New Customers Added
Definition
Count of net-new logo customers signed during the period (a customer is a discrete buying entity — typically an account, not a seat). Paired with sales.new_business gives Average Selling Price (ASP) — a primary input to ICP / segment-fit conversations. Early-stage boards read the logo count as a sanity check on top-of-funnel and PMF before ARR-density grows enough to matter. Common pitfall: counting expansion deals or new contracts from existing customers as "new" inflates the acquisition signal — the count must match the same "first-time customer" criterion as New Business ARR.
Why it matters
Logo count is the most direct read on acquisition-motion volume before contract-value mix dominates the ARR view. Early-stage boards read it before ARR; growth-stage boards pair it with ASP to spot segment drift (e.g. up-market mix-shift where logo count falls while ARR rises).
How it's calculated
New Customers Added = Count of distinct customer entities whose first-ever active contract started during the period. Must apply the same logo-counting unit (account / parent-org / billing entity) consistently across periods so the trend is comparable. How to interpret it
Read alongside New Business ARR to derive ASP (= New Business ARR / New Customers Added). A rising ASP with falling logo count signals up-market drift (often intentional). Stable ASP with falling logo count signals a top-of-funnel problem. Falling ASP with stable logo count usually means discounting pressure — investigate competitive dynamics.
Source
imboard Editorial
Stage relevance
Typically owned by
Related KPIs
Annualized recurring revenue booked from net-new logos (first-time customers) during the period. This is the "hunt" line of the ARR waterfall — the output of the new-customer acquisition motion, distinct from expansion (existing-customer upsell) and from churn / downgrades. Common pitfall: counting renewals or expansion deals as new business inflates the new-logo conversion engine and hides a stalled acquisition motion. The KpiVarianceTable widget shows period forecast vs actual; downstream views compare it to S&M spend to derive new-business CAC and CAC payback.
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.
Fully-loaded sales-and-marketing (S&M) expense incurred to acquire one new customer during the period. Per the SMSB standard, the CAC numerator includes salaries + commissions + benefits + travel + marketing programs + tooling — i.e. all S&M costs, not just direct-attribution paid acquisition. The denominator is new logos, not deals. Common pitfall: omitting fully-loaded comp (especially BDR/SDR base salary and CS-team cost-of-sale where they participate in expansion) understates CAC and inflates every downstream efficiency metric. The board cares about CAC alongside CAC Payback and the CAC Ratio family — single-number CAC is a building block, not a verdict.
Total number of active opportunities in the pipeline (open stages only — excludes closed-won and closed-lost). The volume side of pipeline coverage; paired with pipeline_value gives the average deal size and the deal-count vs deal-size ratio that characterizes the motion shape. Common pitfall: counting non-bona-fide opportunities (orphaned trials, demo requests that never converted to a real evaluation) inflates the number — apply a stage-floor cutoff (e.g. SQL or higher) so the count reflects committed evaluation activity.
Count of opportunities that reached closed-won status during the period — the volume side of the period's sales output. Paired with closed_won_value gives the period's average won-deal size, a critical mix-shift indicator. Common pitfall: counting opportunity stage transitions rather than discrete deal closes (re-opened deals inflate the count). Boards read the trend over 4+ quarters to detect motion-volume stability — sharp drops while pipeline holds usually mean late-stage conversion has broken.
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.
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