Conducting A/B Testing On Sites

lets say, you are QA manager and i am a QA and Manager asked you to start implementing automation testing for regression testcases for a website first. how would you do it? this will be my approach: Since we already have a set of manual regression test cases, I’d begin by reviewing and prioritizing them. Not all test cases are worth automating immediately—some may be too unstable or rarely executed. So, I'd focus first on high-impact, frequently executed tests like login, signup, checkout, and other critical flows. I'd organize these into a clear, shared spreadsheet or test management tool and tag them as "Ready for Automation." a tag always helps. Next, I’d set up a basic Java + Selenium framework. If we don’t already have one, I’d recommend using Maven for dependency management, TestNG or JUnit for test orchestration, and Page Object Model (POM) as the design pattern to keep our tests modular and maintainable. I'd also propose integrating ExtentReports for test reporting and Log4j for logging. I can bootstrap this framework myself or pair with a dev/test automation resource if needed. Once the skeleton framework is ready, I’d start converting manual test cases into automated scripts one by one. I’d begin with the smoke tests and top-priority regressions. For each script, I’d ensure proper setup, execution, teardown, and validations using assertions. then, I’ll commit code to a shared Git repo with meaningful branches and naming conventions. For execution, I'd run the tests locally first, then configure them to run on different browsers. Later, we can integrate the suite with a CI/CD tool like Jenkins to schedule regular test runs (e.g., nightly builds or pre-release checks). This would give us feedback loops without manual intervention. I’d document everything—how to run the tests, add new ones, and generate reports—so the team can scale this effort. I’d also recommend setting aside a couple of hours weekly to maintain and update tests as the app evolves. Finally, I’d keep you in the loop with weekly updates on automation progress, blockers, and test coverage. Once the core regression suite is automated and stable, we can expand into edge cases, negative tests, and possibly integrate with tools like Selenium Grid or cloud providers (e.g., BrowserStack) for cross-browser coverage. what will you be your action plan? let's share. #testautomation #automationtesting #testautomationframework #sdets

📚 Key Test Documentation Types 1. Test Plan Purpose: Outlines the overall strategy and scope of testing. Includes: Objectives Scope (in-scope and out-of-scope) Resources (testers, tools) Test environment Deliverables Risk and mitigation plan Example: "Regression testing will be performed on modules A and B by using manual TC" 2. Test Strategy Purpose: High-level document describing the overall test approach. Includes: Testing types (manual, automation, performance) Tools and technologies Entry/Exit criteria Defect management process 3. Test Scenario Purpose: Describes a high-level idea of what to test. Example: "Verify that a registered user can log in successfully." 4. Test Case Purpose: Detailed instructions for executing a test. Includes: Test Case ID Description Preconditions Test Steps Expected Results Actual Results Status (Pass/Fail) 5. Traceability Matrix (RTM) Purpose: Ensures every requirement is covered by test cases. Format: Requirement ID Requirement Description Test Case IDs REQ_001 Login functionality TC_001, TC_002 6. Test Data Purpose: Input data used for executing test cases. Example: Username: testuser, Password: Password123 7. Test Summary Report Purpose: Summary of all testing activities and outcomes. Includes: Total test cases executed Passed/Failed count Defects raised/resolved Testing coverage Final recommendation (Go/No-Go) 8. Defect/Bug Report Purpose: Details of defects found during testing. Includes: Bug ID Summary Severity / Priority Steps to Reproduce Status (Open, In Progress, Closed) Screenshots (optional) Here's a set of downloadable, editable templates for essential software testing documentation. These are useful for manual QA, automation testers, or even team leads preparing structured reports. 📄 1. Test Plan Template File Type: Excel / Word Key Sections: Project Overview Test Objectives Scope (In/Out) Resources & Roles Test Environment Schedule & Milestones Risks & Mitigation Entry/Exit Criteria 🔗 Download Test Plan Template (Google Docs) 📄 2. Test Case Template File Type: Excel Columns Included: Test Case ID Module Name Description Preconditions Test Steps Expected Result Actual Result Status (Pass/Fail) Comments 🔗 Download Test Case Template (Google Sheets) 📄 3. Requirement Traceability Matrix (RTM) File Type: Excel Key Fields: Requirement ID Requirement Description Test Case ID Status (Covered/Not Covered) 🔗 Download RTM Template (Google Sheets) 📄 4. Bug Report Template File Type: Excel Columns: Bug ID Summary Severity Priority Steps to Reproduce Actual vs. Expected Result Status Reported By 🔗 Download Bug Report Template (Google Sheets) 📄 5. Test Summary Report File Type: Word or Excel Includes: Project Name Total Test Cases Execution Status (Pass/Fail) Bug Summary Test Coverage Final Remarks / Sign-off 🔗 Download Test Summary Template (Google Docs) #QA

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

As UX researchers, we often encounter a common challenge: deciding whether one design truly outperforms another. Maybe one version of an interface feels faster or looks cleaner. But how do we know if those differences are meaningful - or just the result of chance? To answer that, we turn to statistical comparisons. When comparing numeric metrics like task time or SUS scores, one of the first decisions is whether you’re working with the same users across both designs or two separate groups. If it's the same users, a paired t-test helps isolate the design effect by removing between-subject variability. For independent groups, a two-sample t-test is appropriate, though it requires more participants to detect small effects due to added variability. Binary outcomes like task success or conversion are another common case. If different users are tested on each version, a two-proportion z-test is suitable. But when the same users attempt tasks under both designs, McNemar’s test allows you to evaluate whether the observed success rates differ in a meaningful way. Task time data in UX is often skewed, which violates assumptions of normality. A good workaround is to log-transform the data before calculating confidence intervals, and then back-transform the results to interpret them on the original scale. It gives you a more reliable estimate of the typical time range without being overly influenced by outliers. Statistical significance is only part of the story. Once you establish that a difference is real, the next question is: how big is the difference? For continuous metrics, Cohen’s d is the most common effect size measure, helping you interpret results beyond p-values. For binary data, metrics like risk difference, risk ratio, and odds ratio offer insight into how much more likely users are to succeed or convert with one design over another. Before interpreting any test results, it’s also important to check a few assumptions: are your groups independent, are the data roughly normal (or corrected for skew), and are variances reasonably equal across groups? Fortunately, most statistical tests are fairly robust, especially when sample sizes are balanced. If you're working in R, I’ve included code in the carousel. This walkthrough follows the frequentist approach to comparing designs. I’ll also be sharing a follow-up soon on how to tackle the same questions using Bayesian methods.

Already using #TDD for #OperationsResearch projects? You're ahead of 90% of teams. But if you're starting, you need to consider that different kinds of tests have different implications. Let's see a layered pyramid that represents several important aspects of a testing strategy, like: 🕐 Quantity & frequency: Typically, you have more tests at the bottom of the pyramid and fewer as you move up. Lower-level tests run more frequently during development. ⚡ Execution speed: Tests at the bottom are faster to run (milliseconds to just a few seconds) while tests at the top can take minutes or hours. 🔍 Scope & isolation: Lower tests focus on isolated components, while higher tests evaluate entire systems. 🏗️ Cost of creation & maintenance: Tests at the bottom are relatively inexpensive to create and maintain, while comprehensive tests at the top require significant investment. 🔄 Feedback speed: Lower-level tests provide immediate feedback during development, while higher-level tests might run only in nightly builds or pre-release phases. Start implementing from the bottom of the pyramid and work your way up. This approach builds confidence in your foundational components before tackling more complex concerns.

Don’t Focus Too Much On Writing More Tests Too Soon 📌 Prioritize Quality over Quantity - Make sure the tests you have (and this can even be just a single test) are useful, well-written and trustworthy. Make them part of your build pipeline. Make sure you know who needs to act when the test(s) should fail. Make sure you know who should write the next test. 📌 Test Coverage Analysis: Regularly assess the coverage of your tests to ensure they adequately exercise all parts of the codebase. Tools like code coverage analysis can help identify areas where additional testing is needed. 📌 Code Reviews for Tests: Just like code changes, tests should undergo thorough code reviews to ensure their quality and effectiveness. This helps catch any issues or oversights in the testing logic before they are integrated into the codebase. 📌 Parameterized and Data-Driven Tests: Incorporate parameterized and data-driven testing techniques to increase the versatility and comprehensiveness of your tests. This allows you to test a wider range of scenarios with minimal additional effort. 📌 Test Stability Monitoring: Monitor the stability of your tests over time to detect any flakiness or reliability issues. Continuous monitoring can help identify and address any recurring problems, ensuring the ongoing trustworthiness of your test suite. 📌 Test Environment Isolation: Ensure that tests are run in isolated environments to minimize interference from external factors. This helps maintain consistency and reliability in test results, regardless of changes in the development or deployment environment. 📌 Test Result Reporting: Implement robust reporting mechanisms for test results, including detailed logs and notifications. This enables quick identification and resolution of any failures, improving the responsiveness and reliability of the testing process. 📌 Regression Testing: Integrate regression testing into your workflow to detect unintended side effects of code changes. Automated regression tests help ensure that existing functionality remains intact as the codebase evolves, enhancing overall trust in the system. 📌 Periodic Review and Refinement: Regularly review and refine your testing strategy based on feedback and lessons learned from previous testing cycles. This iterative approach helps continually improve the effectiveness and trustworthiness of your testing process.

With new mobile devices constantly entering the market, ensuring compatibility is more challenging than ever. Compatibility issues can lead to poor user experiences, frustrating users with crashes and functionality problems. Staying ahead with comprehensive testing across a wide range of devices is crucial for maintaining user satisfaction and app reliability. I would like to share the strategy that I have used for comparability testing of mobile applications. 1️⃣ Early Sprint Testing: Emulators During the early stages of development within a sprint, leverage emulators. They are cost-effective and allow for rapid testing, ensuring you catch critical bugs early. 2️⃣ Stabilization Phase: Physical Devices As your application begins to stabilize, transition to testing on physical devices. This shift helps identify real-world issues related to device-specific behaviors, network conditions, and more. 3️⃣ Hardening/Release Sprint: Cloud-Based Devices In the final stages, particularly during the hardening or release sprint, use cloud-based device farms. This approach ensures your app is tested across a wide array of devices and configurations, catching any last-minute issues that could impact user experience. Adopting this 3 tiered approach ensures a comprehensive testing coverage, leading to a more reliable and user-friendly application. What is the strategy that you are adopting for testing your mobile apps. Please share your views as comments. #MobileTesting #SoftwareTesting #QualityAssurance #Testmetry

Why visitors drop off before buying and how to fix it Every online store leaves clues in its analytics Take a look at this real conversion funnel breakdown (screenshot 👇), it's from a store we audited (name withheld for privacy) -> 59,000+ sessions -> Only 0.99% added to cart -> Just 0.12% converted Why so low? Let’s zoom in: 👉 Added to Cart: 0.99% Possible reasons for low add-to-cart rates: > No clear trust signals > Product page cluttered with text > Missing hooks like sticky buttons or accessories What we saw: This store had a basic product page layout, lacking trust badges, reviews, and a clear visual structure to guide decisions. The long block of text made it hard to skim and find key details ✔ What’s working: They’ve added express checkout buttons (Google Pay), which is great. But adding Apple Pay and Shop Pay would further increase convenience 👉 Reached Checkout: 0.61% High drop-off from cart to checkout usually means: > Lack of urgency or reassurance > Missing express checkout options > No trust reinforcement in the cart What we saw: More than 50% of users dropped off between the cart and checkout. The cart, like the product page, wasn’t optimized, lacking trust badges, pressure builders (such as low-stock alerts), and cross-sell motivation ✔ Next step: Before experimenting with bundles or upsells, this store needs to fix the fundamentals: > Build trust visually (badges, reviews) > Streamline copy > Add sticky CTA + more payment options > Upgrade cart UX with cross-sell prompts and urgency drivers Small changes = big revenue shifts ––– If your store makes $50K+/mo and you suspect conversion leaks... You might be 1 audit away from fixing them This month, we’re offering a few free audit slots: ✔ Full-funnel review ✔ Specific, prioritized fixes ✔ 10%+ growth guarantee in 60 days, or we work for free Want in? 👉 Comment AUDIT Just leave a comment "audit" and I’ll reach out to you directly 🎁 PS: I’ll also drop a link in the comments to our DIY audit checklist for anyone who wants to self-review

👀 Lessons from the Most Surprising A/B Test Wins of 2024 📈 Reflecting on 2024, here are three surprising A/B test case studies that show how experimentation can challenge conventional wisdom and drive conversions: 1️⃣ Social proof gone wrong: an eCommerce story 🔬 The test: An eCommerce retailer added a prominent "1,200+ Customers Love This Product!" banner to their product pages, thinking that highlighting the popularity of items would drive more purchases. ✅ The result: The variant with social proof banner underperformed by 7.5%! 💡 Why It Didn't Work: While social proof is often a conversion booster, the wording may have created skepticism or users may have seen the banner as hype rather than valuable information. 🧠 Takeaway: By removing the banner, the page felt more authentic and less salesy. ⚡ Test idea: Test removing social proof; overuse can backfire making users question the credibility of your claims. 2️⃣ "Ugly" design outperforms sleek 🔬 The test: An enterprise IT firm tested a sleek, modern landing page against a more "boring," text-heavy alternative. ✅ The Result: The boring design won by 9.8% because it was more user friendly. 💡 Why It Worked: The plain design aligned better with users needs and expectations. 🧠 Takeaway: Think function over flair. This test serves as a reminder that a "beautiful" design doesn’t always win—it’s about matching the design to your audience's needs. ⚡ Test idea: Test functional designs of your pages to see if clarity and focus drive better results. 3️⃣ Microcopy magic: a SaaS example 🔬 The test: A SaaS platform tested two versions of their primary call-to-action (CTA) button on their main product page. "Get Started" vs. "Watch a Demo". ✅ The result: "Watch a Demo" achieved a 74.73% lift in CTR. 💡 Why It Worked: The more concrete, instructive CTA clarified the action and benefit of taking action. 🧠 Takeaway: Align wording with user needs to clarify the process and make taking action feel less intimidating. ⚡ Test idea: Test your copy. Small changes can make a big difference by reducing friction or perceived risk. 🔑 Key takeaways ✅ Challenge assumptions: Just because a design is flashy doesn’t mean it will work for your audience. Always test alternatives, even if they seem boring. ✅ Understand your audience: Dig deeper into your users' needs, fears, and motivations. Insights about their behavior can guide more targeted tests. ✅ Optimize incrementally: Sometimes, small changes, like tweaking a CTA, can yield significant gains. Focus on areas with the least friction for quick wins. ✅ Choose data over ego: These tests show, the "prettiest" design or "best practice" isn't always the winner. Trust the data to guide your decision-making. 🤗 By embracing these lessons, 2025 could be your most successful #experimentation year yet. ❓ What surprising test wins have you experienced? Share your story and inspire others in the comments below ⬇️ #optimization #abtesting

𝗚𝗲𝘁 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝗽𝗲𝗮𝗸 𝘀𝗵𝗼𝗽𝗽𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗮 𝘀𝘂𝗺𝗺𝗲𝗿 𝗼𝗳 𝘁𝗲𝘀𝘁𝗶𝗻𝗴! 🚀 Great visualization of how retailers and brands should prepare for getting ready for Black Friday, Christmas and Q4 peak shopping season. 🏔️ One often overlooked phase of the plan: Test and learn during the summer. 💥 Especially the current (more quiet) summer months can be a great opportunity to do some early testing. 🥳 Whether it’s new campaigns, new creatives, new strategies and processes, website designs or anything else. ✅ You want to avoid changing any of this once September comes around to not risk any major hiccups. 🥵 So here’s  a quick breakdown of how to maximize your peak performance: 𝗡𝗼𝘄 (𝗔𝘂𝗴𝘂𝘀𝘁): 𝗧𝗲𝘀𝘁 & 𝗣𝗹𝗮𝗻.  ↳ Use this time to experiment and test. Set clear business and marketing objectives for what you want to achieve! 🧪 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 - 𝗢𝗰𝘁𝗼𝗯𝗲𝗿 (𝗣𝗿𝗲-𝗣𝗲𝗮𝗸): 𝗥𝗮𝗺𝗽 𝘂𝗽.  ↳ Build awareness and consideration. Start ramping up campaigns and be visible  as demand begins to pick up. 📈 𝗡𝗼𝘃𝗲𝗺𝗯𝗲𝗿 - 𝗗𝗲𝗰𝗲𝗺𝗯𝗲𝗿 (𝗣𝗲𝗮𝗸): 𝗗𝗲𝗹𝗶𝘃𝗲𝗿.  ↳ Bring it home by focusing on capturing demand and driving conversions. 🎯 Being prepared means everything is set up once demand starts picking up. A bit of planning now might go a long way in a few weeks. 💪 What are you testing this summer to get ready for Q4? Let me know in the comments below! 💬 -- 🔔 Follow me for more posts on digital marketing, careers in tech, and life at Google. ♻️ Share this post with your network if you enjoyed it.