玩家的使用模式使也很重要，因为不论他们是否选择游戏，玩游戏的频率怎以及游戏时间长度等都是与他们对游戏的满意程度有关。在那些要求玩家在固定时间后回返的游戏中，我们经常能够看到月活跃独立用户（Monthly Active Uniques，简称MAU）以及日活跃独立用户（Daily Active Uniques，简称DAU）这两个术语。“活跃”是个很重要的定义，因为如此你能够避免将那些已经不玩游戏的僵尸用户帐号也计算在内。“独立”这一词也很重要，因为我们并不能把每天登录10次《FarmVille》的独立用户算作10个用户。而这时候你可能会认为月和日的算法的同等的，只要将以日计算的数值乘以30就可以获得以月计算的数值，但是实际上从用户流失率角度来看，这两个数值是完全不同的概念。所以如果你能够明确区分MAU与DAU，你便能够清晰地看到游戏中有多少新玩家以及多少回头玩家。
Metrics (Part II)
Posted by Sande
In Part I, game designer Ian Schreiber outlines the debate between metrics-driven design and the more touchy-feely intuition-based design. In Part II, he explains the difficulties with trying to measure the “fun” in your game.
How much to measure?
Suppose you want to take some metrics in your game so you can go back and do statistical analysis to improve your game balance. What metrics do you actually take – that is, what exactly do you measure?There are two schools of thought that I’ve seen. One is to record anything and everything you can think of, log it all, mine it later. The idea is that you’d rather collect too much information and not use it, than to not collect a piece of critical info and then have to re-do all your tests.
Another school of thought is that “record everything” is fine in theory, but in practice you either have this overwhelming amount of extraneous information from which you’re supposed to find this needle in a haystack of something useful, or potentially worse, you mine the heck out of this data mountain to the point where you’re finding all kinds of correlations and relationships that don’t actually exist. By this way of thinking, instead you should figure out ahead of time what you’re going to need for your next playtest, measure that and only that, and that way you don’t get confused when you look at the wrong stuff in the wrong way later on.
Again, think about where you stand on the issue.
Personally, I think a lot depends on what resources you have. If it’s you and a few friends making a small commercial game in Flash, you probably don’t have time to do much in the way of intensive data mining, so you’re better off just figuring out the useful information you need ahead of time, and add more metrics later if a new question occurs to you that requires some
data you aren’t tracking yet. If you’re at a large company with an army of actuarial statisticians with nothing better to do than find data correlations all day, then sure, go nuts with data collection and you’ll probably find all kinds of interesting things you’d never have thought of otherwise.
What specific things do you measure?
That’s all fine and good, but whether you say “just get what we need” or “collect everything we can,” neither of those is an actual design. At some point you need to specify what, exactly, you need to measure.
Like game design itself, metrics is a second-order problem. Most of the things that you want to know about your game, you can’t actually measure directly, so instead you have to figure out some kind of thing that you can measure that correlates strongly with what you’re actually trying to learn.
Example: measuring fun
Let’s take an example. In a single-player Flash game, you might want to know if the game is fun or not, but there’s no way to measure fun. What correlates with fun, that you can measure?
One thing might be if players continue to play for a long time, or if they spend enough time playing to finish the game and unlock all the achievements, or if they come back to play multiple sessions (especially if they replay even after they’ve “won”), and these are all things you can measure. Now, keep in mind this isn’t a perfect correlation; players might be coming back to your game for some other reason, like if you’ve put in a crop-withering mechanic that punishes them if they don’t return, or something. But at least we can assume that if a player keeps playing, there’s probably at least some reason, and that is useful information. More to the point, if lots of players stop playing your game at a certain point and don’t come back, that tells us that point in the game is probably not enjoyable and may be driving players away. (Or if the point where they stopped playing was the end, maybe they found it incredibly enjoyable but they beat the game and now they’re done, and you didn’t give a reason to continue playing after that. So it all depends on when.)
Player usage patterns are a big deal, because whether people play, how often they play, and how long they play are (hopefully) correlated with how much they like the game. For games that require players to come back on a regular basis (like your typical Facebook game), the two buzzwords you hear a lot are Monthly Active Uniques and Daily Active Uniques (MAU and DAU). The “Active” part of that is important, because it makes sure you don’t overinflate your numbers by counting a bunch of old, dormant accounts belonging to people who stopped playing. The “Unique” part is also important, since one obsessive guy who checks FarmVille ten times a day doesn’t mean he counts as ten users. Now, normally you’d think Monthly and Daily should be equivalent, just multiply Daily by 30 or so to get Monthly, but in reality the two will be different based on how quickly your players burn out (that is, how much overlap there is between different sets of daily users). So if you divide MAU/DAU, that tells you something about how many of your players are new and how many are repeat customers.
For example, suppose you have a really sticky game with a small player base, so you only have 100 players, but those players all log in at least once per day. Here your MAU is going to be 100, and your average DAU is also going to be 100, so your MAU/DAU is 1. Now, suppose instead that you have a game that people play once and never again, but your marketing is good, so you get 100 new players every day but they never come back. Here your average DAU is still going to be 100, but your MAU is around 3000, so your MAU/DAU is about 30 in this case. So that’s the range, MAU/DAU goes between 1 (for a game where every player is extremely loyal) to 28, 30 or 31 depending on the month (representing a game where no one ever plays more than once).
A word of warning: a lot of metrics, like the ones Facebook provides, might use different ways of computing these numbers so that one set of numbers isn’t comparable to another. For example, I saw one website that listed the “worst” MAU/DAU ratio in the top 100 applications as 33-point-something, which should be flatly impossible, so clearly the numbers somewhere are being messed with (maybe they took the Dailies from a different range of dates than the Monthlies or something). And then some people compute this as a %, meaning on average, what percentage of your player pool logs in on a given day, which should range from a minimum of about 3.33% (1/30 of your monthly players logging in each day) to 100% (all of your monthly players log in every single day). This is computed by taking DAU/MAU (instead of MAU/DAU) and multiplying by 100 to get a percentage. So if you see any numbers like this from analytics websites, make sure you’re clear on how they’re computing the numbers so you’re not comparing apples to oranges.
Why is it important to know this number? For one thing, if a lot of your players keep coming back, it probably means you’ve got a good game. For another, it means you’re more likely to make money on the game, because you’ve got the same people stopping by every day… sort of like how if you operate a brick-and-mortar storefront, an individual who just drops in to window-shop may not buy anything, but if that same individual comes in and is “just looking” every single day, they’re probably going to buy something from you eventually.
[This article is an excerpt from Level 8: Metrics and Statistics, part of Ian Schreiber's course on game balance called Game Balance Concepts.]
Ian Schreiber has been making games professionally since 2000, first as a programmer and then as a game designer. He currently teaches game design classes for Savannah College of Art and
Design and Columbus State Community College. He has worked on five shipped games and hundreds of shipped students. You can learn more about Ian at his blog, Teaching Game Design. （source:gamedesignaspect）