The future of personalization

When you open Instagram, Facebook has 10 million ads to show you. But it chooses just one, to begin with. How? Step by step. To vastly oversimplify for illustrative purposes, Facebook chains targeting filters together in a step-by-step sequence: gender, age, location, hobbies, bidding/budget, etc. The point I want to stress is that the filters don’t happen all at once; they’re sequential.

This is how code interpreter depicts it:

By the way, this is also how prompting an LLM works when you chain prompts together – a process of elimination is employed. Thus you can deliver more personalized content, analysis, or summarization.

In a way, chained filters depicted in the graphic are a form of personalization, but a crude one. I make 4 different ads and use the platform to show them to 4 different kinds of people. Pretty limited.

But over the last 10 years, there are ever more “dynamic” ads – dynamically personalized in real-time based on viewer data. The classic example is the shopping cart-data-as-ad: you put shoes in the cart on one site but don’t buy yet, then you see a massive ad for them on another. That’s called retargeting.

But these are also crude as a form of personalization, in one part because they rely heavily on ethically questionable data mining. But in another part because they offer limited inputs. Or they are just wrong.

You might have seen ads like Home prices dropping in [your city name]”, where the city named is an hour’s drive away.

Generative AI-enhanced ads, on the other hand, let advertisers pull in almost infinite inputs to dynamically personalize an ad to an individual.

It also allows the user to help create the ad they want to see, or customize, in real time using what we now call “prompting”.

If businesses can achieve that, they can leapfrog over the current regime of personal data mining and offer intriguing, noninvasive, and more personalized ads.

BTW, wherever I say ‘ad’, you can also swap in the word ‘content’.