The Best Free-To-Play Models

Get out your credit card

In my last blog, I discussed different ways software companies generate revenue and the recent rise of virtual goods purchases. In general, I like the free-to-play model—it’s great to be able to play a game and never spend a penny on it. But software companies adopting strategy need to be careful, there are good and bad ways to implement this model.

The best in-game purchases

The ideal way to implement in-game purchases is to have them not impact the game play at all, but rather be limited to superficial changes to the appearance of the game (i.e. skins). Players don’t want to look like the generic characters in the game and are often willing to pay to add a hat or a brightly colored vest. This sort of model is a win for everyone—the software company gets revenue, the people who want customized appearances can get their wish, and the people who just want to play a free game don’t need to spend a penny. Thus, I view this model as ideal.

An alternative to this model that is almost as good is the “free to play for a limited time” model. With this monetization strategy, the user can play the game a limited number of times each day for free. If they want to play more frequently than that, they have to pay. Thus, the game play isn’t affected, only the duration of play. This model has the nice feature that the better, more addictive the game, the more likely users will be willing to pay to play more. Thus, good games make more money, and bad games don’t.

Pay-to-win model

Less ideal is the revenue model where the gameplay changes based on whether or not the users have spent money. Thus, the player can pay for better characters, better weapons, and other in-game advantages. These advantages can be extreme, so that it’s literally impossible for a free-to-play player to compete against a player who spends money. In that case, the game become pay-to-win.

While Electronic Arts (Nasdaq: EA) has focused on this model recently, there are major problems with it. The most obvious one is that the game will be less fun if players can’t compete without spending money. Even for players willing to pay, there’s no challenge in obliterating every opponent, which makes the game less fun. And it’s certainly less fun for the free to play player being obliterated by people who simply have big wallets.

What’s more, the game manufacturer will face the constant temptation to inflate the power of new additions to the game and a reluctance to weaken (nerf) overpowered items. New, powerful items encourage people to spend money to get them. If those powerful items are nerfed after people have bought them, then the customer will feel jilted. Thus, you often get into a situation where the game becomes unbalanced, where skill doesn’t matter, but just possession of the magical item. And, if the game manufacturer isn’t willing to nerf the overpowered item, there’s no easy way to correct the imbalance.

When players realize that the gaming company doesn’t actually care about balance, it disincentivizes them to play the game or to purchase the “latest, greatest item”. After all, why would I spend time today trying to improve my account if anything I do is likely to become worthless when the gaming company capriciously introduces a magic “kill everything so I win” wand tomorrow? Why would I buy that magic wand if, a week later, a new “kill everything faster your stupid old wand” wand is likely to be introduced into the game?

Teaching the next generation

In fact, the problems are even worse than that, in that many of these games have added a random component to the acquisitions of paid content. Thus, instead of buying the wand, the player might actually be buying a loot box that has a tiny chance of containing the wand, but a big chance of containing some item that isn’t nearly as useful.

So, since many of these games are targeted toward kids, these games are teaching our kids to gamble—to put up real money for a small chance at getting a good item. And when I say small, I mean tiny—people have said they’ve spent a thousand dollars buying loot boxes to try to get the Millenium Falcon in Star Wars Galaxy of Heroes, and haven’t got it. Thus, these games are essentially selling kids lottery tickets.

The best way to implement pay to win

Despite the many downsides, pay-to-win isn’t necessarily the death of a game, and can be implemented in a reasonable way. All the game needs are ridiculous prices.

If the magical blasting wand costs $10,000, then only people who are willing to pay $10,000 for a virtual item will actually have the wand—a very low proportion of people who will be playing the game. The game will partition into two classes of players—those who have the wand, and those who don’t. Assuming that the ranking system is good (i.e. people only compete against people in the game of similar power), the free-to-play players will make up the vast majority players, and they’ll be able to ignore the people who are willing to dump those levels of cash into the game.

My bottom line

Though I’d like to believe that pay-to-win video game manufacturers will settle on this approach, striving to maintain balance for the long-term health of the game, I suspect that this mostly won’t be the case. Instead, I think gaming companies will focus on extracting the most cash they can from their customers.

To that end, gaming companies, like casinos, will have teams of psychologists trying to find the best way to turn people in compulsive gambling addicts. They’ll look for ways to increase the addictiveness of the game and add even more gambling until regulations are passed to restrict their ability to do so.

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2 thoughts on “The Best Free-To-Play Models

  1. Looks like gaming ‘addiction’ has been around for awhile

    The link below is a CBC Marketplace presentation from 2002!

    Could there be a game whereby one takes a real life situation and then
    scenario games it to better respond in real life?

    Like

    1. Well, there are certainly dating games around that already work like this. I believe that the military also does something like this.

      I guess there’s three major problems. First, it isn’t particularly generalizable–a dating game AI can’t teach you how to drive a car. Second, the game will be reliant on the assumptions that the game developer makes, which may or many not be accurate. Third, AI’s can get overtrained (i.e. trained using a particular data set, and settling on solutions that only work on that exact data set), and I suspect the same thing could happen here to humans, where they learn a solution but that solution only works because of the assumptions of the game developers.

      It is certainly an interesting idea.

      Like

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