AI Personalization: Who We Are and How We Work

Why this site exists

I got interested in AI personalization for a boring reason: I kept noticing how differently the same store, the same app, the same email list treated me versus my partner. Same product, different price shown, different order of results, different “recommended for you.” Once you start pulling that thread, it’s everywhere — recommendation engines, dynamic pricing, chatbots that remember your last three complaints, ad targeting that somehow knows you were just thinking about a new mattress.

This site is where I dig into how that machinery actually works, what it does well, where it quietly breaks, and what it means for the people on the receiving end of it. Not the vendor pitch version. The version where I tell you when a “revolutionary” feature is really just A/B testing with better marketing.

Where I’m coming from — and where I’m not

I’m not a machine learning researcher and I don’t run a personalization platform. What I am is someone who reads the papers, tests the tools, talks to people who build and use this stuff, and writes up what I find in plain language. My background is closer to marketing and business analysis than to data science, which means I’m better at explaining what a recommendation engine does to a business’s numbers than at deriving the math behind it.

That’s a limitation, not a disclaimer I’m hiding. If you’re looking for peer-reviewed ML research, you want an academic journal, not a blog. If you’re trying to understand what AI personalization means for your customers, your budget, or your own inbox — that’s the job I’m actually good at.

How AI fits into how I write

Given the topic of this site, it would be a little absurd to pretend I don’t use AI tools myself — so here’s exactly how:

• I use tools like Claude and Grok to pull together research from public sources and rough out a first structure for an article.
• I do not let a model publish. Every stat gets checked against its original source, every claim gets read against what the source actually says (not what the summary implied), and anything that sounds too clean or too confident gets cut or rewritten.
• The opinions, the framing, the “here’s what I actually think about this” parts — that’s mine. If an article turns out to be wrong, that’s on me, not on the tool that helped draft it.

I’d rather tell you this upfront than have you find out from a clunky sentence I forgot to edit.

What I get wrong

Sometimes. This field moves fast — a vendor claim that’s accurate in January can be outdated by June, and personalization regulation (especially around data privacy) shifts by region faster than most single-author sites can track cleanly. When I catch a mistake, I fix the article and note what changed, rather than quietly editing it and pretending it was always right. If you catch one first, that’s genuinely useful to me — see below.

What you’ll actually find here

Practical breakdowns of how AI personalization works in marketing, e-commerce, customer engagement, and CRM — recommendation systems, ad targeting, chatbots, dynamic pricing, predictive analytics, and the privacy questions that come with all of it. I try to write the article I’d want to read if I were the one trying to decide whether a tool is worth adopting, or whether a “personalized” experience is actually helping me or just extracting more from me.

Tell me when I’m wrong

If something here is outdated, unclear, or just off, say so in the comments or drop me a message — I read all of it, and I’d rather fix a bad article than defend one. That feedback loop is the only real quality control this site has.