Product virality metric.

Viral coefficient (k) measures how many new users each existing user brings. Formula: invites sent × conversion rate. If each user sends 5 invites and 10% convert, k = 0.5. When k ≥ 1, the product grows exponentially without paid marketing - the rare viral state. k below 0.5 means you can't grow on virality alone; 0.2-0.5 is common for healthy products; k of 1+ is rarefied air.

4 invites per user × 15% conversion = 0.6 viral coefficient. Strong. At 7-day cycle time (52 cycles/year), each user eventually brings in 0.6 × (1/(1-0.6)) = 1.5 additional users via viral loop. Not infinite growth but meaningfully amplifies acquired users. The business still needs paid acquisition to seed the base, but each paid user ultimately delivers 2.5x value.

Getting to k above 1 is hard. Products that achieve it have structural virality (can't use alone - like Zoom or Slack), strong social incentive (WhatsApp stickers, Dropbox storage), or pure fun (TikTok, games). Most SaaS products peak at 0.1-0.3 because they're solitary tools. Don't try to force virality into products that aren't fundamentally social.

Run it with sensible defaults

Using invites sent per user of 4, invite conversion of 15%, cycle time of 7, the calculation works out to 0.60. Nudge the inputs toward your own situation and the output recalculates instantly. The defaults are meant as a starting point, not a recommendation.

The levers in this calculation

The inputs — Invites Sent per User, Invite Conversion %, and Cycle Time (days) — do not pull with equal force. Not every input has equal weight. Flip one at a time toward extreme values to feel which ones move the needle most for your situation.

How the math works

Viral coefficient k = invites sent per user × conversion rate %. k ≥ 1 = viral growth. The working is transparent — you can verify every step yourself in the formula section below. No black box, no opaque "proprietary model".

What to do with a low result

A disappointing result is information, not a judgement. Pick the single input that dragged the figure down most and focus the next quarter on that one factor. Breadth-first improvement rarely works; depth-first on the worst input usually does.

What this doesn't capture

The score is a composite of the inputs you provide. Life context — job security, family obligations, health, housing — doesn't appear in the math but shapes the real picture. Use the number as a prompt, not a verdict.