Personalisation works when the message contains something you could only know by looking, and fails when it contains something you could have merged from a spreadsheet. The difference is not effort or word count, it is whether a fact about the recipient was observed or retrieved, and readers tell the two apart instantly.
The harder half of this is the part nobody discusses: at any scale above a handful of messages, the risk stops being blandness and becomes invention. A message that confidently states something untrue about a company is worse than no message.
Why merge fields read as automation
Because they are the only variable thing in a sentence whose shape never changes. A reader who has received four messages built on the same template can see the slot, and the name sitting in it proves a list exists.
Hi Sarah, I noticed Acme is doing great work in the fintech space.
your pricing page lists three tiers and the middle one has no annual option, which is usually where the upgrades come from.
The first sentence works for every company in a category. The second could only have been written after somebody loaded one specific page.
The test is mechanical: take your sentence and swap in a different company's details. If it still reads fine, it was a merge field, however specific the words looked.
What counts as evidence
Evidence is a fact that took a look to obtain and that the recipient can verify. Three tiers, roughly in order of how much they are worth:
- Something about their performance. What their posts reach, whether anyone replies, how their launch actually did. Strongest, because they usually know it and rarely hear it said out loud.
- Something about a specific artefact. A page on their site, a post they published, a job listing they wrote. Verifiable and clearly not from a database.
- Something about timing. That an announcement was three weeks ago and nothing followed it. Weakest alone, strong in combination.
What does not count, regardless of how it is phrased: industry, headcount, funding stage, location, tech stack, the founder's job title. All retrievable in bulk. All therefore proof of a list rather than of attention.
The invention problem
Every scaled personalisation system, whether it is a junior researcher at 2am or a language model, has the same failure: when there is nothing interesting to say about a company, it produces something interesting anyway.
The output is fluent, specific and wrong. It congratulates a company on a product it did not launch, references a post it did not write, cites a number from a different company. And it fails worse than a generic message, because a generic message is merely ignored while a confidently incorrect one tells the recipient exactly how it was made.
The check that stops it
Every factual claim in the message has to trace to something that was actually fetched. Not "probably true", not "consistent with what we know". A specific artefact that was retrieved.
In practice that means separating the two steps. First collect facts about the company and store where each came from. Then write using only those facts. A writing step that has access to general knowledge about a category will use it, and what it produces will be plausible rather than true.
The corollary is the rule most people will not accept: when there is nothing specific to say, the correct output is no message. A pipeline that always produces one is a pipeline that invents, because it has been given no other option.
Personalisation that is not about the recipient
One underused kind: being specific about yourself. "We have run this for four crypto projects and the reply rate landed between 8 and 12 percent" is not personalised to them at all, and it is more credible than any merge field, because it is checkable and nobody puts a real number in a template they are ashamed of.
How much is enough
One observation. Not three.
Messages containing several personalised facts read as a dossier and trigger the exact surveillance feeling you are trying to avoid. One specific, useful, correct observation is the entire budget, and the rest of the message should be about what you would do.
The summary you can apply today
Take your last outbound message. Delete every sentence that would survive being sent to a different company. If what remains is nothing, the message was a template with decoration. If what remains is one sentence that is checkably true and slightly uncomfortable to read, that was the message, and the rest was packaging.
Questions people actually ask
What is the difference between personalisation and evidence?
Personalisation is anything inserted per recipient, including facts retrievable in bulk like industry or headcount. Evidence is a fact that required somebody to look, and that the recipient can verify. Only the second one changes how a message reads.
Does AI personalisation work?
It works when the model is restricted to facts that were actually fetched. It fails when it is asked to be interesting about a company it knows nothing specific about, because it will produce something fluent and false rather than nothing.
How much personalisation is too much?
More than one observation. Several personalised facts in a short message read as a dossier and make the recipient uncomfortable, which costs you more than blandness would.
What should you do when there is nothing to personalise?
Send nothing to that company. A pipeline required to produce a message for every row is a pipeline that will invent one, and an invented fact is worse than silence.
Try Coldpitch and skip the manual part
It finds the accounts worth writing to, spots the weak point, digs out who to talk to on Telegram or X, and writes the first message from what it actually found. Less work than doing this by hand, and better leads at the end of it.
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