Patrick @glinskiii once identified three large buckets of skills in his “it takes an orchestra” argument for web analytics programs. It feels like years ago (it’s probably only been about a year), and it has since evolved. It goes like this: There are three large skillsets in web analytics. T’s, or Technical Analysts, specialize in the technical side of web analytics. They’re the people who can tell you where to put single quotes versus double quotes in the S.Campaign variable of Omniture. S’s, or Strategic Analysts, specialize in strategic side of web analytics. They’re the people who can tell you the social process necessary to take an insight and translate it into action. A’s, or Analytical Analysts, specialize in extracting[…]
Joseph Carrabis wrote something very relevant to our interests. Especially when it comes to planning Web Analytics projects. It’s worth the read. Go check it out. I’ll wait. What’s easily missed on the first scan is the passage: “The purpose of these rules is to tend towards 0 the likelihood that a mistake will be made.” And the two rules, which are the meaty bits are: “Rule #1 – Eliminate Variables” And “Rule #2 – Remove Ambiguities” Rule 1 is important. I categorize knowledge into three broad buckets: What I know that I know. What I know that I don’t know. What I don’t know that I don’t know. It’s the third category that’s the scariest of all. When I[…]
An excellent blog post on Estimating the Effects of Cookie-Deletion is timely and welcome, given the relative degree of contention around the Unique Visitor (UV) definition. The chart above is not gospel, and you should not be running around saying that all websites have 100% human-visitor inflation. That isn’t what Angie is saying. Angie has offered up something valuable: a pretty simple model for estimating UV inflation. What Angie is arguing here that the effect of cookie deletion on your unique visitor to human estimate will depend severely on the use of your website and the inherent habits of its audience. Let’s assume that there’s a fanatical group of humans that visits your website. Let’s also assume that within that[…]
I’m smitten with Rails. Rails conforms to my world view in two ways. DRY stands for ‘Don’t Repeat Yourself’. It’s a great principle, especially when writing difficult SPSS code. MVC stands for Model, View, Controller – and it’s the dominant way that I organize, present, and modify data. There are other biases that are built into Rails that I like, but mostly, it’s those two principles. I’m looking at Rails as an important way of solving a number of lingering problems in Web Analytics, and once I learn enough to actually start experimenting and solving them, I’ll share them.
This post briefly summarizes four threads of thought and a conclusion around problem orientation. I read “Evaluation of Internet Advertising Research” by Juran Kim and Sally J. McMillan. It’s effectively a social graph exercise. The findings themselves are interesting (and you can read about that through the Web Analytics Association once I publish the review), but this reference to “invisible colleges” was especially fascinating – just coming off of the SLAB Karen Stevenson talk at OCAD. Kim and McMillan make the point that visualizing bibliographic graphs (a social graph) is useful for uncovering these colleges. The second thread has to do with “The Market Valuation of Internet Channel Additions”, by Geyskesn, Gielens and Dekimpe. In it, they construct a model[…]
It’s rare that somebody forces me to really look at something differently – but Karen Stevenson in an SLAB lecture at OCAD did. Karen pointed out that the three human variables that matter are: transactions, authority, and trust. Transactions among people are easily handled by technology. It’s been long standardized, and in fact, we’re making incremental improvements in that all the time. Where there’s ambiguity in transactions, you need authority to make decisions. What was really left unsaid, but what I’m concluding, is that since humans are very creative people, they always manage to get themselves into non-standardized problems, and as such, they will always need authority. (Look no further than to Judge Judy for daily evidence of that.) As[…]
I challenge you to refute the following: there may be no truly useful involvement of WA for micro-businesses with no pre-existing data or even market, and that to claim so would be relying on very noisy data (i.e. that of a few testers). How do you permeate “data-driven insight culture” into the nano-scale? -Maciek Adwent in the comments section of the last post Analytics is defined as the application of statistical methods to data to derive business insights. Without data, there is no analytics. QED I’ll argue that the definition of a business is the taking inputs, adding value to them, and the production of outputs with the intent to sell them. A profitable business takes inputs, adds value to[…]
“you need to rework this morning’s blog to apply to a 2-man startup shop. They’ve zero time/budget for navigating data oceans” – A friend by way of twitter I’m not one of those people that’s going to say every decision requires a data input. It doesn’t. We make hundreds of decisions a day when running a business – there literally isn’t enough time, even with very efficient data access processes – for any organization to consider every single input. Just as your brain is programmed with a self-preserving ignorance – it doesn’t process every single input, actively, a two man shop would operate in much the same way. Ignorance can be optimal sometimes. The laws of Recency and Anchor and[…]
Much of my early career is rooted in a simple, but powerful observation: organizations that set concrete targets perform better than those who set relative or abstract ones, regardless if those concrete targets are actually met. (Amazingly, as far as I could tell at the time, nobody else had made that observation in public policy.) A clearly defined Key Performance Indicator (KPI) with a clear, concrete goal, can focus organizational energy towards that goal, and help an organization optimize their efforts. I champion the notion that Key Performance Indicators (KPI) are the dependent variables that matter to the businesses direct goals. This means that all other ‘metrics’, all 4 million of them out there, are independent variables that may, or[…]
Raw Data is a commodity. That’s the overwhelming conclusion I’m running into – that’s the direction of my thinking over the past 4 weeks. I had an excellent talk today with Jennifer Day. It’s a ‘catalytic’ talk. She called me inquiring about a tweet on the pre-click analytics side, and she very patiently listened, in great detail, about the procedures involved and the value of that type of analytics. Somehow I spun off into a rant about data. (Hard to imagine). I said, in effect: “See, the problem with the web analytics vendors today is that they’re in a false trap. They are only as successful as the people who use their tools. And, so many of the people who[…]