In the words of Bill Clinton, “it depends on what you mean by …”
The web analytics is easy / hard discussion among various thought leaders has been interesting but I can’t help but think a little self-serving. I had a long preachy post ready to go but even I was bored by it. Instead, let me offer some observations:
- Web data is messy. It doesn’t fit into cubes (think path analysis by segment) for instance. So it requires special handling.
- Web data is noisy. Think robots and cookie deletion.
- Web analytics is nonstandard. Despite the efforts of the IAB, WAA and others, nobody can say “we use the computation standard to determine this” – because there is no computation standard. Count web 2.0 for me…
If you want to chase the rabbit down the hole, you’ll decide that web analytics is hard. if you want to defer these kinds of things to a tool vendor who will sell you turnkey “best practices” then web analytics is easy.
- In business, speed of deployment is king. Thus it’s easier to look at pre-built reports than create them from scratch.
- Lack of integration with the business goals means lack of actionability. Most analytics is like driving by looking into the glove box – interesting but irrelevant.
- As a result of the first two, there’s an emphasis on quantitative over qualitative analysis — which means there very little “analytics” at all, just metrics trending.
- When you don’t first design your analysis from business goals, you grab as much data as you can, and pick and choose the data that supports your hypothesis — either you’re in report hell, or analysis paralysis. For a very visceral example of this, look at how most people do experimentation.
If you design your analytics to meet your strategy, and staff for it, the implementation is long (and complex, yes) but the resulting analytics are easy. If you want to defer to a tool vendor who will sell you turnkey “best practices” then web analytics is hard.
One thought on “Is Web Analytics Easy or Hard?”
Great post. Similar observations can be made of any kind of research, qualitative or quantitative. It’s all about having a focused, intentional approach to the design-collect-analyze process, rather than being in a collect, scratch-your-head situation.