Nick Gassman’s Post

Key Drivers Analyses (KDA) - How to identify the factors that influence user behaviors. Read about it, learn it, try it yourself: https://lnkd.in/etdPxaxc

The #1 Mistake in CRO (and How to Fix It) 🚫 Most CRO experts focus on numbers and psychological best practices. But here’s the problem: They forget the most important thing - understanding the customer. Quantitative data tells you what’s happening. But it won’t tell you why. Is your product page confusing? Are people stuck trying to find their size? The solution isn’t more numbers. It’s talking to your customers. Surveys, user testing, and interviews uncover the real issues. They show you what your users want, need, and struggle with. But too many CRO agencies stick to “best practices.” They create one-size-fits-all strategies that miss the point entirely. If you want better results, do this: Understand your customers deeply. Solve their problems. Serve them better than anyone else. The secret to sky-high conversions isn’t a formula or an algorithm. It’s empathy. Are you putting your customer first?

In today’s data-driven world, understanding public sentiment is more crucial than ever, and this is where sentiment analysis software comes into play. These robust tools help businesses and individuals gauge emotions and opinions from various text sources, offering invaluable insights. #digitalmarketing #socialmediamarketing #sentimentanalysis https://lnkd.in/gpfUWFpE

Hey LinkedIn friends - This is a great article by the host of one of my favorite podcasts "Curious Minds at Work", Gayle Allen, Ed.D., MBA. This share is kind of typical for Gayle. It is smart and makes you think about how you are operating right now. If you work with people, lead people, or just are curious about how to operate more effectively, I encourage you to listen to "Curious Minds at Work." If you're looking for a good place to start, try the "Simple ways to get unstuck" where she interviews Adam Alter. You won't regret it - https://lnkd.in/g-iQFJE2

Seeing the potential to make changes on your website that improve your conversion rate, AOV, or other key metrics can be hard. To help, I'm starting a series where I take 2 - 3 minutes to walk you through a real website and highlight an idea or two. Today's example comes from the Belk website. Today's lesson is that sometimes A/B testing is another research tool for small, iterative changes. And those changes aren't always about adding or adjusting something. Sometimes, it's about simply removing something. Let me know what you think about doing more of these. --- 🐼 Disclaimer: I have no idea whether any of my ideas are valid without data from analytics or research, or both. These are all generated from high-level heuristic analyses and are not meant to say if this is good or bad. It's just meant to stimulate exploration and learning.

𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐓𝐡𝐞 𝐋𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬 𝐎𝐟 𝐀/𝐁 𝐓𝐞𝐬𝐭𝐢𝐧𝐠? A/B testing is a powerful tool for optimizing user experiences and improving performance metrics, but it also has several limitations that researchers and practitioners should be aware of.

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

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

🎯 When it comes to A/B testing, accuracy is everything. Understanding how to leverage continuous metrics with both Frequentist and Bayesian approaches can take your testing to the next level. 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗺𝗲𝘁𝗿𝗶𝗰𝘀? Metrics like Revenue per Visit or Average Cart Value provide more detailed insights into user behavior, beyond simple yes/no outcomes. 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝘀𝘁 𝘃𝘀. 𝗕𝗮𝘆𝗲𝘀𝗶𝗮𝗻 𝘍𝘳𝘦𝘲𝘶𝘦𝘯𝘵𝘪𝘴𝘵 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩: Traditional methods like t-tests help you measure improvement rates and statistical significance. Ideal for larger sample sizes. 𝘉𝘢𝘺𝘦𝘴𝘪𝘢𝘯 𝘢𝘱𝘱𝘳𝘰𝘢𝘤𝘩: Combines prior knowledge with observed data for a nuanced view, perfect for smaller sample sizes or when you need deeper insights. By understanding and applying these approaches, you can ensure that your A/B tests are not just accurate but also actionable, driving stronger results for your business. Read the full article for a deeper dive into how you can apply these methods effectively using the Kameleoon platform. 👉 https://lnkd.in/eBWwFner

You Need To Iterate Properly. 95% of people who do outbound have decided to put their results in the hands of luck. If you aren’t properly iterating, you're part of that 95%. Most people will send a few messages, lazily track, switch something, repeat. Here’s how you should be doing outbound: REAL Metric Tracking First of all, if you aren't tracking, start. If you are tracking, make sure you're tracking the right things. Just your ABR isn't enough, track the main metric for each “sub system” of your outbound process. Doing this will help you establish your bottlenecks. If you're doing both, make sure your metrics are real. Spend the time every weekend to go over the week's metrics and recount. False metrics will ruin you more than no metrics. Use regression to the mean. Averages don't show themselves in small datasets. If you flip a coin twice and it lands on heads twice, does the coin have a 100% chance of landing on heads? No, you're just a bad scientist. Make sure your tests (in 3) have enough volume of data to allow for regression to the mean. Be scientific When testing an outreach system, you should be using the scientific method. Create a testing pool (amount of outreaches) Do the outreaches, not changing anything Track your results Iterate where you believe the bottleneck to be Don’t change anything else Run the test again Track your results Repeat the last 4 steps