Let’s say you’re developing a game and you want to see who your most profitable players are. Imagine you’ve got a power-law so heavy-tailed you don’t even have a mean. You want to estimate your shape parameter and your statistics are all over the place as expected (no pun intended). Looks like you’re going to have to chop off that long tail and treat it separately. Looks like you’re going whale hunting.

In the casino industry, so-called “high-rollers” are often referred to as whales. It’s easy to see why: while they might come in with an attitude of being able to beat the house by wagering enough money, ultimately they wind up providing the biggest payouts to the casino. A somewhat disparaging term, to be sure, but it’s easy to understand how casinos view their revenue stream: lots of small change with the occasional big kill.

Facebook used to include a little whale tag of its own, the letter ‘g’ for gamer, that the company would include among the usual demographic data that a Facebook app would usually access. What constitutes a gamer? Oh, just some aggregate statistic indicating a high propensity to spend. No actual transaction history. Facebook would have obviously liked to market this tag to app developers, but given the heat that Amazon went through for price adjustments back in 2000, it have obviously tried put the kibosh on this sort of price discrimination:

To protect user privacy, this API call does not disclose specific purchasing information about a particular player, but instead categorizes players into a broad set. In addition, this call must not be used for marketing purposes, or to increase prices for the set of higher monetizing players.

Fair enough. Facebook is actively protecting its customers (at least with this clause) from price gouging. But the existence of this phenomenon begets a few interesting lines of thought:

1. First, this sort of policy can be quickly handed off to the attorneys for some serious debate. Why is price gouging illegal? What is its legal foundation? Does the discrimination of price fall under the umbrella of discrimination in general, à la race, gender and income? Are sellers barred, legally and morally, from trying to capture more of the demand curve?
2. The second thought is how to work around this sort of clause. The answer is that you have to provide the exact same app experience to everyone, gamer or not. But with some incisive analytics, an app developer can refactor the entire app to target the whales. It seems to me, however, that if one is going to be doing such deep analytics (i.e., using Kontagent), that Facebook’s little ‘g’ tag won’t be contributing that much information (except exposing the developer to perhaps some legal liability).
3. The final thing to realize is that only certain types of app and styles of monetization demand identifying and targeting the whales. These are precisely the apps that target the “long-tail” of the population. We recall the 80-20 rule, wherein 80% of revenue generally comes from 20% of customers. But this rule of thumb only applies to certain business models, not all of them. It requires a savvy investigator with the right tools (again, Kontagent), to identify revenue distribution, key customers, and optimal policy, whether he targets the whales or not.

So when investigating the monetization behavior of your user base, ask yourself this: Are my big spenders simply the long tail of a distribution that everyone falls under, or are they qualitatively different? If so, happy hunting.

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About the author: Owen Martin is a marketing data scientist at Kontagent. He received his Ph.D. in statistics with a focus on Bayesian Inference, and has been working in big data since then. You can contact Owen at @owensmartin or owen.martin@kontagent.com.