In Praise of BuzzData

I'm using BuzzData.

It's pretty awesome.

From their own description of what it is:

Data should be free-flowing, well-organized and easy to share.

Wouldn’t it be nice if there was a place where you could store, share and show off your data with just a couple of mouse clicks?

BuzzData lets you publish your data in a smarter, easier way.

Instead of juggling versions and overwriting files, use BuzzData and enjoy a social network designed for data.

Keep data simple. Use BuzzData."

What's quite remarkable is the combination of technologies used to solve a very real problem.

It's important for marketing scientists, analysts, data scientists, and technologists alike to exchange real data and replicatable proof that things are the way we say they are. BuzzData is a step on this path. It represents a utility for the rapid exchange of ideas with the source located right there. That's very powerful.

If used, it could result in some very important acceleration. Let's see.

How will we measure attention when you can play an app on your TV?

How will we measure attention when you can play an app on your TV?

It's coming, and the future won't be the walled garden offered by WebTV. TV came to the Internet through YouTube and Hulu. Now the Internet will go to the TV through open boxes, possibly a new device that completely blurs the line between a computer, a set top box, and a tablet.

The digital medium has the ability to capture 'the event', and it's through these events that we create spectacular pictures of how people consume media.

If I'm generating events through an App while watching TV, what's the attribution model for TV?

Will broadcasters let go of their methods for measuring attention?

No.

From their cold dead hands.

They haven't changed much just because TiVO offers deeper engagement analytics. Why would digital analytics change entrenched behaviors and attitudes?

It doesn't mean we still can't learn more, and create better, impactful, experiences using these emerging platforms. It doesn't mean we shouldn't actively measure them. Yet, this is very much an invasion of digital people generating interactive experiences for broadcast. It isn't broadcasters seeking to augment their medium.

Loathing in Web Analytics

Eric Peterson, who some of you may recall from interview questions and repeated WAW discussions, asked:

"What do you think? Is web analytics hard because the tools are hard to use?"

And this thread really got going. Check it out.

This is all fairly predictable, and it always ends in detente. You may recall a lot of annoyance within the web analytics community back in late 2009. There was one in 2007. I didn't self-identify as a web analyst for the 2005 iteration, but I've seen fingerprints of it.

It's recurring and predictable.

It's akin to the periodic "Information Architecture is DEAD!" line that appears so frequently at their conference that it might as well become a lolcat meme.

So, here's a thesis.

There's a significant group of people who are still pouring into digital, and they don't understand digital analytics because they don't understand digital. The education burden tends to fall on web analysts because, to thrive as a web analyst, you have to understand digital. They do not need many degrees of freedom. Keep it simple. Keep them focused. Keep things on one theory. It's a time sink. But an essential one.

There's a significant group of people who are well along the maturity curve. They need many degrees of freedom, because they expect answers to very complex questions. And they're asking questions that are effectively data mining questions. Worse, there's confusion about the difference between marketing reporting and marketing analytics. They're not the same people, though, misrepresentations are common and constant. The technology has not caught up with most of these people. And it will. Eventually.

I'm personally aware of a very small handful of firms that successfully compete on analytics and adequately balance the quant and qual aspects of marketing strategy. They're exceedingly rare, and they all started up post-2002. Most companies were not born after 2002, nor, do most companies have quants at the helm. There's a lot of creative destruction to go still.

In sum, the beatings will continue until morale improves.

In the meantime, enjoy the debate and make up your own mind.

Hopes for the Analytics of Google+

I have a few questions I'd love to answer in any forthcoming Google+ Analytics package.

These include:

• What is the Recency-Frequency curve of viewing and posting for Google+ users?

• Can we accept or reject the Zuckerberg Hypothesis of List-Making?
  

• What are the characteristics of UGC content that is shared and plused the most often?

• What is the relationship between circle views, stream activity and UGC post frequency to those circles?

• What is the relationship between the number of those in a circle and post frequency?

• What is the average post frequency that results in somebody being booted from a circle and placed into a new circle (or deleted altogether for posting too hard)?

• Do Circles cause organic community of interest formation?

• What are the differences in content shared between/among circles? (Just how Janus-Faced are people?)

Circles are a social experiment on a grand scale. I want to understand how circles are used to moderate signal-to-noise ratios, cause variation in post frequency, self-censorship, and how it affects UGC generation and dissemination.

The answers have very important marketing ramifications. Circles enable a higher degree of consumer control. Consumers are empowered to generate slices of their world to both see and engage with.

Relevance and Divergence will matter more than ever on this platform.

I'm spending a lot of time trying to understand how a piece of physical technology changes social behavior. It's a system. And I hope any Google+ analytics interface helps me understand that system to make better decisions.