Glossary

The terms of outbound and lead generation, defined plainly, with the number or the trap that makes each one matter.

• Bounce rate: what it means for email

  Email bounce rate formula, hard versus soft bounces with SMTP codes, a worked calculation, and what a high bounce rate does to your sender reputation.

• Buying signals: what they mean

  What buying signals are, which ones tend to predict a reply, how to measure a signal's worth, and the mistakes that turn signals into noise.

• Data enrichment: what it means

  Data enrichment explained: the fields you can add, how per-lookup billing works, a worked cost per usable lead, and the mistakes that waste credits.

• Do-not-contact list: what it means

  What a do-not-contact list is, what belongs on it, how suppression matching works, a worked example with free email domains, and common mistakes.

• Email deliverability: what it means

  Email deliverability explained: delivery versus inbox placement, Gmail and Yahoo sender rules since February 2024, the 0.3% spam threshold, and a checklist.

• MQL vs SQL: the difference

  MQL vs SQL explained: who decides each stage, typical criteria, how to calculate the conversion rate between them, and why the handoff often breaks.

• Sales qualified lead (SQL): what it means

  What a sales qualified lead is, qualification frameworks like BANT, a worked qualifying conversation, how to measure SQL quality and the common mistakes.

• Signal-based selling: what it means

  Signal-based selling explained: how it differs from list-based outbound, a weekly workflow, how to measure signals, and where it goes wrong.

• SPF, DKIM and DMARC: what they mean

  SPF, DKIM and DMARC explained with example DNS records, alignment, the 10-lookup SPF limit, and what Gmail and Yahoo require of senders since 2024.

• What are firmographics?

  Firmographics explained: the core fields, where the data comes from, a worked segmentation example, and why firmographics alone rarely predict a sale.

• What are technographics?

  Technographics explained: how technology data is detected, what it can and cannot tell you, a worked targeting example, and common mistakes.

• What is a catch-all email domain?

  What a catch-all email domain is, why verifiers cannot confirm catch-all addresses, how to estimate the risk, and a safe way to send to them.

• What is account-based marketing (ABM)?

  Account-based marketing explained: the three tiers of ABM, a worked example for a 50-account list, how to measure it, and when ABM is the wrong choice.

• What is an email sequence?

  Email sequences explained: a sample three-step sales sequence, timing, the stop rules that matter most, how to measure each step, and common mistakes.

• What is an ideal customer profile?

  What an ideal customer profile is, how to write one you can test, a worked example, and the mistakes that make an ICP too vague to use.

• What is cold email?

  Cold email explained: what separates it from spam, the anatomy of a good first email, the numbers to measure, and the mistakes that hurt deliverability.

• What is dark social?

  Dark social explained: where it happens, why analytics labels it direct traffic, and a worked way to measure it with self-reported attribution.

• What is email verification?

  How email verification works step by step, what valid, invalid, catch-all and unknown results mean, and how to use them to keep bounces down.

• What is GTM engineering?

  GTM engineering explained: what a GTM engineer builds, a worked example of an enrichment workflow, the costs to watch, and when a small team needs one.

• What is intent data?

  Intent data explained: first-party versus third-party, how topic surge scores work, a worked example of reading one, and where intent data misleads.

• What is lead scoring?

  Lead scoring explained: fit versus engagement scores, a points model you can copy, how to test whether your scores are honest, and common mistakes.

• What is reverse IP lookup?

  Reverse IP lookup for website visitors explained: how it works, why it only identifies companies that own their network, a worked example, and privacy points.

• What is social selling?

  Social selling explained: how it works on LinkedIn, Reddit and X, a worked example of a first message, how to measure it, and the mistakes that make it spam.

• What is total addressable market (TAM)?

  TAM explained with a bottom-up calculation example, how TAM, SAM and SOM differ, top-down versus bottom-up methods, and mistakes that inflate the number.

• What is waterfall enrichment?

  Waterfall enrichment explained with a worked cost example: how supplier order changes your hit rate and price, and how billing for misses works.