Glossary

Viral coefficient (K-factor)

The viral coefficient (K-factor) is the average number of new users an existing user brings in over a given period, a formula borrowed from epidemiology: K = invitations sent per user × conversion rate of invitees.

Also known as

  • viral coefficient
  • K-factor
  • viral factor
  • viral coefficient

Invented by Andrew Chen (a16z) and popularized by early social networks, the K-factor measures a product's organic growth potential. Formula: **K = i × c**, where i = average number of invitations sent per user over the period, and c = conversion rate of invitees to active users. If each user brings in more than one other on average (K > 1), the user base grows exponentially without acquisition spend. If K < 1, growth depends on an external input (paid, content, SEO).

2026 benchmarks: Dropbox at launch K ≈ 1.5 (referral program 16 GB vs 2 GB free), TikTok K ≈ 1.3-1.5 (native algorithmic virality), Notion K ≈ 1.0-1.2 (team collaboration), a classic B2B SaaS "loop" K ≈ 0.2-0.4 (rare above 0.5 in B2B where usage is less social). The K-factor must be combined with **cycle time** (average time between invitation and conversion): a K = 1.2 with a 7-day cycle is dramatically more powerful than a K = 1.2 with a 90-day cycle.

In the getchatsocial.com product

getchatsocial.com is primarily a content-led product (growth via SEO/AEO/organic social), not viral-led. The K-factor is not a managed metric — growth comes from public use cases and AI citations, not referrals.

FAQ

  • What K-factor do you need for a product to be viral?

    K > 1 = theoretically infinite exponential viral growth. K between 0.5 and 1 = growth significantly amplified by virality. In B2B, achieving K > 0.5 is rare and indicates excellent product-market fit + a native sharing mechanism.

  • What is the difference between K-factor and NPS?

    NPS measures declared intent ("would you recommend"). The K-factor measures actual behavior (how many users were actually brought in). An NPS of 70 with a K-factor of 0.1 indicates that people say they would recommend but don't actually do so in practice.