Sales Cycle Quarter-to-Quarter
Definition
Container handle for the three-section quarter-over-quarter compare object that tracks average days-to-close trend (lastQuarter / thisQuarter / improvement). Renders via the QuarterToQuarterImprovementGrid widget with three slots. The "is the motion getting faster or slower" diagnostic — cycle length trend is one of the most reliable leading indicators of ICP fit and packaging quality. Common pitfall: comparing without controlling for deal-size mix — if up-market mix is shifting, a flat cycle is actually an improvement (because up-market cycles are inherently longer). Note the mix context in commentary if material.
Why it matters
Cycle compression compounds dramatically — a 20% reduction in cycle time roughly translates to a 20% capacity increase for the same headcount. Cycle expansion does the inverse and usually predicts future-period coverage stress.
How it's calculated
Container — three-slot composite. lastQuarter and thisQuarter slots = average sales cycle in days (sales.avg_sales_cycle_days for that period). improvement = (lastQuarter − thisQuarter) / lastQuarter × 100 (positive = cycle compressed = better). When deal-size mix changes materially, the slots should be ACV-segmented separately and the improvement read per segment. How to interpret it
Cycle compression of 10%+ QoQ at constant ACV mix is a strong signal that ICP / pricing / sales-process changes are working. Expansion of 20%+ at constant mix is the canonical "something is broken in the buyer journey" signal — usually procurement, security, or competitive friction worth a stage-by-stage diagnosis.
Source
imboard Editorial
Stage relevance
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Related KPIs
Average number of days from opportunity creation to closed-won status — measured only on won deals (lost deals are tracked separately). The motion-velocity metric — directly determines how much pipeline coverage is needed, how quickly investment in new reps pays back, and how feedback loops on packaging or pricing experiments compound. Common pitfall: blending segment cycles (SMB and Enterprise often differ 5–10×) into a single average hides material trend signals — segment-cut the metric where deal-volume permits.
Container handle for the per-stage pipeline metrics grid — for each pipeline stage (qualification, discovery, evaluation, proposal, negotiation, closing) tracks dealCount, totalValue, closingProbability, winRateFromStage, and avgTimeToClose. The most diagnostic surface in the pipeline view: where deals are bunching, which stage is the bottleneck, where conversion math is breaking. Rendered via the StageMetricsGrid widget seeded from PipelineStageValues. Common pitfall: trusting unchanged stage probabilities even as the deal mix shifts — re-calibrate the per-stage close rates quarterly against actuals or the weighted forecast drifts unreliably.
Average annualized contract value across new-customer deals signed during the period (ACV). Defines where the company plays on the SaaS deal-size spectrum and dictates the operating model — high-ACV businesses tolerate longer sales cycles and direct sales motions; low-ACV businesses must run product-led or inside-sales motions to keep CAC payback short. Common pitfall: blending new and expansion ACV obscures the new-logo deal-size trend that boards actually want to see. Anchored to KBCM/Sapphire SaaS Survey 2024 §Average Contract Value for cross-company benchmarking.
Percentage of closed opportunities that resulted in closed-won (vs closed-lost) during the period. The single best read on bottom-of-funnel execution and the most direct input to pipeline-coverage math (required coverage = 1 / win rate). Common pitfall: computing win rate without disqualifying "no decision" outcomes inflates losses and depresses the rate artificially; the SaaS norm is to either bucket no-decisions separately or track a two-rate view (raw win rate vs ICP-fit win rate excluding no-decisions). Stage-segment cuts (SMB vs Enterprise) usually differ 2×–4× and should be reported separately when volume permits.
Sum of the dollar value of all active deals currently in the sales pipeline — unweighted (raw deal-value sum, not probability-weighted). Boards read this as the top-of-funnel sufficiency check: if pipeline coverage (pipeline value / forecast) drops below the historic conversion-rate-implied threshold, the forecast is at risk. Common pitfall: confusing pipeline value with weighted forecast — the unweighted number always exceeds the weighted, often by 3–5× depending on the stage mix. Always report both and the implied conversion ratio.
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