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K-factor is your growth multiplier: for every new user who joins, how many more does that user bring in through the product itself, before their influence runs out. For most B2B products the honest number is low, 0.1 to 0.3, so if a form or a dashboard asks for your K-factor and you don't know it, that's normal, not a red flag. Read alone, K tells you almost nothing. Read next to retention, it tells you whether your growth compounds or slowly runs out of road.
K-factor is the number of additional users an existing user brings in through the product itself, not through paid ads or a sales team. The textbook formula is the referral-program version:
K = (Invitations sent per user) × (Conversion rate per invitation)
Invite 5 people, 20% sign up, and K = 1.0: on average, every user replaces themselves. That formula is fine as far as it goes, and it goes exactly as far as your referral program. Most growth actually comes from shared documents, published templates, a "Powered by" badge on a free export, or a teammate invited into a live workspace, not from a referral form at all. The formula that covers all of that is:
K = (Outputs per user) × (Signups per output)
An output is anything a user does that puts your product in front of someone who isn't a user yet. Take a project tool where an active user invites 2 teammates a month and 15% of those invites convert: K = 2 × 0.15 = 0.3, without a single referral incentive in sight. Real products stack several of these loops (invitations, shared documents, integrations, plain branding) and add the individual K's together to get a total. The full breakdown of loop types, worked examples, and how to raise each one lives in How to Improve K-Factor Without a Referral Program; this piece stays on the definition and the one thing that decides whether K matters at all: retention.
K-factor isn't one mechanism, it's a sum across whichever of these four loop types your product actually has:
Here's a rough model of a collaboration tool running several of these loops at once (based loosely on how a product like Notion likely breaks down, not a disclosed real number): a document-sharing loop at K = 0.15, a template-gallery loop at K = 0.10, a workspace-invitations loop at K = 0.30, and a casual-contact "Powered by" loop converting about 0.004 signups per impression at K = 0.05. Add the four together and the product's total K-factor comes out to 0.60, almost entirely from mechanisms nobody would have labeled "referral program." That's the whole case for the loop-by-loop lens over the single naive formula: most of your K lives in things nobody's tracking on a growth dashboard.
For the step-by-step playbook on auditing and raising each of these loops, see How to Improve K-Factor Without a Referral Program.
Say "K-factor" to most operators and you get a blank look. That's why the scilla.studio benchmark tool doesn't lead with the term. It asks: "The growth multiplier: for every new user, how many more do they create?" Same number, same formula, framed as what it does instead of what a growth textbook calls it.
That framing also sets the right expectation before you type in a number. A multiplier close to zero is normal, not a failure. A multiplier above 1 is the rare, self-sustaining kind of growth that gets written up in blog posts precisely because it almost never happens. Most of what you're looking for lives well under 1, and that's where the honest signal actually is.
Here's the part a bare K-factor number hides: K amplifies whatever base you're already growing, or losing. On its own, it creates no growth at all.
The cohort-based growth model behind the benchmark tool makes this concrete. Each month's new users, N0, grow into newUsers[t] = N0 + K × newUsers[t−1]. Run that forward and, as long as K stays under 1, the new-user line doesn't grow forever. It converges to a ceiling of N0 / (1 − K).
Put numbers on it. A product bringing in 1,000 new users a month at K = 0.2, a solid mid-band B2B number, converges to a ceiling of 1,000 / (1 − 0.2) = 1,250 new users a month once the loop fully plays out. Push K to 0.3, the strong end of the B2B band, and the ceiling moves to about 1,430. That's real, it's just not dramatic, and dramatic is what most people expect when they hear the word "viral."
Now bring retention into the same picture. The model's stable base churns at a "mature retention" rate derived from your monthly retention, M1: matureRetention = 1 − (1 − M1) × 0.10, meaning long-tenured users churn roughly ten times slower than a brand-new cohort. If M1 is 55%, mature retention runs about 95.5%, and a K of 0.2 is amplifying a base that mostly holds. If M1 drops to 30%, that same K = 0.2 is amplifying a base that's shrinking every month, and the multiplier makes the shrinkage compound too. K doesn't know the difference between a healthy base and a leaking one. It multiplies whatever you hand it.
So is a K-factor of 0.2 good or bad? It depends entirely on what your retention is doing underneath it, which is exactly why the benchmark tool always shows the two together instead of scoring K in isolation. (See the full cohort-based growth model for the complete stable-base and new-cohort math, and how to plug your own N0, M1 and K into it.)
| Segment | K-factor (avg) | What it means |
|---|---|---|
| B2B SaaS / B2B tech | 0.1 to 0.3 | B2B virality is usually weak. Anything above 0.3 is unusually strong unless the product has built-in collaboration loops. |
| Consumer apps | 0.3 to 0.7 (rarely above 1) | K above 1 is true viral growth and extremely rare. Even 0.5 is considered very strong. |
Sources: Reforge, Andrew Chen.
Two things worth saying plainly. First, these are averages across very different products, so sitting below the band is not automatically a problem: a two-person internal tool with no shareable output has no business chasing a consumer-grade K. Second, B2B and consumer numbers aren't comparable to each other. A B2B tool at K = 0.25 is doing fine. A consumer app at K = 0.25 is underperforming its category. Same number, opposite verdict, because the two grow by different mechanics entirely. (See B2B vs Consumer Growth for why the two shouldn't share a benchmark bar.)
Here's what I actually think: the fastest way to burn a quarter on K-factor is to decide the number looks low and bolt on a referral program to fix it. A referral form is one loop, usually a mediocre one, and it's rarely where the leak actually is.
Raising K starts with an audit, not a feature. Which outputs does your product already create that a non-user could see? Which of those actually convert, and which just get shared into a void? You won't know until you measure outputs per user and signups per output, loop by loop, and most teams have never put a number on either side. This is closer to user research than to a growth hack: watching what people already do with your product and asking who's downstream of it.
And honestly, sometimes the honest answer is that your product has no natural sharing surface, and that's fine too. A vertical, single-player tool with no reason to invite anyone can be a completely healthy product that grows through other channels. Forcing a viral loop onto it usually produces a worse product, and a K-factor that still doesn't move. The full loop-by-loop playbook, including where the real gains tend to sit, is in How to Improve K-Factor Without a Referral Program.
The free benchmark tool asks for K-factor as the growth multiplier, and it's explicitly optional: leave it blank if you don't know it, and the tool still charts your retention and unit economics against B2B and consumer bands. If you do have a number, the tool's growth simulation shows what that K actually does to your 12-month curve once retention is plugged in, so you can see the ceiling for yourself instead of trusting a formula on a page.
What is K-factor in plain terms? K-factor is the growth multiplier: how many additional users each existing user brings in through the product itself, before their influence runs out. A K of 0.3 means every 10 users bring in roughly 3 more.
What's a good K-factor for B2B SaaS? Typically 0.1 to 0.3. Above 0.3 is unusually strong unless the product has built-in collaboration loops that put it in front of non-users constantly. (Source: Reforge, Andrew Chen.)
Can K-factor be above 1? Yes, and it means each user brings in more than one additional user, the rare case of true, self-sustaining viral growth. It shows up in a handful of consumer products and is almost unheard of in B2B.
Does a low K-factor mean my product is failing? No. Most B2B products sit well under 1, and plenty of healthy, growing companies have a K-factor close to zero. K amplifies growth you already have; it doesn't create growth by itself, so a product growing well on retention and paid acquisition alone isn't a broken one.
Why does K-factor matter alongside retention, not on its own? Because K multiplies whatever base you're already growing or losing. The same K-factor compounds a healthy, retained base into faster growth, and compounds a leaking base into faster decline. Read the two together, using the cohort-based growth model, rather than K in isolation.
