Playbooks, product deep-dives and hard-won tactics for generating, scoring and enriching B2B leads at scale.
Every sales team is sitting on lead data it can't use — not because the data is wrong, but because it's written in sentences. Here's the six-step procedure for turning notes, call summaries and forwarded replies into structured records you can actually filter, score and route, including where to automate and where to r
"Acme Corp", "ACME Corporation", "Acme Corp." and "Acme (UK) Ltd" are one account and four CRM records — and nobody typed them wrong. Company-name normalization is the highest-leverage cleaning step in B2B lead data, because dedupe, routing, territory assignment and ABM matching all key off the company. Here's the six-
Most lead lists don't fail because they're too short - they fail because the columns are wrong. Twelve fields that look thorough, of which four drive a decision and eight are decoration. Here's the field-by-field breakdown: four tiers of lead data, a minimum viable schema, which fields you can derive rather than buy, a
Every lead data source demos well — the sample is hand-picked and every field looks populated. Then the file arrives and a third of it is unusable. Here's a seven-step evaluation you can run in an afternoon on any source: provenance, recency, a 50-record sample of records you already know, match rate over row count, fi
Ask three vendors what a lead list costs and you'll get three numbers that aren't comparable. The sticker price is only one of four costs — the others are hours, decay, and waste, and they're usually larger. Here's how to price all four, compare the three real routes, and run the arithmetic on your own numbers.
Every B2B list starts decaying the moment you build it. This FAQ answers what RevOps actually asks — how fast contact and firmographic data decays, what causes it, how to spot a stale list, how often to refresh each field, whether to clean or enrich first, and when to do it yourself versus hand it off.
Pre-call research fails because it has no stopping rule. Here's a repeatable, roughly 5-minute routine — open the ICP brief, read for three fit signals, score the person against your brief, capture the contacts on the page, note two talking points, and draft an opener you review before sending. Platform-broad, and you
"Personalize at scale" usually collapses into a first-name token every buyer reads as automated. Real personalization is evidence that you looked — and the looking can be systematic. Here's a repeatable process to produce genuinely specific outreach across a large list, with an assistant that drafts but never sends.
Firmographic data turns "which companies are worth our time?" from a hunch into a defensible decision. Here's what it is, which fields actually change a sales call, where to source them from public data, and how to turn them into an account score — built yourself with the Company Extractor or handed off as a managed li
A long list isn't a lead problem — it's a prioritization problem. Here's a transparent fit-scoring framework: turn one ICP brief into weighted criteria, run a Fast deterministic pass to triage, a Deep LLM pass on the shortlist, and route on visible thresholds — scoring the account and the person, with you in control of
A messy lead list poisons every CRM number downstream. Here's a repeatable cleaning pass to run before import — profile, standardize, dedupe, validate, enrich only the gaps that matter, and structure to your CRM schema — so garbage never becomes your system of record.
Candidate sourcing eats hours, but most of it isn't judgment — it's manual data handling. Here's a platform-broad workflow to turn a role brief and public profiles into a ranked, structured shortlist you can hand off with confidence.
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