Score Leads

Score Leads rates every row in the table against your ideal customer profile and writes the result back as a column you can sort, filter and gate on.

What it does

• Reads the columns you point it at — title, seniority, headcount, industry, tech stack, signals
• Judges each row against the criteria you describe
• Writes a score back to the table, so good-fit rows sort to the top
• Gives the rest of the table something to branch on: filters, Auto-run conditions, Delete Row

It sits between the cheap enrichment that describes a lead and the expensive work that pursues one. Enrich the fields that identify fit first, score on those, then let only the top rows through to phone lookups, deep research and outreach columns.

How to use it

1. Enrich the columns your scoring depends on first — a score built on empty fields is noise
2. Click Add Action and search “Score Leads”
3. Point it at the input columns that describe fit
4. Describe what a good lead looks like for you — the titles, sizes, industries and signals that matter
5. Run one row, read the score, adjust the criteria, then run the column

Common use cases

• Rank an inbound or scraped list so reps work the top of it first
• Gate paid columns behind a score threshold — score every row, enrich only the good ones
• Split one table into tiers and route each tier to a different sequence
• Re-score an account list after a signal column fires
• Drop clearly out-of-ICP rows before they reach a sequencer, with Delete Row behind a filter

Best practices

• Score on facts already in the table, not on the row’s raw name and domain — enrichment first, scoring second
• Keep criteria concrete and few. “Head of Sales or above at a 50–500 person B2B SaaS company” beats a paragraph of adjectives
• Ask for a bounded output — a number in a fixed range, or one label from a fixed set — so a filter or Auto-run condition can read it reliably
• Sanity-check on a mixed sample of rows, including ones you know are bad fits, before trusting the column
• Put the threshold in a formula column beside the score; that keeps the cutoff free to change without re-running the score
• Re-score when the inputs change rather than editing scores by hand — a hand-edited column stops matching the model

Where to next

• Formula — free columns for thresholds, tiers and flags built on the score
• Auto-run — run the score, and what follows it, as rows land
• Actions — the enrichment columns that feed the score