I agree that intelligent depth is the next step, and necessarily tailored for each user “persona”. At least, in large organizations, developers, designers and content producers care about different things.

Conversely, web analytics tools, at least the best of them, are known for providing fast (even real-time) results from relatively easy-to-learn systems, but get dinged for not being particularly deep.

My vision of “Business Intelligence 2.0″ is the convergence of these types of tools — “easy” to use applications that also provide depth in their results. You see some of this out there today in applications like Quantivo and others who bring a more traditional BI focus to the digital landscape. I suspect we’ll see A LOT more of that in the next three to five years (maybe sooner!)

Regarding journalism, while a little dated you can have a look at a paper I did for the Newspaper Association of America (NAA) awhile back. Free download here: http://www.webanalyticsdemystified.com/sample/KPIs_for_Media_Properties_-_Newspaper_Association_of_America.pdf

Adrian: I’m glad you weren’t driving, that would have been a disaster no doubt! ;-)

Regarding your point about small sample sizes and statistical analysis, I’m not a hardcore statistician but I have worked with enough of them to know that you can surely conduct a more robust analysis with small sample sizes. What will be limiting is the kinds of questions you can ask and your ability to segment the resulting data, but based on the work I see people do all the time you should be fine.

Best example? Have a look at Google Analytics new “Intelligence” feature, fully grounded in good, old-fashioned statistics. If they’re able to provide this feature to low volume sites (which many GA sites surely are) then you should be good to go, at least with some questions worth asking.

Another good example is the use of Voice of Customer solutions like Foresee Results and OpinionLab. These solutions provide great insights based on relatively small samples all the time. In fact, if you’re not already using a survey tool that might be the best way to get deeper insights from the audience you’re targeting.

Let me know if this is helpful and if not I’m happy to hop on the phone to discuss.

Thanks to both of you for your comments and positive feedback!

I am not a statistician, so I have to ask others for some insight (or even pointing me in the right direction) on my situation: how do you conduct BI, or Analytics 3.0, for a firm that manages websites that garner only about 5,000 to 10,000 visits a month? I’ve had another analytics’ industry leaders tell me by email that with numbers like that, instead of doing any fancy analysis I need to focus on just getting more visits period. Yes, more visits is a goal, but I feel our situation is unique. We advertise luxury real estate, with price points starting just below $1M. I feel the potential pool of visits is therefore very small, and the amount of leads smaller, and the amount of walk-ins to the site smaller yet, and then the number of buyers smaller still (we advertised for a building that had *16* homes total, all well over $1M).

So, maybe the question should be this instead: are we chasing the pot of gold at the end of the rainbow, or is Analytics 3.0 is possible for a situation like ours?

@Elias: I’ve thought about your question a lot. I’ve seen some awesome tools for things like analyzing in-store displays, like how website activity is analyzed, and it made me ponder the impending end-to-end measurement of our lives (gives new meaning to Eliot’s Prufrock measuring out his life with coffee spoons); however, my initial reaction is that there will always be outliers, and thus a need for human involvement. Just a thought :-/

Also, what are your thoughts about web analytics for journalists and editors within news publishing? Is anyone writing about it (except me)?

Thanks for the white paper btw, I thought it was an excellent piece.

Of course, this is just my/our experience!

I figure that with Adobe (who actually knows how to do business correctly), there’s a chance of Omniture shaping up.

The hub and spoke model works well in most situations. I like to take the analogy a step further bringing the spokes inwards towards the hub and getting the spokes to spread out to a third generation of users. These aren’t necessarily SiteCatalyst or Google Analytics users, but are users of the reports (at least, initially).

We want to encourage them to ask questions, to look for insights and work out how they can not only use the reports to help themselves, but to want to delve deeper into the system. This way instead of adding more spokes into the hub, you add more spokes into the spokes. The spokes acting like mini hubs.

This way you reduce workload on the hub (you don’t have to spend so long answering n00b questions) so that they can do some real analysis and provide tangible recommendations. It also allows the hub to get back to some of that real political stuff of cajoling senior management into giving you more money!

Alec

Given that you wrote this so many months ago and it is still pertinent is a credit yourself and your writing!

The hub and spoke model works well in most situations. I like to take the analogy a step further bringing the spokes inwards towards the hub and getting the spokes to spread out to a third generation of users. These aren’t necessarily SiteCatalyst or Google Analytics users, but are users of the reports.

We want to encourage them to ask questions, to look for insights and work out how they can not only use the reports to help themselves, but to want to delve deeper into the system. This way instead of adding more spokes into the hub, you add more spokes into the spokes. The spokes acting like mini hubs.

This way you reduce workload on the hub (you don’t have to spend so long answering n00b questions) so that they can do some real analysis and provide tangible recommendations. It also allows the hub to get back to some of that real political stuff of cajoling senior management into giving you more money!

Alec

Marcos: Impossible to argue with you, nicely put. Think “Business Intelligence 2.0″ and we’re on the same page.

Ashley: I haven’t talked to the folks at Wharton … do you mean Peter Fader and that group? I’d love to engage in a conversation with them if you can bridge an introduction.

Elias: “Will the machines replace us …” is an interesting thought, kind of like Terminator for digital measurement huh? In short I think the answer is “no” given the simple fact that IMHO even with more advanced (third-generation) tools it will still take human smarts to analyze the data. Plus, it will always take a human being to explain the data to the rest of the company and make the case for taking action.

Even in the case of automation (testing, personalization) there is a human element — we design tests, evaluate scores, etc. My hope is that with “Business Intelligence 2.0″ we can get away from some of the wasteful arguments (”are these numbers right?”, “is this significant?”, etc.) and start to have a more productive conversation about how to respond.

Great question!

Fulton: Good point, and I could certainly have used your section header. But I’m not sure that the average consumer is really that mindless about data collection — certainly those folks actively deleting their cookies have a notion about data collection, the folks in the UK who figured out what Phorm was doing, anyone listening to the FTC here in the states wonder about data privacy online, etc. I think this is becoming a larger part of the everyday conversation …

But do you think for a second that if people knew exactly what Google knew about them it would ** really ** stop them from using Google? I don’t doubt there would be some saber-rattling and complaining, but would consumers really turn away from the awesomeness that Google gives away every second of every day?

I doubt it.

So both headlines are correct without a doubt. Great point!