July 8, 2014
June 23, 2014
By my unofficial count, there are approximately a million ways to segment your users within Google Analytics. One of my favorite segments for predicting future success is looking at the demographics reports, because they allow us to look at the age range and gender of our customers to a great level of accuracy. Learn how to make the most out of it.
June 10, 2014
This article is a guide to the new Google Analytics Chrome extension: In-Page Analytics. The extension is intended to help its users understand their customers in a better and faster way.
May 19, 2014
This article shows how to easily rewrite multiple URLs in Google Analytics with the help of Google Tag Manager (GTM). This solution is life changing for websites that do not have friendly URLs, which makes analyzing customer behavior on websites very cumbersome.
May 12, 2014
Content experiments for mobile apps can be extremely valuable to a business. We are no longer dependent on long development cycles as soon as we want to test something new. And when we do want to test, we can do so faster, more accurately, and with more certainty to avoid costly mistakes based on hunches. Learn how!
April 29, 2014
Are you struggling with Google Analytics segments every now and then? Would you like to learn how to use/build them instead of just being overwhelmed with a "best segments" posts? If your answer to these questions is yes, I recommend you read this article very carefully!
April 22, 2014
This article provides important statistical references and actionable tips in order to run A/B Tests in a professional way. The ideas reflected in this article will help you improve your A/B testing techniques for better decision making and conversion rates.
We've all been there. Everything on surface looks like it's running smoothly. Data is coming in. The 30,000-foot view of your account looks like business as usual. You start upping your analytics game. Maybe you took some training and you're getting your hands dirty asking the tough questions of your data. But how do you know if you can trust your data in the first place?