Straight answers, real numbers

Scrape when the data you need isn't in any database you can buy — a niche or data-poor market, a field no vendor sells, or freshness a static file can't match. For mainstream, well-covered B2B, buying is faster and cheaper; custom scraping is the right tool in maybe 10–20% of cases. And it's a commitment, not a one-off: a scraper costs roughly 20–30% of its build in maintenance every year as sites and anti-bot defenses change. Scrape for the gap, buy for the bulk — and never scrape what's already a column you can purchase.

GTM engineering is the practice of building the data, software, and automation systems behind a go-to-market motion — instead of throwing more headcount at it. A GTM engineer maps the TAM, sources and scores the data, wires the CRM, and automates the busywork that consumes reps, who spend only about 28% of their week selling. It's RevOps with a builder's toolkit: reverse-engineer what's already closing, turn it into a repeatable system, and let AI augment the team for roughly 2.8× the pipeline of teams that just hire more juniors.

Yes — when it's built on real outcomes rather than guessed point values. Machine-learning lead scoring reaches 40–60% accuracy versus 15–25% for rule-based systems, and converts up to 75% better; adding behavioral signals lifts MQL-to-SQL rates by up to 40%. It matters because only 27% of leads handed to sales are actually qualified. Scoring sorts that 27% from the noise, so reps spend their limited selling time on accounts likely to close instead of working a list top to bottom.

Waterfall enrichment chains data providers in sequence: the first source fills what it can, the next takes the misses, then the next, until coverage is maxed. It exists because single-source data has a hard ceiling — contact accuracy plateaus near 78–84%, and a single database has no fallback when a record isn't in its index. A well-configured waterfall regularly pushes match rates past 90% and lifts direct-dial coverage 20–40% above any one provider.

B2B contact data decays about 30% a year — commonly measured between 22.5% and 30%, or roughly 2.1% a month. The biggest driver is job change: 65.8% of contacts change title or function within 12 months, and 42.9% change phone number in a year. That's why a list is only as good as the day you pulled it, and why a CRM left to drift becomes unreliable. Fighting decay means re-running sourcing and enrichment on a schedule, not buying a static file once.

B2B data enrichment costs roughly $0.01 to $2.50 per record, with high-volume tools near a penny and enterprise providers above $1.50; annual seat contracts run $12k–$80k. But sticker price is misleading. The real figure is effective cost — list price divided by match rate divided by accuracy — because a cheap record that doesn't match, or matches wrong, costs more than a verified one once it wastes rep time and pollutes your CRM.