The world of B2B sales has evolved dramatically in recent years. Digital transformation, artificial intelligence, and automation have introduced a completely new vocabulary that every sales, marketing, and RevOps professional must master.
This comprehensive glossary covers the 100 most important terms defining the modern B2B sales landscape.
From fundamental prospecting concepts to emerging AI-powered technologies, each definition is designed to provide practical clarity, not just abstract theory.
Why is mastering this terminology important? Because common language enables sales, marketing, operations, and leadership teams to align effectively. When everyone understands these concepts, strategies execute better, decisions are more informed, and results improve.
Whether you're an SDR starting your career, a sales leader scaling your team, or a founder building your growth engine, this glossary will be your definitive reference for navigating the modern B2B sales ecosystem.
Proactive process of identifying and contacting potential customers who haven't yet expressed interest in your product or service.
Unlike inbound (where leads come to you), outbound means your team initiates contact via email, calls, LinkedIn, or direct messages. Modern outbound relies on data, deep personalization, and intelligent automation, not mass spam.
Set of marketing and sales strategies designed to create awareness and interest in your solution among your target market. Goes beyond simple generate B2B leads; seeks to educate the market, build brand, and create need even before prospects are ready to buy.
Includes content marketing, events, webinars, and strategic outbound campaigns.
Detailed description of the type of company that gets maximum value from your solution and generates highest ROI for your business. Defines firmographic characteristics (industry, size, revenue), technographic (tech stack), and behavioral (buying process).
A clear ICP is fundamental to focus resources on the right accounts and avoid wasting time on low-quality leads.
Semi-fictional profiles of individual stakeholders involved in the B2B buying process. While ICP describes the ideal company, buyer personas describe the people: their role, responsibilities, pain points, motivations, and typical objections.
In B2B, there's rarely a single decision-maker; you need to map the entire buying committee.
Lead: potential prospect who has shown some level of interest or fits your ICP. Contact: specific information about a person within a target account. A lead can contain multiple contacts.
The difference is important for segmentation, nurturing, and accurate reporting.
Process of determining if a lead has real fit with your solution and probability of converting.
Common frameworks include BANT (Budget, Authority, Need, Timeline), MEDDIC (Metrics, Economic buyer, Decision criteria, Decision process, Identify pain, Champion), and CHAMP (Challenges, Authority, Money, Prioritization).
Effective qualification avoids wasting time on low-quality opportunities.
Process of supplementing basic lead information with additional data from external sources: exact title, company size, technologies used, recent news, intent signals.
Modern enrichment uses waterfall approach querying multiple sources sequentially until obtaining complete and verified information, often supported by specialized data extraction tools.
Quality: accuracy, completeness, and relevance of commercial data. Freshness: how up-to-date it is.
Outdated data (invalid emails, old titles) destroys outbound effectiveness and damages sender reputation.
The best platforms automatically enrich and verify data before each touchpoint.
Set of information and insights that help sales teams sell more effectively. Includes firmographic, technographic, intent data, trigger events, org charts, and prospect behavior.
Sales intelligence transforms prospecting from "spray and pray" to strategic outreach based on real context.
Behavioral data indicating a prospect is actively researching a solution like yours: keyword searches, visits to review sites, educational content downloads, webinar attendance.
Intent data enables contacting prospects at optimal moment when they're in buying mode, not randomly.
Use of artificial intelligence to automate deep research on target accounts.
AI can analyze websites, press releases, LinkedIn, news, financial filings, and more to identify pain points, strategic priorities, and relevance opportunities - work that would manually take hours per account.
Use of technology to execute repetitive sales tasks without constant manual intervention: sending sequenced emails, scheduled follow-ups, activity logging, data enrichment.
Automation frees reps to focus on high-value activities like conversations and closing deals.
Artificial intelligence systems that can execute sales tasks autonomously: answer questions, qualify leads, schedule meetings, nurture prospects.
Unlike simple rule-based automation, AI agents can maintain natural conversations and make contextual decisions. They operate 24/7 without incremental costs.
Automated and coordinated series of touchpoints designed to move a prospect through the funnel: email → LinkedIn → call → follow-up email → valuable content, etc.
Effective sequences combine multiple channels, vary messaging, and adapt based on prospect behavior.
Strategy of contacting prospects through multiple coordinated channels: email, phone, LinkedIn, direct messages, video.
Multichannel approach increases visibility, respects prospect preferences, and reinforces messages - studies show up to 3x better response rates vs. single-channel.
Cold email: first contact by email with a prospect who doesn't know you. Deliverability: ability of your emails to reach primary inbox, not spam.
Critical factors include sender reputation, email warmup, message content, and engagement rates.
Poor deliverability destroys campaigns before they start.
Strategic use of LinkedIn (and other professional networks) to build relationships, generate credibility, and initiate sales conversations.
Goes beyond sending connection requests with pitches; includes content creation, thoughtful engagement with prospects' posts, and warm outreach based on social activity.
Direct phone contact with prospects — often referred to as phone outreach — for qualification, discovery, or closing. Includes cold calling (first call without prior context) and warm calling (after previous touchpoints).
Modern telephony integrates auto-dialers, call recording, automatic transcription, and CRM synchronization.
Ability to customize outreach for each individual prospect without sacrificing volume. Modern technology enables inserting dynamic variables based on enriched data: name, company, industry, technologies used, recent trigger events.
Genuine personalization can 2-3x response rates.
Engagement: prospect's interaction with your outreach (opens email, visits site, responds to message). Response: direct reply to your communication.
High engagement without response may indicate interest but bad timing; low engagement suggests lack of relevance or poor deliverability.
Customer Relationship Management: centralized system to track all interactions with prospects and customers. Stores contact data, communication history, deals in progress, and performance metrics.
Modern CRMs (Salesforce, HubSpot, Pipedrive) are the sales team's single source of truth.
Revenue Operations: function that unifies sales, marketing, and customer success operations to optimize the entire revenue engine. RevOps manages tech stack, data governance, process design, and analytics.
Teams with dedicated RevOps generate up to 19% more revenue growth according to studies.
Seamless connection between all tools the sales team uses: CRM, prospecting platform, email sequencing, telephony, analytics. Effective CRM integration ensures these systems work as one. Fragmented stack generates data silos, duplicated work, and lost context.
Native integrations or via API are critical for efficiency.
Pipeline: set of deals in progress at different stages of the sales cycle. Forecasting: prediction of future revenue based on current pipeline and historical conversion rates.
Pipeline health is leading indicator of revenue - if pipeline thins, revenue will drop months later.
KPIs measuring sales process effectiveness: activity (emails sent, calls made), engagement (open rates, reply rates), conversion (leads → meetings → opportunities → closed-won), velocity (average time in each stage), and revenue.
What gets measured, gets improved.
Customer Acquisition Cost (CAC): total cost of acquiring a new customer, including team salaries, tech stack, marketing spend, etc. CAC payback period: how long it takes to recover acquisition investment.
CAC must be significantly lower than Customer Lifetime Value for sustainable business.
Use of data and analytics to obtain actionable insights on sales performance, buyer behavior, and pipeline health.
Revenue intelligence platforms identify patterns of deals that close, predict outcomes, and recommend next best actions based on machine learning.
Measurement of individual and collective sales team effectiveness: quota attainment, win rate, average deal size, sales cycle length. Includes both lagging indicators (final results) and leading indicators (activity predicting future results).
Performance dashboards enable data-driven coaching.
Process of growing revenue predictably and sustainably without costs growing proportionally.
Requires replicable processes, scalable tech stack, effective onboarding, and clear metrics.
Premature scaling (before product-market fit) is common cause of startup failure.
Growth strategy where outbound sales are the primary engine of acquisition.
Typically requires dedicated sales team that prospects, qualifies, and closes deals.
Contrasts with product-led growth. It's the standard approach for complex products, large deals, or markets where buyers don't discover solutions organically.
Strategy where the product itself is the primary acquisition driver: free trials, freemium models, integrated virality.
Users adopt the product first, sales intervenes later for expansion and enterprise deals. Works well for intuitive products with quick time-to-value.
Comprehensive plan of how a company brings its product to market: ICP definition, positioning, pricing, distribution channels, sales team structure, and marketing strategy.
GTM strategy aligns product, sales, and marketing toward common revenue objectives.
Highly focused approach on specific high-value accounts instead of mass prospecting. Requires deep research, hyper-personalized messages, and coordination across multiple touchpoints.
ABS inverts the traditional funnel - you identify target accounts first, then create demand specifically in them.
Marketing strategy that treats individual accounts as their own markets. Marketing and sales collaborate to create personalized campaigns for each target account.
Typically used for enterprise deals where an account's value justifies significant investment in customized marketing.
Division of total market into homogeneous groups based on shared characteristics: industry, size, tech stack, behavior, including niches such as cibersecurity leads.
Enables more relevant messages and focused resources on highest-value segments. Effective segmentation can 5x campaign ROI.
Descriptive characteristics of companies (equivalent to demographics for individuals): industry, annual revenue, employee count, location, growth stage, organizational structure. Used to define ICP and segment prospecting lists.
Data about technologies a company uses: CRM, marketing automation, cloud infrastructure, analytics tools, etc.
Extremely valuable to identify fit with your solution, buying timing (if using old competitor), and personalize technical messages.
Process of ranking target accounts by conversion probability and potential value. Factors include ICP fit, intent signals, previous engagement, potential deal size.
Effective prioritization ensures reps focus time on highest ROI opportunities, not alphabetical order.
Use of machine learning to assign scores to leads based on conversion probability. Analyzes hundreds of variables (firmographics, behavior, engagement) and historical patterns of leads that converted.
Superior to manual scoring based on simple rules because it identifies non-obvious correlations.
Automated system that manages follow-ups based on prospect behavior: if opens email but doesn't respond, schedule call; if no engagement after 3 attempts, pause sequence.
Ensures consistent follow-up without reps manually tracking each prospect.
Technology enabling AI systems to maintain natural conversations similar to humans. Uses natural language processing (NLP) and machine learning to understand intent, context, and language nuances.
Foundation of sophisticated chatbots and AI sales agents.
Automated conversational interfaces that can qualify leads, answer questions, schedule meetings, and nurture prospects.
Basic chatbots follow pre-programmed scripts; advanced AI assistants understand context and dynamically adapt responses. They operate 24/7 without cost increase.
Model where AI automates tasks but maintains human supervision at critical points. For example, AI generates personalized email draft, human reviews and approves before sending.
Combines automation efficiency with human judgment and creativity.
Low-value activities consuming significant time: data entry, activity logging, meeting scheduling, basic prospect research, manual follow-up sending.
These tasks are prime candidates for automation - freeing reps to focus on high-value conversations.
Measure of effective output from Sales Development Reps: quality meetings scheduled, qualified leads generated, conversion rates.
Productive SDRs require: clear processes, tech stack automating repetitive tasks, consistent coaching, and well-defined metrics.
Process of training new reps until they're productive: product knowledge, sales process, tools, messaging, objection handling.
Effective onboarding accelerates time-to-productivity from months to weeks, critical for rapid scaling.
Strategic and operational alignment between marketing and sales teams: shared ICP definition, SLAs on lead quality, feedback loop on which messages convert. Misaligned sales and marketing lose up to 10% of revenue according to studies.
Process of determining which touchpoints or campaigns contributed to a conversion. Models include first-touch (credit to first contact), last-touch (credit to last), multi-touch (distributes credit).
Accurate attribution informs decisions on where to invest resources.
Analysis of sales data to extract actionable insights: which tactics work, where bottlenecks are, which segments convert best, how to optimize process. Includes descriptive analysis (what happened), diagnostic (why it happened), predictive (what will happen), and prescriptive (what to do).
Interactive visualizations of key sales metrics: pipeline by stage, forecast vs. quota, win rates, team activity, conversion funnels.
Effective dashboards make insights visible to reps, managers, and leadership, enabling quick data-based decisions.
Active supervision of the complete process from first contact to close: prospecting → qualification → discovery → demo → proposal → negotiation → close. Includes identifying and removing bottlenecks, reducing friction points, and accelerating movement between stages.
Win rate: percentage of opportunities you close successfully (closed-won / total opportunities).
Typical B2B close rate: 20-30% for mid-market, 15-25% for enterprise. Improving win rate 5% has greater impact than increasing top-of-funnel 20%.
Retention: keeping existing customers (reducing churn). Expansion: growing revenue from current accounts via upsell, cross-sell, or increased users.
In B2B SaaS, expansion revenue can be 30-50% of total - it's more efficient than acquiring new customers.
Total revenue a customer generates during their entire relationship with your company. Calculated as: (Average revenue per account × Gross margin) / Churn rate.
LTV should be minimum 3x CAC for healthy unit economics.
Total cost of acquiring a new customer: sales and marketing salaries, tech stack, advertising spend, divided by number of customers acquired.
CAC payback period (time to recover investment) should ideally be < 12 months for B2B SaaS.
Return on Investment: revenue generated divided by cost of generating it. Applicable at campaign, channel, rep, or complete program level.
Clear ROI justifies investment in tech stack, additional headcount, or new initiatives.
Adherence to regulations on handling personal and commercial data: GDPR (Europe), CCPA (California), CAN-SPAM (US email marketing). Includes obtaining consent, respecting opt-outs, protecting sensitive data, and maintaining audit trails.
General Data Protection Regulation: European law regulating personal data processing. Requires legal basis for processing data (consent, legitimate interest), right to be forgotten, data portability.
Fines can reach 4% of global revenue.
Compliance with email marketing laws: CAN-SPAM (US), GDPR (EU), CASL (Canada). Typical requirements: clear unsubscribe, sender identification, no misleading subject lines, honor opt-outs within 10 days.
Violations result in fines and sender reputation damage.
Framework of policies and processes for corporate data management: who has access, how it's stored, what can be done with it, how it's protected.
Solid data governance prevents breaches, ensures compliance, and improves data quality.
Protection of sensitive data against unauthorized access, disclosure, or destruction: encryption, access controls, monitoring, incident response plans.
In B2B sales, includes protecting customer information, access credentials, and proprietary data.
Potential dangers of poorly implemented automation: robotic-sounding messages, emails sent to wrong contacts, prospect saturation, loss of human touch.
Mitigation requires: exhaustive testing, continuous monitoring, human oversight at critical touchpoints.
Principles for effective and ethical outbound: deep research before contacting, genuine personalization, providing value from first touchpoint, respecting prospect preferences, giving clear opt-outs, being persistent but not annoying, continuously measuring and optimizing.
Mistakes destroying effectiveness: buying unverified lists, using generic messages, abandoning after 1-2 attempts, ignoring timing and intent signals, not testing or optimizing, measuring activity instead of results, saturating single channel.
Practices that seem logical but are counterproductive: prioritizing quantity over lead quality, pitching before discovery, ignoring disinterest signals, not qualifying prospects adequately, competing on price without demonstrating value, overselling and creating impossible expectations.
Over-utilization of an outreach channel to the point of diminishing returns. Example: B2B email increasingly saturated, inboxes full of similar cold emails.
Solution: diversify channels, increase quality and relevance vs. simply increasing volume.
Spam: unsolicited emails recipients don't want. Domain reputation: score ISPs assign to your domain based on engagement, bounce rates, spam complaints.
Damaged reputation results in emails going to spam folder - recovery takes months.
Ability of your emails to reach primary inbox, not promotions tab or spam. Critical factors: sender reputation, email authentication (SPF, DKIM, DMARC), content quality, engagement rates.
Deliverability < 90% indicates serious problems.
Excessive proliferation of tools in sales stack: different tools for prospecting, enrichment, sequencing, calling, analytics.
Generates data silos, complexity, high costs, and team friction. Consolidation into unified platforms is growing trend.
Consolidation of commercial data in unified repository instead of dispersed across multiple systems.
Benefits: single source of truth, consistent reporting, duplicate elimination, better visibility of complete customer journey.
Unique system containing the definitive and updated version of all important commercial data. Typically the CRM.
Eliminates confusion when different systems show contradictory information about a prospect or deal.
Automatic bidirectional data flow between systems: changes in CRM reflect in prospecting platform and vice versa.
Real-time synchronization eliminates manual data entry work, prevents inconsistencies, and ensures everyone works with updated information.
Immediate access to actionable insights on prospects and accounts based on fresh data: trigger events that just occurred, recent tech stack changes, spikes in intent signals.
Enables timely outreach when timing is optimal.
Continuously updated information, not static snapshots. Includes activity streams, engagement tracking, behavioral signals.
Critical for modern sales because context and timing change rapidly - weeks-old data can be obsolete.
External indicators suggesting changes in demand, competition, or market conditions: search trends, news mentions, competitor movement, regulatory changes.
Help anticipate shifts and proactively adapt strategy.
Moment when a prospect is most receptive to your solution. Influenced by trigger events (funding, expansion, acute pain point), budget cycles, organizational changes.
Outreach at right timing can 5x conversion rates vs. random timing.
Strategy of activating outreach based on specific signals instead of fixed cadence: when prospect visits pricing page, when company announces funding, when competitor has outage.
Signal-based selling is more relevant and timely than mass blasting.
Adapting sales approach based on prospect's complete context: industry, current challenges, tech stack, buyer journey stage, previous interactions.
Requires access to rich data and ability to act on those insights in real-time.
Extreme level of personalization going far beyond [FirstName] - messages created specifically for each prospect's unique context using multiple data points: trigger events, inferred pain points, references to recent activity.
Technology enables this at scale.
Outreach content dynamically adapted according to: buyer journey stage, previous behavior, prospect characteristics, current timing.
Example: prospect who saw demo receives different email vs. cold prospect who never interacted.
Use of artificial intelligence to improve sales effectiveness: predictive lead scoring, message generation, conversational AI, deal analysis, forecasting.
AI in sales analyzes patterns humans don't detect and automates cognitive tasks, not just repetitive ones.
AI models (like GPT) that create original content: personalized email drafts, objection responses, call summaries.
Generative AI accelerates content creation but requires human oversight to ensure accuracy and appropriate tone.
Machine learning algorithms that predict outcomes: which leads will convert, which deals will close, when prospect is ready to buy, which message will resonate best.
Based on historical pattern analysis of thousands of data points.
Automated workflows following simple if-then logic: if prospect opens email, send follow-up in 2 days.
Useful but limited - doesn't adapt to complex or unexpected contexts. Predecessor to AI-based automation.
Systems making contextual decisions without exhaustive pre-programmed rules: AI determines best message, timing, and channel based on prospect and current situation analysis.
ore flexible and effective than simple rules but requires sufficient training data.
Ability to significantly increase revenue without costs growing proportionally. Requires replicable processes, effective automation, tech stack supporting growth, and metrics to identify bottlenecks before they block scaling.
Sales approach for early-stage companies: founders doing sales initially, focus on learning and product-market fit over scale, high personalization, rapid experimentation.
Lean tech stack, less formalized processes than enterprise.
Selling to companies with 50-1000 employees: more structured deals than SMB but faster than enterprise, multiple stakeholders but not massive committees, greater emphasis on ROI than enterprise, cycles typically 1-3 months.
Selling to large corporations (1000+ employees): multiple decision-makers, long approval processes, security and compliance critical, customization requirements, $100K+ deals, 6-18 month cycles.
Requires account-based approach and cross-functional coordination.
Selling across geographies and cultures: timezone considerations, language, differences in communication and negotiation styles, local regulations (GDPR, etc.), payment preferences.
Requires global data coverage and cultural sensitivity.
Ability to operate sales in multiple languages beyond simple translation: adapting messages to local culture, respecting regional business conventions, compliance with local regulations.
Critical for effective international expansion.
Dashboards and reports designed for C-level: high-level metrics (revenue, growth rate, CAC, LTV), pipeline health, forecast accuracy, strategic initiatives progress.
Less operational detail, more insights on business outcomes and trends.
Commercial decisions based on data analysis instead of intuition or anecdote: which segments to prioritize, where to invest resources, which tactics to scale.
Requires quality data, accessible analytics, and culture valuing evidence over opinion.
Level of sales operation sophistication: from founders doing ad-hoc sales to organization with documented processes, integrated tech stack, training programs, advanced analytics.
Maturity correlates with revenue predictability and scalability.
Adoption of digital technologies to fundamentally change how sales is executed: from manual to automated, from intuition-driven to data-driven, from mono-channel to omnichannel. It's not just buying tools - requires cultural and process change.
Current B2B sales paradigm: data-driven, technology-enabled, buyer-centric, consultative instead of transactional, multichannel, emphasis on education and value.
Contrasts with "traditional sales" characterized by aggressive cold calls and product pitches.
Integrated set of tools today's B2B sales teams use: CRM as base, sales engagement platform, data enrichment, conversational AI, revenue intelligence, analytics.
Trend toward consolidation in unified platforms vs. fragmented point solutions.
Approach of building sales operation with AI at the center from the start: automating repetitive tasks with AI, predictive lead scoring, AI-assisted content generation, conversational agents.
AI-first doesn't mean without humans - means humans focused on high-value activities.
Expected evolution of B2B prospecting: greater personalization via AI, less volume but more relevance, AI agents handling first touchpoints, signal-based timing over fixed cadences, privacy-first approach, tech stack consolidation, emphasis on multi-threading accounts.
Directions of sales tools ecosystem: consolidation (all-in-one platforms vs. point solutions), native AI in all functions, real-time data over static snapshots, verticalization (industry-specific tools), embeddable sales tools, focus on complete workflow automation.
After exploring these 100 essential concepts, it's clear that the modern B2B sales ecosystem is complex and interconnected.
From outbound prospecting to AI automation, from data enrichment to compliance, each element plays a critical role in building an effective sales engine.
The challenge sales teams face: all these concepts typically require multiple disconnected tools. Data enrichment on one platform, outbound sequences on another, analytics in spreadsheets, separate CRM - generating fragmentation, data silos, and operational complexity.
Genesy AI represents the evolution toward unified platforms that natively integrate the most important capabilities this glossary has covered:
Intelligent Prospecting and Lead Generation:
Advanced Sales Automation:
Real-Time Sales Intelligence:
Integration and Operations:
Genesy AI enables sales teams to be significantly more productive by automating the repetitive tasks (#44) that consume 60-70% of an average SDR's time.
Companies like Factorial, Sequra, Metricool, and Red Points use Genesy to transform their outbound:
These results come from applying modern B2B sales principles this glossary documents - not fragmented across multiple tools, but unified in a single intelligent platform.
The 100 terms in this glossary aren't isolated concepts - they're interconnected pieces of a system.
The future of sales tech (#100) isn't in accumulating point solutions for each individual concept, but in platforms that integrate them natively.
Genesy AI represents this convergence:
In a market where speed, relevance, and efficiency define success, having these 100 concepts working together seamlessly - not fragmented in disconnected tools - is the difference between scaling effectively and falling behind.
Genesy makes these advanced concepts accessible and actionable, transforming B2B outbound from dark art to predictable science, from tedious manual work to intelligent automation, from guesswork to data-driven decisions.
This glossary covers the essential vocabulary of the modern B2B sales professional. Whether you're building your first sales motion, scaling an existing team, or optimizing mature operations, mastering these concepts is fundamental.
The terms don't exist in a vacuum - they reinforce each other.
The best data enrichment (#7) feeds effective personalization (#19). Solid intent data (#10) enables perfect timing (#76). Intelligent automation (#85) builds on quality data (#8).
The B2B sales ecosystem will continue evolving, with new technologies, methodologies, and terms emerging. But the fundamental concepts documented here will form the foundation on which the next generation of sales innovation is built.
Bookmark this glossary as a reference. Share it with your team. Use it to align vocabulary and understanding across sales, marketing, product, and operations.
Common language is the first step toward aligned execution and exceptional results.
