Peretti’s argument is that brand advertising is the only way to inspire consumers to try products they might not otherwise have bought. Targeted behavioural advertising, conversely, is merely a way to pounce on someone and shove them down the sales funnel when you’ve spotted them lingering uncertainly around its entrance. He’s right, to a degree, but there’s also more to it than that.
Yes, there’s a problem in the philosophy of data-driven marketing, in that it’s often only useful for converting an obvious propensity to buy, watch, read or click on something into a concrete action. That still leaves the question of how marketing can reach and appeal to those people who have a good chance of liking your products but have never exhibited any behaviour that tells you as much.
Old-fashioned brand advertising is one way of building the necessary awareness and desire, but is there something more scientific, something with a higher hit rate?
Channel 4 thinks so. As its head of viewer relationships Steve Forde told Marketing Week recently, one of the big new things it is working on for the next refresh of its 4oD video-on-demand platform is a way of introducing viewers to new programmes.
He said: “We are working on the idea of the anti-algorithm, the algorithm of surprise. How can we create recommendations based on what we know a viewer likes, but also how do we nudge them to watch shows that they don’t know they will like?”
For Channel 4, this is still in research and development, but the broadcaster will undoubtedly be helped by the fact that much of the behavioural data it will need for calculating these algorithms is first-party data, coming from what viewers do when they’re logged in to its service.
For brands that aren’t lucky enough to have such data, a lot of creative thinking needs to be done. The words ‘data scientist’ and ‘big data’ are used far too often and far too uncriticially, but this is one instance where data scientists with a grasp of big data will be invaluable.
The key for most marketers will be finding the unexpected proxy behaviours that indicate a strong likelihood that someone will be interested in their brand. Doing so will mean analysing a lot of different data sets for some very obscure patterns that will only emerge given a certain amount of investment in technology and technical knowledge.
Brands should already be working on their own versions of the algorithm of surprise. It could be the difference between your next new launch being a break-out hit or a feeble flop.
But then again, there are always blindfolds and dartboards.