Return on Attention

Ed Batista at AttentionTrust wrote a great response to my post on the importance of Context (Beyond WHAT) in the analysis of attention data. In it, he says:

“I’m also not ready to concede that context can’t be wrung from the clickstreams and other attention data.”

I completely agree with Ed – I am not ready to concede that either. But the key, I think, lies in “other attention data”. I don’t think clickstreams are enough. Looking at clicks just tells you what someone has paid attention to in the past. In order to make sense of it, you need to know why they paid attention to it, and even more importantly, how much they valued the experience. In other words, to borrow John Hagel’s expression (not sure if John coined it or borrowed it from somewhere else), you need to know their Return on Attention.

In a stable environment, you can figure out the Why and the ROA through analysis of a large enough set of clickstream data. If someone returns to a source often, it is likely it provides them with a high Return on Attention. If someone buys coffee at Starbucks every day, it is safe to infer that they like Starbucks.

The vast majority of environments, however, are not stable. Ed alludes to this by noting that analysis of December clickstreams would likely lead to incorrect conclusions, because of holiday shopping. This is a great example of a very common phenomenon. People’s tastes change; Their contexts change, and not just at Christmas time. Take a vacation, work at home, buy a present for a friend’s birthday, go to a charitable event, think about joining the Army, have a kid, get married, change jobs – all of these are events that dramatically alter your context. Every one of these events diminishes the value of time-series clickstream data by some small amount, and makes it more likely you will draw the wrong conclusions. They also make driving actions much harder – you can see patterns, but with no understanding of the context changes, it is very difficult to provide recommendations that increase your Return on Attention.

(This is one of my big frustrations with the Amazon recommendation system, by the way – a little while ago, I was shopping for a present for my 3 yr old niece – no occasion, just a random present. I bought one from Amazon. Ever since then, I have been getting recommendations about other kids books, videos, toys, etc, even though I have relatively little ongoing interest. This only stopped when I went in to my ratings and history and provided Amazon with more context – specifically, that I wasn’t interested in those items).

This is not to say that clickstreams are not important or useful. They are. I have used clickstream data in the past to segment consumers, to target recommendations, to improve offers. It is certainly a part of any solution. But in dynamic environments, the value of clickstream data becomes hugely amplified when we have “other Attention data”. Specifically, data about Why and Return on Attention. They help us make sense of the clickstream data, to understand what still applies and what does not.

Where we at yapaZOO net out on this is that the issue is not about how much data we have, but the right types. Each set of data provides insight into consumers. Putting them together, however, yields far more than the sum of the parts.

BTW, also mentioned in Ed’s post is that he has no idea what we do, because we haven’t said anything about who we are or why we exist. I know our silence is annoying, and we’ll be coming out of stealth soon, probably in a few weeks. But we are working generally on this problem; we don’t claim to have a complete solution to all aspects of it, but we think we’ll provide a good step forward. We’re also huge supporters of what Ed and the others at AttentionTrust are trying to do, and will be joining as soon as we’re a little further along.