One of the really interesting things about Farmville is that it has some fairly strong incentives for people to keep coming back and playing Farmville.  Put in more generic terms, Farmville does a great job at providing incentives for user retention.  Here’s the basics of how it works: you buy seeds and plant them in a plot on your form.   Then you have to wait; it takes anywhere from 4 hours (real, wall-clock time) to 3 days or more for the seeds to grow.  (Note: I’m only a level 5 farmer; I’m sure there are much fancier things you can grow as you advance.)  This waiting means that you can’t just harvest or keep playing right now; with the shortest growing time at 4 hours you also cannot just wait it out.   Once the seeds have finished growing, you can come and harvest them for coins and experience points.  However, if you wait too long to harvest your crops, then they whither and die and you lose your money.

It is really interesting to hear stories about how people adjust to this structure.   They plan a lot: people plan to be at their computer at certain times so they can harvest their crops; they plan which crops to plant by thinking about when they will be available to harvest them, and they stick to their plans because there are penalties for not doing so.  Basically, this structure provides a strong incentive for people to plan out how to fit this game, this social media system, into their life on a regular basis.

This is exactly the goal of user retention: we want to encourage users of a social media system to keep coming back on a regular basis, and to make visiting the site part of their daily life.  When you combine this with their viral marketing incentives (to buy certain things you need to convince a number of your friends to become your neighbors in the game), it is not surprising that this game has 62 million users.

I have a guess (a hypothesis, if you will) about  how we can learn from Farmville in designing other types of social media.Farmville schedules times for things to happen.   At 5pm today, my strawberries finish growing and I need to return to Farmville to harvest them.  This works for two reasons: 1) it gives me a reason to come back to the site later.   This is an individualized reason; they are my strawberries that need to be harvested, and only I can harvest them.  And 2) it gives me a reason to wait.  I cannot harvest the strawberries now.  I cannot accomplish everything I want to accomplish on the site right now.  I have to wait, be patient, and come back for more later.  This second reason is counter-intuitive: by making the site not serve all of my needs, at least not right now, I have a reason to return later.  And to keep coming back.  Less functionality leads to more use.  Farmville retains users by not meeting all their immediate needs, but scheduling a time in the future when they can return and get those needs met.

However, they add one additional twist.  They add a deadline; if you don’t come back by a certain time, your crops have withered.  You can’t procrastinate your return; you have to return relatively soon if you want to actually get the rest of your needs met.   This is the real key incentive that induces people to plan around Farmville.   Without this, you could just play farmville in your free time, and as people get busy, free time disappears.   But since your crops wither, you can’t just wait till you have free time.  You have to go harvest them now (or within 4 hours).  You have to plan a time in your day to tend your crops.  You have to force time into your busy schedule to visit the site.  You have to integrate Farmville into your life.  And once a person has integrated a social media site into their daily life and their daily routines, then the site really has retained the user.

This strategy of waiting with a deadline has been used by other types of social media.  For example, Facebook uses a very similar strategy to retain users.  You can’t get all your needs met right now; you have to wait for more people to contribute status messages in the future.   By forcing users to wait for the status messages, Facebook gets people to come back later.  Many sites accomplish this by having regularly updating content; this is a well-known important feature of most social media systems.   However, Facebook also uses the deadline approach.  Eventually, new status messages scroll off the screen.  If you wait too long to check Facebook, then you miss status messages from your friends.   And its not very easy to just check the friends you really care about; most people use the “all status messages” live news feed.  Since messages basically expire by falling off the bottom of the page, users feel like they have a deadline for checking Facebook.  This deadline provided the incentive that people needed to integrate Facebook into their daily lives.

Also, this incentive has the interesting property that it is the strongest for people who are the busiest.  Normally, it is the busiest people who can’t afford to fit a social media system into their lives.  However, the busiest people are often the ones with the most “friends” on Facebook.   And that means that status messages fall off the page faster as new messages from friends appear.   The busiest people are the ones with the shortest deadline, and therefore the strongest incentive to integrate Facebook into their lives.

I recently got an iPhone, and I don’t know how I lived without it.  For the not-so-brief period of time that I didn’t have Internet at home, it was my lifeline connecting me to my Email and my distant wife.  One of the fun things about the iPhone is the App Store.  You can browse through and download applications from a collection of thousands of applications.  When browsing an application in the App Store, each application displays a distribution of ratings on a scale of one to five stars.  This really helps evaluate applications; if lots of people didn’t like it, then I usually won’t download it.

One of the things that really surprised me when I got my iPhone is the large number of ratings.  Most applications that I looked at had hundreds or thousands of ratings.  That’s as much or more than the number of ratings that products on Amazon.com get.  Why would so many people spend their time rating applications they have used?

It turns out that the way most of the ratings are collected is by prompting users for a rating when they delete an application.  I guess that Apple figures people will have used an application enough to have an opinion about it if they are deleting it.

This reminded me a lot of an “incentive mechanism” that is well known in the field of Philanthropy: the “Power of the Ask.” (Anderoni, 2006)  Basically, when you want someone to give money to your charitable organization, one of the most effective methods is to approach them and ask them to donate.  Outright asking works for a couple of reasons: 1) it changes the question from “which charity should I donate to” to “should I donate to this charity.”; this helps the charity doing the asking at the potential expense of other charities.  2) It solves the problem of when to make the donation; people cannot procrastinate or put off donation when being asked to do so now.

Thinking a little deeper, though, the mechanism used by Apple’s App Store is subtly different. The main difference is that Apple is asking for a contribution of information rather than money.  All information isn’t the same; there is good information and bad information.  Or, more accurately, there is useful information and biased / wrong information.  When asking for money, a charity doesn’t have to worry about getting bad money; if they get any money at all it is good.   However, Apple needs to worry that people will provide bad information.   Asking isn’t enough; you need to somehow ensure that the information is high quality and useful.

I think Apple might have done this wrong.  Specifically, they only ask for a rating when a user is deleting an application.  So all the unsatisfied users who delete an application rate it low.  All the satisfied users who don’t delete it and keep using it never get asked, and never contribute a rating.   This means that the overall ratings are biased to be much lower than the community truly thinks they are.  This illustrates one reason why “the power of the ask” doesn’t always for in information settings in the way that it does in charity / money settings.

Unfortunately, solving this problem isn’t easy.  You could randomly prompt people for ratings, or ask them N days/weeks/months after they install an application, but that would be very annoying for the users.  If you don’t ask, then your ratings will be more unbiased, but very few people will contribute.

J. Andreoni. Philanthropy. In S.-C. Kolm and J. M. Ythier, editors, Handbook of Giving, Reciprocity and Altruism, pages 1201–1269. North Holland, Amsterdam, 2006.

One of the interesting things about this new game is that it supports user-contributed content: anyone can use the built-in level creator to create new levels and upload them to a central content server.  You can also download and play levels that others have created.  As expected, I found a few really fun levels and a bunch of not-so-good levels.  Fortunately, the Boom Blox online system allows users to rate levels with 1-5 stars, and you can sort the levels by rating when looking for new levels.

This is a great example of how this user-contributed content is becoming pervasive.  Even games like Boom Blox have a user-contributed content portion of the game.  And I think that this feature could have benefited from hiring someone who studies incentive-centered design.  There is a basic contribution question here: why should users contribute levels?  And more importantly, what types of levels should users contribute?  So far, the creators of the game have been rewarding complex Rube Goldberg type levels by highlighting them on YouTube.  It would be interesting to think about different ways that the game can reward other types of levels; for example, it could pick the highest rated levels of each type currently in the game and make them available as a downloadable “level pack.”  This might encourage users to create interesting new levels to extend the game’s current gameplay.

Another ICD issue is the rating system.  Right now, after playing an online level, the user is asked to rate it with 1-5 stars.   But, why should the user provide a rating?   And, more importantly, what metric should he or she employ?  A level might get 5 stars on “cool” but 1 on “playability”.  By thinking about what behavior you want people to do, you can design the system to elicit that behavior.  For example, you might want people to rate levels based on how much fun they are to play over and over. Do elicit that type of rating, the designers might allow people to save levels from online (which is currently supported) and automatically sort them by the rating I gave it by default.   Levels I give 5 stars are shown first, then 4 stars and on down.  This way, levels I have an incentive to rate highly the levels I want to play again (to make them easy to find), and rate poorly the levels I don’t.

This is actually an example of the side effect mechanism I’ve talked about before.  Users have a private reason to rate levels — to make it easier to find the ones they want to play again.   And the incentive to rate levels is aligned with the goals of the consumers, who want to use the ratings to know what levels are best to play.

In order to use Facebook, a new user must do three things: 1) sign up for an account, 2) join a network, and 3) contribute a list of her friends (her social network).  Users who don’t do all of these don’t receive access to any of the information on Facebook.  Notice #3 (and to some extent #2); it includes contributing information to the system that benefits others.   Facebook doesn’t have lurkers in the classic sense of someone who doesn’t contribute anything.  They decided that it was worthwhile to lose the true lurkers in exchange for the increased contributions.   By knowing everyone’s social network, Facebook can offer additional services (like strong privacy controls and better friend suggestions) that they wouldn’t be able to offer if the social network information was less complete.   This is a great example of the minimum threshold in action; you gain a little bit of information from everyone at the expense of losing the lurkers.   For Facebook, they figured out a “little bit” of information that allows them to really make their system valuable.

If you are skeptical, compare Facebook with Twitter.   Twitter does not have a minimum threshold of contribution.   There are true lurkers on Twitter; many people read twitter without accounts.  These people have never contributed to twitter but still use it.  Twitter values these lurkers and as such does not require a minimum contribution like Facebook does.

And sure enough, a number of the predictions of my minimum threshold paper have come true on Facebook.  A number of people have chosen to join Facebook, but only contribute the minimum.  I’ve seen a number of users who are my “friends” who never contribute pictures or status messages; all they do is contribute their social network information so they can get access to the information of others.   Also, as Facebook has gotten larger, more people who would have preferred to lurk have decided to contribute the minimum threshold.   Larger systems provide more value, and hence more people are willing to make that minimum contribution in order to join.

The Facebook example also illustrates a useful point for using the minimum threshold mechanism: everyone who remains in the system will have contributed at least the minimum.  If the threshold is chosen carefully, the system can take advantage of the knowledge that everyone has contributed this information.   On Facebook, all users have to contribute social network information.  Facebook is then designed to take advantage of the fact that all Facebook users have provided their social network information to the system; many features such as the privacy controls assume the existence of contributed social network information.  This well-chosen threshold enables many useful features.

But there are a few sites for which this is not true.   The one that comes to mind first is Wikipedia, which is a large online user-contributed encyclopedia.  Most of the information in Wikipedia doesn’t change, or at least doesn’t change very often.   For example, the mathematical constant Pi has been known since antiquity and hasn’t changed much since then.  Yes, there is a lot of information that does change in Wikipedia (like the list of Presidents of the US) but much of the basic knowledge remains static.

Since not much changes, the biggest problem in building an online encyclopedia is generating the initial corpus of articles.  An article I once read but cannot find again claimed that the vast majority of users made exactly 1 edit; and that edit was usually the writing the first version of an article.  Most articles were written by someone who contributed nothing else.  A person would hear about Wikipedia, look at it, try writing an article, decide that it wasn’t really worth it, and never do that again.  The incentives for contribution failed.

However, Wikipedia is mostly made up of information that doesn’t change.  So if enough people do this (try their hand at writing 1 article and then stop) then Wikipedia can build up the vast collection of knowledge that it contains.   The hype around Wikipedia might have been enough to get the basic encyclopedia written.  Lots of news stories were proclaiming how novel Wikipedia is because anyone can write an article; so lots of people did!  Even though those people decided, in the end, that contributing wasn’t worth it, their trying it out was enough to generate most of the base content Wikipedia needed.  Wikipedia is therefore a hype-generated social media site.  This only works because Wikipedia doesn’t depend on novelty to continually draw viewers.   This wouldn’t work for WikiNews, for example, but does work here because the basic information on the site doesn’t change much.

Wikipedia depends on a core group of dedicated Wikipedians that edit these articles, cleaning them and making them conform to the standards of the site.   Without this core group, those initial articles wouldn’t have been high-quality enough to make a good website.   And this core group is important for the articles that are changing, like current events and cutting-edge research.  But for the basic, been around for centuries knowledge that is in Wikipedia, the hype around the site was enough to generate the needed content.  Now that the hype has died down, there are many fewer people trying to write articles; providing incentives for contribution is becoming more important.

We can learn from this example.  For certain types of social media sites where the important thing is building up a base corpus of content, it might be possible to get away without having real incentives for contribution.   Tricking enough people into contributing a little bit, even if in the end they decide contributing is not worth it and stop, can build the intial corpus.   Remember to have a core group that cleans up the contributions, because first-time contributors rarely do a high-quality job.  This is one way of solving the bootstrapping problem that many social media systems face.