What it takes to compete (and win!) on analytics

Things have come a long way since the days of analyzing Web server logs (raise your hand if you were at THAT party.) Web analytics tools like Google Analytics, SiteCatalyst, and WebtrendsAnalytics have brought basic site tracking and reporting to the masses, often with little more than dropping some javascript on the page.  Closer to home, Business Intelligence (BI) and data warehousing tools such as MicroStrategy and Microsoft’s suite of tools provide a structured view into in-house data.  Google Site Optimizer, SiteSpect, and tools like Omniture Recommendations go so far as to provide intelligent testing, targeting, and recommendation abilities to companies that even a few years ago would have met serious challenges developing these competencies in-house.

But these tools are not how you compete on analytics. Continue reading “What it takes to compete (and win!) on analytics”

Why “The One Number You Need To Grow” is one number you should probably avoid

In 2003, Frederick Reichheld published a Harvard Business Review article entitled The One Number You Need to Grow.  Reichheld’s article described a method for computing a simple, easy-to-understand customer satisfaction metric called the Net Promoter Score — and ushered in a flavor-of-the-month management practice that has left a bad taste in the mouth of academics and serious marketing researchers Continue reading “Why “The One Number You Need To Grow” is one number you should probably avoid”

Is the online dating industry propping up Google’s ad revenue numbers?

A few days ago, Frédéric Peters (Cupidon at Cupidon.be) posted an interesting question to the LinkedIn’s Internet Dating Executive Alliance group: Given the oft-cited success of the online dating industry, what percentage of Google’s ad revenue is driven by the online dating industry?

Based on Frédéric’s question, I felt compelled to run a quick back-of-the-envelope estimate of the share of Google’s ad revenue contributed by the U.S. online dating industry based on Continue reading “Is the online dating industry propping up Google’s ad revenue numbers?”

There is no room at the top for the hired help

Avinash Kaushik recently addressed a question perenially on our minds — how to make more money as a web analyst — by offering some very specific advice on how to choose a career path in web analytics based on your strengths and aspirations.  It’s a long post but definitely worth a read if you’re in the field.

But the subject of career path has been on my mind for awhile. Over time I’ve observed that most companies (or possibly business units if we’re talking about very large organizations) offer a rock-star career path for only one specific skillset. That is, before you can answer the question “How can I get ahead?” you’ve got to answer the question Continue reading “There is no room at the top for the hired help”

Omniture SiteCatalyst Variables 101

Data analysis begins with understanding the available data. To social scientists, this usually involves understanding your data type — nominal, ordinal, scalar, ratio. Web analysts, however, must always be concerned with the level of measurement as well — page level, session level, user level. If Omniture SiteCatalyst is your data source, understanding SiteCatalyst’s levels of measurement is key to being a good analyst. Those esoteric SiteCatalyst variables: sProps, Success Variables, and eVars, are easy to remember once you’ve seen their levels of measurement and Continue reading “Omniture SiteCatalyst Variables 101”