Omniture and HBX Tips and Tricks Papers
If you’re a regular reader of this blog, you’ll probably have noticed one pretty glaring area of omission. I’ve almost never talked about tools. There are reasons for this – not the least of which is that with five or six different solutions all commanding a fair amount of marketplace respect it’s hard to talk about tools and not lose a good chunk of your readers. In addition, I think there is a lot to be said for keeping as much of your thinking about web analytics as tool independent as possible.
One of our clients paid us a compliment recently that really stuck in my mind. She said, "What I like about you guys is that you don’t think about what the tool can do – you think about what you want to measure and then you find a way for the tool to do it!" I think the more often that’s true, the better – and one of the main features of the Functional methodology I’ve been banging away at is that it’s pretty much tool independent.
As my SEMphonic co-founder Joel Hadary loves to say, "A fool and a tool is still a fool." Tools do matter though. And no matter how clever you are with a tool, each and every one has basic limitations that can’t be transcended and strengths that can be exploited.
So I’m going to try some blogs about tools over the next couple of months – not a formal series like Functionalism but enough, I hope, to make a noticeable dent in the topic. I’m not going to cover every tool and I’m not going to do any classic product comparisons. Instead, what I’m going to cover is a mixture of how tool capabilities and real-world needs seem to me to come together or fall apart in specific cases. I’ll probably talk quite a bit about SiteCatalyst and HBX since those are the tools we use most frequently. But I’m also going to talk about some products we haven’t used much but that seem to me to add real value to the web analytics tool kit in one respect or another.
I’d also like to point you to a couple of "White Papers" we’ve produced recently that are very tool focused. The two are somewhat different in tone – largely unintentionally – but I think nicely illustrate both sides of what you need to know about tools. The first is called the Art of Omniture. It’s not on our web site yet, but if you post a request here I’ll send you one. This paper is a fairly broad overview of some of the most important Omniture capabilities. It includes a good discussion of the various customer segmentation options in Omniture (Warehouse, ASI, Discover) and some pointers on building and using visitor filters. It also has a very detailed set of tips for solving or avoiding some common problems with the Excel tool. So pretty much all of the paper is focused on using the tool when you already have a specific task in mind.
The second paper was written for HBX and is also a Tips & Tricks article. However, it provides much less detail about specific tool issues and more about how to think about using the tool when you have certain business/analysis problems. There are some techniques in here (like using Campaigns to track Longitudinal behavior) that are quite clever in their own right – but they also illustrate some common business problems that you may not even have considered if your field-of-view has become tool-restricted. I think tool-blindness (focusing on what your tool provides instead of what you need) is a real problem in analytics – especially since most analysts and even vendors only really work with a single tool. You can get the HBX article on our web site at http://www.semphonic.com/resources/whitepapers.asp - it really isn’t a White Paper at all but it seemed like the best place to put it. I’m working on a considerably expanded version of this, so I’d love to hear feedback or suggestions for it!
I’m going to try and write both ways about tools (how to use them, how to think about using them) in some upcoming blogs. And I’m also going to talk about what I’d really like to see web analytics tools do that they currently don’t. Why? Well, in addition to giving vendors some good ideas, these suggestions serve to clarify what kinds of problems analytics should be solving and what we need to know to really solve them.