Whether it was Larry Light from McDonald's or Jack Klues of Starcom, the big dogs of media seemed to be barking about media's responsibility to make evident its connection to consumer action.
The call for accountability is one with a rough voice. It's been made for a long time and it has issued forth from a lot of the same throats. For years, the Internet-as-ad-medium sell had been one of accountability. To a large degree, it still is.
But how does one determine accountability? The answer is, with data.
The accountability sell required that as much data as could be collected was collected. Clients everywhere online wanted to get as much data as they could about each and every electron moving about the Web.
In short order, terabytes of data were collected (though perhaps not always stored) on behalf of clients who felt that knowing everything they can about a potential consumer would help them net that consumer.
What so many advertisers - and to some degree, their agencies - don't understand is that data is not the same thing as knowledge, and knowledge more often than not is a wider pale than the pickets of data which set its boundaries.
Too often advertisers set about gathering data with no clear idea of what they want to do with it. Rolling out a campaign, clients want to conduct all manners of research, or collect all manners of data from potential customers through some manner of registration process, but they don't plan what to do with the data. This leads to a frenzied gathering of bits, collecting any and all manner of jetsam and flotsam. There is a subconscious belief that the marketer will know what it is that they are looking for once they find it.
This can sometimes be true, but in advertising, we are ultimately about the business of business, and though some expenditure for the sake of learning what doesn't work can be as valuable as that which yields answers about what does work, collecting data for its own sake isn't very helpful.
I once had a client for whom the agency would perform a monthly summary of his online campaign's performance. This is a valuable exercise, mind you, and I hope many advertisers are still asking for this. But this client always wanted to have the some fifty pages of raw data accompanying the written summary. He believed that if he just looked harder and harder at the data, a secret would be revealed to him that the rest of us, looking at the emergent patterns as seen from a distance, would miss.
Data is turned to information, and information is turned into knowledge. Sometimes though, data is meaningless and provides for useless information that results in inapplicable knowledge.
If it could be deduced from data that men who wear blue boxers eat more omelets, would that be meaningful? Unless you were doing a cross-promotion with the Egg Board and Jockey, the answer is no. And if that is your concept, then you have the necessary a priori form that the data will eventually fill out. Before inventing the knife, early humans had a sense of "knifeness" that was necessary to create it.
Without some sense as to where the data might take you, the resources expended against collecting all of it could have instead been used to get more media, messages, or both.
Sometimes, even the most basic data isn't useful. A few years ago I remember a fast food chain doing a promotion; I think it was asking patrons to enter for a chance to win an Xbox, when the gaming console first came onto the market.
I remember listening to a case study presentation - some enormous number of e-mail addresses over an incredibly short period of time had been collected. I thought to myself afterward, what would a fast food restaurant do with those e-mail addresses and the other data that were collected at the time? How much of a relationship do teenage boys really want to have with a place that sells tacos on the cheap? How sublime is the value proposition of fast food any more?
Perhaps the chain is now making extraordinary use of their database and engaging in all manner of meaningful consumer relationships; I don't know. But I suspect that this was an example of resources allocated to gather data but then no plan or capacity to digest it.
Remember: data is turned into information; information is turned into knowledge that you can use. But just like in science - and what we are talking about here is closer to science than anything else - you've got to have a thesis around which your experiments are structured or you don't discover anything because you don't know what you are out to discover.