A/b Testing Strategies for Better Results

Product development entails inherent risks where hasty decisions can lead to losses, while overly cautious changes may result in missed opportunities. To manage these risks, proposed changes undergo randomized experiments, guiding informed product decisions. This article, written by Data Scientists from Spotify, outlines the team’s decision-making process and discusses how results from multiple metrics in A/B tests can inform cohesive product decisions. A few key insights include:  - Defining key metrics: It is crucial to establish success, guardrail, deterioration, and quality metrics tailored to the product. Each type serves a distinct purpose—whether to enhance, ensure non-deterioration, or validate experiment quality—playing a pivotal role in decision-making.  - Setting explicit rules: Clear guidelines mapping test outcomes to product decisions are essential to mitigate metric conflicts. Given metrics may show desired movements in different directions, establishing rules beforehand prevents subjective interpretations during scientific hypothesis testing.  - Handling technical considerations: Experiments involving multiple metrics raise concerns about false positive corrections. The team advises applying multiple testing corrections for success metrics but emphasizes that this isn't necessary for guardrail metrics. This approach ensures the treatment remains significantly non-inferior to the control across all guardrail metrics. Additionally, the team proposes comprehensive guidelines for decision-making, incorporating advanced statistical concepts. This resource is invaluable for anyone conducting experiments, particularly those dealing with multiple metrics. #datascience #experimentation #analytics #decisionmaking #metrics – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gewaB9qC

Most A/B tests look simple on the surface, two versions, one outcome, run a t-test, done. But what if your entire analysis is built on a faulty assumption? To help my students spot these hidden traps, I created a synthetic dataset (https://lnkd.in/epyqmxTC) that mirrors a common real-world scenario. In this example, we simulate 75 students spread across 10 classes. Each class, not each student, is randomly assigned to either Design A (control) or Design B (treatment) of an educational platform. Students then use their assigned version, and we measure how long they spend on the page as a proxy for engagement. At first glance, Design B appears to outperform Design A. A few of the B-assigned classes show noticeably higher average time on page. This is exactly where things can go wrong. Without proper statistical training, someone might look at this and immediately run Welch’s t-test to compare students in Design A versus Design B. The logic sounds straightforward. There are two conditions, one continuous variable, and Welch’s test even adjusts for unequal variances, so it seems like a safe choice. But it is not the right tool in this case. The issue is that treatment was assigned at the group level. Classes, not individual students, were randomized. That means the data points are not truly independent. Students within a class tend to behave similarly because of shared dynamics such as the same teacher, classroom environment, or peer effects. Welch’s t-test, just like any traditional t-test, assumes each observation is unrelated to the others. When that assumption is violated, the p-values it produces can give a false sense of certainty. In this dataset, Welch’s t-test produced a very small p-value (p = .00007), suggesting a strong and statistically significant effect of Design B. But when we analyzed the same data using a linear mixed-effects model that properly accounted for the fact that students were nested within classes, the result changed. The effect of Design B was no longer statistically significant (p = .109). What seemed like a convincing treatment effect was actually driven by just two of the ten classes. The other classes showed no clear difference. This example has direct consequences for how teams make decisions, and can result in wasted development time, unnecessary marketing costs, and operational effort spent on a change that delivers no real value. While the scenario may seem straightforward, it highlights a deeper issue: without a strong grasp of experimental design and statistical modeling, it’s easy to apply the wrong test, misread the outcome, and move forward with misplaced confidence. Even one misstep can turn into a failed product launch or a missed opportunity. Statistical reasoning isn’t just a technical skill, it’s a critical part of producing research that supports sound, evidence-based decisions. Please learn stats and methods before doing UX research!

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!)

A/B testing can increase conversions by 161%+. Yet, most people don't know how to do A/B testing properly. Here are 10 copy elements you should test (and how to do it). From a $200M Marketer: Before we start... When running an A/B test, it's important to only test one variable at a time. This way, you'll know exactly what change impacted the metric you’re optimizing for. Various tips below pertain to landing pages, emails, ads, etc.  1) Headline Testing headlines is crucial as they directly impact how many people read the rest of your copy. You can test headlines by changing: - Tone - Length - Emotional appeal - Use of Numbers This will help you understand what catches your target's attention the best. 2) Call-to-Actions Testing CTAs is vital because it can lead to higher click rates & purchases. You can test headlines by changing: - Copy - Placement - Color & Design - Urgency and Scarcity This will give you insights into what combo of attributes drives the most clicks. 3) Value Proposition Testing different value propositions allows you to communicate what your product offers in a better way. Test your value props by changing: - Format - Benefits - Pain Points This will uncover the value proposition that resonates most with your target. 4) Body Copy Length Testing the body copy length helps you find the balance between information and engagement. This testing is made by comparing short-form copy and long-form copy. This experimentation will reveal the ideal copy length that keeps readers engaged. 5) Emotional Appeal Testing different emotional triggers allows you to tap into your target's desires and motivations. To test this, experiment with different emotions: - Fear - Anger - Desire This will help you create copy that deeply connects with your prospect. 6) Social Proof Testing different presentations of social proof helps establish trust and credibility with your audience. To test this, try different formats such as: - UGC - Testimonials - Case studies This will highlight the most compelling way to present social proof. 7) Pricing Strategies Testing pricing strategies helps you optimize your pricing model for maximum conversions. Test pricing strategies by offering: - Discounts - Bonuses - Financing This will uncover the pricing approach that resonates best with your target.   8) Storytelling Using storytelling techniques allows you to captivate your audience more easily. To test this, try incorporating: - Stories - Characters - Narrative arcs Into your copy. This will help you create emotion-evoking and thought-provoking copy. 9) Formatting Testing different types of formatting enhances the readability and scannability of your copy. Test formatting by varying: - Text alignment - Sentence length - Word styling (bold/italics) This will improve your copy's presentation & lead to higher engagement. ( #10 in the comments )

founder learnings! part 8. A/B test math interpretation - I love stuff like this: Two members of our team (Fletcher Ehlers and Marie-Louise Brunet) - ran a test recently that decreased click-through rate (CTR) by over 10% - they added a warning telling users they’d need to log in if they clicked. However - instead of hurting conversions like you’d think, it actually increased them. As in - Fewer users clicked through, but overall, more users ended up finishing the flow. Why? Selection bias & signal vs. noise. By adding friction, we filtered out low-intent users—those who would have clicked but bounced at the next step. The ones who still clicked knew what they were getting into, making them far more likely to convert. Fewer clicks, but higher quality clicks. Here's a visual representation of the A/B test results. You can see how the click-through rate (CTR) dropped after adding friction (fewer clicks), but the total number of conversions increased. This highlights the power of understanding selection bias—removing low-intent users improved the quality of clicks, leading to better overall results.