Fishpool

I've been talking recently about a few particularities in the business models based on end-user micropayments that have created lots of followup discussion and questions. So much, in fact, that I decided it's time to try to explain one crucial and somewhat counter-intuitive detail in writing for later reference.

First, a bit of background: this information is based on my work with Habbo over the last 5 years, and is half learned from experience, half based on theoretical models built from that experience. I'm sharing this with the world because while it's been an interesting ride to build an online social game with an end-user business model, breaking pretty much every conventional rule in the process ("games have to have objectives", "there is no profit in micropayments", and so on), it's still better for our business if people understand why it works. If this allows a competitor to fix a problem in their product and get off the ground, so be it - there's plenty of growth to go around here, and failures don't help anyone. As a disclaimer, the numbers I'm discussing here have no relation to Habbo, though the basic observations certainly apply.

Let's start with an obvious statement and follow it up with something less obvious: Everyone wants to maximize revenue per player. However, in a free-to-play environment, where the majority of players do not contribute direct revenue, the right tool for the job is not to try to extract the maximum amount of money from those who do pay - rather, to increase the number of players buying anything at all - even if it's just $1 over their entire lifetime. In other words, it's good to have a lot of very low individual value players.

To explain it in detail, lets look at two assumptions behind a flexible pricing business model: first, that the number of customers grows as the cost of goods drops, and second, that the maximum consumption is unrelated to the minimum. There is no average customer who would spend more than half of others, and less than half of the rest. If there were, the picture of that customer base would look something like the image here, and it's pretty strange looking, wouldn't you say? You've probably seen pictures resembling this one where they don't start from the dominating $0 value point - that's the normal distribution.

The first assumption really is very simple: more people are willing to buy a product at a lower price. This is true for most goods, with some notable exceptions in the luxury goods market, where the perception and desirability of a product goes up with its price. However, it is difficult to create a mass-market luxury item, and those do tend to be cheap (and small).

The second is perhaps slightly more involved especially if one is used to thinking of fixed-price models such as one-time purchase of a boxed product or monthly subscriptions, both of which are difficult to scale up on a revenue per customer basis, so scaling them down is highly undesirable as well. However, it's more clear, if not obvious, by looking at other consumer goods - whether tangible such as drink- and foodstuff or intangible like movies, music and other entertainment. Buying these once certainly does not exclude further sales of the same product to the same customer - rather, it's a strong indicator of sales potential!

The free-to-play, pay-for-stuff model follows both of these assumptions. Cheap purchase price attracts more customers out of the existing free users, and transactional item-based sales allows repeat purchases of theoretically unlimited amount. Those who are willing to buy more will do so, up to some practical maximum of consumable goods and discretionary spending.

In this environment, focusing on higher-paying customers makes sense only if the number of customers drops by less than half when the revenue per customer doubles. Again, with the exception of some luxury goods segments, this rarely happens. Think about it: how many chocolate bars of standard quality would you expect to sell for $1? How about for $2? More or less than half? How about for $10 for the exact same package? I'd wager chocolate bars sell at least 10x better at the price of $1 than at the price of $10 each, and the increase of customer base more than covers the lower per-unit revenue.

This is a simple exhibit of power-law market dynamics, and is easiest observed when looked at through a logarithmic chart. Readers of books like The Long Tail or Critical Mass should not be surprised. There's a twist through - because this starts from zero gains (at the free players), the exponential behaviour follows a different path in the beginning. This model also turns Pareto's Law on its head - due to the (in my experience) relatively high exponent, the highest total value is at the lowest end of the spending.

Now, of course there is a minimum profitable price for a bar of chocolate that does not become near-$0 even at very high volumes, unlike purely digital products, so increasing chocolate-sales revenue by dropping prices does not necessarily increase profits, and I'm completely ignoring the effects of packaging and marketing on the perceived value of items. For digital sales, where packaging is more flexible and material costs are effectively non-existent, we still have to consider not-unsubstantial fixed development costs, a certain amount of costs associated to servers and bandwidth, some transaction-related pricing friction, and so forth, but certainly the minimum value (and price) of one unit of digital sales can be driven much lower than a bar of chocolate.