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Customers

The count of distinct new customers acquired during a period — business entities that signed at least one term-committed contract in the window. It is a tally of customers acquired, not the cumulative customer base, and a single entity is counted once no matter how many contracts it signs.

Count

Formula

Customers={eEntities  :  cPeriod,  Customerc=e    Termc>0}\text{Customers} = \left|\,\left\{\, e \in \text{Entities} \;:\; \exists\, c \in \text{Period},\; \text{Customer}_c = e \;\wedge\; \text{Term}_c > 0 \,\right\}\right|
A distinct customer entity (business), counted at most once in the periodCardinality: the number of distinct entities in the setThe customer entity that signed contract c Contract duration in months; an entity qualifies only if it signed at least one contract with a term greater than zero

What it measures

Customers is a raw count — a tally of distinct business entities that newly signed in a defined period, where each entity signed at least one contract with a term greater than zero months. It strips away deal size, seat count, and revenue to show pure acquisition volume. An entity that signs more than one contract in the period still counts once. Entities whose only records carry a zero or undefined term are excluded, because an open-ended record without a committed term is not yet a closed customer. This metric is period-specific: it counts inflows for the window, never the standing total — use Total Logos or Total Users for the cumulative base.

Why it matters

Customers is the clearest signal of acquisition velocity — the company's ability to convert pipeline into signed, term-committed relationships. A team closing 12 new customers in a month is winning; a team closing 2 is struggling, regardless of deal size. Boards and investors latch onto "we signed 40 new customers this quarter" because it is easy to understand and hard to fake. Tracked period over period, it shows whether sales is accelerating, plateauing, or declining, and it helps diagnose where growth is breaking: if acquisition is healthy but recurring revenue is flat, the problem lives in retention or expansion, not in the top of the funnel.

How to read it

Read Customers as a trend, and always alongside revenue. More customers is usually better, but context decides. A period with a high customer count but low new ARR points to small deals or pilot-heavy mix; a period with few customers but high ARR points to large enterprise deals — neither is good or bad without the other number beside it. A 20% jump in customers should flow through to new MRR within the same or next period; if it doesn't, average deal size is shrinking — dig into New ACV. Compare month-over-month and quarter-over-quarter, and against plan, to see whether the sales engine is speeding up or stalling.

What good looks like

Good

New customers grow month-over-month and the sales team signs a steady or expanding number of term-committed customers without leaning on ever-larger deals to hit target.

Watch

Customer count is flat or choppy month to month, hinting at inconsistent pipeline or a lengthening sales cycle — confirm whether a shift toward fewer, larger deals is intentional.

Bad

Customer acquisition is declining, with fewer signings than the same period last year and no expansion offsetting the drop — a sign of weakening sales momentum.

Watch-outs

  • Counting contracts instead of entities. A single customer that signs two contracts in the period — software plus services — is one Customer, not two. Counting contracts overstates your acquisition rate.
  • Counting free trials or non-binding pilots. A prospect on a 30-day trial is not a customer until they commit to a contract with a defined term. Inflate this figure and your true acquisition rate disappears from view.
  • Including renewals or amendments. A customer renewing an annual contract or upgrading mid-term is not a new customer — those are retention and expansion events tracked separately. Count only the initial signature.
  • Mishandling zero-term records. If your system permits contracts with no committed term, decide once whether they count and apply it consistently — silently dropping or including them distorts the trend either way.
  • Comparing raw counts across uneven periods. A month with 22 working days and one with 18 are not directly comparable; normalize to a daily acquisition rate or adjust for calendar variance before reading the trend.

Worked example

Hypothetical

Customers={12 term-committed contracts}distinct entity=121 shared=11\text{Customers} = \left|\,\{\text{12 term-committed contracts}\}\,\right|_{\text{distinct entity}} = 12 - 1\ \text{shared} = 11

In June, 12 term-committed contracts are signed: five one-year deals, four two-year deals, two three-year deals, and one month-to-month subscription with a 12-month auto-renew commitment. Every contract has a term greater than zero. But two of those contracts — one software deal and one professional-services deal — were signed by the same customer. So while there are 12 contracts, they trace to 11 distinct customer entities, and the Customers count for June is 11.

Variants & windows

The same metric re-expressed by a mechanical transform — a trailing window, a growth rate, a per-unit scaling, or a book/segment cut. Each is computed from Customers above.

  • Total New Contracts New

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