How many people does one person bring in? Below 1 you buy every user forever

What this is

One of our published methods, written in plain language: what it can do, when to use it, what goes in and what comes out.

Why it matters

Most methods are published as papers or code. This one is written so you can decide whether it fits your problem before you invest time in it.

How to use it

Read the page top to bottom. If it matches your question, use the online tool - or subscribe and run it on your own data.

What you get

A clear answer on what the method does and does not do, plus a live curve from our own demand series. Free to read.

Free to readOnline tools from $5 per runSee it →

Demand curve read by this method — A live reading from our own demand series - the shape of the signal this method works on. 570 industries are tracked daily; this is one of them.

Data base: InfoGap own records - 391 Chinese cities x 8 dimensions, 213,208 city-industry rows, 40,499 POIs (central / lifechain, 2026-10-08).

Algorithm Handbook · one algorithm per page · no jargon anywhere · formal name: K-factor viral spread (the "K" is the "average new people one person brings in" below)

1In one sentence: what it does

Works out "how many new people one person brings in on average". Below 1, the crowd passes on smaller every round, and the only option left is to keep paying to buy people.

2When you use it

Three situations where it helps:

You spent USD 700 on a forum and got 77,000 impressions and 180 clicks — and not one signup.

What most people do: "The channel is no good, switch to another one."

Use it: put in the three numbers — 180 people, 0.23 people brought in per person, 0.0013 of those becoming new users — and it tells you straight out that right now this is entirely bought, and the crowd will not grow by itself.

You set up a "refer a friend and get a reward" scheme, but nobody refers.

What most people do: raise the reward.

Use it: first see what the number is today. If it is still far below 1, a bigger reward is a drop in the bucket — the problem may well be "how many of the people brought in are willing to stay".

You want to know when you can stop the advertising.

What most people do: go on feel — the moment the ads stop, the people stop.

Use it: check whether this number has gone above 1. Only above 1 can it grow on its own; below 1, stopping the ads means stopping the people.

3What you put in · what comes out

Input (this is the exact set used for the real run)

· How many people came at the start: 180
· Average people each one brings in: 0.23
· Of the people brought in, how many really stay: 0.0013

What comes out

These are the numbers it produced on the spot, not a single character changed:

New people one person brings in: 0.0013
Which stage it is in now: seed stage
Can it grow on its own: no
How far it is from "can grow on its own": 0.9987 (99.87% short)
Its advice: this number is too low (still seed stage) — first improve the two things, "willing to bring people in" and "willing to stay"

—— change "0.23 people brought in per person" to 3, and the stay rate to 0.4 ——
New people one person brings in: 0.4
Stage becomes: growth stage
How far it is from "can grow on its own": 0.6

How to read it: watch one number: the average new people one person brings in. Below 1, each generation of the crowd is smaller than the last, and the business always has to buy people in from outside. What is missing is not "spending a bit more", it is "are the people who stay willing to help you bring others in". You get a blunt conclusion plus one line on where to push, with no hedging.

📈One chart: K below 1 or above 1, two trajectories

Two real parameter sets, one run each — see whether people keep passing it on or not.

4How to use it

There is no public calculator for this yet — the method above is the whole procedure; on this site the inputs have to be keyed in by hand.

The input is three items: how many people at the start / how many each one brings in / how many of those brought in stay. There is no page or form for it here: the rule above is the whole method, and the numbers have to be looked up and worked through by hand.

Measure three numbers first: how many people at the start, the average brought in per person, and how many of those brought in stay
Work the three numbers out with the rule above
Check whether "new people one person brings in" has reached 1
Change the numbers and work it out again to see how the answer moves

5Who it is for

People running acquisition or referral campaigns; anyone who has paid for ads and wants to know whether they can stop; people building a community or a content business who want users to bring in other users.

6Common questions

Q: What counts as a good value for this number?

A: Reaching 1 is what counts as growing on its own. Below 1, each round passes on fewer people. So the real dividing line in this field is exactly 1, not a vague "the higher the better".

Q: Where do I get the three numbers?

A: From your own back-office data. If you have no data, estimate from how things actually stand today and then use it to see how much would change if each item went up a little.

Q: Why did I put in 3 people and it still says 0.6 short?

A: Because it still has to be multiplied by whether the people brought in are willing to stay. You bring in 3 people but only four in ten stay, so 0.4 is what is left.

7Related algorithms

decay · how many are left after each round water-flow · turn spend data into a curve motivation-level · why they are willing to promote for you ← All algorithms Handbook index

✓Why this scenario exists (where the content comes from)

This scenario comes from the records of the "algorithm meets demand" round: the cold-start need of an overseas Chinese community was run against several algorithms, and this one was judged useful, on the grounds that "invites × stay rate → 0.0013, stage = seed stage, one-line conclusion: the value is too low, priority is to raise the invite rate and the stay rate".

The input and output above are what you get by re-running the set of numbers in that record: 180 people / 0.23 brought in per person / 0.0013 stay rate → 0.0013, seed stage.

Source: the overseas scouting report, section 5, product 4 (first spend USD 150 = USD 50 on a forum + USD 100 on two vertical publications); the measured wording in the report is "USD 700 → 77,000 impressions, 180 clicks, 0 signups". The D7 entry in the pairing ledger.