Marketing · Lead Generation / 03 of 09

Account Filtering & Scoring

Cut the accounts that do not fit, then score whatever survives by how many buying signals it carries, so that the list arrives sorted into who gets a bespoke approach and who gets a sequence.

How it runs

Most rows resolve before a model ever sees them

Trigger

Raw account list in

Step 01

Keyword pass resolves most

Step 02

Model council on the rest

Step 03

Drop what fails

continues

Signals — 4 checks

Active ad spend
Hiring for the roles
Technology in use
Recent public activity

Step 04

Score by signal count

Routes — 3 layers

Layer 1: sequence
Layer 2: light touch
Layer 3: bespoke asset

The score decides what each account is worth

What it is

Every sourced account gets classified against your criteria, then scored by how many buying signals it currently shows.

You need this if your team is manually eyeballing websites to decide who is worth contacting, which caps your list at whatever one person can read in a day.

Worse, the judgement drifts, so the same company gets a different verdict depending on who looked and when.

If you already filter, scoring by signal count is what tells you where to spend the expensive personalisation.

What it does for you

Filtering runs cheap-first and only escalates the ambiguous rows to a model

so cost per account stays flat as the list grows.

Every account carries a score and the reason for it

so nobody has to re-litigate why a company is in or out.

The list arrives pre-split by layer

so the bespoke asset goes to the accounts that earn it and everyone else gets a sequence.

Built and running

Solar On Steroids

Live

Sourced installers were filtered and enriched into a purpose-built base for the campaign, so that what reached the sending infrastructure was already sorted rather than raw. The classifier resolves most rows on a keyword pass, sends only the genuinely ambiguous ones to a model council, and writes back the verdict, a confidence score and the reasoning.

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