Web Analytics 2.0
Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.
Avinash Kaushik is the author of the leading research & analytics blog Occam's Razor. He is also the Analytics Evangelist for Google and the Chief Education Officer at Market Motive, Inc. He is a bestselling author and a frequent speaker at key industry conferences around the globe and at leading American universities. He was the recipient of the 2009 Statistical Advocate of the Year award from the American Statistical Association.
The author donates all proceeds from his books to two charities, The Smile Train and The Ekal Vidyalaya Foundation.
Web Analytics 2.0
Chapter 1: The Bold New World of Web Analytics 2.0
For years it has been clear that web analytics holds the promise to truly revolutionize how business is done on the Web. And why not? You can track every click of every person on your site. How can that not be actionable? Unfortunately, the revolution has not quite panned out. The root cause is that analysts and marketers have taken a very limited view of data on the Web and have restricted it just to clickstream data. In this chapter, I make the case for why you need to drastically rethink what it means to use data on the Web. The Web Analytics 2.0 strategy adapts to the evolution of the Web and dramatically expands the types of data available to help you achieve your strategic business objectives.
- State of the Analytics Union
- State of the Industry
- Rethinking Web Analytics: Meet Web Analytics 2.0
- Change: Yes We Can!
State of the Analytics Union
Let's start with a tale about the paradox of data. Professionally speaking, I grew up in the world of data warehousing and business intelligence (BI). I worked with massive amounts of enterprise data; multiterabytes; and sophisticated extract, transform, and load (ETL) middle layers-all fronted by complex business intelligence tools from companies such as MicroStrategy, Business Objects, and SAS. Although the whole operation was quite sophisticated and cool, the data set wasn't really that complex. Sure, we stored customer names and addresses, products purchased, and calls made, along with company metadata and prices. But not much data was involved. As a result, we made lots of great decisions for the company as we valiantly went to battle for insights.
But the lack of breadth and depth of data meant that often, and I say this only partly in jest, we could blame incompetence on the lack of sufficient types of data. So, we always had a get-out-of-jail-free card, something like, "Gosh darn it. If I knew our customers' underwear sizes, I could correlate that to their magazine subscriptions, and then we would know how to better sell them lightweight laptops."
I know, it sounds preposterous. But it really isn't.
With that context, you'll appreciate why I was ecstatic about the world of web analytics. Data, glorious data all around! Depth and breadth and length. Consider this: Yahoo! Web Analytics is a 100 percent free tool. It has approximately 110 standard reports, each with anywhere from 3 to 6 metrics each. That number of 110 excludes the ability to create custom reports covering even more metrics than God really intended humanity to have.
But after a few weeks in this world, I was shocked that even with all this data I was no closer to identifying actionable insights about how to improve our website or connect with our customers.
That's the paradox of data: a lack of it means you cannot make complete decisions, but even with a lot of data, you still get an infinitesimally small number of insights.
For the Web, the paradox of data is a lesson in humility: yes, there is a lot of data, but there are fundamental barriers to making intelligent decisions. The realization felt like such a letdown, especially for someone who had spent the prior seven years on the quest for more data.
But that's what this book's about: shedding old mental models and thinking differently about making decisions on the Web, realizing data is not the problem and that people might be, and focusing less on accuracy and more on precision. We will internalize the idea that the Web is an exquisitely unique animal, like nothing else out there at the moment, and it requires its own exquisitely unique approach to decision making. That's Web Analytics 2.0.
Before we go any further, let's first reflect on where we are as an industry today.
State of the Indus