“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.
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:
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.
Over the last several years, email has been pronounced dead half a dozen times, if not more. The truth is, even today, that email is very much alive and, for most optimizers, it’s far from being on its proverbial deathbed.
How can there be such a divided opinion? Segmentation and personalization are the answer.
Optimizers who take advantage of it are seeing real ROI. Optimizers who don’t? Well, they’re likely declaring that “the email blast is dead.”
When you hear “data segmentation”, it’s tempting to feel overwhelmed. Why? Segmentation can seem daunting (or boring) to those unfamiliar with it.
It’s an unfortunate because segmentation is perhaps one of the most effective tools at our disposal. The ability to slice and dice your Google Analytics data is the difference between mediocre, surface-level insights and meaningful, useful analysis.
In this article we’ll show you how to setup your Google Analytics to unlock actionable insights.
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.
With Google processing over 70,000 searches every second and Facebook being a hub for 2.7 billion monthly active users, Google Ads and Facebook are obvious choices for PPC campaigns.
But is one better than the other? Are the optimization processes for both similar? What about A/B testing?
These are the questions optimizers need answers to before they can really reap the benefits of two very powerful advertising platforms.
Why spend hours and thousands of dollars redesigning your website from scratch when someone has already done the work for you?
Millions of businesses turn to website templates to make the design process more efficient. But there’s something almost no one is talking about and it’s a big problem.
Website templates are not optimized for conversions.
For a web analytics analyst or a data-driven marketer, these are words to live by: “Without data, you’re just another person with an opinion.”
Optimization isn’t about educated guesses and hunches, no matter how many years you’ve been in the industry. It’s about doing the research, asking the right questions, digging for clues in problem areas, paying attention to the signs when they appear, and running smart A/B tests.
Web analytics analysis is a big part of that. It helps separate the optimizers from just another person with an opinion.