Sales Intelligence Glossary: 50 Essential Terms

Introduction to the sales intelligence glossary

Sales intelligence encompasses all the data, signals and processes that make it possible to prioritise accounts, align Sales and RevOps teams, and increase pipeline predictability. Without a common language, each team interprets the same indicators differently, which creates friction, duplication and loss of efficiency.

This sales intelligence glossary brings together 50 essential terms to structure your prospecting, qualification and revenue management practices. The definitions are oriented towards field use, to help Sales, SDR, AE, CSM, Marketing and RevOps teams speak the same language, secure data reliability and make consistent decisions.

Fundamental concepts of sales intelligence

The basic concepts structure the way you segment your market, organise your CRM and manage your sales activities. Without these foundations, more advanced initiatives (scoring, intent data, ABM) quickly lose impact.

1. Target account

Priority company identified as having strong revenue potential, good fit and high likelihood of purchase. Target accounts structure account plans, sales territories and ABM (Account-Based Marketing) campaigns. They are generally defined using ICP criteria, intent signals and historical closing data.

2. ICP (Ideal Customer Profile)

Description of the type of company that derives the most value from your solution and generates the best return for your organisation. The ICP includes firmographic criteria (size, industry, geographic area), technographic criteria (IT stack, tools already in place) and contextual criteria (maturity, challenges). It guides the selection of target accounts, scoring and prospecting priorities.

3. Persona

Typical profile of a stakeholder within an account (role, responsibilities, objectives, frustrations, KPIs). The persona is used to adapt messaging, content, prospecting sequences and product demonstrations. A single account may involve several personas (CFO, CMO, CIO, business user) with different expectations.

4. Buying group

Set of people involved in a B2B purchasing decision. It often includes sponsor, economic decision-maker, key user, finance, IT, security, procurement. Sales intelligence aims to map this group, understand internal influence and orchestrate targeted interactions with each member.

5. Champion

Internal contact at the client who actively supports your solution, facilitates access to the buying group and shares information about the decision-making process. A good champion has internal credibility, a personal interest in the project’s success and the ability to influence decision-makers.

6. Economic buyer

Person who has the final authority to approve the budget and sign the contract. To be distinguished from the operational sponsor, who drives the project on a daily basis but does not always have signing authority. Identifying the economic buyer early in the sales cycle is key to securing budget and timelines.

7. Opportunity

Structured deal in the CRM, associated with an account, an estimated amount, a closing probability and a close date. The opportunity is the basic unit of the pipeline and follows a standardised qualification and progression process (sales stages).

8. Pipeline

All active opportunities, segmented by stage of the sales cycle. The pipeline is used to measure coverage, velocity and revenue predictability. Intelligent pipeline management relies on reliable data, well-defined stages and rigorous CRM hygiene.

9. Marketing–sales funnel

Representation of the stages of progression of a prospect, from the first interaction to signature. It generally includes anonymous phases (visits, content engagement), marketing leads (MQL), sales accepted leads (SAL), opportunities and customers. Sales intelligence makes it possible to measure conversion rates at each stage and identify bottlenecks.

10. Sales cycle

Duration and sequence of stages between the first qualified contact and contract signature. The sales cycle is measured in days or months and varies by segment (SMB, mid-market, enterprise). Analysis of the sales cycle helps optimise resources, calibrate forecasts and identify stages that slow down deals.

Data, signals and scoring

Sales intelligence relies on the ability to collect, enrich and interpret data from multiple sources. Scoring and signal analysis make it possible to prioritise actions and focus efforts on the accounts most likely to buy.

11. Firmographic data

Descriptive information about a company: industry, size (revenue, headcount), location, legal structure, growth, fundraising. Firmographic data is used to segment the market, define the ICP and personalise messages.

12. Technographic data

Data on the technologies used by a company (CRM, ERP, marketing tools, competing solutions). It makes it possible to identify compatible environments, replacement opportunities and signals of digital maturity.

13. Intent data

Signals showing that a company is actively searching for a solution on a given topic (searches, article reading, solution comparisons, participation in webinars). Intent data helps detect projects early and prioritise accounts to engage.

14. Engagement signals

Measurable interactions between an account and your brand: email opens, clicks, visits to key pages, downloads, responses to campaigns, participation in events. Analysis of these signals makes it possible to qualify interest and trigger targeted sales actions.

15. Lead scoring

Scoring system assigned to a contact or lead based on its profile (fit) and behaviour (engagement). Lead scoring helps marketing and SDR teams decide when to hand a lead over to sales and how to prioritise follow-ups.

16. Account scoring

Overall score assigned to an account based on ICP criteria, intent signals and multi-stakeholder engagement. Account scoring is central to ABM approaches and to the prioritisation of account plans for sales teams.

17. Fit score

Scoring component that measures the structural alignment between an account (or a contact) and your ICP. It is based on firmographic, technographic and sometimes geographic criteria. A high fit score indicates strong theoretical potential, even without strong intent signals.

18. Intent score

Scoring component that measures the intensity of intent and engagement signals. It aggregates browsing data, consumed content, thematic queries and participation in campaigns. A high intent score indicates a favourable moment to engage the account.

19. First-party data

Data collected directly by your organisation (website, product, campaigns, CRM, support). It is generally more reliable and more actionable than third-party data. Sales intelligence aims to better exploit this first-party data to guide decisions.

20. Third-party data

Data from external providers (industry databases, intent data platforms, aggregators). It complements your internal data to enrich accounts, detect weak signals and expand your addressable market.

Sales processes, RevOps and revenue management

Sales intelligence is not limited to data: it also structures processes, responsibilities and management rituals. The objective is to align Marketing, Sales, Customer Success and Finance around a shared view of the pipeline and performance.

21. RevOps (Revenue Operations)

Function or team responsible for aligning marketing, sales and customer success operations to optimise revenue generation. RevOps manages tools, data, processes and reporting in order to reduce silos and improve predictability.

22. Marketing–sales SLA

Formalised agreement (Service Level Agreement) between Marketing and Sales on the definition of leads, qualification criteria, handling times and volume targets. A clear SLA reduces friction and strengthens shared accountability for the pipeline.

23. MQL (Marketing Qualified Lead)

Lead considered sufficiently qualified by marketing based on profile and engagement criteria. The MQL is passed on to SDRs or salespeople for further qualification. The definition of the MQL must be aligned with sales to avoid irrelevant leads.

24. SQL (Sales Qualified Lead)

Lead validated by sales as having a real need, a potential budget and a reasonable probability of turning into an opportunity. The SQL often marks the creation of an opportunity in the CRM.

25. SAL (Sales Accepted Lead)

Intermediate stage where sales officially accept to handle a lead passed on by marketing. The SAL makes it possible to measure the perceived quality of MQLs and identify definition gaps between teams.

26. BANT qualification

Qualification method based on four criteria: Budget, Authority, Need, Timeline. BANT helps salespeople structure their questions and assess the maturity of a project.

27. MEDDIC / MEDDPICC qualification

Advanced qualification frameworks (Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, Champion, Competition). They are used in complex sales to secure deals and reduce end-of-cycle surprises.

28. Forecast

Revenue forecast for a given period (month, quarter, year), based on the pipeline, closing probabilities and performance history. A reliable forecast is a key indicator of maturity in sales intelligence.

29. Pipeline coverage

Ratio between the total pipeline amount and the revenue target for a period. For example, coverage of 3x means that the pipeline represents three times the target. This ratio helps anticipate underperformance risks.

30. Pipeline velocity

Speed at which opportunities progress through the pipeline, from creation to closing. Velocity combines the number of opportunities, conversion rate, average deal size and sales cycle length. It helps identify levers to accelerate growth.

Sales intelligence tools, channels and tactics

Sales intelligence tools and tactics make it possible to turn data into concrete actions: targeted prospecting, orchestrated campaigns, nurturing and expansion within existing accounts.

31. CRM (Customer Relationship Management)

Central system that stores customer and prospect data, sales activities and opportunities. The CRM is the source of truth for the pipeline and the forecast. Good CRM hygiene is essential to secure sales intelligence.

32. Sales engagement platform

Platform that enables orchestration and tracking of multichannel prospecting sequences (email, phone, LinkedIn, etc.). It provides analytics on response and engagement rates and integrates with the CRM.

33. ABM (Account-Based Marketing)

Marketing–sales approach focused on a list of strategic accounts, with highly targeted and personalised campaigns. ABM relies heavily on sales intelligence to select accounts, personalise messages and measure engagement.

34. Outbound

Active prospecting initiated by the company towards accounts or contacts that have not yet expressed explicit interest. Modern outbound relies on intent data, scoring and engagement signals to avoid purely “cold” outreach.

35. Inbound

Strategy aimed at attracting prospects through content, SEO, social networks and events, then converting them into leads. Sales intelligence makes it possible to qualify and prioritise these inbound leads according to their fit and behaviour.

36. Nurturing

Set of actions aimed at maintaining the relationship with non-mature leads or accounts, through content, automated campaigns and regular touchpoints. Nurturing relies on engagement signals to adapt pace and content.

37. Sales playbook

Structured set of best practices, scripts, sequences and resources to handle a given situation (prospecting, follow-up, objection, renewal). Playbooks rely on sales intelligence data to define triggers and messages.

38. Trigger event

Event that creates a sales opportunity or increases the likelihood of purchase: fundraising, management change, acquisition, product launch, regulatory change. Identifying trigger events makes it possible to contact accounts at the right time.

39. Cross-sell

Sale of complementary products or services to an existing customer. Sales intelligence helps identify additional needs based on usage data, support tickets and intent signals.

40. Upsell

Increase in the value of an existing contract (more users, advanced features, higher tier). Upsell is based on a good understanding of perceived value, adoption and the growth potential of the account.

Performance measurement and continuous optimisation

Sales intelligence is an iterative process. Performance indicators make it possible to test hypotheses, adjust strategies and align teams around shared objectives.

41. Conversion rate

Percentage of leads, accounts or opportunities that move from one stage to the next (for example MQL → SQL, SQL → opportunity, opportunity → customer). Tracking conversion rates by segment and channel helps identify optimisation levers.

42. Win rate

Percentage of opportunities won compared with the total number of opportunities handled over a period. Win rate is a key indicator of sales effectiveness and pipeline quality.

43. Churn

Loss of customers or recurring revenue over a given period. Churn can be measured in number of accounts (logo churn) or in value (revenue churn). Sales intelligence also aims to anticipate churn through warning signals.

44. NRR (Net Revenue Retention)

Indicator that measures the change in recurring revenue on an existing customer base, including upsell, cross-sell and churn. NRR above 100% indicates that the customer base is growing even without new logos.

45. LTV (Lifetime Value)

Estimated total value of a customer over the duration of the relationship. LTV helps calibrate acquisition investments (CAC) and prioritise the most profitable segments.

46. CAC (Customer Acquisition Cost)

Average cost to acquire a new customer, including marketing, sales and tools. The LTV/CAC ratio is a key indicator of the viability of the business model.

47. Product adoption

Actual level of use of your solution by customers (frequency, depth, key features). Adoption data is essential to detect churn risks, identify upsell opportunities and feed sales intelligence.

48. Health score

Synthetic score that assesses the “health” of a customer account by combining product usage, support, satisfaction, engagement and risks. The health score makes it possible to prioritise CSM actions and anticipate renewals.

49. Sales–product feedback loop

Structured process for feeding information from sales teams back to product teams (objections, unmet needs, competitors, use cases). This loop feeds the roadmap and strengthens product–market fit.

50. Data-driven culture

Corporate culture in which sales decisions are made based on data that is reliable, shared and understood by all. Data-driven culture is the foundation of sales intelligence: without team adoption, even the best tools remain underused.

Conclusion: bringing your sales intelligence to life

A shared glossary is a first step towards aligning teams around a common language. The challenge is then to embed these definitions in your processes: team training, playbook documentation, CRM configuration, management rituals and feedback loops.

By clarifying these 50 terms, you create the conditions for better account prioritisation, more targeted prospecting, a healthier pipeline and more predictable revenue. Sales intelligence is not a one-off project, but an ongoing discipline that is strengthened with every interaction, every data point collected and every decision made.