So much knowledge out there, so little time. Below is an attempt to outline why I’m investing most of my attention into learning about user data consolidation, user modeling and personalization. Not exactly first principles, but I try.
(Disclaimer: this is by no means universal advice. It’s my subjective experience and an attempt to rationalize why I’m so excited about this field.)
The world is incredibly noisy. We waste lots of precious attention consuming irrelevant information. When we make big decisions about what to buy, where to work or who to date, lots of time goes into considering and comparing options. Just think about it, how much mental energy do you spend on making choices each day?
What we want is to reduce the noise and to increase the relevancy of potential choices around us. I can think of countless examples where people around me just settle for whatever good option comes in their way, simply because it’s too expensive to search further (expensive in terms of attention). Human attention is wasted on filtering, left and right. And we still end up with sub-optimal choices. It’s unbelievable how many domains this applies to.
The Economics of Relevancy
Of course it’s obvious that personalization is a huge area and many companies are already successfully offering personalized experiences (Netflix, Facebook, Okcupid). But there’s SO MUCH more noise to be reduced. Here’s my prediction for the future:
Every information stream that comes in our brains will be optimized. Ads will be so relevant that you’ll buy the thing 95% of the time. That feeling when a product or service just shows up in your life, right when you need it, is going to be all the time.
Apps and products will solve your problems, flawlessly. They’ll make an attempt to understand you in your current context, and will be optimized for your personal needs.
A unit of attention
Advertisers are already paying for units of our attention. Facebook determines the price for each ad we see based on the engagement of its audience (measured in clicks and impressions). This will increasingly be the case, and it makes sense. Whoever can benefit the most from getting a unit of your attention will pay the most for it. There’s an incentive for Facebook to understand what is the most relevant ad to show to an individual, so that they can ask the highest price for selling each unit of attention.
The same goes for products. We’ll pay more for genuinely useful features, and will stick around on platforms that are tailored for our needs.
This sounds SO OBVIOUS. I know. But that’s good, since the point of this article is to outline the most basic assumptions about the future of data, to justify what exact fields I want to dedicate my attention to.
A Career in Cutting the Noise
Besides the economic reasons why it makes sense to learn about designing personalized products, I’m super excited to delve into user data sets. Human data, baby! It’s genuinely interesting for me to understand how humans navigate, click, read, and engage with content. I’m excited to predict personality traits from digital footprints, and to cluster users based on archetypal behaviors. I want to know more about psychometrics, and I want to understand what factors contribute to different choices and preferences. I want to model information rich marketplaces, and match individuals to each other, or products or whatever. There are so many use cases I can think of where understanding individuals will be a game changer. It makes me genuinely happy to think about search processes becoming more optimal!
Suggestions on how / where I could master this field are welcome!
About me: for now, I’m working on a chatbot (m.me/meetjobie) that interviews and matches people to jobs, based on their digital footprints. I built a “candidate model” with over 200 variables that describe the person from a cultural / skills / personality perspective. I’m also working on a semantic matching algorithm that finds hyper-relevant roles or companies for individuals. And it’s all about cutting the noise.
