It’s a pretty cryptic tweet, yes. A paper was published in the Journal Marketing Science that just came to my attention. A review will be forthcoming on the Web Analytics Association website. I’ll publish the link once I write it. The article in question is very theoretical and mathematical, but it leverages an area of mathematics that has yet to be applied, practically, to web analytics. What’s most exciting is that the physical technology to make it happen exists. The case study that it works is there. The one sentence summary is: “There’s a way to radically increase the tempo of optimization while drastically lowering both the monetary and personhour cost”. Now the bad news: The social technology is another[…]
Begin with five statements: 1. The set of business questions that could be asked is infinite. 2. The subset of business questions that could be answered by our current toolset set is very large, but ultimately finite. 3. The subset of business questions that, when answered, could be valuable, is a smaller, finite set. 4. This subset is volatile. 5. The current analytical paradigm is ill equipped to grapple with statements 1 through 4. Unpack these statements: 1. The set of business questions that could be asked is infinite. Consider every single combination of characters and numbers that can be packed into quotation marks, ended with a question mark. Consider writing all of these down. You’d end up with millions[…]
In my previous post, I argued that Web Analytics was not easy because of complexity, much of it caused by people. Things can get lost in translation when translating data into actionable insight and actionable insight into action. Let’s turn to the solution, something that Jacques Warren, fellow tweeter (and #wa guru), has termed “Organizational Engineering”. What follows is a laundry list of the elements, considerations, and biases that should feed a successful web analytics organizational design pattern. 1. It all starts with a great web analyst, a few things a great web analyst does or understands: a) Takes the site map, goes through the site, and understands it. b) If no site map exists (which is common), then that[…]
If Web Analytics is so easy, why then is it so hard to get something done with them? Start with physical technology – the machines and software: 1) The accuracy of the data is in question (See the case of Peterson v. Unique Visitors / Cookies) 2) Web Analytics tools are incapable of automating actionable insight 3) Web Analytics tools report hindsight. They typically do not forecast or perform what-if analyses 4) Some Web Analytics tools require very careful calibration to report anything of value Solve the above 4 issues with social technology – the rules, processes, culture, norms, hierarchies and networks that people, tribes, communities, departments, companies and associations have developed over centuries: 1) The accuracy of web analytics[…]
Assume we had all the data we needed. Assume that all the systems were talking together. Assume that insights could be executed flawlessly. Assume that an analyst wasn’t weighed down by reporting. Assume we’ve reached the gates of paradise. Would most analysts know what to do with it? To be sure, we tend to build these systems with some idea of the business questions we want answered. These include: “Who are my most profitable customers?”“What are my most profitable customers like?”“Where can I find new customers like them, cheaply?”“Who are my least profitable customers?”“What are my least profitable customers like?”“How can I avoid attracting those customers, cheaply?” In reality, the set of business questions might be far less powerful. Or[…]
Guerrilla Analytics is pretty much what it sounds like – it’s about going out, without permission and without sanction, and conducting analytics on publicly available information, purely for the purpose of curiosity, case study, or for the common advancement of the discipline or technology. In many ways, I admire the work that has been done by the dev community. JQUERY is an example of a developer led open-source technology, a common library that many front-end devs dip into. It’s just an awesome because it saves them so much time and effort. Many developers are truly technologists. And the really awesome ones go out and experiment. They actually really push the science, and frequently, when it comes to many of these[…]
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.