How is the K-factor calculated?

Multiply invites sent per user by the acceptance rate. For 4 invites at 20% acceptance, K is 0.80.

Is the K-factor the same as the viral coefficient?

Yes — they are the same quantity. The K-factor framing emphasizes invite volume and acceptance rate as separate, diagnosable levers.

What K-factor do I need for viral growth?

Above 1.0, where each user generates more than one new user, growth sustains itself. Below 1.0 the loop still lowers blended acquisition cost.

Should I focus on invite volume or acceptance?

Diagnose which is weak. Acceptance usually has more headroom — a better offer and less friction often move K more than pushing users to send more invites.

Calculator · 103

K Factor Calculator

Measure the strength of a referral loop — and decide whether invite volume or acceptance is the constraint.

Invites sent per user

invites

Acceptance rate

K-factor

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Average

Scenario lens Current · Benchmark · Optimized

Leverage

Formula

K-factor = Invites sent per user × Acceptance rate

Understanding the K-factor

Reference material — the calculator above stays the primary tool.

What the K-factor measures

The K-factor is the number of new users each user generates through referral — invites sent multiplied by the acceptance rate. It is the same quantity as the viral coefficient, framed from the invite mechanics: how many go out, and how many stick.

Splitting it into volume and acceptance is what makes it actionable — it shows which half of the loop is the constraint.

How to read your result

The result is judged against the 1.0 self-sustaining threshold. The scenario lens then shows how a higher acceptance rate moves K, isolating acceptance from invite volume so you can see which lever to pull.

Below 1.0 — the loop assists but does not sustain. At 1.0 — self-replacing. Above 1.0 — compounding referral growth.

Volume vs acceptance

K splits into two levers with different fixes. Treat these as orientation.

Context	Typical median
Low invites, high acceptance	Prompt more invites
High invites, low acceptance	Fix the offer
Both low	Rebuild the loop
Both high	Rare, compounding

Levers that raise K

Diagnose which half is weak: if invite volume is low, prompt and incentivize sending; if acceptance is low, strengthen the offer and cut friction. Acceptance usually has more headroom. Model a higher acceptance rate as a scenario above.

K-factor in context

Read this alongside the viral coefficient and referral growth tools, which the related tools cover. A sustained K above 1.0 is rare, so most teams use the K-factor to reduce blended acquisition cost rather than to grow purely virally.