed up decision-making, and align your team. Growth isn’t just about testing – it’s about learning, improving, and scaling smarter. Keep experimenting, but do it with structure. ––– 🤘 Follow me, Gadashevich, for more insights on growing your #ecommerce business #shopify

Not every A/B test will be a winner. But here’s the good news about A/B testing: → You prevent harmful elements from hurting your shop’s performance. → You gain insights for future tests to optimize your shop for users. At SNOCKS, we removed the default selection of credit card payment options in an A/B test. Our goal? Give users a better sense of control. The result? This variant performed worse. But we didn’t stop there. In a follow-up test, we pre-selected the most popular payment method—PayPal (with nearly 70% of users). The result: This variant performed better and increased ARPU by 1.74%. Why? → It requires fewer cognitive resources to see the preferred payment method. → Pre-selecting PayPal shows fewer form fields, making checkout feel easier. So, never give up after a flop. Learnings from these tests are worth their weight in gold! What insights have you gained from your A/B testing experiences? #startup #ecommerce #abtesting #cro

How I Helped My E-Commerce Client Measure Postcard Mailing ROI with A/B Analysis A client approached me with a common challenge—do postcard mailings actually increase revenue, or are they just an unnecessary expense? They wanted data-backed proof before deciding whether to continue the campaign. Using A/B testing, I conducted an in-depth analysis to compare the revenue impact between customers who received a postcard vs. those who didn’t. The Approach: 🔹 Data Preparation – Cleaned and structured customer transaction data from multiple months. 🔹 Segmentation – Categorized customers into two groups: Group 1: Received a postcard. Group X: Did not receive a postcard. 🔹 T-Test for Statistical Significance – Used statistical analysis to determine if there was a real impact on revenue. Key Findings: → Customers who received postcards (Group 1) had higher revenue per customer compared to those who didn’t (Group X). → Despite Group X having more customers, their revenue contribution per customer was lower. → T-Test results confirmed a statistically significant difference—proving the postcard campaign had a measurable impact. Final Insights & Recommendation: → The postcard campaign positively influenced revenue. → It’s worth continuing and optimizing for better targeting. → Future tests should explore personalized postcards or different frequency strategies. What This Means for Businesses By using data-driven A/B testing, businesses can move away from assumptions and make decisions with real evidence. This method isn’t just for postcards—it applies to ads, email campaigns, pricing strategies, and customer retention efforts. When you track what works and what doesn’t, you’re not just spending on marketing—you’re investing in profitable growth. By applying data analytics and A/B testing, I provided my client with clear insights to make an informed decision—turning what seemed like a guessing game into a data-driven strategy. ------------------ Are you tracking your marketing ROI the right way? Let’s connect and analyze your campaigns! #Ecommerce #MarketingAnalytics #DataDriven #ABTesting #CustomerInsights #ROI

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

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.