Category

Prospect intelligence

Prospect intelligence is the research a seller does on a company before contacting it: what the company is doing, what problem that creates, who owns that problem, and why now. Done well it replaces volume with relevance. Done badly it produces a personalised first line stapled to a generic pitch, which recipients recognise instantly.

The four things worth knowing before you write

A research pass that takes ten minutes and actually changes the message answers four questions:

  • What has changed at this company recently, according to the company itself?
  • What problem does that change create, in the language of the person who owns it?
  • Who owns it, and how do we know - is that person named in a document, or guessed from a title?
  • Why would they act this quarter rather than next year?

Named in a document, or guessed

The difference matters more than it appears. A person named in a filing, a press release, a conference listing or their own company leadership page is a fact about the company. A person inferred from a job title in a contact database is a guess that may be two role changes out of date.

A research system that cannot tell you which of the two it is doing will eventually have a seller address someone by a title they left a year ago, which ends the conversation before it starts.

Where personalisation goes wrong

The failure mode is mechanical personalisation: a template with a variable slot, filled with something scraped. "Loved your post about Q3" reads as automation because it is. The recipient has seen hundreds.

What works is narrower and harder to fake: a specific observation about their situation that could not be true of another company, followed by a claim about what usually goes wrong next. If the first two sentences would survive being read aloud to the recipient, the research was real.

Judging a research tool

Ask it for an account you know well and read what it produces. The questions that separate the useful from the plausible: does every claim link to a document, does it distinguish what the company said from what someone said about them, and does it tell you when it found nothing rather than filling the space?

Where QuikSignal fits

QuikSignal builds an account picture from named sources for the companies in a workspace: what changed, which signals fired, and the decision makers it can name, with the document that names each one.

A person is only listed where a document names them. Where no document names anyone, the section says so instead of guessing from titles.

See it on your own market →

What it does not do
  • No purchased contact data, no email addresses or phone numbers, no LinkedIn scraping.
  • It names people only from documents that name them. There is no inference from job title tables.
  • It does not send outreach. It produces the research; the message and the sending stay with your team and your own tools.
Questions

What people ask

How long should prospect research take per account?
If a person is doing it by hand, ten minutes is the practical ceiling before it stops scaling. The point of automating the collection is to spend those ten minutes on the message instead of on the reading.
Is this the same as account-based marketing?
It is the research layer underneath it. Account-based marketing is the programme; prospect intelligence is what tells that programme which accounts and which message.
Does more research produce better reply rates?
Up to a point, and then it stops. Two specific, checkable observations beat a page of context. The failure is usually not too little research but a message that reports the research instead of using it.

Read your own market the same way.

Eleven agents, the companies you choose, every night, with the document behind every line.