Glossary
RevOps Glossary
Plain-English definitions of the terms we use - from RevOps and marketing operations to lifecycle stages, attribution models, LLMs and AI agents. Search, or browse by area.
MOps (Marketing Operations)
The everyday name for marketing operations - the people, process, data and martech that turn marketing activity into pipeline the business can measure. What MOps runs →
Martech stack (marketing technology)
The set of tools marketing runs on - CRM, marketing automation, a CDP, analytics, enrichment, ad platforms. MOps owns how they connect, so data flows instead of pooling in silos. The stack by stage →
Marketing automation
Software that runs marketing workflows so a human doesn’t have to - triggered emails, lead nurturing, list management, lead handoff. The engine underneath the lifecycle. Automations that hold →
Demand generation (demand gen)
The programs that create awareness and interest and turn it into pipeline - content, events, webinars, paid, ABM. MOps makes it measurable; without that, it’s just activity. Campaigns tied to revenue →
Lead nurturing
The sequences that develop a lead who isn’t ready to buy yet - staged content triggered by behaviour, handed to sales at the right moment instead of chased once and dropped. Where leads leak →
Campaign operations (campaign ops)
The execution engine for campaigns - build, QA, launch, track, report - run the same way every time, so a launch is repeatable instead of a fire drill.
Agile marketing
Running marketing in short, iterative cycles with a prioritised backlog and a visible board - decide, ship, measure, adjust - instead of one rigid annual plan. Kanban is the common way to see the work.
Kanban
A visual board that moves work through columns - backlog, in progress, review, done - so a marketing team can see what is flowing and what is stuck. The everyday face of agile marketing.
Marketing-sales SLA (service-level agreement)
The agreement between marketing and sales on what a qualified lead is and how fast it gets worked - the contract that stops leads dying in the gap between the two teams. The handoff map →
Houred (houred.io)
A CRM built for solo operators and small teams, not the enterprise. The brief was to make the software disappear - fast capture, a clean Kanban board, and automation that hands back the hours a heavy CRM eats. Built by RevOps XL; see it at houred.io. How we built it →
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Core RevOps & GTM
RevOps (Revenue Operations)
The function that aligns marketing, sales and customer success around one revenue engine - shared data, shared definitions, and the systems that connect them. When it works, the whole go-to-market motion runs on the same source of truth. What we do →
RevOps maturity model
A RevOps maturity model describes the stages a revenue operation moves through as it grows: from scattered spreadsheets and tribal knowledge, through defined processes and a CRM people actually trust, up to a connected go-to-market motion with reporting the whole team believes. It is a way to place where you are honestly and see what the next stage takes. Run the GTM diagnostic →
Go-to-Market (GTM)
Everything a company does to take a product to customers: positioning, demand generation, the sales motion, pricing, and the operations underneath. GTM strategy decides who you sell to and how; RevOps makes it run. GTM strategy & build →
Marketing Operations (MOps)
The systems under marketing - lead lifecycle, the martech stack, lead scoring, routing and attribution. MOps turns marketing spend into pipeline you can trust. Marketing operations →
Sales Operations (SalesOps)
The counterpart to MOps on the sales side: pipeline management, forecasting, deal stages, territory and quota, and the CRM hygiene that keeps sales data honest.
Fractional RevOps
A senior RevOps operator working part-time or on a fixed scope instead of a full-time hire - the seniority without the headcount, and, done right, a system your team can run after they leave. De-risking fractional RevOps →
Managed RevOps
Managed RevOps is an ongoing arrangement where a senior outside team runs your revenue operations for you, instead of hiring and managing the function in-house. In practice it is the same engagement as fractional RevOps, described from the service side: the data model, pipeline and forecasting, reporting and handoffs, owned on a retainer by someone accountable for the result. How fractional RevOps works →
Pipeline
The set of open deals moving toward closed, organised by stage. Your pipeline number is only as honest as the stages behind it. Stages that mean something →
Sales forecast
A prediction of how much will close in a period, built from the deals in your pipeline and their probability. A forecast built on hope instead of data is why so many miss. Why forecasts are wrong →
Ideal Customer Profile (ICP)
A precise description of the accounts most likely to buy, stay and expand - industry, size, tech stack, pains. It’s what lead scoring and AI qualification should be measured against. AI lead qualification →
TAM mapping (total addressable market)
TAM mapping turns total addressable market from one big number in a pitch deck into something you can work from. It breaks the market into segments you can size and rank by fit and by how winnable each one is, so the team knows who to go after first. How to build a TAM map →
Qualified lead (MQL / SQL)
A lead judged ready for the next step. A Marketing Qualified Lead (MQL) is ready for sales to look at; a Sales Qualified Lead (SQL) is ready to work as a deal. The catch: ‘qualified’ has to mean the same thing to everyone. Agreeing what qualified means →
CRM & Data
CRM (Customer Relationship Management)
The system of record for your customers and deals - contacts, companies, pipeline, activity and the properties that describe them. HubSpot and Salesforce are the common ones. A CRM is only as useful as the data people actually put in it. Designing a CRM people use →
HubSpot
A CRM and marketing/sales platform popular with B2B SaaS. Strong on marketing automation, lifecycle stages and workflows - the platform we most often architect and clean up.
Salesforce
The enterprise CRM standard, built around objects like Lead, Contact, Account and Opportunity. Powerful and configurable - which is exactly why it needs disciplined stages and governance.
CRM object
A type of record in the CRM. In Salesforce: Lead, Contact, Account, Opportunity. In HubSpot: contacts, companies, deals, tickets. A Lead converts into a Contact, an Account and an Opportunity.
Property (field)
A single piece of data on a CRM record - lead source, deal amount, renewal date. Properties are the context every report and every plan is built on; a blank field is a question you can’t answer later.
Lifecycle stage
Where a contact sits on the journey from stranger to customer - subscriber, lead, MQL, SQL, opportunity, customer. It’s a marketing-and-lifecycle concept, distinct from a deal’s pipeline stage. Stages vs statuses →
Deal stage (Opportunity stage)
Where a deal is on the path to closed - prospecting, qualified, proposal, negotiation, closed. Each stage should be a promise that specific things are true, with a probability and forecast category behind it. Clear staging →
Lead status
What’s happening to a lead right now - open, working, nurturing, qualified or unqualified. It answers a different question than stage: status is ‘what’s happening’, stage is ‘how far along’.
Conditional field (dependent field)
A property that only appears when it’s relevant - the closed-lost reason when a deal is lost, not while it’s still open. It keeps forms focused instead of drowning people in fields that don’t apply yet. Personalised CRM design →
Attribution model
The rule that decides which touchpoints get credit for a deal - first touch, last touch, or weighted across the whole path. Pick one before the deal exists, or every attribution argument is unwinnable. Attribution done right →
Data hygiene
The ongoing work of keeping CRM data clean - deduping, fixing formats, filling gaps, catching decay. Clean data is the first domino in whether your customers feel known. From clean data to NPS →
Data governance
The rules for who can write to which field, what values are allowed, and what happens on a sync conflict. Without it, a ‘single source of truth’ is just the single place all your confusion piles up.
Deduplication
Finding and merging duplicate records so one customer isn’t three. Merging first, without fixing the inputs that create duplicates, is why they keep coming back. Cleaning a messy CRM →
Single source of truth
One system everyone trusts for a given fact, instead of five that disagree. It’s a governance outcome, not a purchase - a CRM is only a source of truth if its fields are governed. Why the stack lies →
Data enrichment
Automatically adding firmographic or contact data to a record from an external source - company size, industry, tech stack. Useful when it’s verified; dangerous when it overwrites good data with worse. AI-to-CRM enrichment →
AI & Automation
LLM (Large Language Model)
A model trained on huge amounts of text that can read, summarise, classify and generate language - the engine behind tools like ChatGPT and Claude. In RevOps it can qualify leads, summarise calls into CRM fields, and draft outbound. AI built into your CRM →
AI agent
An AI system that doesn’t just answer once but takes steps toward a goal - reading data, deciding, calling tools and writing results back. An agent might spot a job signal, score a prospect against your ICP, enrich it, and drop it into the CRM, hands-off. Custom AI builds →
Automation
A rule the system runs so a human doesn’t have to - ‘if a form comes in, route it to the right rep’. Deterministic, cheap and reliable. If you can write the rule down, it’s automation, not AI. Automation for B2B companies and SMBs →
Workflow
A defined sequence of automated steps triggered by an event - enrol a contact, set a property, send a task, wait, branch. Workflows are how a CRM fills in context without anyone dreading the admin. Workflows that hold →
AI-native RevOps
Building AI into the revenue engine as infrastructure - enrichment, qualification, governance - rather than bolting a chatbot on the side. The combination of GTM strategy, RevOps execution and production AI. AI-native RevOps →
Prompt
The instruction you give an LLM. In production RevOps prompts aren’t typed by hand each time - they’re engineered, tested and version-controlled so the model behaves the same way every run.
Automation vs AI
The line between the two: automation follows a rule you can write down; AI makes the call a rule can’t. Mix them up and you pay a model to do a spreadsheet’s job. Knowing the difference →
Marketing & Sales
Lead
A person who’s shown some interest - filled a form, downloaded something, replied. A lead has a shelf life measured in minutes, not days. Where leads get lost →
Lead routing
The rules that send each new lead to the right owner instantly - by territory, size, product or round-robin. Bad routing is one of the places leads leak. Lead routing →
Lead scoring
Ranking leads by fit and intent so sales works the best ones first - ideally scored against your ICP by a model, not brittle point rules. AI lead scoring →
Sales handover (handoff)
The moment a lead becomes a deal, or a deal becomes a customer - where context goes to die without a protocol. A good handover is a gate: certain fields must exist before it can happen. Handover protocols →
NPS (Net Promoter Score)
A measure of customer loyalty - how likely someone is to recommend you, on a 0–10 scale. It’s downstream of whether your data and systems make customers feel known. Data hygiene and NPS →
Not sure which of these apply to you? Talk to us or run the free GTM diagnostic.
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