Every arcade cabinet collected a certain number of quarters in a month. At month's end, the arcade owners counted these up, paid their bills, and sunk the rest into their futures. These were the people who decided which new arcade machines to buy.
You can imagine the effect this system had on game design, and you can think of it as a primitive form of analytics. The majority of analytics in current Facebook games are essentially the same, only one step away from counting profit. However, with the realities of digital distribution and real time metrics, this world is quickly changing.
As a refresher, analytics is the process of crunching data to inform decisions. They came to games via web developers, as the early Facebook games were not created by the traditional console studios. Web developers knew the value of analytics in a way the console world is only beginning to appreciate, and built them into the game.
Webpage "hits" and "bounce rate" translate fairly well into the world of Facebook games as "Daily Average Users" and "Monthly Average Users," and the combo stat "DAU/MAU." (It's used to measure retention.)
Beyond helping producers sound important, they provide a clear and inarguable metric of success: points on the graph! Through simple metrics like these you can see in real time how quickly your game is gaining users, keeping them, and turning a profit.
However, analytics used at the level of DAU are fundamentally limited. They show only the present, and say little or nothing as to why and how the game is creating these numbers. It's through a combination of design insight, deeper analytics and experiment that takes it to the next level.
We're putting games in the cloud now. Steam was one of the early digital distribution channels, and got into analytics right away with its hardware surveys. Facebook has more advanced demographics and built in analytic functionality (not to mention its pioneering work in redesigning our concept of privacy).
Android and iOS devices all deliver through the cloud. Google has numerous online APIs for crunching analytics, and I'm just waiting for the day websites like OkCupid start hosting apps. (It's an analytics goldmine!) The current and next generation consoles all have digital channels, and I don't see any indication of this letting up.
The more we continue to shift (or float) in that direction, so too will the analytics become easier and easier to access, and what's more, with the social graph that accompanies it all. The relationships between all the players, their in-game interactions, and the histories of players over time and across games can now be tied to a unique profile for each user. This massive well of information is where web analytics and game design begin to synthesize into something new.
Because games offer a much deeper level of user activity than websites, you can use analytics to probe much deeper into player behavior, too. Will Wright was a pioneer in using analytics for game design long before we'd dreamed of Facebook. In a fantastic 2001 interview with Game Studies he spoke of how data revealed two main types of play in the Sims: House Building vs. Relationship Building. The game was then tailored to further support these two player goals.
He went on to speak of how analytics might one day be used to customize games on a per-user basis. It's something now possible, and in real time. Rather than just gathering information on player attendance, every aspect of player behavior can now be collected as analytical data. Questions of what, when, who, and why can be asked, and a whole slew of predictive information can now be developed.
Dividing players into different demographic groups allows developers to tailor (both in content and mechanics) to particular types of players. If charging $10 for the rare Purple Penguin in Zoo Collector will not make up for your monthly budget deficit, then introduce the $10 Violet Bugatti in the nearly identical Car Collector (catering to a segment of the Middle Eastern audience with disposable income).
Not all demographics are of equal worth. By mastering specific content for a number of smaller demographics you can expand into niches beyond the mainstream, or divide the mainstream up into more manageable developmental targets. Not all players are of equal worth, either.
It's very valuable to identify your early adopter players and the opinion-generating players who act as social hubs. These players are the agents of virality, and your means to success. Identify them and let them know how special they are with rewards and privilege. Tell them early about your next game, and encourage them to move on to it. The rest will follow.
Don't forget this can all be automated, too. Games on a server can be patched twice a day, if needed, and there's no reason every user will see the same version of your game. Not only can you give your most popular users the "Special Version," you can also release "Design Version A" to fifty percent of your users and "Design Version B" to the rest.
Watch the analytics to see which version does better, then shift over the entire game to that design. Wash, rinse, repeat. This is known as "A/B testing," and you can use it to explore almost every aspect of design. Just be careful not to test various costs on the same digital content, as people might compare notes on the forums and get suspicious.
I see no reason to be so blatant, though. Rather than being reactive -- staring at your feet while trying to get ahead -- find success through forward-looking design. As outlined earlier, we now have a strong theoretical framework for interfacing with players and developing a compulsive play response through behavioral theory and neurology. A/B testing, demographics, social graphs, and an endless stream of willing players is the perfect laboratory setup for perfecting such designs.
I would recommend experimenting on subsets of users to see how well they condition to certain mechanics such that the timing and distribution of rewards can be optimized. Hire or train statisticians. Users should be tracked according to their sensitivity, and targeted to maintain engagement.
Rather than rehashing the same designs over and over, predict the effectiveness of certain designs on specific market segments and transition them from an old design to a new, more complex one the moment they are ready. This can be verified as increased retention via the MAU/DAU ratio. Meanwhile, the old design can be reapplied to a fresher audience segment.
I'd recommend that if I was a tool, that is, and unfortunately it's already happening.