Plain definitions for the terms behind B2B account selection and ICP scoring, no marketing fluff.
What an Ideal Customer Profile (ICP) is, why it's different from a buyer persona, and how small B2B teams actually define one without a data analyst.
What a lookalike company is in B2B prospecting, how it differs from ad-platform lookalike audiences, and how teams find them without buying a database.
A plain definition of firmographic data, the company-level attributes B2B teams use for segmentation, ICP scoring, and lookalike company discovery.
How firmographic scoring works: turning industry, size, and geography fit into a single number you can actually sort a prospect list by.
The difference between Total Addressable Market (TAM), Serviceable Addressable Market (SAM), and Ideal Customer Profile (ICP), explained without the VC-deck framing.
What an account brief is, what it should contain, and why a one-page brief beats a bare company name before a cold call or demo.
What a target account list is, how it differs from a generic lead list, and why most spreadsheet versions go stale within weeks.
What an account fit score measures, how it's typically calculated, and how to read one when deciding which prospects to call first.
The difference between prospect scoring (before contact, company-level fit) and lead scoring (after contact, behavior-based), and where each applies.
What an AI ICP engine does: continuously finding, scoring, and researching lookalike companies against your ideal customer profile, instead of a one-time list.
Buyer persona and ICP aren't the same thing: one describes the person, the other describes the company. Here's how they work together.
What technographic data is, how it's used alongside firmographics for ICP scoring, and its main limitation as a signal.