Virtually all the success and growth of web/social gaming was built on analytics. Our company’s analytics tools are what led us to where we are today. One of the benefits of web/connected games is the ability do deep analytics. Statistics and sampling are certainly useful, but analytics give you an ongoing, live, 100% sample rate for whatever you are looking at. It is so effective that we have been using it on our console games now as well.
For example, we use it for things like Joe pointed out, player progress. Players actions are recorded when the play a level which is used for sharing battles. That information is also used by the designers to see how levels perform, and if there are problems, where. We can heat map all the players actions and see and discover where people are getting stuck, potential holes, and overall balance the gameplay.
A common application of deep analytics, is AB testing. Where you can test a feature or modification or even tuning by rolling it out to a subset of your player base. This is very common, and very useful. Say you want to introduce a new mechanic, you can creating a few silos of players, (a group with the feature, one with the feature with different variables, and a control group). Run it for a few weeks (or however long), and see what impact it has. This very useful for an established game as often changes, no matter how good, can turn off regular players.
This type of testing really shines when it comes to challenging conventional wisdom. Often we make choices to do things a certain way because it has worked in the past (when to introduce players to features, NUFs, certain flows). If a designer wants to explore a new way, AB testing can allow them to test it against real players without having to compromise the whole game. Occasionally this lead us down new paths, and sometimes it just reinforced previous methods. Sometimes common design choices work differently in different contexts, being able to test multiple choices in the same context is very valuable.
Certainly there are downsides. It can be easy for some designers to rely on it to heavily. To the point where they aren’t actually designing, they are just tossing ideas into a live game and seeing what sticks. Several years back, we had one designer who insisted on AB testing everything. Right down to the color choices in the UI. It got the point where most of the tests we ran were statistically pointless. Also we were running so many tests at once, that they became meaningless. At one point the game had nearly 60 unique shards (versions of the game). Players were all playing different games, the results were nearly useless, and the only thing we were actually doing is annoying our players.
Analytics also provide valuable insights into patterns and trends. We can offer incentives like sales or special features at times of the year that usage is low. Things like that. Overall, they can be very useful and informative.