SaaS Metrics

Sales Cycle Variance

Sales cycle variance describes how spread out your time-to-close is (some deals close fast, others stall). High variance makes forecasting harder.

Updated 2026-01-24

Definition

Sales cycle variance describes how spread out your time-to-close is (some deals close fast, others stall). High variance makes forecasting harder.

Example

If p50 is 45 days and p90 is 140 days, variance is high and forecast risk rises.

How to use it

  • Track median and percentile cycle lengths (p50/p75/p90), not just averages.
  • Use variance to identify deal types that consistently slip.
  • Separate new business vs expansion deals; variance often differs.

Common mistakes

  • Using only average cycle length and missing long-tail delays.
  • Mixing segments with different buying processes and calling it variance.

Why this matters

This term matters because small changes compound in SaaS metrics. Use consistent definitions by cohort and segment so you can diagnose retention, payback, and growth quality.

Practical checklist

  • Write a 1-line definition for "Sales Cycle Variance" that your team will use consistently.
  • Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
  • Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
  • Sanity-check with a related calculator from the same category on MetricKit.
  • Read the related guide (e.g., Pipeline coverage and sales cycle math: set realistic targets (and avoid sandbagging)) for context and common pitfalls.

Where to use this on MetricKit

Calculators

Guides