Do you remember when Slack launched? At the time, I was a diehard HipChat fan. Needless to say, I wasn’t interested in trying Slack.
I considered it nothing more than a passing trend. Now? I use it for an average of 10 hours a day for personal and professional reasons. (Sorry, HipChat.)
What’s going on here? How’d I go from loathing something to using it daily in the span of just 3-4 weeks? It’s called the mere-exposure effect, which means we tend to develop a preference for things just because we’re familiar with them.
“Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital,” said Aaron Levenstein, a former professor of business administration at Baruch College. [Tweet it!]
The same is true of your data in Google Analytics. Most of what you spend your time looking at (and re-looking at) is merely suggestive.
Sites that don’t work, don’t convert.
That’s why optimizers conduct quality assurance on sites, landing pages, test treatments, email campaigns, you name it—to make sure they work the way they’re supposed to.
While it’s common knowledge that quality assurance is something you should do, not enough optimizers complete it properly. If they did, there wouldn’t be so many sites that just plain don’t work.
You’d like to think that you’re a completely rational person making completely rational decisions, right? It’s nice to believe that you haven’t made major life decisions based on how you were feeling.
Well, you have. Many times.
C.F. Kettering once said, “My interest is in the future because I am going to spend the rest of my life there.”
When we look at data and analytics, we’re focused on the past. How did we do last quarter? What happened H1 2019? And how does that compare to H1 2018? How well did landing pages X, Y, and Z convert last Monday at 1:03 p.m.? (I’m kidding, I’m kidding.)
Data becomes more valuable when we use it to predict the future instead of just analyzing the past. That’s where propensity modeling comes in.
There’s a popular user experience quote: “A user interface is like a joke. If you have to explain it, it’s not that good.” While clever, that statement is far from true.
User interfaces shouldn’t be complicated, but you can’t expect a new user to understand a new interface without any direction. Similarly, you can’t expect an existing user to understand an updated interface or a new feature without any help.
We’re all familiar with the standard “best practices” of CRO. Always use social proof, always reduce form fields, never use image sliders, and so on.
As someone who believes that best practices are merely common practices, I’m always looking to test the tried and true to see how, well, true it really is.
First up? Social proof. Does it really work as well as we all assume? Why? And more importantly, what’s the best way to implement it?
Update: Google has announced that Optimize and Optimize 360 will shut down on September 30, 2023. After that, all experiments will end and the tool will no longer be usable.
If you are looking for A/B testing alternatives to Google Optimize, check out our post on the best A/B testing tools.
Chances are, you’ve heard of Google Optimize by now. It’s Google’s solution for A/B testing and personalization. Over the years, it has become a popular solution for optimizers around the world who wanted a freemium tool to do A/B testing.
In this post, you will learn what you can really expect from this tool. How do you configure it properly? How do you run your first experiment? Let’s go into details:
One of my favorite UX quotes comes from Chikezie Ejiasi, UX lead at Nest.
He wrote: “Life is conversational. Web design should be the same way. On the web, you’re talking to someone you’ve probably never met—so it’s important to be clear and precise. Thus, well-structured navigation and content organization goes hand in hand with having a good conversation.”
Can tabbed navigation be clear and precise? Of course it can, which makes it a valid form of navigation and content organization. What matters, as with most things related to UX, is how you implement it and how you optimize it.
Sean Ellis coined the term “growth hacking” over a decade ago in 2010. Since then, the term has taken on a life of its own.
“Growth hacking” is the focus of dozens of books, new roles, new departments and teams, new methods of thinking, hundreds of articles, hundreds of guides, hundreds of webinars… you get the idea.
Yet, it still feels very elusive. High-growth companies simply have something most companies don’t, right?
Wrong. The truth is, they simply had a solid growth marketing process.