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.
Measured as
Measure Sales Cycle Variance on the same customer segment, time window, and revenue basis each time you review it.
Misused when
- Using only average cycle length and missing long-tail delays.
- Mixing segments with different buying processes and calling it variance.
Operator takeaway
- 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.
- Keep Sales Cycle Variance consistent by cohort, segment, and period before you use it as a decision signal in planning or reporting.
- Interpret the metric alongside retention, margin, or payback so one ratio does not hide the real operating trade-off.
Next decision
- Read Pipeline coverage and sales cycle math: set realistic targets (and avoid sandbagging) if the decision depends on interpretation, policy, or trade-offs beyond the raw formula.
- Decide whether Sales Cycle Variance is a growth, retention, or efficiency signal before you set targets around it.
Where to use this on MetricKit
Guides
- Pipeline coverage and sales cycle math: set realistic targets (and avoid sandbagging): A practical guide to pipeline coverage: connect quota, win rate, sales cycle length, and CAC/payback constraints to set realistic growth targets.