Using Retargeting Ads In Ecommerce

A company runs an A/B test. Version B wins—12% lift, statistically significant. Champagne. 🎉 Six months later, revenue is flat. What happened? They averaged over their customers. Rookie move. (I've done it too.) Version B: +20% for new users. But -8% for returning customers. New users outnumbered returners in the test, so B "won." Then the customer mix shifted. More returners. The "winning" variant was slowly bleeding its best users. This is Simpson's Paradox—when aggregate trends reverse at the subgroup level. It's not exotic. It's everywhere. Data-driven teams walk into this constantly when the first rule of being data-driven is "run the test and trust the average." The fix isn't more data. It's asking: for whom did each version win? Averages describe populations. They don't describe people. The most dangerous phrase in analytics isn't "we don't have data." It's "the data is clear." For those wrestling with weird A/B results—I see you! Ask who's in your sample before you pop the champagne.

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!)

Supercharge Your Facebook Ads: 6 Data-Backed Experiments Digital Marketing Pros Can't Ignore Let's dive into some high-impact Facebook Ads experiments backed by real data. Here's what you need to be testing: 1. Hook Magic: Those first few seconds are crucial! Test different hooks to grab attention. Bold statements, intriguing questions, or eye-catching visuals can make all the difference. 2. Thumbnail Impact: Don't sleep on thumbnails! In one study, changing thumbnails led to a 96% difference in cost per install[3]. Test these proven performers:   - Close-ups of faces (especially those resembling your target audience)   - Close-up patterns   - Thumbnails highlighting pain points with an "X" sign 3. Landing Page Optimization: Got the click? Now convert! Test various designs and ensure consistency between your ad and landing page. A/B testing can significantly boost your conversion rates. 4. Single Image vs. Carousel: Not sure which format to use? Test both! Single images can be powerful, but carousels offer a dynamic way to showcase multiple products or features. 5. Audience & Creative Testing: Dive deep into A/B testing. One study found that dynamic creative campaigns with 12 different combinations (e.g., two thumbnails, three headlines, two video lengths) yielded statistically significant data. 6. Ad Length Dynamics : Is shorter always better? Test 30-second ads against 60-second ones. One experiment showed a $10,000 video outperformed both $1,000 and $100,000 versions, proving that mid-range production can be most effective. Remember, Facebook's A/B testing tool allows you to compare performance across variables like copy, images, audiences, or campaign objectives. Keep testing, keep learning, and watch your ROI soar! Which experiment are you itching to try first? Drop your thoughts below! 💡 #FacebookAds #metaads

Your A/B test launched 4 weeks ago. And the p-value is 0.0501. In this situation you usually find three types of CRO practitioner or data scientist: The bad: "It's fine, we won, the uplift looks great, ship the variant!" The ugly: "Let's just wait one more week and see what happens." The good: "Let's repeat it and meta-analyze." The closer your p-value sits to alpha (0.05), the more likely your recommendation lands on a false positive (you call the variant a winner when it isn't) or a false negative (you call it a loser when it isn't). Think about it: a p-value of 0.051 is almost identical to 0.049, yet your recommendation flips completely. In most cases a handful of extra conversions is all that separates them. That points to an uncomfortable truth: interpreting a p-value is gradual, not binary. So when a p-value lands close to alpha, don't rush to declare a winner or a loser. Most of the time you are sitting in an FP/FN gray zone. What can you do instead? Repeat it. If you run the experiment again under strictly the same conditions (same design, same metrics, same audience definition), you get a second p-value that you can combine into a meta p-value: a single combined probability that aggregates the results of multiple independent studies testing the same hypothesis. Sometimes you can't rerun the test, because you can't preserve independence between tests or because of security/legal constraints. But in digital products, especially in product and UX, tests can often be repeated safely. How do you combine two p-values? There are plenty of resources online (link in comments) with easy calculators using different methods: Stouffer, Pearson, inverse-variance weighting... What's the payoff? If two experiments give you 0.061 and 0.0501, Stouffer's method returns a meta p-value of around 0.0067. When both tests point the same way, the combined p-value comes out smaller than either of them. One important caveat: when you replicate and combine, hold yourself to a stricter bar. A common best practice (Ron Kohavi) is to demand p-value < 0.01 rather than 0.05, especially when the change is costly to ship or the prior that it works is low. The combined number looks reassuringly small, so raise the threshold (alpha) to match the higher standard you are now claiming. Next time you see a p-value hugging 0.05, don't cry and don't celebrate. Just say "let's repeat" and let the statistics do the work. The cost of shipping a decision that doesn't move the needle (false positive) is just as real as missing a genuine opportunity (false negative). #CRO #Growth #stats #abtesting #data #analytics

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 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

At $100k+/month in ad spend, creative velocity is the whole game. Andromeda burns through creatives faster than anything before it. What ran for 30 days in 2023 lasts 10-14 days now. Here's the process we use to maintain 100-200+ new variations per week across our portfolio without scaling headcount: Step 1: Identify your winners. Pull any creative that's been profitable for 7+ days at meaningful spend. These are your seeds — everything grows from here. Step 2: Feed the winning ad copy into Claude with this framework — generate variations across 5 dimensions, 4 each: hook swaps (different opening, same body), angle shifts (same benefit, different emotional lens), proof swaps (different evidence type), format changes (listicle, question-led, story-led, testimonial-style), audience pivots (different demographic, same offer). Step 3: That's 20 copy variations from one winner. Now multiply visually. Take each copy variation and generate 3-5 image versions through Midjourney or GetHookd. Different backgrounds, different product angles, different visual hooks. Step 4: Feed the best-performing copy into Higgsfield or Veo for video variations. Same script, different visual treatment. AI-generated UGC. Different presenters. Different settings. Step 5: Launch all variations in your testing campaign at $50-100/day per ad set. Within 3-5 days you know which combinations work. Winners move to your main Advantage+ campaign. The ratio that works: 80% iterations on proven winners, 20% net-new concepts. Most brands test 3-5 new creatives a week and wonder why their account is flatlined. They're bringing a knife to a gunfight. The AI tools (Claude for copy, Midjourney for statics, Higgsfield and Veo for video) are what make 200+ variations possible with a 2-person creative team. Without them you'd need a team of 10+ doing this manually. Creative velocity isn't a strategy. It's a survival requirement at scale in 2026.