Tilen Bobek’s Post

Our brand new tech blog is live! 🙌 First up: Senior Data Scientist Rok Piltaver delivers a decade’s worth of A/B testing wisdom, including some hard-won lessons learned. Find out how data-driven decisions can enhance user experience, boost monetization, drive business growth, and more in the first instalment of this two-part series. 👉 https://lnkd.in/gqDFxz9e

Feeling overwhelmed by intent data? 🤯 You’re not alone! B2B intent data is a powerful tool, but making it work can be tricky. Our latest blog breaks down how to use intent data effectively from setting clear goals to choosing the right metrics and tools. Check it out and let us know your thoughts! 👇 https://bit.ly/3T7hcoN

Feeling overwhelmed by intent data? 🤯 You’re not alone! B2B intent data is a powerful tool, but making it work can be tricky. Our latest blog breaks down how to use intent data effectively from setting clear goals to choosing the right metrics and tools. Check it out and let us know your thoughts! 👇 https://bit.ly/3T7hcoN

A/B test = core tool for comparing the results of two or more recommender variants 💪 And above all, the starting line of our cooperation with the client, when we can clearly see whether the recommendation tool makes sense or not. And also in what form. For example, it may look like the e-shop already has basic product recommendation functionality and is about to compare it with an external tool that offers higher performance. In practice, the tools may then differ in the effectiveness of different algorithms, parameters or recommendation strategies.  👇 We have run dozens of higher A/B tests and always pay close attention to it. And what have we learned, for example? ✅ You can't relevance test on a small sample of data.  ✅ If the results seem too good or bad in favor of one of the options, there is a mistake somewhere!  ✅ You can't look at the results every day, AB-testing is like investing in the stock market. ⚠ Most importantly: If an e-shop really wants to know what is best, it will cost time and (probably) money. And we are pleased that in 90 % of the cases our A/B tests show that years of research and development of our tool have a high added value for the client and can bring concrete improvements in referrals and sales. 💸

Day 15: A/B Testing for Business Decisions 📈 We’re halfway through my 30-day Data Science journey! Today, we’re looking at A/B Testing—an essential tool for making informed business decisions. A/B Testing involves comparing two variable versions (like a webpage, email, or ad) to determine which performs better. By splitting your audience into groups (Group A and Group B) and showing each a different version, you can measure which delivers better results, such as higher conversions, clicks, or sales. Key steps in A/B Testing: (1) Hypothesis Formation: Clearly define what you're testing and what you expect to happen. (2) Split Testing: Divide your audience into two groups randomly. (3) Measurement: Track metrics such as conversion rate, engagement, or revenue. (4) Analysis: Compare results and decide which version to implement. 💡 Real-Life Example: In 2012, Google ran over 7,000 A/B tests on its search algorithm to continuously improve the user experience. Even the smallest changes, like tweaking font colors or the placement of ads, were tested to maximize performance. A/B Testing helps ensure every adjustment is data-backed. A/B Testing empowers businesses to make data-driven decisions, ensuring that changes lead to measurable improvements. Looking forward to tomorrow’s post on Data Privacy and GDPR, a key concern for data-driven businesses today! 🚀 #DataScience #ABTesting #BusinessDecisions #Google #ConversionOptimization #30DaysOfDataScience #LearningJourney

I hate not knowing if the feature we just launched moved the needle or not. It’s tempting to measure success by the uptick in revenue. But that’s like waiting for a year-end bonus to decide if your last meeting was worth it. Here’s my 3-step approach to know if the new feature is making a difference: 1. What’s the goal? Whether you’re building games for teenage kids or selling internet software to Fortune 500 companies, every big feature you build is tied to a bigger business goal. The features you ship not only help your users solve their pain points, but also help grow your business by increasing acquisition, engagement, retention, converting free users to paid. Establish what your feature is trying to accomplish for your company. 2. Define metrics that matter Metrics aren’t one-size-fits-all. Ask yourself, “What do I want to learn?” instead of “What do I want to track?” It’ll help you avoid vanity metrics and hone in on true user behaviour. It’s not just about tracking data; it’s about making it actionable. 3. Collect qualitative data Data can tell you “what” happened, but talking to your users will tell you “why”. Skip the yes/no questions and dig deeper with open ended Qs. Bottom line? Don’t just track for the sake of tracking. I wrote in detail in my substack: https://lnkd.in/g4HC3EEm

How about simplifying your Google Analytics 4 test analysis process forever? This is our new feature... Over the past few months, we've been quietly working on something significant, and we're about to reach an new milestone at Varify.io® A/B Testing. We can't wait to share what we've created with you in our upcoming webinar next week! This feature will make it much easier to evaluate your test results. What you can expect: 💡 How the integration of the feature works ➕ What are the benefits and changes 🔧 Use cases how you can take advantage of it Curious to know what awaits you? Join our webinar with our Founders Steffen Schulz and Thomas B. Kraus to find out 😊. Register for free – it will be worth your time! 📅 Date: 28.05.2024 You can choose whether you want to take part 🇩🇪 at 11.30 am in German or 🇬🇧 at 5 pm in English If you have any questions, feel free to comment below! 👇🏼 You’ll find the german & english registration link in the comments.

🚀 FREE A/B Testing Use Case: Skyrocket Your Conversion Rates with Data-Driven Insights 🚀 Are you ready to take your website's performance to the next level? 📈 A/B testing is one of the most powerful tools in a Business Analyst or Data Analyst's toolkit. By experimenting with variations and analyzing real-time data, you can optimize your business strategies, improve conversion rates, and make data-backed decisions that drive results! 🎁 As a special resource, I’m offering a FREE A/B Testing Use Case that you can download to get started or refine your approach. In this use case, you’ll learn how to: Boost Website Conversion Rates by testing small but impactful changes like button colors 🎨 Make data-driven optimizations that continuously improve your user experience 🌟 Implement a step-by-step A/B testing framework that’s easy to follow and backed by real metrics 🔗 Download the FREE use case here: Download A/B Testing PDF ( https://lnkd.in/gnRdb33S ) #ABTesting #DataAnalytics #BusinessAnalytics #ConversionRateOptimization #Experimentation #WebsiteOptimization #BusinessGrowth #FreeResource #DataDrivenDecisions #ContinuousImprovement

I love the magic of MVPs. Here’s how we launched in just 3 months: 𝗝𝘂𝗻𝗲: 𝗪𝗲 𝗵𝗮𝗱 𝗮𝗻 𝗶𝗱𝗲𝗮. - What would happen if we reached out to 30 people a day? - Result: 20 new contacts & 1-2 demos a day. - Product stage: Manual labor. 𝗝𝘂𝗹𝘆: 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 - Can we get people interested in this approach? - Result: Yes, and they were excited! - Product stage: Stitched-together pieces (only running on our own accounts) 𝗔𝘂𝗴𝘂𝘀𝘁: 𝗕𝗲𝘁𝗮 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 - Can we build this reliably? - Result: Happy beta testers! - Product stage: Low-code beta. (our UI was Google Looker Studio for 2 weeks 😂). 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿: 𝗟𝗮𝘂𝗻𝗰𝗵 & 𝗙𝗶𝗿𝘀𝘁 𝗣𝗮𝘆𝗶𝗻𝗴 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 - Will anyone actually pay for this? - Result: Yes, they are! - Product stage: Stable release. To celebrate, we’re offering an 80% discount until September 25th and a 14-day free trial of Cascaddy. Just comment “Free trial” if you want in.

I'm thrilled to share my recent work on A/B testing for optimizing call-to-action buttons on our platform! 🚀 Project Overview: We tested two different buttons—"Secure Free Trial" vs. "Enroll Now"—to determine which one drives more user engagement. Key Procedures: Power Analysis: Conducted to ensure the test was sufficiently powered to detect a meaningful difference. Data Collection: Summed the total number of clicks per group to analyze user behavior. Pooled Estimates: Calculated pooled estimates for clicks per group to get an overall view of the performance. Pooled Variance & Standard Error: Computed to assess the variability and reliability of our estimates. Z-Test & p-Value Calculation: Performed a Z-test to determine the statistical significance of our results. Practical Significance Test: Evaluated whether the observed differences were not just statistically significant but also practically meaningful. Standard Normal Distribution: Used to set parameters and further validate our findings. Results: The insights from this A/B test will help us make data-driven decisions to enhance user experience and conversion rates! Looking forward to applying these results to our ongoing projects and continuing to drive impactful changes. 💡 #ABTesting #DataScience #UserEngagement #DataDriven #ConversionRateOptimization #Analytics https://lnkd.in/d-dJn6pc