A/B Testing Procedures

Using insights from tens of thousands of A/B tests, I break down Ramp’s homepage hero ➡️ highlighting both the smart bets and areas for optimization. 1) Social proof at the top of homepages is typically a poor bet. Ramp includes both G2 stars above the header and a logo bar beneath the hero. In our testing, when brands place third-party review stars above headers, these versions consistently lose (I cover this in detail in my LinkedIn article, The Problem with Social Proof). Why? 🔎 Distrust of third-party reviews, often perceived as pay-to-play. 🔎 Key content and CTAs get pushed down, especially on mobile. 🔎 Unintentional signaling, for example "4.8 stars from 200+ reviews" can actually make the brand seem small. 2) Embedded email capture + clear CTA text is a winning combo. About two years ago, Ramp A/B tested an embedded email capture form versus a standard button. The embedded form won. Since then, across dozens of site iterations, they’ve kept it. Brands like Buffer and Rippling have similarly tested into and retained embedded capture forms. Their CTA text, “Get Started for Free,” is also strong: it clearly communicates that it’s a free trial. The only improvement I’d suggest is adding reassurance text below the CTA, clarifying that no credit card is required. We’ve seen this small detail improve conversions in multiple A/B tests (see Twilio’s homepage for a good example). 3) Secondary CTAs are a good bet. Ramp’s secondary CTA, “Explore Product,” beneath the main CTA is smart. We’ve seen extensive testing on one vs. two CTAs in homepage heroes for B2B SaaS and fintech brands. Two CTAs typically win. Why? Most of these companies have both self-service and enterprise buyers, with varied traffic sources (and intent levels). Offering two clear paths lets each group choose their preferred next step. 4) Product imagery works. Across hundreds of tests, product imagery consistently outperforms stock photos, branded graphics, or stylized backgrounds. Prospects want a preview of the actual product. 5) Customer logo bars typically underperform. I’ve written extensively on this, but here’s a quick recap of why logos usually lose: 🚧 Logo blindness: If you’re an industry leader, customers assume you serve top brands, so listing them adds little credibility. 🚧 Logo fit: Irrelevant logos create disconnect. Prospects want proof that companies like theirs trust your product. 🚧 Logos mislead: Many sites display big-brand logos when just a small team or individual used the product, or worse, when that company has already churned. If you do use logos, make them interactive or segmented. Brands like Clay and Hex link logos to case studies, providing depth. Others, like 7shifts, segment logos by industry to improve relevance. Hope this is helpful. Any other brands you would love to see analyzed based on DoWhatWorks's database of tracked tests?

💡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

At Swiggy every product feature goes live via A/B testing. Experimentation is such a goldmine for decision-making, and as someone who didn't even know how to do it right a year back - below is my step-by-step approach to statistical analysis. The problem statement is - You’re working to improve the conversion rate for a product signup flow. You’ve implemented several changes—now, how do you figure out which one really makes a difference? 1️⃣ Define the Hypothesis Before diving into the test, clearly define what you're testing. Example: "Will the new signup flow increase the conversion rate (CVR)?" 2️⃣ Define Clear Metrics What does success look like? Are you aiming to increase the percentage of sign-ups, or reduce drop-offs at a specific funnel stage? Success Metric: Conversion rate or step completion rate. Check Metric: Have a secondary metric to ensure nothing else breaks (e.g., page load times or errors). 3️⃣ Test One Change at a Time Testing multiple changes (e.g., a new form layout and an incentive like a discount) at once won’t help you pinpoint which worked. 4️⃣ Split Your Traffic Determine the sample size you need and split users randomly into two groups: - Group A (Control): Users see the current version - Group B (Test): Users see the new version 5️⃣ Collect Data & Analyze Monitor key metrics like CTR, conversion rates, or churn - choose metrics tied to the business goal. Example: Track how the new signup form impacts the completion rate of the entire signup process. 6️⃣ Analyze Statistical Significance You see a 15% increase in conversions with the new form—great! But is it statistically significant, or could it be due to random variation? Use p-values and Z scores to validate if the changes are meaningful and not just due to chance. 7️⃣ Interpret Results & Take Action Once you’ve confirmed statistical significance, interpret the results in a business context. Example: If the new form significantly increases conversion but doesn’t impact overall user satisfaction, it’s time to implement it at scale. 💡 What’s are some of your go-to strategies for an effective A/B test? Share your insights in the comments! ___________ 🔔 Follow Sanya Swain ♻ Repost to help others find it 💾 Save this post for future reference #businessanalysis #dataanalytics #dataanalyst #analytics #businessinsights #womenintech #product #sql #datascience #abtesting

Day 5 - CRO series Strategy development ➡A/B Testing (Part 1) What is A/B Testing? A/B testing, also known as split testing, is a method used to compare two versions of a marketing asset, such as a webpage, email, or advertisement, to determine which one performs better in achieving a specific goal. Most marketing decisions are based on assumptions. A/B testing replaces assumptions with data. Here’s how to do it effectively: 1. Formulate a Hypothesis Every test starts with a hypothesis. ◾ Will changing a call-to-action (CTA) button from green to red increase clicks? ◾ Will a new subject line improve email open rates? A clear hypothesis guides the entire process. 2. Create Variations Test one element at a time. ◾ Control (Version A): The original version ◾ Variation (Version B): The version with a change (e.g., a different CTA color) Testing multiple elements at once leads to unclear results. 3. Randomly Assign Users Split your audience randomly: ◾ 50% see Version A ◾ 50% see Version B Randomization removes bias and ensures accurate comparisons. 4. Collect Data Define success metrics based on your goal: ◾ Click-through rates ◾ Conversion rates ◾ Bounce rates The right data tells you which version is actually better. 5. Analyze the Results Numbers don’t lie. ◾ Is the difference in performance statistically significant? ◾ Or is it just random fluctuation? Use analytics tools to confirm your findings. 6. Implement the Winning Version If Version B performs better, make it the new standard. If no major difference? Test something else. 7. Iterate and Optimize A/B testing isn’t a one-time task—it’s a process. ◾ Keep testing different headlines, images, layouts, and CTAs ◾ Every test improves your conversion rates and engagement Why A/B Testing Matters ✔ Removes guesswork – Decisions are based on data, not intuition ✔ Boosts conversions – Small tweaks can lead to significant growth ✔ Optimizes user experience – Find what resonates best with your audience ✔ Reduces risk – Test before making big, irreversible changes Part 2 tomorrow

A/B Testing in Google Ads: Best Practices for Better Performance Introduction to A/B Testing A/B testing in Google Ads is a crucial strategy for optimizing ad performance through data-driven insights. It involves comparing two versions of an ad to determine which one delivers better results.  Set Clear Goals Before conducting A/B tests, define clear objectives such as increasing click-through rates or conversions. Having specific goals will guide your testing process and help you measure success accurately.  Test Variables To effectively A/B test ads, focus on testing one variable at a time, such as the ad copy, images, or call-to-action. This approach will provide clear insights into what elements are driving performance. Create Variations Develop distinct ad variations with subtle differences to compare their impact. Ensure that each version is unique enough to produce measurable results but relevant to your target audience.  Implement Proper Tracking Set up conversion tracking and monitor key metrics closely to evaluate the performance of each ad variation accurately. Use tools like Google Analytics to gather meaningful data. Monitor Performance Metrics Regularly review performance metrics like click-through rates, conversion rates, and cost per acquisition to identify trends and patterns. Analyzing these metrics will help you make informed decisions. Scale Successful Tests Once you identify a winning ad variation, scale it by allocating more budget and resources to drive maximum results. Replicate successful strategies in future campaigns. Continuous Optimization Optimization is an ongoing process, so continue to test, refine, and adapt ad elements to enhance performance continuously. Stay updated with industry trends and consumer preferences. Analyze Results After conducting A/B tests, analyze the results comprehensively to understand the impact of your optimizations. Use the insights gained to inform future ad strategies. Summary  Following best practices for A/B testing in Google Ads can significantly improve the performance of your campaigns. By testing, analyzing, and optimizing ad variations, you can enhance engagement, conversions, and overall ROI. #MetaAds, #VideoMarketing, #DigitalAdvertising, #SocialMediaStrategy, #ContentCreation, #BrandAwareness, #VideoBestPractices, #MarketingTips, #MobileOptimization, #AdPerformance

What’s Working for You? (How you can test to see if you are right!) One common method to find out which product offering Or which email outreach style is doing better Is to perform an A/B Test. The premise of the test is simple Obtain feedback or observe behaviors of customers That are exposed to either product A or product B And see if there is a clear difference in preferences. Let us consider the example of Marketing LLC Who wanted to see which email style was resonating more With their potential clients. After conducting required background research On their Ideal Client Profile (ICP), They decided to test their email styles using the A/B Testing method. We sent out 300 emails of Style A to one group And 300 emails of Style B to another group. The groups were randomly selected from their ICP list And the content of the emails was very similar. The subject line and first two sentences of the emails were different. Observation & Proportions: -         100 or 33% of Style A emails were opened. -         120 or 40% of Style B emails were opened. -         Total or joint open rate was 220 out of 600 or 37% Clearly the numbers show that Style B had a higher rate of opening. However, it is essential to test this statistically before deciding Whether to go with Style B or Style A for sending future emails to ICPs. We can use a Test of Proportions at a 95% confidence level To ensure that Style B is better, using statistical significance. Actual Test: * Joint p* = 0.37 * Std. Error Sp = sqrt((0.37 x 0.63/300) = 0.03 * Test Z-value = (0.4 – 0.33)/0.03 = 2.33 * 95% Z-value = 1.96 (this is a very important and constant critical value) Since the Test Z-value is greater than 1.96, we can now conclude with 95% confidence that: Emails sent using Style B, were doing better. Actionable Insights from A/B Testing: 1. Deep Dive: Analyze the elements of Style B that contributed to the higher open rates. This could include the subject line, tone, or specific keywords. 2. Limit Variables: When conducting A/B tests, focus on one or two variables at a time to isolate the impact of each change. 3. Scale Up: Increase volume of emails following Style B to further validate the results & reach a larger audience within your ICP. 4. Content Quality: Ensure that the content of the email is compelling & relevant. An opened email is just the first step; the content must result in engagement and conversions. 5. Continuous Testing: Regularly perform A/B tests to keep refining your email strategies. Market dynamics & customer preferences can change over time. 6. Segmentation: Segment ICP further to tailor email styles to different sub-groups, for personalization & relevance. 7. Feedback Loop: Collect feedback from recipients to understand their preferences & pain points, to improve future email campaigns. #PostItStatistics #DataScience Follow Dr. Kruti or Analytics TX, LLC on LinkedIn (Click "Book an Appointment" to register for the workshop!)

One of the most common A/B testing questions is: how long should I run my experiment? 🗓 The answer is. . . ⬇ It depends! Although a frustrating response, “it depends” is actually based on specific, measurable criteria. Here’s the 5 factors to consider: 1️⃣ CALCULATE REQUIRED SAMPLE SIZE Calculate your required sample size before starting the test! Doing so helps you determine the: ✅ Traffic required for trustworthy test results ✅ Duration you need to run the test to meet sample requirements For most tests, “Ronny’s Rule” (Ron Kohavi) recommends a minimum of 125,000 visitors per variant, based on realistic 2-5% MDE, at a standard power of 80% with an alpha of 5%. 2️⃣ ALIGN TO SALES CYCLES Align your tests with your sales cycles to capture behavior changes across typical purchase timelines. Every company will have different sales cycles. Know what yours is and adjust your testing timeframe accordingly. 3️⃣ CONSIDER SEASONAL FACTORS Holidays like #BlackFriday and #CyberMonday impact buying behavior, often altering consumer choices and, consequently, skewing data. Consider waiting to test or extending the testing timeframe to smooth out data anomalies. 4️⃣ TAKE WEEKEND BLIPS INTO ACCOUNT To balance weekend effects, run tests for at least two weeks to capture a full cycle of weekday and weekend traffic activity. 5️⃣ AVOID DATA DRIFT While 2 weeks is the minimum you should consider running a test -- even if you reach statistical significance much sooner -- you shouldn't let a test run too long. How long is too long? Most stats experts agree, between 6-8 weeks is the maximum length a test should run. Anything longer, the data may start to become muddied. The one caveat is: you should always run your test to reach the required pre-calculated sample size. If that timeframe is much longer than 8 weeks, question if you really should be testing. 💡 SUMARY AND REAL-LIFE EXAMPLE In short, aim to run your study for 2-6 weeks. Here's a real-life example showing why. Had this client ended their test early -- before 2 weeks -- they would have missed out, significantly, thinking the variant was a loser when in fact, it became the winner. Questions? Thoughts? Comments? Reach out! #abtesting #optimization #experimentation

A/B Testing/Experimentation almost always is just regression under the hood. Basic A/B tests, Multivariate tests/ANOVA, interaction checks between simultaneous A/B tests, checks if different user segments respond differently to treatments, and variance reduction - all them can be performed with regression. Based on the choice of the data coding scheme and what/if additional data to include in the model the following can be easily recast/performed with basic regression: 1) Pairwise t-tests - this is the basic difference in means A/B Test. Design Matrix coded with an Intercept and single dummy variable, with the 'Control' as reference class. 2) MVT/Factorial ANOVA/f-tests - Design Matrix Effects coding such that the grand mean is 'reference'. 3a) User Segment Treatment Effects via partial F-test. Two Design Matrices: a) Main effect dummy variables only; b) A Fully interacted Design Matrix. F-test is ratio of residual sum of squares of main effects model over interacted model. 3b) Pairwise A/B Test interaction Partial F-test (see 3a). 4 Covariate Adjustment - CUPED/ANCOVA. Design matrix includes treatment dummy, mean centered pre experiment covariate(s) and dummy interaction term of covariate with treatment dummy* AND, it turns out that ALL of these test design matrices can be constructed from K-Anonymous data! That means that not only do all of these disparate tasks roll up into one general approach, but with a bit of upfront thought, all of them can be performed when following privacy by design. * technical note that unless Na=Nb, one prob should account for heteroskedastic errors rather than use the OLS st errs.

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.