I’ve been fairly silent on my blog lately, partly because it’s time to scale my social media marketing business. (I’m in that insane phase where I need more talent to streamline my work.) I’ve also been busy with a project for Bedrocket Media Ventures.
In case you haven’t heard, Bedrocket enlisted me two months ago to create a marketing and insights platform on top of YouTube. It’s been a wonderful ride so far, and every day as I envision new features I believe I’m exactly where I want to be.
Working in a fairly new field, I’m pushing the limits of what’s been done before to discover what’s possible. Can I mine prospective customers’ social streams, for example, to rank their affinity to a specific niche and predict how receptive they will to becoming a brand advocate? What else can I find out about them, and how can I frame my messaging to them to create the most excitement about what I’m presenting?
Awhile back I read something in Wired about an emerging field of study that uses information about people to figure out what motivates them. Can I present a discount to Nicole and a value proposition to Adam in order to maximize the perceived value of what I’m offering them? Better yet, how can I automate that process as much as possible?
Creating something like this is dependent on mining social streams to extract user values. But how do you hack a natural language processing library to discover one person is best sold through discounts while another likes anything that will make them smarter?
Taking it all a step further, how do you figure out that value and then make it simple for a company to twist an offer around for multiple customers with the least value possible. The answer has everything to do with the next generation of big data, targeted marketing, and business in general.
It’s an amazing problem to be approaching, but as the above video full of nonsensical infographics demonstrates, sometimes you’ve got to get lost before you’re found.
Lucky for us, each day we’re getting closer.