Blogs

Google Analytics is now showing "Chrome" as a browser type in your reports! All current and future visits will be labeled as such.

Over the next few days, the visits from Google Chrome since its beta launch on Tuesday will be identified and updated to correctly show "Chrome" as the browser type for that period.

In our last post, we discussed Omniture SiteCatalyst Traffic Data Correlations and learned how they could be used to break one Traffic Variable (also known as an sProp) down by another Traffic Variable.  But what if you want to break down a SiteCatalyst Conversion Variable (also known as an eVar) down by another Conversion Variable?  Since these are not Traffic Variables, Traffic Data

A couple of weeks ago I decided to start Twittering. I’ve had a Twitter account for a while, but never really got into it. But after observing some friends for a while, and reading up on how others use Twitter, I started to see some value in the service.

Once upon a time, when I was a young turk, I would assiduously download every last doodad that my employer created as soon as it shipped - or often long before, happily reaching for the pile of floppy disks as I rebuilt my computer for the umpteenth time following the latest toxic combination of untested software.

Today, we announced the release of the beta version of a new open-source browser: Google Chrome. We encourage you to download it and give it a try.

There’s been quite a storm brewing around the best methodology to use for multivariate testing: fractional-factorial vs. full-factorial. (For a quick primer, definitions of both are included at the bottom of this post.) I have to say that some of the arguments I’ve heard border on ideological in both their passion and rigor. Are there scenarios where one methodology makes more sense than the other? Absolutely. Is it possible that one methodology is right for every scenario?

Now that we have covered the basics of the three Omniture SiteCatalyst variable types, it is time to see how we can use them to our advantage to do analysis.  In this post we will learn about Traffic Data Correlations which allow you to leverage the work you have already done by tagging your Traffic Variables (also known as sProps).

One of the more interesting concepts presented at last week’s SES show in San Jose detailed the use of predictive revenue modeling for SEO and paid search. A former colleague of mine, Dave Roth of Yahoo!, runs search programs on behalf of Yahoo!’s properties. He discussed their use of predictive data models to gauge how they were performing against their competition.

In SEO it works like this: