The Pipeline Physics Glossary

This glossary compiles every framework, methodology, and metric introduced across Pipeline Physics: Engineering Revenue Certainty. Use it as a reference for terminology and as an index to the chapters where each concept is explained in depth.

A

Attention Dilution Effect: The degradation of win rates when reps spread focus across too many low-quality opportunities rather than concentrating on high-value deals. A consequence of coverage obsession. (Chapter 6)

B

Best Case (Forecast Category): Deals that could close this period if conditions align. Requires: qualified opportunity, buyer intent expressed, plausible timeline. Factors to resolution are not fully identified or controlled. (Chapter 7)

Bottleneck Diagnosis: The process of using conversion physics to identify which stage transition is underperforming and causing overall yield problems. Reveals where intervention produces results. (Chapter 4)

Buyer Commitment Ladder: The progression of buyer actions from low-stakes (sharing information) to high-stakes (committing budget, engaging legal). Stages should align to rungs on this ladder. (Chapter 3)

Buyer-Centric Stages: Stage definitions based on buyer actions (stakeholders identified, budget approved). These measure actual commitment and correlate with close probability. (Chapter 3)

C

Category Migration: Movement of deals between forecast categories during a period. Some migration is normal; excessive migration (>30%) indicates categorization problems. (Chapter 7)

Commit (Forecast Category): Deals that will close this period. Requires: verbal commitment from economic buyer (documented), contract in legal/procurement, no outstanding blockers, close date confirmed by buyer. (Chapter 7)

Commit Conversion: The percentage of Commit-category deals that close as forecasted. A calibration metric for Commit evidence criteria. Target: 90%+. (Chapter 7)

Commit Protocol: An evidence-based framework for forecast category assignment. Requires documented evidence for each category (Commit, Upside, Best Case) rather than subjective confidence. (Chapter 7)

Conversion Cascade: The multiplicative progression of deals through stages. Overall win rate equals the product of all stage conversion rates. (Chapter 4)

Conversion Physics: The mathematical patterns governing how deals progress through pipeline stages. Includes stage-to-stage conversion rates, cumulative conversion, and the Decay Curve. (Chapter 4)

Conversion Trend: The quarter-over-quarter trajectory of stage conversion rates. Declining trends indicate structural problems requiring diagnosis. (Chapter 4)

Coverage Paradox: The phenomenon where obsession with raw coverage quantity incentivizes behavior (garbage creation, zombie retention) that undermines revenue outcomes. (Chapter 6)

Credibility Tax: The organizational cost of repeated forecast misses. Destroys trust in sales leadership and creates parallel planning processes that undermine alignment. (Chapter 1)

D

Data Governance: The practice of regular audits, enforcement of data quality standards, and accountability for CRM hygiene. Essential for maintaining the data foundation of predictability. (Chapter 10)

Days-in-Stage Threshold: The time limit, specific to each stage, beyond which deal probability begins declining. Typically set at 1.5x to 2x average stage duration. (Chapter 5)

Decay Curve: The pattern where early pipeline stages have lower conversion rates than late stages. Reflects the filtering effect of qualification and the momentum effect of buyer commitment. (Chapter 4)

Decision Audit: A framework for documenting what must be true for a deal to close and what evidence exists for each condition. Answers: Who decides? What is the process? What is the timeline? What conditions must be met? What evidence exists? (Chapter 8)

Developing Stage: A non-pipeline holding area for opportunities that do not yet meet the PAIN Threshold. Excluded from pipeline metrics, coverage calculations, and forecast. (Chapter 2)

E

Evidence Chain: The hierarchical relationship between forecast categories, where each higher category requires the evidence of the lower category plus additional specific evidence. (Chapter 7)

Evidence Landscape: The totality of verified information supporting deal progression. Distinguished from assumptions, hopes, and unverified claims. (Chapter 8)

Exit Criteria Protocol: A methodology requiring each pipeline stage to have defined exit criteria based on buyer-verifiable actions. Deals cannot advance without documented evidence that criteria are met. (Chapter 3)

F

Forecast Accuracy: Actual closed revenue divided by forecasted revenue. The primary metric for assessing forecast reliability. Target: 90-100%. (Chapter 7)

Forecast Integrity Index (FII): A diagnostic measuring pipeline's ability to support accurate forecasting. Scores three components: Qualification Rate, Stage Integrity, and Close Date Validity. Score of 0-6; below 4 indicates structural issues that methodology cannot fix. (Chapter 1)

Forcing Function: A tactic for creating urgency on stalled deals through legitimate constraints (pricing expiration, capacity windows, timeline requirements). (Chapter 8)

G

Garbage Inflation Effect: The tendency for coverage pressure to inflate pipeline with unqualified deals, stalled opportunities, and inflated deal sizes. (Chapter 6)

Graceful Exit: The practice of closing dead or dying deals professionally, preserving the relationship for future reengagement while freeing resources for active opportunities. (Chapter 8)

H

Hero Dependence: The dysfunction where a small number of top performers mask systemic issues. When heroes leave, revenue craters. A sign that systems are not working. (Chapter 10)

M

Manager as Diagnostician: The mindset shift from manager-as-advisor (prescribing generic solutions) to manager-as-diagnostician (examining evidence to identify root cause before prescribing). (Chapter 9)

N

The 90-Day Rule: The principle that deals older than 90 days (adjusted for sales cycle) have dramatically lower close rates. Such deals should be requalified or archived. (Chapter 5)

P

PAIN Threshold: A qualification gate requiring documented evidence of Problem (specific, quantified), Authority (budget and decision access), Impact (urgent and consequential), and Need (solution match). Minimum score of 6/8 required for pipeline entry. (Chapter 2)

Phantom Opportunities: Deals that exist in pipeline without meeting qualification standards. Consume resources, corrupt forecasts, and inflate pipeline metrics without corresponding close probability. (Chapters 1, 2)

Pipeline Physics: The governing laws that determine pipeline behavior and forecast accuracy. The recognition that revenue systems operate according to measurable, predictable dynamics. (Introduction)

Pipeline Review Protocol: A structured approach to pipeline inspection including preparation, prioritization, three-question inspection, and documented actions. Replaces unstructured deal narrative reviews. (Chapter 9)

Predictability Roadmap: The stage-appropriate infrastructure requirements at each revenue milestone, from founder-driven ($1M-$5M) to sophisticated systems ($50M+). (Chapter 10)

Predictability Threshold: The set of metrics that must hold for forecasting to remain reliable: Qualification Consistency, Stage Integrity, Conversion Stability, Velocity Consistency, and Forecast Accuracy. (Chapter 10)

Prior Action Review: Starting each pipeline review by examining actions from the prior review. Did they happen? What resulted? Essential for accountability. (Chapter 9)

Q

QAC Ratio: Quality-Adjusted Coverage divided by quota. A QAC Ratio of 1.2-1.5 indicates healthy pipeline; below 1.0 indicates deficit. (Chapter 6)

QAC/Raw Ratio: The proportion of QAC to raw coverage. Reveals pipeline quality. A ratio below 0.4 indicates significant quality problems. (Chapter 6)

Qualification Debt: The accumulated cost of allowing unqualified opportunities into pipeline. Manifests as wasted rep time, corrupted forecasts, and inflated pipeline-to-revenue ratios. (Chapter 2)

Quality-Adjusted Coverage (QAC): Pipeline coverage weighted by qualification score, stage probability, and velocity factor. Reflects actual revenue potential rather than raw pipeline volume. (Chapter 6)

R

Retroactive Demotion: The practice of moving deals to earlier stages when evidence review reveals they do not meet the stated exit criteria. Uncomfortable but essential for maintaining stage integrity. (Chapter 3)

Revenue System Architecture: The organizational and technical infrastructure supporting predictable revenue: Data Infrastructure, Process Infrastructure, Enablement Infrastructure, and Governance Infrastructure. (Chapter 10)

Roll-Up Problem: The compounding of forecast error as individual forecasts aggregate up management layers. Solved by requiring evidence at deal level rather than judgment at manager level. (Chapter 7)

S

Scaling Fracture: The phenomenon where systems that work at one revenue scale break at a larger scale. Anticipating and building for the fracture maintains predictability through growth. (Chapter 10)

Segment Segmentation Imperative: The requirement to maintain segment-level metrics (not just blended) as companies serve multiple markets with different physics. (Chapter 10)

Segment-Level Coverage: Coverage analysis broken down by segment (SMB/Mid-Market/Enterprise), source, or time horizon. Reveals allocation imbalances hidden by aggregate metrics. (Chapter 6)

Seller-Centric Stages: Stage definitions based on seller activities (discovery completed, demo delivered). These measure effort, not progress, and cannot reliably predict outcomes. (Chapters 1, 3)

Signal Pattern Recognition: The practice of assessing deal risk based on combinations of Stall Signals. Yellow (caution), Orange (at risk), Red (critical) status levels guide intervention intensity. (Chapter 8)

Skip-Level Trap: The dysfunction where senior leaders inspect individual deals (which undermines first-line managers) instead of inspecting the inspection system. (Chapter 9)

Stability Principle: The observation that conversion rates cluster around a stable baseline when qualification and stage architecture are rigorous. Deviation from baseline signals underlying change. (Chapter 4)

Stage Conversion Baseline: The historical average conversion rate between two adjacent stages. Serves as the benchmark for diagnosing deviation and projecting outcomes. (Chapter 4)

Stage Integrity: The degree to which stage definitions are meaningful, exit criteria are enforced, and deals in each stage actually meet the stated criteria. A prerequisite for forecast reliability. (Chapter 3)

Stall Signals: Observable behaviors that correlate with deal failure. Include engagement signals (champion dark, meeting quality decline), process signals (timeline slips, internal alignment delays), competition signals (new questions, proposal revisits), and decision signals (decision maker absent, urgency fading). (Chapter 8)

T

The Three Lies: The foundational assumptions that guarantee forecast failure: (1) deals in pipeline belong there, (2) stages indicate probability, (3) close dates are real. All three must be addressed for forecasting to become reliable. (Chapter 1)

The Three-Question Framework: The diagnostic core of effective inspection: (1) What changed since last review? (2) What is the next buyer action? (3) What is blocking that action? (Chapter 9)

Threshold Violation Rate: The percentage of current pipeline that exceeds Days-in-Stage Thresholds. A leading indicator of forecast risk and pipeline health. (Chapter 5)

Time Tax: The hidden cost of unqualified pipeline in rep hours. Unqualified deals require disproportionate follow-up and management attention, stealing time from deals that could close. (Chapter 2)

Time-Boxing: The practice of limiting deal discussions to a fixed duration (typically 3-5 minutes) to maintain review efficiency and prevent narrative dominance. (Chapter 9)

U

Upside (Forecast Category): Deals that should close this period, pending resolution of identified factors. Requires: champion confirmed intent, decision timeline within period, budget identified, known factors to resolution documented. (Chapter 7)

V

Velocity-Adjusted Forecasting: Forecasting methodology that modifies stage probability based on deal age. Produces more accurate projections than stage-only weighting. (Chapter 5)

Velocity Decay Rate: The rate at which probability decreases for each week a deal exceeds its threshold. Typically 5-10% per week depending on stage. (Chapter 5)

Velocity Mechanics: The dynamics of how time affects deal probability. Includes Days-in-Stage Threshold, Velocity Decay Rate, and velocity-adjusted forecasting. (Chapter 5)

W

Weighted Probability Matrix: The methodology of assigning calibrated probabilities to each forecast category and calculating expected value by weighting deal values accordingly. (Chapter 7)

Z

Zombie Deals: Opportunities that remain in pipeline far beyond reasonable close timelines. Consume resources and corrupt metrics without corresponding probability of closing. (Chapter 5)

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Using This Glossary

This glossary is designed for multiple uses:

1. Quick Reference: Look up unfamiliar terms while reading Pipeline Physics
2. Onboarding: Use as training material for new revenue team members
3. Diagnostic Index: Find the chapter that addresses a specific pipeline problem
4. Communication Alignment: Establish shared vocabulary across sales, RevOps, and leadership

For deeper exploration of any concept, refer to the chapter indicated in parentheses.