The B2B marketing glossary.

A precise description of the company most likely to buy and stay — defined by industry, size, geography, tech stack and buying behaviour. Used to focus marketing and sales effort on accounts that fit, instead of chasing everyone.

The total revenue opportunity if every company that could buy your product did. The upper-bound number, useful for board decks and rarely for week-to-week planning.

The slice of TAM you can realistically reach given your geography, language, and product fit. The market you actually compete in.

The portion of SAM you can plausibly win in the next one to three years given your team, channels and competition. The number that should anchor your annual plan.

The named list of companies your sales and marketing teams actively pursue — usually a tighter slice of your ICP, often 100 to 500 accounts.

A contact who has shown enough engagement — downloads, email opens, page visits, webinar registrations — for marketing to pass them to sales for follow-up.

A lead that sales has reviewed and confirmed is worth pursuing — typically with confirmed need, budget direction, and timing to engage.

The handover stage between MQL and SQL: sales has accepted the lead from marketing but hasn't yet validated it as fully qualified.

A user who has tried your product — usually through a free trial or freemium tier — and shown usage signals that suggest they're ready to buy.

Assigning numeric values to lead behaviours (downloads, replies, page visits) and attributes (company size, role) to rank who sales should call first.

The ratio of pipeline value to your revenue target for a period. Most B2B teams aim for 3x — meaning $3M of pipeline for every $1M of target — to absorb normal slippage.

How fast deals move through the funnel — calculated as (deals × win rate × deal size) ÷ sales cycle length. The single number that captures whether your pipeline is healthy.

The full set of activities that create awareness, interest and intent in your product among target buyers — content, ads, events, outbound, partnerships.

The narrower discipline of capturing contact information from interested buyers — typically through gated content, webinars or paid ads.

The visual model showing how prospects move from awareness through interest, evaluation and purchase — narrowing at each stage as some drop off.

The three broad layers of the funnel: TOFU is awareness content, MOFU is evaluation content, BOFU is decision-stage content like case studies and ROI calculators.

A go-to-market approach where marketing and sales jointly target a defined list of named accounts with co-ordinated campaigns. Replaces the broad-funnel mindset with focused, account-by-account precision.

An evolution of ABM that extends co-ordinated targeting beyond marketing into sales, customer success and product — so the buyer experiences one company, not five disconnected teams.

The most intensive ABM tier — fully personalised programmes for a small number of strategic accounts (often under 25), with bespoke content, custom microsites and named-buyer outreach.

ABM run against clusters of similar accounts (50–200), typically grouped by industry or use case. Personalisation is by segment rather than by individual company.

ABM at scale — hundreds or thousands of accounts in the same programme, with personalisation driven by data and automation rather than custom content.

The specific companies on your target list. Used interchangeably with TAL — "named" emphasises that the list is hand-picked rather than algorithmically scored.

Ranking your target accounts by fit (matches your ICP) and intent (showing buying signals). The output is a daily-refreshed priority list for SDRs and AEs.

Signals that a company is researching topics related to your product — usually surfaced from third-party data networks (Bombora, G2, 6sense) tracking content consumption across the web.

An observable behaviour suggesting a company is moving toward a purchase — a job posting for a role that uses your tool, an executive change, a press release about a new initiative.

A composite metric tracking how much an account has interacted with you across channels — emails opened, content downloaded, meetings attended. Used to flag warming accounts.

An inbound-focused rep whose job is to qualify leads coming from marketing and pass the good ones to AEs. Usually the first sales conversation a buyer has with your company.

An outbound-focused rep whose job is to prospect into target accounts cold — researching, sequencing, and booking initial meetings. Sometimes used interchangeably with SDR.

The closer — the rep who runs full sales cycles, demos the product, negotiates terms and signs the contract. Usually owns a quota.

The rep who owns the customer relationship after the deal closes — driving renewals, expansions and references.

The executive accountable for the entire revenue function — typically owning sales, marketing, customer success and revenue operations under one roof.

The team that owns the systems, data and processes connecting marketing, sales and customer success — making sure all three teams work off the same numbers and definitions.

The classic four-question qualification framework: do they have the money, are they the decision-maker, do they have a real problem, and when do they need to solve it? Older but still widely used.

A more thorough qualification framework popular in enterprise sales. Asks deeper questions about who decides, how they decide, what success looks like, and who inside the account is helping you.

MEDDIC with two additions: "Paper Process" (the legal/procurement steps to actually sign) and "Competition" (who else is being evaluated). The de facto standard for enterprise deal qualification today.

The percentage of qualified opportunities that close as won. Healthy B2B benchmarks vary by ACV and segment but typically sit between 15% and 30%.

The average time from first qualified meeting to closed-won deal. Enterprise B2B cycles often run 6–12 months; SMB can close in weeks.

The early-stage call where sales asks open questions to understand the buyer's situation, problems and goals — before pitching a solution.

A scoped trial of your product against the buyer's real data and use case. Used to de-risk a purchase decision before contract.

The two terminal states for an opportunity. Closed-won means the deal signed; closed-lost means the buyer chose not to proceed (or chose a competitor).

An opportunity that hasn't progressed to the next stage within an expected window. Stalled deals usually need an executive escalation or a graceful exit, not more nudge emails.

Software that uses an LLM to plan and execute multi-step tasks — calling tools, reading data, making decisions — with limited human input. Different from a chatbot, which mostly just responds.

A sequence of steps where an AI agent decides what to do next based on results so far — rather than running a fixed script. Common in sales prospecting, research and triage tasks.

The underlying AI that powers tools like ChatGPT, Claude and Gemini — trained on vast text data to understand and generate language. The brain inside an agent.

The discipline of crafting clear, specific instructions for an LLM to get reliable, high-quality output. The single highest-leverage human skill in modern AI workflows.

The standing instruction that defines how an LLM should behave across an entire conversation — its role, tone, constraints and goals. Set once, applies always.

Showing the LLM two to five examples of the kind of output you want, so it can pattern-match rather than guess. Hugely effective for consistent formatting.

A prompting technique that asks the LLM to think step-by-step before answering. Improves accuracy on complex reasoning tasks dramatically.

An architecture where the LLM looks up relevant documents from a knowledge base before answering — so it grounds responses in your data, not just its training set.

Further training a base LLM on your own data so it specialises in your domain, voice or task. Powerful but increasingly replaced by good prompting + RAG.

When an LLM generates plausible-sounding but factually wrong output. The reason every AI workflow needs human checkpoints on anything high-stakes.

The maximum amount of text an LLM can consider at once — measured in tokens. Modern models handle 100K to 1M+ tokens, enough for whole books or codebases.

The unit of text an LLM processes — roughly 0.75 words in English. Pricing, context limits and rate limits are all measured in tokens.

A mathematical vector representing the meaning of a piece of text. Lets you find semantically similar content ("customer churn" matches "users leaving") instead of just keyword matches.

A database optimised for storing and searching embeddings — the backbone of most RAG systems. Examples: Pinecone, Weaviate, pgvector.

An open standard for connecting AI models to external tools and data sources in a consistent way. Lets one agent talk to your CRM, calendar, files and inbox without bespoke integrations.

An architecture where multiple specialised agents work together — one researcher, one writer, one reviewer — each with its own role and prompt. Often produces better results than one large agent.

An LLM's ability to call external functions (search the web, query a database, send an email) and use the results in its reasoning. The capability that turns models into agents.

The act of running a trained model to get an output. Every time you ask ChatGPT a question, you're paying for inference.

An agent design pattern where humans review or approve AI outputs at defined checkpoints — before sending an email, before updating a record, before making an irreversible call.

A composite profile of a typical buyer at your target accounts — their role, goals, pain points, daily tools and decision criteria. Used to tune messaging per audience.

The path a buyer takes from realising they have a problem through evaluating options to making a purchase. Typically modelled in three stages: awareness, consideration, decision.

The person with formal authority to approve a purchase. In enterprise B2B, this is usually a VP, director or C-level — rarely the person you first speak to.

The person who controls the budget for the purchase. Often the same as the decision-maker but not always — in larger deals procurement and finance can have veto power.

The person who evaluates whether your product actually works as advertised — often an engineer, IT lead or security reviewer. Can block a deal even if the economic buyer is sold.

An internal advocate at the target account who actively wants you to win — and will do work inside their organisation to help. Without a champion, enterprise deals tend to stall.

A person whose role is to protect the decision-maker's time — an executive assistant, a procurement officer, sometimes an analyst. Usually needs to be navigated rather than fought.

Someone in the buying group who shapes the decision without owning it — a peer, a consultant, an analyst the buyer trusts. Often left out of CRM but disproportionately important.

The person who'll actually use the product day-to-day. May not be in the buying conversation but is critical for renewal and expansion.

The full set of people involved in an enterprise purchase decision — typically 6 to 10 in B2B SaaS. Modern selling motions target the group, not just the lead contact.

Marketing or sales motions where you reach out to buyers first — cold email, LinkedIn outreach, SDR calls. Higher control over targeting, lower response rates than inbound.

Motions where buyers come to you — driven by search, content, referrals or paid ads. Higher response rates but harder to predict volume and timing.

The schedule of touches an SDR follows when reaching out to a prospect — for example: day 1 email, day 3 LinkedIn, day 5 call, day 8 follow-up email. Discipline matters more than length.

The automated version of a cadence — a pre-built series of emails, LinkedIn messages or calls that runs on a schedule until the prospect replies or the sequence ends.

An email programme that sends a series of pre-written messages over time — usually triggered by a sign-up or a behavioural event.

Long-running, lighter-touch content sent to prospects who aren't yet ready to buy — keeping you top-of-mind until they are.

Content (a report, guide, template) only accessible after the visitor fills out a form. The classic lead-gen mechanism — though falling out of favour as buyers resist forms.

A piece of high-value content offered in exchange for contact details. The thing on the other side of the form.

A substantial, definitive piece of content (often a long-form report or guide) that anchors a topic — and gets sliced into many smaller assets across channels.

Content that stays relevant for years — explainers, how-tos, glossaries — as opposed to news or trend pieces with a short shelf-life.

A joint marketing programme run with a partner — shared webinar, co-authored report, joint event — pooling audiences for mutual benefit.

Distributing your content through third-party channels (industry publications, paid networks) to reach audiences beyond your owned list.

The total sales and marketing spend divided by the number of new customers acquired in the same period. The headline efficiency metric for B2B growth.

The total revenue a customer generates over their entire relationship with you. In subscription B2B, calculated as ARR ÷ churn rate.

How many times you earn back what you spent to acquire a customer. 3:1 is healthy; below 1:1 means you're losing money on every customer.

(Gain from investment − cost) ÷ cost. The general-purpose efficiency metric — answers "was this worth doing?"

Revenue generated for every dollar spent on advertising. The narrower paid-media equivalent of ROI — common in performance marketing.

The percentage of people at one funnel stage who progress to the next — visitor-to-lead, lead-to-MQL, MQL-to-SQL. Tracked at every transition.

The discipline of assigning credit to marketing touches for the revenue they influenced. Notoriously hard in B2B because deals involve many touches over months.

Gives 100% of revenue credit to the first marketing touch a buyer had. Useful for measuring top-of-funnel programmes; poor for measuring deal-stage influence.

Gives 100% of credit to the final touch before close. Easy to track but credits the closer at the expense of everything that warmed the deal up.

Distributes credit across all touches in a buyer's journey using a model (linear, time-decay, U-shaped). More accurate but harder to operationalise — most B2B teams approximate.

Asking buyers directly how they found you, usually in a form field. Often more accurate than data-driven attribution for B2B because so much influence happens off-platform.

Pipeline that touched a marketing programme at any stage — broader than "sourced" pipeline. Useful for proving that marketing affects deals it didn't originate.

Pipeline that originated from a marketing programme — the prospect first appeared in your CRM via that programme. Stricter and harder to claim.