The secret to a free-to-play game is not volume. It is not about getting millions of users and relying on only a tiny percentage of that enormous volume to cover your costs.

It is about understanding the power-law. You can read more about the concept in my post How much is your game worth? but the secret to success in free-to-play is this:

Free to play not only removes barriers by letting players play the game for free; it removes the upper limit on how much a committed fan spends by removing the purchase price or subscription.

For an ironic take on this concept, see this cartoon on free-to-play from Penny Arcade.

I know of one company with around a quarter of a million registered users that is grossing $3 million a year and another with fewer than 1.5 million MAUs that grosses $20 million. The power-law business model works.

What is the power-law, and how does it work with online games

The power law simply expresses the idea that not all customers are equal. Some love your game, some will think it’s so-so. Some will have lots of time and no money, others will be vice versa. Some users are happy spending money for many reasons, ranging from convenience to social status.

By designing your game to allow users to spend different amounts of money – by offering consumable items, aesthetic items, power-ups and the ability to exchange time for money – you unlock the ability to let your biggest fans spend a lot of money with you.

(A note on terminology: the term “whales” for your biggest spenders has become dominant. I don’t like it, because it is a deeply unflattering term. I prefer true fans. But then I realised there is a difference between whales and true fans: true fans spend money because of what you do; whales spend money because of who you they are. Enabling true fans to spend lots of money because they love what you do is entirely ethical; targeting whales who can’t help themselves may not be. For more on this, read Whales, true fans and the ethics of free-to-play gaming.)

Modelling the freemium power-law

For the purposes of this spreadsheet, I split your gamers into three groups:

Minnows spend the smallest amount possible in a month, typically $1

Dolphins spend a “middling” amount. Typically I forecast they spend an average of $5 per month

Whales spend a lot. Typically I forecast they spend an average of $20 per month.

Freeloaders (see Whales, power-laws and the future of media) are, of course, the fourth group. They are covered by the conversion rate and not considered here)

For more details on ARPPU, see the separate post on this topic – ARPPU in freemium games.

My starting point for what percentage of your users fit in which bucket is:

Minnows: 50% of payers

Dolphins: 40% of payers

Whales: 10% of payers

Note that this is an approximation of the shape of the power law. You can change the percentages and change the ARPPUs as you like. Just be aware that changing the percentages and ARPPU changes the curve that you are predicting.

Benchmarks

It is pretty hard to get accurate, public benchmarks for how to separate the minnows from the dolphins and the whales. Many companies talk about their ARPPU in round terms. Bigpoint, for example, says that its ARPPU is larger than that of World of Warcraft. That hides a massive concentration amongst the whales.

We’ll keep digging to find publicly available splits of users into whales, dolphins and minnows. However, since it is an approximation to the power law curve, that may take us a long time.

In the meantime, I suggest you work with:

Minnows: 50%

Dolphins: 40%

Whales: 10%

It’s what I’ve seen across many of my clients, but you’ll just have to take that on trust.