New Customers Added
Count of net-new customer logos acquired during the period (excludes expansion of existing accounts and re-activated churned logos unless they signed a fresh contract). The board reads this alongside `customers.customers_churned` to derive the net logo change and alongside `customers.prior_quarter_total_customers` to reconcile the logo bridge (prior total + new − churned = current total). Common pitfall: counting signed-but-not-yet-live logos here while counting them as live in `customers.total_customers` — keep the activation cut-off consistent across both. — Customers KPI, I'mBoard-authored (editorial tier).
I'mBoard-authored (editorial tier)
No public third-party standard anchors this KPI yet, so I'mBoard authors and maintains the definition — transparently labeled as editorial tier. See the ontology methodology for the published vs editorial tier system and the back-attribution workstream.
Rogue ID: customers.new_customers_added
Type: Number
Domain: Customers
Definition
Count of net-new customer logos acquired during the period (excludes expansion of existing accounts and re-activated churned logos unless they signed a fresh contract). The board reads this alongside customers.customers_churned to derive the net logo change and alongside customers.prior_quarter_total_customers to reconcile the logo bridge (prior total + new − churned = current total). Common pitfall: counting signed-but-not-yet-live logos here while counting them as live in customers.total_customers — keep the activation cut-off consistent across both.
Formula
Count of customer logos that signed their first paid contract in the period. Excludes expansion/upsell on existing logos. Net logo change = new_customers_added − customers_churned.Why it matters
The acquisition half of the logo bridge — pairs with churn to show whether the customer base is growing by count, independent of ARR mix.
How to interpret
Read with customers.customers_churned and customers.prior_quarter_total_customers to reconcile the logo bridge. Absolute counts are stage- and ACV-specific (industry folk-wisdom, not citation-grade) — compare to the company's own trailing trend.
Calculation policy
How an AI agent should compute this KPI from messy company data. Free-text rules consumed at reasoning time — not a deterministic DSL. The most common ways to get this wrong are listed under Common miscomputations.
Inclusion rules
- Count one per customer logo that signed its first-ever paid contract during the period — a period-flow count.
- Use the same logo unit as
customers.total_customers. - Keep the activation cut-off (signed vs. live) consistent with how the same logo is counted in
total_customers.
Exclusion rules
- Expansion / upsell on existing accounts — no new logo.
- Re-activated previously-churned logos, unless they signed a fresh contract (document the policy and apply consistently).
- Trials / pilots until the first paid contract starts.
- Sub-accounts under an existing parent when the parent is the counting unit.
Required inputs
- Per-logo first-paid-contract date within the period.
- The logo unit + activation cut-off (consistent with
total_customers). - Period boundaries.
Data-source priority
- CRM / billing with a stable customer-since (first-paid) field per logo unit.
- Billing first-billed-date as a fallback.
Edge cases
- Logo signs in period N but goes live in N+1: pick signing-date vs. go-live-date and apply it consistently with how
total_customerscounts that logo. - Re-activated former customer: pin "count as new" vs. "count as recovery" and apply consistently; most boards report it separately.
- Parent / child shift mid-period: a consolidation is not a logo gain.
Validation checks
prior_quarter_total_customers + new_customers_added − customers_churned = total_customers(the logo bridge).- Non-negative integer.
new_customers_added × new-logo ACV≈ new-logo ARR within rounding when the populations match.
Common miscomputations
- Counting expansion or sub-account adds as new customers — inflates the acquisition signal.
- Counting signed-but-not-live logos here while excluding them from
total_customers(or vice versa) — breaks the logo bridge. - Logo-unit inconsistency across periods.
- Double-counting a trial conversion (once at trial start, once at paid start).
Related KPIs
customers.total_customerscustomers.customers_churnedcustomers.prior_quarter_total_customerscustomers.avg_contract_value
Source
I'mBoard editorial — authored and maintained by I'mBoard, first published 2026-04-01. No third-party standard is cited for this KPI; when one emerges, the definition is back-attributed and promoted to the published tier (a minor version bump). Read the ontology methodology for the published vs editorial tier system, attribution rules, and dispute process.
Stage relevance
| Company stage | Priority |
|---|---|
| Pre-Seed | Core |
| Seed | Core |
| Series A | Core |
| Series B | Core |
| Series C+ | Core |
| Public | Core |
Suggested for stages: Pre-Seed, Seed, Series A, Series B, Series C+, Public.
Default owning functions
- Sales
Machine-readable
- This KPI as JSON:
/api/ontology/customers/new_customers_added.json - All Customers KPIs:
/api/ontology/customers.json - Full catalog:
/api/ontology/index.json
Net Revenue Retention (NRR)
Recurring revenue retained from the cohort of customers present at the start of the period, including expansion (upsell, cross-sell, price increases) and net of churn and contraction — but excluding revenue from net-new logos acquired in-period. Per the SaaS Metrics Standards Board (SMSB) NRR standard. NRR above 100% means the cohort grew faster than it lost — a hallmark of strong product-led expansion. The board reads NRR alongside GRR (`customers.gross_revenue_retention`) to separate the "keep + expand" signal from the "just keep" signal. Common pitfall: mixing GAAP revenue and ARR in numerator vs. denominator, or letting net-new logo revenue leak in — both inflate the number; SMSB is explicit that the cohort is closed at period start. — Customers KPI anchored to SaaS Metrics Standards Board.
NPS Responses
The number of survey responses the current `customers.nps_score` is computed from — the confidence qualifier the board must read alongside any NPS value. Per the NPS methodology (Reichheld/Bain), a score from a small or unrepresentative sample is unreliable; surfacing the response count lets the board discount low-n scores. Common pitfall: celebrating (or alarming at) an NPS swing that is actually a sample-size artifact — always read the score and the response count together. — Customers KPI, I'mBoard-authored (editorial tier).