I’ve been reading Mark Tungate’s history of global advertising Ad Land over the past few weeks (reading books takes on a new time frame when you have toddlers). It’s been an interesting background to the ad and marketing industry, and has given me a few surprises not least, how old most of the major ad agencies are, and how egotistical most ad people must be that almost all of the agencies are named after their founders…
One of the things that has really struck me, though, is how the debate between scientific marketers who believe in measurement and predictability, and those that believe in creativity and inspiration leading decisions has been going on throughout the lifetime of the modern advertising industry.
This ideological split, and the almost religious zeal that both does can have in believing that they are right, was brought to mind in news stories that I have heard in the past couple of days, and how hard the measurement of things can be.
How long does it take to go through border control at British airports is a matter of political contention at the moment, mostly it seems, because no one can quite agree on how to measure how long a queue waiting time is. As soon as you start to look at the issue, you realise that it is actually a fairly complex problem, with issues of fair averages and sampling being top of the list of challenges.
Yesterday’s announcement of trading results by Sainsbury’s also showed that something as seemingly clear cut as profit also its subject to interpretation as the supermarket’s “underlying” profit (excluding items like property deals) was significantly better than its overall bottom line results portrayed.
One of the things that technology has brought to the mix in the world of marketing its that it offers new levels of measurability previously unheard of in more traditional mass media. This is shaping how technology spend is happening in organisations, with Gartner earlier this year predicting that the CMO would be spending more on IT than the CIO by 2017.
But as both of the earlier examples show, even if you can ‘measure’ something, you shouldn’t assume that everyone will agree on your numbers.