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This article builds on previous articles I've written and talks I’ve given. In particular, it is a follow-up to my recent article on Dynamic Pricing, Personalized Offers, and Modern Gaming. That article was an extended discussion of the recent uproar around randomized price-points in Zynga’s CSR 2 and a survey of how prevalent sophisticated data-driven merchandising techniques already are in mobile gaming.
Readers of that article objected to dynamic pricing as a practice, using words like “shady” or “shameless” and claiming that dynamic pricing, if adopted widely, would destroy the gaming industry. This FAQ helps to set the record straight by providing explanations and addressing the underlying concerns associated with dynamic pricing.
Dynamic pricing is the practice of altering prices for goods or services in real-time without altering the goods or services (e.g. “just changing the price”). Different users, or possibly even the same user at different times, will see different prices for the same good or service.
That’s a broad definition, and encompasses many different pricing strategies. For example:
That’s a lot of examples! Perhaps too many, but the point I want to make is simply this: prices change all the time, for a wide variety of reasons. In fact, the idea of a “fixed price” that is stable and available to all buyers is the exception, not the norm in commerce.
But if you only take away one thing from this first section, it's this:
The idea of a “fixed price” that is stable and available to all buyers is the exception, not the norm in commerce.
Yes, they do. Dynamic offer management is a best practice in gaming as well. My article Dynamic Pricing, Personalized Offers, and Modern Gaming covers this in depth, but the key point is simply that big data and machine learning make it possible to tailor ads, prices, and bundles to individual users with a high degree of effectiveness. This has been true in advertising for a long time (see, for example, this overview from 2010) and is now equally true in bundle management, offer management, and pricing.
In fact, dynamic pricing for digital goods inside a web-page or from an application can be incredibly effective as a general technique. This relies on two observations:
When you marry this to modern data science, you have an amazing ability to price goods to the demand curve.
I'm not just telling you. I’m telling EVERYONE. As an industry, we need to get past the dismissive rhetoric (“shady” and “shameless”) and apocalyptic predictions (“The result is harm to ALL F2P developers”) and move on to a discussion of when and where dynamic pricing is appropriate.
In a world where most video games lose money (especially indie games), most games studios constantly struggle to stay in business, and the cost of user acquisition reaches record highs each year, rejecting best practices from other industries is irresponsible.
In some cases, yes absolutely. The classic case is usually phrased in terms of a life-saving medicine and the scenario goes along the lines of:
This is a classic example of “predatory” pricing (as are the examples above where batteries get more expensive during storms).
Consider the following example instead. A software publisher decides to sell bags of gold coins inside their game. They’re not really sure what the demand curve is, but they think it looks something like Figure 1 below
Based on this, they pick a point on the curve that seems pretty good, and pretty fair (“I’d pay that much”). They wind up with the prices and revenue illustrated in Figure 2
But then someone on their data science team realizes: there are complex predictive models that will reveal which users won’t buy at that price. And that it is possible to offer these price-sensitive users, who won’t buy at the initial price, a second and lower price. Figure 3 illustrates what happens when this new price point is created and rolled into production.
You might object that this is an artificial example. And that’s true—nobody can target this perfectly. What’s going to happen is that some of the “light green” buyers (e.g. players would would buy at the higher price point) will get the “dark green” price (and benefit from the lower prices).
Who could object to this?
Now, it’s also true that most companies will immediately think “are there some people we can charge more”, and that anger at this impulse drives a lot of the knee-jerk objections to dynamic pricing.
But while it’s fun to imagine robber-barons twirling their mustachios ala Snidely Whiplash while they raise prices to an arbitrarily high degree, this isn’t a very realistic scenario. Gamers, when presented with excessive prices, have options:
The “fairness” objection to dynamic pricing is usually phrased along the following lines: Yes, but isn’t dynamic pricing inherently unfair in a game where players play against each other. Charging different prices to different people in a competitive game is wrong..
Candidly, I don’t understand this argument at all. It seems to assume that the value of the real-world currency is the same for players. Suppose we have two players. Player 1 is single, makes $50,000 dollars a year, and lives in a cheap apartment. Player 2 is married, has three children, owns their house (e.g. owes mortgage payments) and makes the same $50,000 a year. Is it really fair to charge them the same prices for in-app-purchases?
I’d claim the answer is no. The only defensibly fair thing to do is to switch over to a premium model (e.g. remove all in-app-purchases from the game) and then give the game away for free to Player 2.
Most game companies which decide to adopt this practice will wind up out of business very quickly.
I can’t speak for the industry. But at Scientific Revenue, we don't gouge whales. We often wind up lowering prices. The scenario illustrated above with the introduction of lower price points isn’t just a hypothetical example, it’s a simplification of what really happens in a lot of situations.
It’s also worth noting that modern dynamic pricing, based on big data and machine learning, can’t really target whales effectively (in order to do machine learning, you need lots of data. There aren’t enough whales to target them effectively with machine learning).
If you want to target whales, use people. Machine learning won’t work well.
No, with some exceptions:
More generally, given the wide variety of examples in the first answer (and many more traditional forms of offering targeted discounts, such as coupons), it’s hard to imagine a legal framework which outlaws dynamic pricing in any significant way.
People love a good story. People especially love a good story which involves an underdog fighting for what’s right against an evil and corrupt overlord who wishes to despoil the galaxy.
But we are not living in Thunderdome, and this is not a Mad Max tale.
What’s more likely to “kill” the gaming industry:
I think the answer is clear.