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The K-factor formula

K-factor (or viral coefficient) measures how many new users each existing user brings in through invites or sharing. The formula:

K = i × c

where:
  i = average invites sent per user
  c = conversion rate of those invites (0 to 1)

K = 1 means each user brings exactly one new user. K = 0.5 means half a new user. K = 2 means each user brings two new users. K > 1 is the threshold for sustained exponential growth from virality alone, with no paid acquisition needed.

The growth projection

With viral cycle time T (the time for a new user to invite the next batch), starting users U₀, and time horizon t, the projected total users after N = t / T cycles:

If K = 1: U(t) = U₀ × (1 + N)
If K ≠ 1: U(t) = U₀ × (1 - K^(N+1)) / (1 - K)

For K below 1 the formula converges to U₀ / (1 - K) as N grows. So a K = 0.5 product with 1,000 starting users will asymptotically approach 2,000 users from virality alone. The first cycle adds 500, the second adds 250, the third 125, etc. Decaying contribution.

For K above 1 the formula explodes exponentially. K = 1.5 with 7-day cycles takes 1,000 users to ~80,000 in 90 days. K = 2.0 takes them to ~14M. Real-world products almost never sustain K > 1 for long because audiences saturate, but the early-stage windows where K > 1 is the kind of growth that made Hotmail, Dropbox, and Slack famous.

Why cycle time matters more than K

Cycle time controls how quickly K compounds. Two products with the same K = 1.0 but cycle times of 7 days vs 30 days will end up at radically different sizes after a year. The short-cycle product has 52 cycles; the long-cycle product has 12. With K = 1, that's 52x growth vs 12x growth in the same calendar time. Cycle time is often a bigger lever than K-factor for early-stage viral products.

What to do at each K range

• K > 1.5: Truly viral. Don't break it. Most teams over-engineer features that drop K. Protect the loop.
• K = 1.0 to 1.5: Genuinely viral. Optimize for cycle time and retention, K is doing its job.
• K = 0.5 to 1.0: Sub-viral but accretive. Halves your effective CAC. The healthy zone for most products.
• K = 0.2 to 0.5: Referral lift. Worth measuring but paid acquisition is the main growth driver.
• K < 0.2: Effectively no virality. Either build a real loop or invest the resources elsewhere.

The K decay curve

K is rarely constant over time. It tends to start high in the early adopter phase (K = 1.5 to 2.0 is common in the first six months for products that go viral), then decays as the audience saturates, the most-likely-to-invite users have already invited their network, and the invitee pool becomes less and less responsive. Most successful products settle into K = 0.3 to 0.7 for the long run, which is still highly accretive even though it's no longer technically viral. Plan budgets assuming today's K will halve within 18 months.

A worked example

Your product has 1,000 users. Each user invites 5 others on average. 15% of invites convert to new users. K = 5 × 0.15 = 0.75. Sub-viral. Cycle time is 7 days, so over a 90-day period (~13 cycles), the projection is 1,000 × (1 - 0.75^14) / (1 - 0.75) ≈ 3,930 users. Per-cycle contribution drops rapidly because K is under 1. Bump i to 8 (more aggressive invite flow) and K jumps to 1.2. Same 90 days now project to 1,000 × (1 - 1.2^14) / (1 - 1.2) ≈ 59,000 users. Same i improvement (5 to 8) at lower c (15% to 12%) and K = 8 × 0.12 = 0.96, projection at 90 days is ~10,500. Tiny moves across the K = 1 threshold matter enormously.