The types of "neurotargeting" describes in the article - using data from emojis, personality tests, OCEAN models, fMRI studies - are all very noisy compared to what actually does work in advertising. You perturb the ad shown, measure conversions, and then just directly optimize for conversions. It's a variant of A/B testing described in the article, but you aren't comparing just two variants and having a human decide based on metrics, you're comparing thousands of variants and then training a machine learning model to predict conversion events.
All of the major online ad networks - Google, Facebook, etc - actually function like this, and have for the past 15 years. And it is effective to the tune of about $300B/year, looking at Google's ad revenue.
Barely related, but do you have advice for someone who wants to work on a very specific project at Google? I'm not at the point of hireability yet, but in the long-term I want to optimize my chances to get hired to work on AndroidXR. Should I just put my effort into writing tangentially-related open source projects? Should I be trying to grab positions at similarly-focused companies first? I don't really care about optimizing for comp, and I don't even really mind if I'm only contracting and not at Google proper, I just very specifically want to work on that project.
It would be > 0, but pretty significantly < $300B.
I think a lot of people underestimate the importance of good targeting to the online ads market. It's taken over from print/media/TV brand advertising because it's orders of magnitude more efficient. When Google was young (~2005) advertisers were reporting that they'd get 3-5x more conversions from Google at 1/10th the ad budget because the conversion rate was that much higher.
All of the major online ad networks - Google, Facebook, etc - actually function like this, and have for the past 15 years. And it is effective to the tune of about $300B/year, looking at Google's ad revenue.