Generative Advertising with Feedback: Bahio Coffee

M&C Saatchi have combined generative design with feedback analysis to try evolve the most engaging ad. The campaign is called Bahio coffe. I think this is an interesting idea and would like to talk about the good, the bad and the ugly. It has been written about here and has a good website to explore progress here. A two minute video overview is on YouTube.

The generative algorithm is given “copy, layout, fonts, colours and images” and this is expressed as a gene-string. There is still considerable human expertise that goes into the basic assets that are input into the Bahio generative system.

The feedback is attention – which appears to be tracked by watching the amount of eyeball engagement viewers have with the poster. Individual posters are scored by the amount of attention they get – with better posters having their genes preserved for future generations.

The algorithm to generate new posters is genetic. Mathematically this is a method for searching a large multi-variate space in a non-exhaustive manner. It works best when we assume the fitness landscape is hill-like – that is, there are smooth ways to improve towards a “best” poster. Though I suspect it has limited usefulness without some sort of similarity measurement between input possibilities. What that means is, how does it determine that a small mutation on the “image” variable results in an image that is different in a similarly small way? Though, for the relatively small number of images the campaign appears to run, this doesn’t appear to be a big problem.

However, in the offline world, we don’t yet have an easy way to target particular demographics. This technique is, without modification, limited to products that have a general appeal to everybody. Some control could be exercised by creative directors on the input materials to the generative system but then the evaluation is still going to be limited.

That doesn’t make this a bad approach at all. M&C Saatchi will learn a lot of useful things from conducting this experiment. So far, this is a form of multivariate testing which is a technique already employed in the web world. It’s great to see experiments with transferring this to the offline world.

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