Sales analytics is the discipline of measuring, modeling, and acting on data generated throughout the sales cycle — from first touch to closed revenue. It sits at the intersection of RevOps, business intelligence, and sales strategy, turning raw CRM activity into decisions about hiring, territory design, quota setting, and forecasting. The single most important mental model to carry into this topic: every metric is either a leading indicator (predicting future outcomes — pipeline adds, activity rates, stage conversions) or a lagging indicator (confirming past outcomes — closed revenue, win rate, quota attainment). Building dashboards that track both, in the same view, is what separates reactive reporting from genuinely predictive analytics.
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This topic spans 13 focused tables and 110 indexed concepts, 97 flashcards. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
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Table 1: Core Pipeline Health Metrics
Pipeline health metrics are the vital signs of a sales organization — the numbers leadership checks first in every forecast call or board update. They answer whether the team has enough pipeline to hit targets, how fast deals are moving, and where the biggest risks to revenue are hiding.
| Metric | Example | Description | |
|---|---|---|---|
$V = \frac{\text{Opps} \times \text{Avg Deal Size} \times \text{Win Rate}}{\text{Cycle Days}}$ | • Revenue generated per day • formula is (Opportunities × Deal Size × Win Rate) ÷ Cycle Length• the four levers are opps, deal size, win rate, and cycle speed | ||
$500K quota → $1.5M–$2M pipeline | • Total pipeline value ÷ sales quota • healthy benchmark is 3x–4x • below 2x signals near-term risk, above 5x may indicate bloated, low-quality pipeline | ||
Win Rate = (Won Deals ÷ Total Closed Deals) × 100 | • Percentage of opportunities won vs. all decided deals (won + lost) • B2B average is 16–30% • segment by deal size, territory, and rep for actionable coaching | ||
Total closed-won revenue ÷ Number of won deals | • Drives quota design and territory planning • a rising ADS with stable win rate accelerates velocity • a falling ADS with constant volume means more work for the same revenue | ||
Total days to close all won deals ÷ Number of won deals | • Average days from opportunity open to closed-won • predictor of cash flow timing • shorter cycles + same win rate = higher pipeline velocity | ||
((Current pipeline − Prior pipeline) ÷ Prior pipeline) × 100 | • Month-over-month or quarter-over-quarter expansion of total open pipeline value • a shrinking pipeline is the earliest leading indicator of a future revenue shortfall | ||
(Opps moving to next stage ÷ All opps in stage) × 100 | • Percentage of deals advancing from each stage to the next • exposes choke points — e.g., high Discovery-to-Demo but low Demo-to-Proposal signals proposal quality issues | ||
Stage duration average = Σ days in stage ÷ total opps | • Average days an opportunity sits in each pipeline stage • deals exceeding 2× the stage average are statistically at elevated close risk. | ||
Score = f(ICP fit, engagement touches, close probability) | • Weighted composite of ICP fit, buyer engagement, and win likelihood • prevents false pipeline optimism — two $1M pipelines can have very different quality scores | ||
Avg response time = Σ(response minutes) ÷ new leads | • Average minutes/hours from lead creation to first rep touch • responding within 5 minutes vs. 30 minutes can increase conversion rates by a multiple in competitive markets |