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 will always be in question – there’s a legal limit to how accurate we can get. The primary goal has always been to improve accuracy, however, we have a branch of science that enables us to estimate the risk associated with making a decision based on a data feed whose accuracy is in question. Statistics, for most, is not ‘easy’. But assume that it is.

2) Machines do not produce actionable insight. They produce the data outputs that humans use to extract insight. There are very simple analyses that people can do if they have a book that shows them, step by step, how to do it. For most subsets of business questions though, the process of extracting insight out of data, and putting the data in context, is not easy. But assume that it is.

3) Web analytics vendors are not incented to invest in the processing cycles necessary to forecast data into the future. Data is typically extracted, transformed, and loaded into a machine that is capable of produce predictive outputs. This process is not easy, especially since the extraction and transformation processes are hard. But assume that it is.

4) Setting up a web analytics tool is easy to do – poorly. Dump the tags in and go! gogogogogo. However, most web analytics tools require the technitian to know the entire set of potential business questions that every user group might want an answer to. Otherwise the tags don’t track it and it’s gone! Forever. This process of gathering requirements and translating them into requirements is not as easy as dumping the tags in and going. But assume that this is all easy.

Assume we’ve reached a point where we can answer all the business questions necessary and we’re in a place to extract a killer insight.

What now?

If you’re also the developer, IA, creative and project manager of a website, it’s no problem. You just go about getting your Google Optimizer tags on and your creative on and away you go. Awesome, you’ve just spent an hour a day and got a 900% lift in ROI. You’re the man now, dog.

But what if you’re in a company with an IT department. That happens to have an IA. Creative. Business stakeholders.

What if your company lacks the physical technology necessary to get those Optimizer tags in place?

What if the social technology of your company is so rooted in the pre-web-analytics-is-easy revolution that the processes and institutions necessary to make actionable insight actioned simply doesn’t exist?

The answer becomes: build those processes and institutions, right? Lead. Build them. Argue for the value. Show them the value!

1. Do this
2. PROFIT!!! (1% boost in conversion = 5 million dollars in revenue. Oh yeah!)

And the building of institutions is easy, because inertia – the tendency of a body at rest to stay at rest, doesn’t really apply to human hierarchies or networks. And humans instantly see the value that the web analyst is trying to bring and will willingly remove all obstacles.

To be fair – there are many very easy things you can derive from Google Analytics on your blog. You can tweak alot, and you don’t necessarily need to extract the data and forecast things into the future. If all that knowledge is in your head, then you’re optimizing just fine. The Analyst-Technologist can improve a website very easily. Web analytics, for them, is easy.

It’s easy because they don’t have to confront human complexity. Problems 1 to 4 are abstracted right out of existence. The user is doing some pretty simple analyses, some pretty good hindsight, and doing some inductive reasoning. The user trusts the users own judgement. There’s nothing social about it.

If you’re in a rather large organization though – the type of organization where a 1% bump in conversion results in a 5 million increase in revenue – the effort is totally well worth it. No question, there’s loads of value in analytics.

Deriving an insight and following it through to execution is not as easy as we’ve all been led to believe. And yes, I can clearly say that we’ve all been led to believe that it is so easy. To be sure, as the industry matures and more of the process of optimization is automated, it’ll become easier. (Keep it up guys!)

It’s only by confronting the realities and building the bridges, processes, culture and institutions necessary that we’ll be able to make optimization as routine as spaceflight. That we’ll be able to realize the Optimization dream.

Web analytics, as a single person with all the power, all the authority – that’s easy.

Web analytics, in the context of an organization, with distributed authority and all the design patterns that go along with it – that’s not easy.

Now get off my lawn.