Web Analytics Key Performance Indicators (KPIs) are critical for understanding the huge volume of data in your Web analytics tool. While it's wonderful to have a tool that lets you cut, cross, and slice datasets in innovative ways, it can be a challenge to frame the data or put it in context in a way that helps your business optimize the Web site and meet customer needs. That's why I like KPIs -- they identify meaningful, business-focused relationships in your analytics data. By understanding KPI drivers, setting expectations for KPI performance, and analyzing your KPIs toward defined goals for those …

While watching the Olympics, I was struck by similariites between the scoring of the decathlon and Internet metrics. In the decathlon, everyone who ran got a score, and that score accumulates towards the gold. Announcers explained, for example, that one runner came in fifth -- but that was fine because the 100-yard dash was not something he did well on and he would make it up on other events. So, what was being presented (times in the race) was not actually the cumulative score. That's like Internet metrics, where we look at things like click-through rates, but in fact evaluate …

"How do I make my widget viral?" This is such a common question that I hear when it comes to widget distribution. But what does the word viral really mean? The term virality (which is not recognized in Webster's dictionary, but well-documented on Wikipedia) is thrown around almost as much as the word engagement. Virality can be used to describe the efficacy in distribution for widgets, social applications, email and your traditional word-of-mouth marketing. When it comes to widgets specifically, virality boils down to the number of times that a widget is grabbed from a source and installed to a …

In my last Metrics Insider column, I wrote about the question of how advertising works. The column generated some great comments, and I got some very thoughtful responses via email; some of you even sent me papers on different components of ad effectiveness measurement. This week, I want to revisit the topic, and perhaps amplify a few points.

As the debate rages over which measurement methodology provides the best perspective on digital (or analog, for that matter) advertising effectiveness, I think there's a tendency to overlook something important: segmentation.

With the introduction of more and more new information sets characterized as media research (Google Ad Planner) and other data sets that people are using as surrogates for research (log files/Google Analytics), it seems an appropriate time to talk about research and proper use of same.

One of the things that daunts many online marketers, most marketers new to the job and every single senior executive in business today is the merciless multiplicity of metrics available to dazzle, amaze and confuse. In a world of lies, damned lies and statistics, every stat-quoting, graph-toting, trend-spotting front-line manager with a laptop can run circles around professionals armed only with 30 years of doing it by gut feel. Here then, is the short course in the wide variety of things that can be measured to help you sort out the interesting from the useful and the useful from the …

I've been in a lot of conversations and meetings recently where the subject has turned to the question of "How does advertising work?" Invariably, when this topic comes up, you'll hear two things: someone providing empirical quantification of the ROI of a particular type of advertising (or of an entire campaign); and, someone bemoaning the fact that, at this late stage in the evolution of marketing science, we still don't really know -- or can't prove -- how advertising works.

Targeting refers to the process of delivering content or ads to segments or visitors based on their known attributes. The goal of targeting is simple to understand: maximizing the performance of content or an ad by serving it to visitors at a time when they are most open to the receiving the message.