New Data startups are (nearly) making 24th century science fiction happen today
If you’re like me and have worked in data your whole career, you’ve probably spent a disproportionate amount of time trying to explain to your friends and family what you actually do. “No, I don’t sell lists, I don’t send junk mail, I actually try to stop people sending junk mail. I look for patterns of behaviour in data that marketers can react to. What do you mean you don’t get it?”.
So when my dad gave me a book written by L.E. Modesitt: “Flash” – a book categorised as science fiction – and asked ‘is this what you do, but in far distant future’ (according to Wikipedia the 24th century, and I wouldn’t argue with them) I couldn’t wait to read it. And lo and behold, I’d finally found a family member who understood what I do.
The storyline’s not important here, but the hero’s job is… he is the world’s expert on measuring the effectiveness of “prod” – product placement, the only advertising which viewers will allow through their sophisticated ad filters. This “prod” is reinforced with “sublims” (I think a self explanatory term, even for the non SF fans out there) via “res” – resonant frequencies, a form of sonic branding.
Well, a little, but the likes of UnTapTV are already getting your mobile to react to TV content using impossible-to-discern embedded sounds. Embed their code in your app, and you can supplement what’s happening on the TV screen with additional content on the second screen. So “res” is pretty much already here.
As a data wonk, I’m interesting in how we can optimise this experience. Testing different triggers, volume and rate of triggers, different content, different host apps. Just like we used to test which sort of envelope worked best. It’s the wrapper for the message after all.
Then that got me thinking about optimising product placement. Product placement companies are out there already measuring overall media placement and trends on purchase success, so there’s already science applied. But what if we could systematise and understand it at a much more granular level? Taking the data generated at each stage of the process and combine them – for example the exact co-ordinates of the product on screen, the length of time it’s on there, in what context, any concurrent app usage. Now that would be pretty cool marketing science. Fact.
Oh, and the book gets mixed reviews. But I liked it.