Perhaps you’d like to deepen user engagement—either the amount of time that users spend in your community or the number and breadth of activities that they partake in. For a host of reasons, users who are more engaged are more valuable, both to your community and to your business.

Offering incentives to users may persuade them to try more of your offering than they would try by virtue of their own natural curiosity alone. Offline marketers have long been aware of this phenomenon; promotions such as sweepstakes, contests, and giveaways are all designed to influence customer behavior and deepen engagement.

User engagement can be defined in many different ways. Eric T. Peterson offers up a pretty good list of basic metrics for engagement. He posits that the most engaged users are those who do the following (list adapted from http://blog.webanalyticsdemystified.com/weblog/2006/12/how-do-you-calculate-engagement-part-i.html):

This list is definitely skewed toward an advertiser’s or a content publisher’s view of engagement on the Web. It’s also loaded with subjective measures. (For example, what constitutes a “long” session? Which content is “critical”?) But that’s fine. We want subjective—at this point, we can tailor our reputation approach to achieve exactly what we hope to get out of it.

So what would be a good set of metrics to determine community engagement on your site? Again, the best answer to that question for you will be intimately tied to the goals that you’re trying to achieve.

Tara Hunt, as part of a longer discussion on Metrics for Healthy Communities, offers the following suggestions (list adapted from http://www.horsepigcow.com/2007/10/03/metrics-forhealthy-communities/):

This is a good list, but it’s still highly subjective. Once you decide how you’d like your users to engage with your site and community, you’ll need to determine how to measure that engagement.

Figure 5-1. No matter how you measure it, Bernadette e. is one active and engaged “Yelper.”

Perhaps you’re interested in building brand loyalty among your site’s visitors, establishing a relationship with them that extends beyond the boundaries of one visit or session. Yahoo! Fantasy Sports employs a fun reputation system, shown in Figure 5-2, enhanced with nicely illustrated trophies for achieving milestones (such as a winning season in a league) for various sports.

Figure 5-2. “Boca Joe” has played a variety of fantasy sports on Yahoo! since 2002. Do you suppose the reputation he’s earned on the site helps brings him back each year?

This simple feature serves many purposes: the trophies are fun and engaging, they may serve as an incentive for community members to excel at a sport, they help extend each user’s identity and give the user way to express her own unique set of interests and biases to the community, and they are also an effective way of establishing a continuing bond with Fantasy Sports players—one that persists from season to season and sport to sport.

Any time a Yahoo! Fantasy Sports user is considering a switch to a competing service (fantasy sports in general is big business, and there are any number of very capable competitors), the existence of the service’s trophies provides tangible evidence of the switching cost for doing so: a reputation reset.

Maybe you are concerned about your site’s ability to attract advertisers. User-generated content is a hot Internet trend that’s almost become synonymous with Web 2.0, but it has also been slow to attract advertisers—particularly big, traditional (but deep-pocketed) companies worried about displaying their own brand in the Wild West environment that’s sometimes evident on sites like YouTube or Flickr.

Once again, reputation systems offer a way out of this conundrum. By tracking the high-quality contributors and contributions on your site, you can guarantee to advertisers that their brand will be associated only with content that meets or exceeds certain standards of quality.

In fact, you can even craft your system to reward particular aspects of contribution. Perhaps, for instance, you’d like to keep a “clean contributor” reputation that takes into account a user’s typical profanity level and also weighs abuse reports against him into the mix. Without some form of filtering based on quality and legality, there’s simply no way that a prominent and respected advertiser like Johnson’s would associate its brand with YouTube’s user-contributed, typically anything-goes videos (see Figure 5-3).

Figure 5-3. The Johnson’s Baby Channel on YouTube places a lot of trust in the quality of user submissions.

Of course, another way to allay advertisers’ fears is by generally improving the quality (both real and perceived) of content generated by the members of your community.

Reputation systems really shine at helping you make value judgments about the relative quality of content that users submit to your site. Chapter 8 focuses on the myriad techniques for filtering out bad content and encouraging high-quality contributions. For now, it’s only necessary to think of “content” in broad strokes. First, let’s examine content control patterns—patterns of content generation and management on a site. The patterns will help you make smarter decisions about your reputation system.

When your staff is in complete control of all of the content on your site—even if it is supplied by third-party services or data feeds—you are using a Web 1.0 content control pattern. There’s really not much a reputation system can do for you in this case; no user participation equals no reputation needs. Sure, you could grant users reputation points for visiting pages on your site or clicking indiscriminately, but to what end? Without some sort of visible result to participating, they will soon give up and go away.

Neither is it probably worth the expense to build a content reputation system for use solely by staff, unless you have a staff of hundreds evaluating tens of thousands of content items or more.

In this content control pattern, the site encourages users to petition for removal or major revision of corporate content—items in a database created and reviewed by staff. Users don’t add any content that other users can interact with. Instead, they provide feedback intended to eventually change the content. Examples include bug tracking and customer feedback platforms and sites, such as Bugzilla and GetSatisfaction. Each site allows users to tell the provider about an idea or problem, but it doesn’t have any immediate effect on the site or other users.

A simpler form of this pattern is when users simply click a button to report content as inappropriate, in bad taste, old, or duplicate. The software decides when to hide the content item in question. AdSense, for example, allows customers who run sites to mark specific advertisements as inappropriate matches for their site—teaching Google about their preferences as content publishers.

Typically, this pattern doesn’t require a reputation system; user participation is a rare event and may not even require a validated login. In cases where a large number of interactions per user are appropriate, a corporate reputation system that rates a user’s effectiveness at performing a task can quickly identify submissions from the best contributors.

This pattern resembles the Submit pattern (see Submit-publish: Users create, staff evaluates and removes), though the moderation process in that pattern typically is less socially oriented than the review process in this pattern (since the feedback is intended for the application operators only). These systems often contain strong negative feedback, which is crucial to understanding your business but isn’t appropriate for review by the general public.

This popular content control pattern—the first generation of online reputation systems—gave users the power to leave ratings and reviews of otherwise static web content, which then was used to produce ranked lists of like items. Early, and still prominent, sites using this pattern include Amazon.com and dozens of movie, local services, and product aggregators. Even blog comments can be considered user evaluation of otherwise tightly controlled content (the posts) on sites like BoingBoing or The Huffington Post.

The simplest form of this pattern is implicit ratings only, such as Yahoo! News, which tracks the most emailed stories for the day and the week. The user simply clicks a button labeled “Email this story,” and the site produces a reputation rank for the story.

Historically, users who write reviews usually have been motivated by altruism (see Incentives for User Participation, Quality, and Moderation). Until strong personal communications tools arrived—such as social networking, news feeds, and multidevice messaging (connecting SMS, email, the Web, and so on)—users didn’t produce as many ratings and reviews as many sites were looking for. There were often more site content items than user reviews, leaving many content items (such as obscure restaurants or specialized books) without reviews.

Some site operators have tried to use commercial (direct payment) incentives to encourage users to submit more and better reviews. Epinions offered users several forms of payment for posting reviews. Almost all of those applications eventually were shut down, leaving only a revenue-sharing model for reviews that are tracked to actual purchases. In every other case, payment for reviews seemed to have created a strong incentive to game the system (by generating false was-this-helpful votes, for example), which actually lowered the quality of information on a site. Paying for participation almost never results in high-quality contributions.

More recently, sites such as Yelp have created egocentric incentives for encouraging users to post reviews: Yelp lets other users rate reviewers’ contributions across dimensions such as “useful,” “funny,” and “cool,” and it tracks and displays more than 20 metrics of reviewer popularity. This configuration encourages more participation by certain mastery-oriented users, but it may result in an overly specialized audience for the site by selecting for people with certain tastes. Yelp’s whimsical ratings can be a distraction to older audiences, discouraging some from contributing.

What makes the reviews content control pattern special is that it is by and for other users. It’s why the was-this-helpful reputation pattern has emerged as a popular participation method in recent years—hardly anyone wants to take several minutes to write a review, but it only takes a second to click a thumb-shaped button. Now a review itself can have a quality score and its author can have the related karma. In effect, the review becomes its own context and is subject to a different content control pattern: Basic social media: Users create and evaluate, staff removes.

In the surveys content control pattern, users evaluate and eliminate content as fast as staff can feed it to them. This pattern’s scarcity in public web applications usually is related to the expense of supplying content of sufficient minimum quality. Consider this pattern a user-empowered version of the reviews content control pattern, where content is flowing so swiftly that only the fittest survive the user’s wrath. Probably the most obvious example of this pattern is the television program American Idol and other elimination competitions that depend on user voting to decide what is removed and what remains, until the best of the best is selected and the process begins anew. In this example, the professional judges are the staff that selects the initial acts (content) that the users (the home audience) will see perform (content) from week to week, and the users among the home audience who vote via telephone act as the evaluators and removers.

The keys to using this pattern successfully are as follows:

In the submit-publish content control pattern, users create content that will be reviewed for publication and/or promotion by the site. Two common evaluation patterns exist for staff review of content: proactive and reactive. Proactive content review (or moderation) is when the content is not immediately published to the site and is instead placed in a queue for staff to approve or reject. Reactive content review trusts users’ content until someone complains and only then does the staff evaluate the content and remove it if needed.

Some websites that display this pattern are television content sites, such as the site for the TV program Survivor. That site encourages viewers to send video to the program rather than posting it, and they don’t publish it unless the viewer is chosen for the show. Citizen news sites such as Yahoo! You Witness News accept photos and videos and screen them as quickly as possible before publishing them to their sites. Likewise, food magazine sites may accept recipe submissions that they check for safety and copyright issues before republishing.

Since the feedback loop for this content control pattern typically lasts days, or at best hours, and the number of submissions per user is minuscule, the main incentives that tend to drive people fall under the altruism category: “I’m doing this because I think it needs to be done, and someone has to do it.” Attribution should be optional but encouraged, and karma is often worth calculating when the traffic levels are so low.

An alternative incentive that has proven effective to get short-term increases in participation for this pattern is commercial: offer a cash prize drawing for the best, funniest, or wackiest submissions. In fact, this pattern is used on many contest sites, such as YouTube’s Symphony Orchestra contest (http://www.youtube.com/symphony). YouTube had judges sift through user-submitted videos to find exceptional performers to fly to New York City for a live symphony concert performance of a new piece written for the occasion by the renowned Chinese composer Tan Dun, which was then republished on YouTube. As Michael Tilson Thomas, director of music, San Francisco Symphony, said:

The agents content control pattern rarely appears as a standalone form of content control, but it often appears as a subpattern in a more complex system. The staff acts as a prioritizing filter of the incoming user-generated content, which is passed on to other users for simple consumption or rejection. A simple example is early web indexes, such as the 100% staff-edited Yahoo! Directory, which was the Web’s most popular index until web search demonstrated that it could better handle the Web’s exponential growth and the types of detailed queries required to find the fine-grained content available.

Agents are often used in hierarchical arrangements to provide scale, because each layer of hierarchy decreases the work on each individual evaluator several times over, which can make it possible for a few dozen people to evaluate a very large amount of user-generated content. We mentioned that the contest portion of American Idol was a surveys content control pattern, but talent selection initially goes through a series of agents, each prioritizing and passing them on to a judge, until some of the near-finalists (selected by yet another agent) appear on camera before the celebrity judges. The judges choose the talent (the content) for the season, but they don’t choose who appears in the qualification episodes—the producer does.

The agents pattern generally doesn’t have many reputation system requirements, depending on how much power you invest in the users to remove content. In the case of the Yahoo! Directory, the company may choose to pay attention to the links that remain unclicked in order to optimize its content. If, on the other hand, your users have a lot of authority over the removal of content, consider the abuse mitigation issues raised in the “Surveys: Staff Creates, Users Evaluate and Remove” pattern (see Surveys: Staff creates, users evaluate and remove).

An application that lets users create and evaluate a significant portion of the site’s content is what people are calling basic social media these days. On most sites with a basic social media content control pattern, content removal is controlled by staff, for two primary reasons:

Often, operators of new sites start out with an empty shell, expecting users to create and evaluate en masse, but most such sites never gather a critical mass of content creators, because the operators didn’t account for the small fraction of users who are creators (see Honor creators, synthesizers, and consumers). But if you bootstrap yourself past the not-enough-creators problem, through advertising, reputation, partnerships, and/or a lot of hard work, the feedback loop can start working for you (see The Reputation Virtuous Circle). The Web is filled with examples of significant growth with this content control pattern: Digg, YouTube, Slashdot, JPG Magazine, etc.

The challenge comes when you become as successful as you dreamed, and two things happen: people begin to value their status as a contributor to your social media ecosystem, and your staff simply can’t keep up with the site abuse that accompanies the increase in the site’s popularity. Plan to implement your reputation system for success—to help users find the best stuff their peers are creating and to allow them to point your moderation staff at the bad stuff that needs attention. Consider content reputation and karma in your application design from the beginning, because it’s often disruptive to introduce systems of users judging each other’s content after community norms are well established.

What? You want to give users complete control over the content? Are you sure? Before you decide, read the section Basic social media: Users create and evaluate, staff removes to find out why most site operators don’t give communities control over most content removal.

We call this content control pattern the Full Monty, after the musical about desperate blue-collar guys who’ve lost their jobs and have nothing to lose, so they let it all hang out at a benefit performance, dancing naked with only hats for covering. It’s kinda like that—all risk, but very empowering and a lot of fun.

There are a few obvious examples of appropriate uses of this pattern. Wikis were specifically designed for full user control over content (that is, if you have reason to trust everyone with the keys to the kingdom, get the tools out of the way). The Full Monty pattern works very well inside companies and nonprofit organizations, and even in ad hoc workgroups. In these cases, some other mechanism of social control is at work—for example, an employment contract or the risk of being shamed or banished from the group. Combined with the power for anyone to restore any damage (intentional or otherwise) done by another, these mechanisms provide enough control for the pattern to work.

But what about public contexts in which no social contract exists to define acceptable behavior? Wikipedia, for example, doesn’t really use this pattern: it employs an army of robots and professional editors who watch every change and enforce policy in real time. Wikipedia follows a pattern much more like the one described in Basic social media: Users create and evaluate, staff removes.

When no external social contract exists to govern users’ actions, you have a wide-open community, and you need to substitute a reputation system in order to place a value on the objects and the users involved in it. Consider Yahoo! Answers (covered in detail in Chapter 10). Yahoo! Answers decided to let users themselves remove content from display on the site because of the staff backlog. Because response time for abusive content complaints averaged 12 hours, most of the potential damage had already been done by the time the offending content was removed. By building a corporate karma system that allowed users to report abusive content, Yahoo! Answers dropped the average amount of time that bad content was displayed to 30 seconds. Sure, customer care staff was still involved with the hardcore problem cases of swastikas, child abuse, and porn spammers, but most abusive content came to be completely policed by users.

Notice that catching bad content is not the same as identifying good content. In a universe where the users are in complete control, the best you can hope to do is encourage the kinds of contributions you want through modeling the behavior you want to see, constantly tweaking your reputation systems, improving your incentive models, and providing clear lines of communication between your company and customers.

When analyzing what role reputation may have in your application, you need to look at what motivates your users and what incentives you may need to provide to facilitate your goals. Out of necessity, this will take us on a short side trip through the intersection of human psychology and market economics.

In Chapter 4 of his book Predictably Irrational (HarperCollins), Duke University Professor of behavioral economics Dan Ariely describes a view of two separate incentive exchanges for doing work and the norms that set the rules for them; he calls them social norms and market norms.

Social norms govern doing work for other people because they asked you to—often because doing the favor makes you feel good. Ariely says these exchanges are “wrapped up in our social nature and our need for community. They are usually warm and fuzzy.” Market norms, on the other hand, are cold and mediated by wages, prices, and cash: “There’s nothing warm and fuzzy about [them],” writes Ariely. Market norms come from the land of “you get what you pay for.”

Social and market norms don’t mix well. Ariely gives several examples of confusion when these incentive models mix. In one, he describes a hypothetical scene after a family home-cooked holiday dinner, in which he offers to pay his mother $400, and the outrage that would ensue, and the cost of the social damage (which would take a long time to repair). In a second example, less purely hypothetical and more common, Ariely shows what happens when social and market norms are mixed in dating and sex. A guy takes a girl out on a series of expensive dates. Should he expect increased social interaction—maybe at least a passionate kiss? “On the fourth date he casually mentions how much this romance is costing him. Now he’s crossed the line (and has upset his date!). He should have known you can’t mix social and market norms—especially in this case—without implying that the lady is a tramp.”

Ariely goes on to detail an experiment that verifies that social and market exchanges differ significantly, at least when it comes to very small units of work. The work-effort he tested is similar to many of reputation evaluations we’re trying to create incentives for. The task in the experiments was trivial: use a mouse to drag a circle into a square on a computer screen as many times as possible in five minutes. Three groups were tested: one group was offered no compensation for participating in the test, one group was offered 50 cents, and the last group was offered $5. Though the subjects who were paid $5 did more work than those who were paid 50 cents, the subjects who did the most work were the ones who were offered no money at all. When the money was substituted with a gift of the same value (a Snickers bar and a box of Godiva chocolates), the work distinction went away—it seems that gifts operate in the domain of social norms, and the candy recipients worked as hard as the subjects who weren’t compensated. But when a price sticker was left on the chocolates so that the subjects could see the monetary value of the reward, it was again market norms that applied, and the striking difference in work results reappeared—with volunteers working harder than subjects who received priced candy.

When considering how a content control pattern might help you develop a reputation system, be careful to consider two sets of needs: what incentives would be appropriate for your users in return for the tasks you are asking them to do on your behalf? And what particular goals do you have for your application? Each set of needs may point to a different reputation model—but try to accommodate both.

Ariely talked about two categories of norms—social and market—but for reputation systems, we talk about three main groups of online incentive behaviors:

Interestingly, these behaviors map somewhat to social norms (altruistic and egocentric) and market norms (commercial and egocentric). Notice that egocentric motivation is listed both a social and a market norm. This is because market-like reputation systems (like points or virtual currencies) are being used to create successful work incentives for egocentric users. In effect, egocentric motivation crosses the two categories in a entirely new virtual social environment—an online reputation-based incentive system—in which these social and market norms can coexist in ways that we might normally find socially repugnant in the real world. In reputation-based incentive systems, bragging can be good.

Altruistic, or sharing, incentives reflect the giving nature of users who have something to share—a story, a comment, a photo, an evaluation—and who feel compelled to share it on your site. Their incentives are internal. They may feel an obligation to another user or to a friend, or they may feel loyal to (or despise) your brand.

Altruistic or sharing incentives can be characterized into several categories:

When you’re considering reputation models that offer altruistic incentives, remember that these incentives exist in the realm of social norms; they’re all about sharing, not accumulating commercial value or karma points. Avoid aggrandizing users driven by altruistic incentives—they don’t want their contributions to be counted, recognized, ranked, evaluated, compensated, or rewarded in any significant way. Comparing their work to anyone else’s will actually discourage them from participating.

Crusader, opinionated incentives, and know-it-all

Some users are motivated to contribute to a site by a passion of some kind. Some passions are temporary; for example, the crusaders are like those who’ve had a terrible customer experience and might wish to share their frustration with the anonymous masses, perhaps exacting some minor revenge on the business in question. Some passions stem from deeply held religious or political beliefs that they feel compelled to share; these are the opinionated. The know-it-all users’ passions emerge from topical expertise and others who are just killing time. In any case, people seem to have a lot to say that has very mixed commercial value. Just glancing at the comments on a popular YouTube video will show many of these motivations all jumbled together.

This group of altruistic incentives is a mixed bag. It can result in some great contributions as well as a lot of junk (as we mentioned in There’s a Whole Lotta Crap Out There). If you have reason to believe that a large portion of your most influential community members will be motivated by controversial ideas, carefully consider the costs of evaluation and removal in the content control pattern that you choose. Having a large community that is out of control can be worse than having no community at all.

On any movie review site, look at the way people respond to one another’s reviews for hot-button movies like Fahrenheit 9/11 (Figure 5-4) or The Passion of the Christ. If the site offers “Was this review helpful?” voting, the reviews with the highest total votes are likely to be very polarized. Clearly, in these contexts the word helpful means “agreement with the review-writer’s viewpoint.”

Figure 5-4. In the context of movie reviews, it appears as if the community has interpreted the “Was This Helpful?” question in its own way. They’re probably using that input to agree or disagree with a viewpoint, rather than gauging how “useful” it may or may not be.

Commercial incentives fall squarely in the range of Ariely’s market norms. They reflect people’s motivation to do something for money, though the money may not come in the form of direct payment from the user to the content creator. Advertisers have a nearly scientific understanding of the significant commercial value of something they call branding. Likewise, influential bloggers know that their posts build their brand, which often involves the perception of them as subject matter experts. The standing that they establish may lead to opportunities such as speaking engagements, consulting contracts, improved permanent positions at universities or prominent corporations, or even a book deal. A few bloggers may actually receive payment for their online content, but more are capturing commercial value indirectly.

Reputation models that exhibit content control patterns based on commercial incentives must communicate a much stronger user identity. They need strong and distinctive user profiles with links to each user’s valuable contributions and content. For example, as part of reinforcing her personal brand, an expert in textile design would want to share links to content that she thinks her fans will find noteworthy.

But don’t confuse the need to support strong profiles for contributors with the need for a strong or prominent karma system. When a new brand is being introduced to a market, whether it’s a new kind of dish soap or a new blogger on a topic, a karma system that favors established participants can be a disincentive to contribute content. A community decides how to treat newcomers—with open arms or with suspicion. An example of the latter is eBay, where all new sellers must “pay their dues” and bend over backward to get a dozen or so positive evaluations before the market at large will embrace them as trustworthy vendors. Whether you need karma in your commercial incentive model depends on the goals you set for your application. One possible rule of thumb: if users are going to pass money directly to other people they don’t know, consider adding karma to help establish trust.

There are two main subcategories of commercial incentives:

Incentives through branding: Professional promotion

Various forms of indirect commercial incentives can together be referred to as branding, the process of professionally promoting people, goods, or organizations. The advertiser’s half of the direct revenue incentive model lives here, too. The goal is to expose the audience to your message and eventually capture value in the form of a sale, a subscriber, or a job.

Typically, the desired effects of branding are preceded by numerous branding activities: writing blog posts, running ads, creating widgets to be embedded on other sites, participating in online discussions, attending conferences, and so on. Reputation systems offer one way to close that loop by capturing direct feedback from consumers. They also help measure the success of branding activities.

Take the simple act of sharing a URL on a social site such as Twitter. Without a reputation system, you have no idea how many people followed your link or how many other people shared it. The URL-shortening service Awe.sm, shown in Figure 5-5, provides both features: it tracks how many people click your link and how many different people shared the URL with others.

Figure 5-5. Awe.sm turns URL shortening into a reputation system, measuring how many people click your URL and how many share it with others.

For contributors who are building a brand, public karma systems are a double-edged sword. If a contributor is at the top of his market, his karma can be a big indicator of trustworthiness, but most karma scores can’t distinguish inexperience from a newly registered account from complete incompetence—this fact handicaps new entrants.

An application can address this experience-inequity by including time-limited scores in the karma mix. For example, B.F. Skinner was a world-renowned and respected behavioral scientist, but his high reputation has a weakness—it’s old. In certain contexts, it’s even useless. For example, his great reputation would do me no good if I were looking for a thesis advisor, because he’s been dead for almost 20 years.

Egocentric incentives are often exploited in the design online in computer games and many reputation-based websites. The simple desire to accomplish a task taps into deeply hardwired motivations described in behavioral psychology as classical and operant conditioning (which involves training subjects to respond to food-related stimulus) and schedules of reinforcement. This research indicates that people can be influenced to repeat simple tasks by providing periodic rewards, even a reward as simple as a pleasing sound.

But, an individual animal’s behavior in the social vacuum of a research lab is not the same as the ways in which we very social humans reflect our egocentric behaviors to one another. Humans make teams and compete in tournaments. We follow leaderboards comparing ourselves to others and comparing groups that we associate ourselves with. Even if our accomplishments don’t help another soul or generate any revenue for us personally, we often want to feel recognized for them. Even if we don’t seek accolades from our peers, we want to be able to demonstrate mastery of something—to hear the message, “You did it! Good job!”

Therefore, in a reputation system based on egocentric incentives, user profiles are a key requirement. In this kind of system, users need someplace to show off their accomplishments—even if only to themselves. Almost by definition, egocentric incentives involve one or more forms of karma. Even with only a simple system of granting trophies for achievements, users will compare their collections to one another. New norms will appear that look more like market norms than social norms: people will trade favors to advance their karma, people will attempt to cheat to get an advantage, and those who feel they can’t compete will opt out altogether.

Egocentric incentives and karma do provide very powerful motivations, but they are almost antithetical to altruistic ones. The egocentric incentives of many systems have been overdesigned, leading to communities consisting almost exclusively of experts. Consider just about any online role playing game that survived more than three years. For example, to retain its highest-level users and the revenue stream they produce, World of Warcraft must continually produce new content targeted at those users. If it stops producing new content for its most dedicated users, its business will collapse. This elder game focus stunts its own growth by all but abandoning improvements aimed at acquiring new users. When new users do arrive (usually in the wake of a marketing promotion), they end up playing alone because the veteran players are only interested in the new content and don’t want to bother going through the long slog of playing through the lowest levels of the game yet again.

We describe three subcategories of egocentric incentives:

This question may seem simple, but it’s one that often goes unasked: what, exactly, is the purpose of this community? What are the actions, activities, and engagements that users expect when they come to your site? Will those actions be aided by the overt presence of content- or people-based reputations? Or will the mechanisms used to generate reputations (event-driven inputs, visible indicators, and the like) actually detract from the primary experience that your users are here to enjoy?

Many of the models and techniques that we cover in this book are equally applicable, whether your community is a brand-new, aspiring one or has been around a while and already has acquired a certain dynamic. However, it may be slightly more difficult to introduce a robust reputation system into an existing and thriving community than to have baked-in reputation from the beginning. Here’s why:

We don’t mean to imply, however, that designing a reputation system for a new, greenfield community is an easy task. For a new community, rather than identify the community characteristics that you’d like to enhance (or leave unharmed), your task is to imagine the community effects that you’re hoping to influence, then make smart decisions to achieve those outcomes.

Is your community a friendly, welcoming place? Helpful? Collaborative? Argumentative or spirited? Downright combative? Communities can put a whole range of behaviors on display, and it can be dangerous to generalize too much about any specific community. But it’s important to at least consider the overall character of the community that you plan to influence through your reputation system.

A very telling aspect of community character (though it’s not the only one worth considering) is the level of perceived competitiveness in your community (see Figure 5-6). That aspect includes the individual goals of community members and to what degree those goals coexist peacefully or conflict. What are the actions that community members engage in? How might those actions impinge on the experiences of other community members? Do comparisons or contests among people produce the desired behaviors?

Figure 5-6. The competitive spectrum may help suggest the appropriate reputation model for your community’s needs.

In general, the more competitive a group of people in a community is, the more appropriate it is to compare those people (and the artifacts that they generate) to one another.

Read that last bit again, and carefully. A common mistake made by product architects (especially for social web experiences) is assuming a higher level of competitiveness than what really exists. Because reputation systems and their attendant incentive systems are often intended to emulate the principles of engaging game designs, designers often gravitate toward the aggressively competitive—and comparative—end of the spectrum.

Even the intermediate stages along the spectrum can be deceiving. For example, where would you place a community like Match.com or Yahoo! Personals along the spectrum? Perhaps your first instinct was to say, “I would place a dating site firmly in the ‘competitive’ part of the spectrum.” People are competing for attention, right? And dates?

Remember, though, the entire context for reputation in this example. Most importantly, remember the desires of the person who is doing the evaluating on these sites. A visitor to a dating site probably doesn’t want competition, and she may not view her activity on the site as competitive at all but as a collaborative endeavor. She’s looking for a potential dating partner who meets her own particular criteria and needs—not necessarily “the best person on the site.”