Revenue Action Glossary

Definition: An automatically executed action triggered by buying signal detection (email sending, calendar booking, etc.) that focuses on "execution" rather than "suggestion".

Definition: Real-time detection of buying intent signals from web behavior, email engagement, and deal stage changes.

A design philosophy where AI executes actions automatically rather than just suggesting them. The evolution from "System of Record" (CRM) to "System of Engagement" (Sales Engagement) to "System of Action".

Definition: A ledger system that tracks every AI action with a UUID and attributes actual revenue contribution using holdout testing.

The incremental revenue increase attributed to AI actions, measured against a holdout control group that received no AI intervention.

A single, prioritized stream of AI-generated tasks (calls, emails, meeting nudges) triggered by real-time buying signals. It replaces scattered alerts and static task lists with one execution rail where reps work top-to-bottom.

A composite metric that scores pipeline health by weighting deal cycle time, win rate, deal size, and active pipeline per rep. It tracks how fast revenue moves, not just how much exists.

Definition: A closed-loop workflow that captures intent signals, routes them to the right owner, triggers the next best action, and measures outcomes automatically.

A system that independently detects signals, selects playbooks, executes multi-channel actions, and measures causal lift without waiting for human triggers.

Definition: A causal-first framework that attributes incremental revenue to specific plays and signals using holdouts or geo splits, not just multi-touch click weights.

Definition: The use of AI and workflow tools to automatically execute sales playbooks—predefined sequences of actions for specific scenarios (e.g., lost deal re-engagement, pricing page follow-up). Transforms static documentation into living, self-executing processes.

Pre-built, customizable multi-touch email and call sequences designed for specific outbound scenarios (cold outreach, re-engagement, event follow-up). Includes timing, messaging frameworks, and A/B test variants. Designed to accelerate time-to-first-meeting for SDR teams.

AI Lead Qualification uses machine learning models to automatically evaluate and score incoming leads based on their likelihood to convert and potential value. Unlike rule-based scoring, AI models analyze hundreds of data points—firmographic, behavioral, and engagement signals—to predict which leads deserve immediate sales attention. This enables sales teams to focus on high-probability opportunities while ensuring no valuable leads slip through the cracks.

Automated Follow-up systems execute timely, personalized outreach sequences without manual intervention. These systems trigger emails, calls, or tasks based on prospect behavior (email opens, page visits, form fills) or time-based rules (no response after 3 days). Modern automated follow-up combines AI-generated personalization with signal-based timing to achieve response rates comparable to manually crafted outreach—at scale.

Definition: A Revenue Intelligence Platform captures, analyzes, and surfaces insights from all revenue-related activities—calls, emails, meetings, CRM updates—to help sales teams close more deals. Unlike CRM systems that store data, revenue intelligence platforms use AI to analyze conversations, identify deal risks, coach reps, and forecast outcomes. They answer questions like "Why did we lose this deal?" and "Which deals will close this quarter?" with data, not opinions.

A unified platform that coordinates signal detection, automated actions, and ROI measurement across the entire revenue cycle. Emerged in Forrester Wave 2024 as a new category.

The coordination of automated revenue-generating actions across multiple channels (email, calls, social) based on real-time signals and buyer journey stage.

A sales methodology that prioritizes outreach based on real-time buying signals rather than static lead lists or scheduled cadences.

Definition: A testing methodology where a percentage of accounts receive no AI actions (control group) to measure the true incremental revenue impact of automation.

The detection and immediate activation of marketing/sales actions when a prospect shows sudden increases in buying intent signals.

Definition: Statistical methods that establish causation (not just correlation) between marketing/sales actions and revenue outcomes using experimental design.

A measurable digital behavior that correlates with near-term revenue impact—e.g., pricing-page revisits, proposal reopen, renewal objections—that should trigger an action.

Coordinating automated actions across channels and roles so buyers receive the right touch, on the right channel, at the right time, without duplication.

Applying machine learning to forecast deal outcomes, churn risk, and next best action using intent, product usage, and historical CRM data.

An autonomous or semi-autonomous agent that executes sales tasks—responding to inbound, booking meetings, drafting replies—within guardrails, not just suggesting actions.

Definition: Automation that advances deals between stages—sending mutual action plans, scheduling next steps, nudging stakeholders—based on milestones and risk signals.

End-to-end automation of revenue-critical workflows (lead routing, enrichment, follow-up, renewals) across tools, eliminating manual steps and swivel-chairing.

The rate at which detected intent signals are converted into qualified pipeline through rapid routing, personalization, and follow-up.

Definition: Automated outbound and follow-up communications generated and personalized by AI across email, SMS, and social channels.

Identifying roles, influence, and engagement levels of all stakeholders in a deal, then tailoring outreach and content to each persona.

Definition: Applying process mining to revenue data to discover the real buyer journey, detect bottlenecks, and recommend automations that improve conversion and cycle time.

An AI assistant embedded in the revenue stack that doesn’t stop at suggestions. It drafts multi-channel outreach, proposes call talk tracks, books meetings, updates CRM fields, triages inboxes, and triggers sequences with human-in-the-loop guardrails. It lives in the same UI reps use, learns from replies and outcomes, runs safe A/B variants, enforces SLAs, and routes edge cases to humans. The goal is faster execution, higher coverage, and cleaner data without sacrificing control or compliance.

Models that continuously scan pipeline data to flag unusual deal patterns—stage stalls, sudden value drops, missing buying roles, unexpected discount or legal requests, competitor mentions, negative sentiment, or long gaps in activity—so teams can intervene before quarter-end. Each deal is benchmarked against healthy cohorts by segment and size, then alerts are routed with a ranked fix to the owner. The aim is to surface slippage weeks early, not during the forecast call.

Automations that detect and fix broken CRM records—duplicates, missing firmographics, outdated owners, invalid emails/phones, orphaned accounts—using enrichment, validation, merge rules, and reassignment. IDs are standardized across tools so scoring, routing, and reporting stay accurate without manual cleanup. Changes are logged with who/what/why, and failures trigger alerts/retries so Ops can audit and roll back safely.

A model that scores likelihood of slippage or loss by analyzing multi-threading depth, stage age, activity gaps, discount/legal requests, procurement blockers, budget signals, competitor mentions, and sentiment in emails or calls. Scores refresh daily and use segment-adjusted benchmarks to keep noise low, letting managers triage saves instead of inspecting every deal manually. Large deals are weighted separately to avoid overconfidence.

Definition: Automatically creating situational playbooks—messaging, steps, talk tracks, assets—based on segment, signal, stage, persona, and objection set, then refreshing them as data or win/loss patterns change. It replaces static PDFs with living recipes aligned to the latest offer, pricing, and objection handling, and can be launched directly from signals or the Action Feed so reps execute without hunting for docs.

Definition: AI that records and analyzes sales calls and meetings to surface objections, intent signals, commitments, risks, and next steps, then feeds structured tasks back into the workflow. It benchmarks talk/listen balance, topic coverage, pricing discussions, and sentiment to enable coaching, and links insights to accounts/opportunities automatically.

Definition: AI that records, transcribes, and summarizes meetings, extracting decisions, owners, deadlines, risks, open questions, commitments, and follow-ups, then syncing them to CRM and task queues. It preserves context when attendees change, and links notes to accounts, opportunities, and tickets automatically so nothing is lost.

Behavioral events that indicate purchase intent—pricing-page revisits, competitor comparison views, proposal reopens, trial starts, documentation deep reads, budget page visits, or RFP downloads. These signals are ranked by recency, frequency, and depth to drive prioritization and next best actions.

An AI-powered Sales Development Representative that automates prospecting, outreach sequencing, and lead qualification tasks traditionally performed by human SDRs. Unlike chatbots or simple automation, AI SDRs use natural language processing to personalize messages, respond to replies, and book meetings autonomously.

Definition: A secure, branded online space where sellers and buyers collaborate throughout the deal cycle. Centralizes proposals, contracts, stakeholder communications, and content sharing in one trackable location, replacing scattered email threads and file attachments.

Definition: A category of software that captures and analyzes customer interactions across email, calls, and meetings to provide data-driven insights for revenue teams. Combines conversation intelligence, deal analytics, and forecasting to surface risks, opportunities, and coaching moments.

A visual representation of the customer journey from initial awareness through purchase, divided into stages (typically: Awareness, Interest, Decision, Action) that help sales teams track and optimize conversion at each step.

The complete experience a customer has with your company, from initial awareness through purchase and beyond, including all touchpoints across marketing, sales, and customer success.

Definition: The process of providing sales teams with the resources, tools, content, and information they need to effectively engage buyers and close deals.

The ability of a company to keep its customers over time, measured as retention rate (percentage of customers who continue doing business over a period).

A methodology for ranking prospects based on their perceived value to the organization, using demographic/firmographic attributes and behavioral signals to prioritize sales outreach.

Definition: The use of software to automate repetitive sales tasks such as email outreach, follow-ups, data entry, and lead assignment, freeing reps to focus on high-value activities.

The process of tracking and managing sales opportunities as they move through stages, including forecasting, prioritization, and identifying at-risk deals.

The interactions between sales reps and prospects across multiple channels (email, phone, social), and the platforms that enable, track, and optimize these interactions.

A customer relationship management system that centralizes contacts, activities, deals, and reporting. Modern CRM layers automation, AI routing, enrichment, and analytics to cut manual entry, surface next actions, and keep data synchronized across email, calendar, and billing.

A CRM tuned for sales workflows—lead routing, sequences, pipeline tracking, forecasts—with tight email/calendar integration, task automation, SLA monitoring, and auto-logging. It prioritizes speed-to-lead, clean ownership, and reducing manual entry so reps spend time selling, not typing. Routing and stage rules are kept simple so adoption stays high.

Definition: Monitoring sales activities (calls, emails, meetings), deal progress, outcomes, SLAs, and next steps to ensure pipeline visibility and compliance. Modern tracking auto-logs data from email/phone and highlights risk when tasks or stages age out.

Definition: Storing and maintaining accurate person-level data—roles, company, interactions, consent, ownership—and syncing it across CRM, marketing automation, support, and billing. It resolves duplicates and preserves history so outreach and reporting stay reliable.

Coordinating stages, stakeholders, documents, approvals, and next steps to keep opportunities moving to close. It ensures mutual action plans, deadlines, and owners are visible, and surfaces risks like missing finance or security stakeholders.

A visual board showing pipeline, activities, SLAs, forecasts, conversions, and risks, ideally linked to actions so reps can work directly from insights. The best dashboards include drill-downs and one-click task creation.

Definition: Regular reporting of pipeline, activities, conversion rates, revenue, and forecast vs. actual to guide decisions. Effective reporting pairs metrics with recommended actions and owners.

Connecting CRM with email, calendar, marketing automation, billing, support, and data warehouses to sync data and trigger workflows. Good integrations are bi-directional, event-driven, and resilient to failures with retries and alerts.

Definition: The sequence of steps a sales team follows from lead to close, including routing, outreach, qualification, demo, proposal, legal, and follow-up. A clear workflow defines owners, SLAs, and branching paths for key signals.

Analytics on CRM data—pipeline velocity, win rate, activity effectiveness, forecast accuracy, SLA adherence—to guide revenue decisions. It combines dashboards with alerts and play suggestions so insights turn into actions.

Total sales and marketing spend divided by new customers gained in a period. It includes media, tools, payroll, agencies, and overhead allocated to acquisition. Teams track CAC alongside payback period and LTV to know whether growth is profitable.

The percentage of leads or opportunities that turn into closed-won deals. Commonly tracked by stage (demo→proposal→close) to locate the biggest drop-off and prioritize fixes. Consistent definitions of "SQL" and "opportunity" are essential.

Closed-won deals divided by total closed deals (won + lost) in a period. Often segmented by segment, source, or competitor to find where messaging or qualification is weakest.

Average time from first qualified touch (e.g., SQL) to closed-won or closed-lost. Best tracked by stage to reveal where legal, security, or internal approvals create bottlenecks.

Definition: A composite metric combining deal size, win rate, cycle length, and pipeline per rep to show how fast revenue moves through the funnel. It highlights which lever—volume, value, speed, or quality—is limiting growth.

How close revenue forecasted is to actual results, typically measured as |forecast−actual|/actual. Accuracy improves when stage probabilities are consistent and adjusted by leading signals such as multi-threading, activity freshness, and procurement status.

Percentage of reps hitting or exceeding their sales quota in a given period. Often split by role, tenure, or territory to see where enablement or pipeline is lacking.

Total revenue from closed-won deals divided by number of deals in a period. Segmenting by industry, buyer size, and package highlights where value positioning or pricing needs refinement.

Definition: Counts of sales actions—calls, emails, meetings, sequences launched—used as leading indicators. Interpreted with quality signals (reply rate, signal alignment) to avoid vanity volume.

Total revenue (or ARR) generated divided by number of sales reps over a period. It captures productivity and reveals whether to hire, coach, or re-segment territories.

The time elapsed between a lead's first interaction (form submission, demo request, pricing page visit) and a sales rep's first response. Research consistently shows that response time is the single biggest controllable factor in conversion rates, with optimal windows measured in minutes, not hours.

The measured duration between when a lead takes a qualifying action (form fill, chat initiation, demo request) and when a sales representative makes first contact. This metric directly correlates with conversion probability and is considered the most controllable factor in sales performance.

The practice of regularly auditing, updating, and cleaning sales pipeline data to ensure accuracy in forecasting and resource allocation. This includes removing stale deals, updating stage probabilities, verifying close dates, and ensuring consistent data entry across the team.

Definition: The systematic process of analyzing won and lost deals to identify patterns, then implementing changes to sales processes, messaging, or qualification criteria to increase the percentage of opportunities that convert to closed-won status.

The percentage of outreach attempts (emails, calls, LinkedIn messages) that result in a scheduled meeting. A critical efficiency metric for SDRs and outbound sales teams, indicating the effectiveness of messaging, targeting, and timing.

Definition: Behavioral data that indicates a prospect's likelihood to purchase, collected from web activity, content consumption, and research patterns across first-party and third-party sources.

The process of transforming raw intent signals into automated sales and marketing actions, including lead routing, personalized outreach, and campaign targeting.

Definition: Comprehensive data and insights about target accounts, including firmographics, technographics, buying signals, organizational structure, and relationship mapping.

Definition: AI-driven recommendation of the optimal action for a rep to take with a specific prospect at a specific moment, based on historical patterns, current signals, and predicted outcomes.

Definition: Technology that records, transcribes, and analyzes sales calls and meetings using AI to extract insights about customer sentiment, competitive mentions, objection patterns, and rep performance. Goes beyond call recording to provide actionable coaching recommendations and deal intelligence.

Definition: The alignment of marketing, sales, and customer success operations to drive efficient, predictable revenue growth through shared data, processes, and goals.

The ratio of total pipeline value to sales quota, indicating whether there are enough opportunities to meet revenue targets given historical conversion rates.

A metric measuring how quickly deals move through the pipeline and generate revenue, calculated as: (Number of Opportunities × Win Rate × Average Deal Size) / Sales Cycle Length.

Definition: Observable online behaviors—scroll depth, repeat visits, content mix—that indicate interest or intent before a form fill or direct outreach.

A weighted scoring model that ranks accounts or leads based on recent and intense interactions across web, email, and product signals.

Definition: Methods for assigning revenue credit to marketing and sales interactions. Modern practice blends multi-touch models with causal tests to validate lift.

Definition: An attribution approach that distributes credit across multiple touchpoints in the buyer journey rather than only first or last touch.

Applying statistical and machine learning models to forecast deal outcomes, churn risk, or next best action, using historical CRM and intent data.

Definition: Observable actions that indicate buyer intent or engagement, such as repeat visits, long time on pricing, trial usage spikes, competitor page views, or rapid return sessions. Weighted by recency and depth, they are used to prioritize outreach and next best actions.

Definition: Monitoring on-site behavior—pages viewed, scroll depth, time on page, return visits, downloads—to infer intent and trigger outreach or personalization. Identity resolution (login, email clickback, IP) links visits to accounts so sales can act.

Measures such as open rate, click rate, reply rate, bounce, spam flag, and thread continuation that indicate how recipients interact with emails. Modern teams emphasize replies and meetings booked over opens.

Definition: Documented, reusable sequences of messaging, assets, talk tracks, and steps for specific scenarios (industry, persona, product, objection). The best playbooks are embedded in tools so reps can launch actions directly.

Definition: A scheduled sequence of touches (email, call, social, SMS) used to engage prospects over time. Modern cadences blend fixed steps with signal-based branching to avoid over-contact and improve timing.

Definition: Proactive outreach to prospects who have not raised their hand, using targeted lists, enrichment, signal-based prioritization, and sequenced touches across email, phone, and social.

Definition: Responding to hand-raising prospects from forms, chat, or product activity with rapid, personalized follow-up. Strong inbound motions use routing, SLA timers, and templated first-touch to secure meetings quickly.

Definition: A strategy focusing sales efforts on a defined list of target accounts with multi-threaded outreach, personalized value propositions, and coordinated marketing/CS support. Success depends on accurate account scoring and stakeholder mapping.

Definition: Processes and tools to plan quotas, coach reps, track KPIs, and align incentives to revenue outcomes. It closes the loop between planning, execution, and adjustment through dashboards, scorecards, and coaching workflows.

Predicting future revenue using pipeline data, conversion rates, cycle times, seasonality, deal risk signals, and macro factors. Accuracy improves when stage probabilities are adjusted by live signals and large deals are modeled separately.

Definition: The process of forecasting required sales headcount and quota distribution based on revenue targets, rep productivity benchmarks, ramp times, and expected attrition. Effective capacity planning prevents both understaffing (missed targets) and overstaffing (burned cash).

The percentage of assigned sales quota that a rep or team actually achieves in a given period. Calculated as (Actual Revenue ÷ Assigned Quota) × 100. A key indicator of both individual performance and the accuracy of quota-setting processes.

The percentage deviation between forecasted and actual revenue over a given period. Calculated as 1 - |Actual - Forecast| / Actual. High accuracy (within 5-10%) indicates healthy pipeline visibility and rep discipline; low accuracy suggests poor data hygiene or unrealistic projections.

Definition: The complete set of software tools used by a sales team to execute their revenue-generating activities. Typically includes CRM, sales engagement platform, conversation intelligence, data enrichment, and analytics. Modern stacks increasingly incorporate AI-native tools for automation and insights.

Definition: A sales qualification framework developed by IBM. Assesses four criteria: Budget (can they afford it?), Authority (are you talking to the decision-maker?), Need (do they have a problem you solve?), Timeline (when do they need to decide?). Used to determine if a lead is worth pursuing.

Definition: An enterprise sales qualification and methodology framework. MEDDIC: Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, Champion. MEDDPICC adds: Paper Process (procurement) and Competition. More comprehensive than BANT, designed for complex B2B sales with multiple stakeholders.

The predictable revenue a SaaS company expects to receive every month from active subscriptions. MRR = Sum of (customers × monthly subscription price). It normalizes annual and monthly contracts into a single metric for tracking growth velocity.

The annualized value of recurring subscription revenue. ARR = MRR × 12. It represents the yearly run-rate assuming no growth or churn, used for valuation, planning, and investor communication.

The percentage of recurring revenue retained from existing customers after accounting for expansion, contraction, and churn. NRR = (Starting MRR + Expansion - Contraction - Churn) ÷ Starting MRR × 100. NRR >100% means you grow even without new customers.

The total revenue a business expects from a single customer account over the entire relationship. LTV = ARPU × Gross Margin × Customer Lifespan (or ARPU × Gross Margin ÷ Churn Rate). It determines how much you can spend to acquire customers profitably.

The number of months it takes to recover the cost of acquiring a customer through their gross margin contribution. CAC Payback = CAC ÷ (Monthly ARPU × Gross Margin). Shorter payback means faster reinvestment into growth.

The average annualized revenue per customer contract. ACV = Total Contract Value ÷ Contract Years. For recurring business, it represents the annual subscription value excluding one-time fees. ACV guides sales strategy, compensation, and market positioning.

A SaaS health metric where Revenue Growth Rate + Profit Margin should equal or exceed 40%. A company growing 30% with 10% margin hits 40. It balances growth investment against profitability, showing sustainable business performance.

Additional recurring revenue from existing customers through upsells (higher tier), cross-sells (additional products), or seat expansion. Expansion Revenue = Ending MRR from Existing Customers - Starting MRR from Same Customers (excluding churn).

The time it takes for a new sales rep to reach full productivity (typically measured as hitting 100% of quota). Ramp time includes onboarding, training, territory assignment, and building pipeline. Shorter ramp = faster ROI on sales hiring.

A SaaS efficiency metric measuring how much ARR you generate per dollar of sales & marketing spend. Magic Number = (Current Quarter ARR - Previous Quarter ARR) × 4 ÷ Previous Quarter S&M Spend. It shows whether growth investments are paying off.

The percentage of opportunities that successfully move from one pipeline stage to the next. Each stage (e.g., Discovery → Demo → Proposal → Negotiation → Closed Won) has its own conversion rate. Tracking these identifies where deals drop off.

Pipeline value adjusted by probability of close at each stage. Weighted Pipeline = Σ(Deal Value × Stage Probability). A $100k deal at 50% stage counts as $50k weighted. It provides more realistic revenue forecasts than raw pipeline totals.

Definition: Two related but distinct churn metrics. Logo Churn (customer churn) measures the percentage of customers who cancel. Revenue Churn measures the percentage of revenue lost to cancellations and downgrades. They often differ significantly based on which customer segments churn.

The percentage of recurring revenue retained from existing customers, excluding expansion revenue. GRR = (Starting MRR - Churned MRR - Contraction MRR) / Starting MRR. Unlike NRR, GRR cannot exceed 100% because it only measures retention, not growth.

The weighted average customer acquisition cost across all channels—paid, organic, referral, and partner. Unlike channel-specific CAC, blended CAC gives a single number that represents your true cost to acquire a customer from any source. Formula: Total Sales & Marketing Spend ÷ Total New Customers.

Customer acquisition cost that includes all direct and indirect costs: advertising spend, sales and marketing salaries, tools/software, agencies, events, and allocated overhead. Unlike simple CAC (ad spend ÷ customers), fully-loaded CAC reveals the true cost of acquiring a customer.

The ratio of customer lifetime value to customer acquisition cost, measuring the return on acquisition investment. LTV:CAC = Customer Lifetime Value ÷ Customer Acquisition Cost. A ratio of 3:1 means you earn $3 for every $1 spent acquiring a customer.

Definition: Voluntary churn occurs when customers actively decide to cancel (dissatisfaction, switching to competitors, budget cuts). Involuntary churn happens when customers lose access due to payment failures—expired cards, insufficient funds, or billing errors. Understanding this split is critical because prevention strategies differ completely.

The percentage of customers retained over a period, accounting for new logos added from existing accounts (referrals, subsidiaries). Net Logo Retention = (Starting Logos - Churned Logos + New Logos from Existing) ÷ Starting Logos. Unlike NRR which tracks revenue, net logo retention tracks customer count.

A documented, repeatable set of actions triggered by churn risk signals. The playbook defines: (1) Risk indicators that trigger action, (2) Specific interventions for each risk type, (3) Escalation paths, and (4) Success metrics. Unlike reactive retention, playbooks are proactive and systematic.

Definition: MRR (Monthly Recurring Revenue) is the sum of all recurring subscription revenue normalized to a monthly amount. ARR (Annual Recurring Revenue) is MRR multiplied by 12, representing the annualized value. ARR is used for investor reporting and valuation; MRR is used for monthly operations and forecasting.

Definition: ACV (Annual Contract Value) is the total annualized value of a single contract, often including one-time fees like implementation or setup. ARR (Annual Recurring Revenue) is the sum of all recurring subscription revenue across all customers, excluding one-time fees. ACV is used for deal-level analysis; ARR is used for company-level metrics.

Definition: GRR (Gross Revenue Retention) measures revenue kept from existing customers, excluding expansion—it shows how well you retain what you have. NRR (Net Revenue Retention) includes expansion revenue (upsells, cross-sells), showing total revenue change from existing customers. GRR reveals churn health; NRR reveals growth potential.

Definition: A lead that has shown enough engagement with marketing content to be considered sales-ready based on predefined criteria. MQLs typically meet demographic fit (ICP match) and behavioral thresholds (downloads, page views, email engagement). Marketing hands MQLs to sales for follow-up.

Definition: A lead that sales has accepted and confirmed as worth pursuing through direct qualification. SQLs have passed BANT or similar criteria (Budget, Authority, Need, Timeline) during a conversation. SQLs enter the active pipeline as opportunities.

Definition: A lead that sales has agreed to follow up on after receiving from marketing, but before full qualification. SAL is the interim stage between MQL and SQL, representing sales acknowledgment that the lead meets basic criteria worth investigating.

Definition: A B2B marketing strategy that focuses resources on a defined set of target accounts rather than broad audience marketing. ABM treats individual accounts as "markets of one," with personalized campaigns tailored to each account's specific needs and stakeholders.

The cost to acquire one lead (name + contact info) through marketing efforts. CPL = Total Marketing Spend ÷ Number of Leads Generated. It measures top-of-funnel efficiency but should be evaluated alongside lead quality (MQL rate, SQL rate, close rate).

The dollar value of sales opportunities that originated from marketing activities (vs. sales-sourced from outbound). Marketing Sourced Pipeline = Total pipeline where first touch was a marketing campaign. It measures marketing's contribution to revenue generation.

Definition: A detailed description of the type of company that would get the most value from your product and provide the most value to your business. ICP defines firmographic attributes (industry, size, geography, tech stack) and behavioral characteristics (pain points, buying triggers, decision process).

Definition: A semi-fictional representation of your ideal customer based on market research and real data about existing customers. Includes demographics, behavior patterns, motivations, goals, and challenges. B2B personas focus on job role, responsibilities, KPIs, and buying authority.

Definition: The marketing function focused on creating awareness and interest in a company's products or services. Unlike lead generation (capturing contact info), demand generation creates demand before capture. Includes content marketing, thought leadership, brand awareness, and market education.

Definition: MQL (Marketing Qualified Lead) is a lead that has engaged with marketing content and meets basic demographic criteria. SQL (Sales Qualified Lead) is a lead that sales has vetted and confirmed has budget, authority, need, and timeline. The MQL-to-SQL handoff is where most lead leakage occurs.

Definition: ICP (Ideal Customer Profile) defines the type of company that gets the most value from your product—typically described by industry, size, tech stack, and budget. Buyer Persona defines the individual people within those companies who influence or make purchase decisions—described by role, goals, challenges, and behavior. ICP answers "which companies?"; Persona answers "which people?"