User Testing Methods for Conversion Rate Improvement

Conversion optimization pros won't like this: but listening to your users is more valuable than 90% of the experiments I review. I've made this mistake too. My teams spent years running dozens of "high-impact" experiments to improve our signup rate. It helped, but we knew something was missing. Then, we started running a survey on the high-intent pages of our site that changed everything... The question was simple: "Hey, thanks for visiting the site. Mind sharing what's stopping you from creating a free account today?" But the answers were super helpful. "I don't understand the product" "Not sure if I'm your ICP" "The pricing model is confusing" "I need to see what it looks like first" "Can't figure out if you solve for [specific use case]" Some were painful to read. But they refocused us on solving the right problems for our users. Instead of running blind experiments based on what WE thought the problem was, we started brainstorming new impactful ways to improve our conversions: copy changes, video updates, image adjustments, page layout changes - based on the THEIR feedback. Not sure how to take your signup rate to the next level? Try asking some flavor of this question. You’ll get some incredible insights. PS we used Hotjar | by Contentsquare to run the survey, but there's plenty of other tools out there to do this. It's about getting input from real people. That's where the magic happens.

Not every user interaction should be treated equally, yet many traditional optimization methods assume they should be. A/B testing, the most commonly used approach for improving user experience, treats every variation as equal, showing them to users in fixed proportions regardless of performance. While this method has been widely used for conversion rate optimization, it is not the most efficient way to determine which design, feature, or interaction works best. A/B testing requires running experiments for a set period, collecting enough data before making a decision. During this time, many users are exposed to options that may not be effective, and teams must wait until statistical significance is reached before making any improvements. In fast-moving environments where user behavior shifts quickly, this delay can mean lost opportunities. What is needed is a more responsive approach, one that adapts as individuals utilize a product and adjusts the experience in real time. Multi-Armed Bandits does exactly that. Instead of waiting until a test is finished before making decisions, this method continuously tests user response and directs more people towards better-performing versions while still allowing exploration. Whether it's testing different UI elements, onboarding flows, or interaction patterns, this approach ensures that more users are exposed to the most optimal experience sooner. At the core of this method is Thompson Sampling, a Bayesian algorithm that helps balance exploration and exploitation. It ensures that while new variations are still tested, the system increasingly prioritizes what is already proving successful. This means conversion rates are optimized dynamically, without waiting for a fixed test period to end. With this approach, conversion optimization becomes a continuous process, not a one-time test. Instead of relying on rigid experiments that waste interactions on ineffective designs, Multi-Armed Bandits create an adaptive system that improves in real time. This makes them a more effective and efficient alternative to A/B testing for optimizing user experience across digital products, services, and interactions.

Rapid testing is your secret weapon for making data-driven decisions fast. Unlike A/B testing, which can take weeks, rapid tests can deliver actionable insights in hours. This lean approach helps teams validate ideas, designs, and features quickly and iteratively. It's not about replacing A/B testing. It's about understanding if you're moving in the right direction before committing resources. Rapid testing speeds up results, limits politics in decision-making, and helps narrow down ideas efficiently. It's also budget-friendly and great for identifying potential issues early. But how do you choose the right rapid testing method? Task completion analysis measures success rates and time-on-task for specific user actions. First-click tests evaluate the intuitiveness of primary actions or information on a page. Tree testing focuses on how well users can navigate your site's structure. Sentiment analysis gauges user emotions and opinions about a product or experience. 5-second tests assess immediate impressions of designs or messages. Design surveys collect qualitative feedback on wireframes or mockups. The key is selecting the method that best aligns with your specific goals and questions. By leveraging rapid testing, you can de-risk decisions and innovate faster. It's not about replacing thorough research. It's about getting quick, directional data to inform your next steps. So before you invest heavily in that new feature or redesign, consider running a rapid test. It might just save you from a costly misstep and point you towards a more successful solution.

I boosted an 8-figure coaching brand’s conversion rate by 50% in one month. Here's how I did it in 6 steps: 1 – Listen to your client. This sounds obvious, but you’d be amazed at how much you can learn if you actively listen and read between the lines. In this scenario, the client briefly mentioned a landing page revamp the year prior. As a result, other channels saw an uptick in conversion rate, but paid search was flat. Why? 2 - Be proactive. The client was curious and asked a question, but never asked me to take any specific action.  Instead of letting it go, I did this instead: - Pulled a landing page report – identified the page with the most click traffic - Pulled a device report – learned that 65% of click traffic was from mobile - Analyzed the landing page based on mobile CRO best practices 3 – Develop a hypothesis. I found the landing page was not optimized for mobile users. I made the following recommendations to the client: - Create a new version of the landing page - Use one clear image - Use one compelling call-to-action - Move the CTA button above the fold - Shorten the lead form to only 2 fields (name and email) 4 – Test your hypothesis. My hypothesis was the new landing page would beat the old landing page based on conversion rate (CVR). I implemented the following test: - Used Google Ads Experiments - Ran A/B landing page test - 50/50 traffic split (test page against control page) - Measured success or failure based on CVR - Let the test run until statistical significance was reached (approx. 30 days) - Didn’t make any big changes to the campaign while the test was underway 5 – Share the results. After 30 days, I analyzed the results and shared them with my client.  The results: - My hypothesis was correct - The new landing page had a 50% higher CVR compared to the old landing page - The 50% higher CVR led to an additional 500 leads per month for my client - 500 additional leads per month without spending an extra dime  - HUGE WIN! 6 – Learn and iterate. I rolled out the new landing page across the entire Google Ads account. Delivering this kind of major value not only strengthens client trust, but also makes the testing process rewarding.

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

After generating $1 BILLION+ dollars for clients, we can tell you Conversion Rate Optimization isn't about guessing. So want to know what ACTUALLY moves the needle in Conversion Rate Optimization? - It's not random A/B tests. - It's not changing button colors. - It's not "gut feelings." Here's the process we recommend at SiteTuners: 1️⃣ Start with your analytics. Look for the crucial signals: - Where are users dropping off? - Where's the engagement lacking? - How much time are people spending on site? - Which pages are they leaving from? 2️⃣ Add heat mapping. This is where it gets interesting. You need: - Video recordings of real user sessions - Accumulated heat maps showing visitor behavior - Clear data on what's actually happening on your site 3️⃣ Create informed hypotheses. Before testing, calculate: - Expected uplift from the change - Required effort to implement - Potential ROI of the test Here's what most people miss... Testing has real costs: 1. Heat mapping tools 2. Testing software 3. Development time 4. Traffic split for testing So this is important to know… Not every test is worth running. Just because you have an idea doesn't mean it deserves your resources. Let the data guide your decisions: - Use analytics for statistical proof - Watch heat maps for behavioral insights - Calculate the math before testing Stop guessing with your conversion rates. Start letting real data drive your optimization.