2010 Measurement Metrics Testing 
Occam's Razor
JANUARY 6, 2010
Before all the excitement of the new year wears out, here are five simple things I would love for you to try so that your company will have a glorious truly data driven 2010! #1: No longer can tools or "analysts" just puke 15 metrics on a report and hope to survive. This is usability testing for ultra cheap, $29 per person.
Occam's Razor
SEPTEMBER 15, 2014
In blue is how much time we spent in 2010 and in blue the time spent in 2014. was the dramatic shift between 2010 to 2014 to mobile content consumption. But why blame others, in this post let's focus on one important reason whose responsibility can be squarely put on your shoulders and mine: Measurement.
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Occam's Razor
APRIL 19, 2010
"What is the difference between a metric and a key performance indicator (KPI)?" " "Are goals metrics?" There seems to be genuine confusion about the simplest, most foundational, parts of web metrics / analytics. The post will end with a Web Analytics Measurement Framework. " And many more.
Sisense
JUNE 9, 2021
It’s no surprise that rivals followed suit and that by 2010 analytics were widely used by top teams in leading international leagues. And also like their counterparts in the business world, coaches are relying on metrics to guide their decision-making. These developments have added a whole new dimension to data analysis.
Occam's Razor
MARCH 23, 2010
Too many new things are happening too fast and those of us charged with measuring it have to change the wheels while the bicycle is moving at 30 miles per hour (and this bicycle will become a car before we know it – all while it keeps moving, ever faster). Usually at least a test. And I doubt it is going to happen soon.
Occam's Razor
FEBRUARY 20, 2012
My normal recommendation to address this supremely corrosive issue is to encourage each company to go through the process of creating a Digital Marketing and Measurement Model. What content areas seem very expensive to create (hence more important to measure if they are adding any value!)? It is that simple. Good context.
The Unofficial Google Data Science Blog
OCTOBER 12, 2017
With more features come more potential post hoc hypotheses about what is driving metrics of interest, and more opportunity for exploratory analysis. Yet when we use these tools to explore data and look for anomalies or interesting features, we are implicitly formulating and testing hypotheses after we have observed the outcomes.
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