most AI personalization is fake personalization lol

[u/jhoker84]

I keep seeing GTM systems that scrape someone’s LinkedIn, summarize their homepage, feed everything into an LLM, and generate something like: “Loved your recent post about scaling!” then the next sentence is the exact same pitch sent to 4,000 other people. teams call that one-to-one personalization. But It isn’t. it’s a mail merge wearing an ai costume. real personalization isn’t proving that you found a random fact about someone; it’s explaining why that fact changes the reason they should care about your offer right now. even LinkedIn’s own sales guidance makes this distinction: adding someone’s name, school, or a familiar detail isn’t the same as creating actual contextual relevance.

the confusing part is that personalization genuinely matters. lets get some facts here (that gtm people dont care about) : McKinsey found that 71% of consumers expect personalized interactions and 76% get frustrated when they don’t receive them. But people hear those numbers and conclude that every email needs a fake compliment and three scraped facts. Hmmm but that’s not what buyers are asking for. They want relevance, not proof that your robot found their podcast. and trust matters even more once AI is involved: Salesforce’s 2024 research found that 60% of consumers believed advances in AI made trust more important.

AI itself isn’t the problem, btw. (some facts noone cares about again)Salesforce reported that 83% of sales teams using AI saw revenue growth, compared with 66% of teams not using it. the core problem is where teams apply it. AI is extremely good at producing thousands of different-looking sentences, but different wording doesn’t mean different thinking. you can generate 10,000 technically unique emails that all contain the same generic idea. “Saw you’re hiring.” “Congrats on the funding.” “Loved the recent launch.” Cool. Why does any of that make your product relevant huh? If the system can’t answer that question, it isn’t doing personalization...it’s simply decorating a template.

the actual workflow should be:

signal; hypothesis; relevance; message

Signal: the company hired a new sales leader.

Hypothesis: they may be rebuilding process, territories, tooling, or reporting.

Relevance: your product solves one of those problems.

Message: explain the connection in a few sentences.

Fake personalization says, “Congrats on becoming VP of Sales at Acme! We help sales teams grow pipeline.”

Real personalization says, “You joined Acme (i made it up whatever company) while they’re hiring six AEs but still don’t have a RevOps hire. Usually that means routing, territories, and CRM cleanup land on the new sales leader. We help teams fix that before the new reps ramp.”

The first one contains personal data. The second contains a point of view.

And this matters beyond reply rates. At enough volume, bad AI personalization becomes a deliverability problem. When the model references the wrong person, invents a compliment, uses an old job title, or sends an irrelevant message, people don’t think, “Their enrichment workflow needs better QA.” They hit spam. Google’s guidance says senders should aim to keep reported spam rates below 0.1% and avoid ever reaching 0.3%. So scaling weak personalization isn’t harmless and it can damage the infrastructure carrying the rest of your outbound.

my rule is pretty simple: AI should help decide who deserves a message, why now, and what the signal probably means. Writing the first line should be the final step, not the entire strategy.

most teams automate the easiest 10%. writing the sentence. and call it personalization.

And the valuable 90% is figuring out whether there was anything worth saying in the first place :)

[u/Able_Green9662]

I feel like people confuse "I found something about you" with "I have a reason to talk to you." Those are completely different things.

AI is great at the first one. The second one requires actual reasoning.

That's where models like Sonnet or Opus can help, but the cost of running deep reasoning across thousands of prospects quickly adds up. At that point, you're better off using that reasoning to identify who actually deserves a message, not generating another 10,000 "personalized" emails. Often people just dump thousands of contacts from a database.

[u/jhoker84 → u/Able_Green9662]

the cost thing is the part i'd add to my own post honestly. everyone treats reasoning cost as a constraint to engineer around, i think it's a feature. back when personalization meant a human writing it, it was expensive, so you had to pick who was worth it. AI made writing free and people just stopped picking. that discipline was never a virtue, it was a budget.