I’ve been bloggingalotaboutsocialgamesoverthepastcoupleofyears and have been a big proponent of the space. However, over the last few months I’ve started to question whether social gaming is a separate category at all. I now believe that the true category is free to play gaming, and that social gaming is simply a tactic (albeit a very important and differentiating tactic) within this category. Although I’ve been saying this in private a fair bit recently, I brought it up at the VC panel at Gamesbeat yesterday and I hear that it caused a bit of a stir. Rather than being quoted out of context in 140 characters, I thought it would be helpful to explain how I came to this view.
At the most basic level, free to play games (with a digital goods or subscription upsell model) need to focus on only two metrics, Lifetime Player Value (LPV) and Player Acquisition Cost (PAC). If LPV > PAC then you’ve got a business. If not, you don’t. This applies to flash MMOs, virtual worlds, facebook games, asynchronous text based MMOs, client downloadable games, myspace games and a whole host of other games, with the key unifying element being the business model, and the importance of those two statistics, LPV and PAC.
The term “social gaming” has been used in two main, and related, ways. I think that both of these definitions are potentially limiting. The first has been to describe games that are played (and spread) on social networks. The second has been to describe games that spread virally, with a PAC of zero because current players invite new players with a K factor above 1.
Let’s start with games played on social networks. This is a terrific distribution tactic as open platforms and distribution are opposite sides of the same coin, and as I’ve said in the past, in the early stages of a new category distribution is the key driver of success. Free to play gaming is certainly in it’s early stages, with many games having to create demand versus simply fulfilling demand. But there is no reason why these games have to be limited to only social networks, and indeed companies like SGN and Zynga have already started to port their games to other platforms including the iPhone and the open web. Social networks offer an easy starting point for new free to play games because of the large concentration of potential players, but there is no reason for free to play games to stop at social networks.
Now lets address viral growth for games. Obviously, this is a wonderful characteristic. It is the cheapest possible channel for player acquisition as with a PAC of zero, you can make money at any level of LPV. However, once again, there is no reason to limit your player acquisition channels to viral growth. You should acquire new players through any channel where your PAC < LPV. For some game developers this is a religious issue; viral is best and nothing else is acceptable. I disagree with such a fundamentalist approach. If your LPV is high enough to allow you to buy users through advertising, distribution deals, search marketing or any other channel, then you should. Mark Pincus, the CEO of Zynga, has been preaching this approach since early 2008. Here is an excerpt from my blog post from the social games panel that I moderated at the Graphing Social Patterns conference in March 2008:
We next talked about how social games can grow. Viral growth has obviously been the key driver of growth up to this point for all the panelists. Shervin noted that they had seen a strong positive correlation between App Rating and rate of viral growth – high quality games spread faster. Mark talked about the importance of supporting a game with advertising, especially at launch.
One reason that Zynga is the largest social gaming company today is that they have been able to afford to promote their games on both Facebook and Myspace, and have done so aggressively.
Obviously, building social factors into games is increasingly important. Multiplayer is the “user generated content” of games, and social interaction is a key part of that. Furthermore, even if your K factor is less than one, it can be a very important force multiplier on your player acquisition. Buying one player if your K factor is 0.8 means that you will generate 5 new players, and this can dramatically average down your PAC, even if it doesn’t take it all the way down to zero.
In conclusion then, I find the term “social gaming” to be limiting. The best publishers and developers of free to play games will make frequent use of social gaming tactics, but they will not refuse to go beyond social networks and viral channels to grow to their full potential.
Last week Andrew Chen wrote an excellent post about the growth and potential decay of viral apps. Rather than just focusing on the elements of viral growth, Andrew also took into account the declining likelihood of an accepted invitation as you saturate a population, and the impact of churn. He provided a useful model to social media founders who are trying to estimate their growth, and what can go wrong when a viral app “jumps the shark”:
He notes:
* Early on, the growth of the curve is carried by the invitations
* However, over time the invitations start to slow down as you hit network saturation
* The retention coefficient affects your system by creating a “lagging indicator” on your acquisition – if you have good retention, even as your invites slow down, you won’t feel it as much
* If your retention sucks, then look out: The new invites can’t sustain the growth, and you end up with a rather dire “shark fin.”
I think this is a very useful model, but that it doesn’t quite predict what we typically see in real life. Rather than dropping to zero, failed viral apps typically hover at a steady level much lower than their peak. Since Andrew made the model available under “copyleft”, I made a small edit to his model. Rather than treating churn as a constant percentage of users in each time period, I treated it on a cohort level, with a higher churn rate in the early periods and lower churn as time goes on. This is similar to the churn profiles seen for subscriptions businesses such as AOL’s ISP business. (I was at AOL from 2002-2005 as SVP of Corporate Development, and then as GM of Netscape.) This model better matches active user graphs that we typically see for failed viral apps.
If you’re interested, the model is available for download here. Viral growth assumptions are in the yellow cells on the “viral acquisition” tab and churn assumptions and output are on the “user retention” tab.
I am not a hard core gamer by background; more of a casual gamer. But casual gaming is now widespread; we’re all gamers now. My interest in the area has grown out of my interest in social networks and social media. I’ve long noted the increasing application of simple game mechanics to social web sites and how this can meaningfully increase the levels and types of interactions that users have with each other and the site.
As an investor in Flixster and Rockyou, both highly viral Facebook and Myspace “app” and “widget” makers, I’ve been tracking closely the spread of emergent user behaviors in these social networks. One Facebook app that really caught my attention is Scrabulous, an online scrabble game that can be played asynchronously, ie players don’t all have to be online at the same time.
Online multiplayer games have long been popular at all the big casual games portals. Multiplayer gaming can be viewed as user generated content for games, one of the drivers of Games 2.0. These games typically have a “lobby” where players can meet and match up before entering into a game against each other in real time.
Making the gameplay asynchronous fits better with the “continuous partial attention” world that we increasingly live in. The reason I never became a hard core gamer is that the serial monogamy requirements (one game at a time, total dedication, long periods of gameplay coordinated with others) doesn’t mesh well with my lifestyle. Scrabulous is a better match for the “play a little bit when you have some time, at various points throughout the day” life that many of us lead. Single player casual gaming (whether Bejeweled online or Brickbreaker on the Blackberry) has been filling that need for many players. These are fun, and at least have the “high score” dynamic, but they lack the social aspect that turn based asynchronous games offer.
Asynchronous games also make it easier to play against friends. You don’t have to coordinate to be online at the same time. Playing friends makes games more fun, and gives them a social aspect (the games have context if you have an ongoing relationship with an opponent). Playing with friends also offers an opportunity for true viral growth for the game, as players invite their friends to play.
Although these turn based multi-player games (especially those derived from boardgames) have some social dynamic, they lack the breadth of social interaction of synchronous MMOGs (not just the direct social interaction, but also the perfomative aspects of gameplay) that help make them such compelling experiences. Part of the appeal of MMOGs (whether World of Warcraft or Puzzle Pirates) is knowing that you’re “in game” with thousands of other people at the same time, each of them interacting with the same universe that you are.
So what would an asynchronous massively multi-player game look like? It can’t be turn based because most players would spend most of their time waiting for someone else to move. That’s not fun. It would have to be time based instead. Players would need to make their moves against a real world clock. Games like Duels.com (swords and sandals PvP fighting game), Manager Zone (soccer manager game) and Kings of Chaos (real time strategy game) all employ this dynamic. Massively multi-player games offer even more opportunity for viral growth because a players invitation ability is unbounded by the number of seats at a board game.
This led to me think about games using the framework below:
I think that we’ll see a lot more innovation in the two sections of asynchronous multiplayer and massively multiplayer games over the next few years. I’m actively interested in investing in these areas. What are the most interesting such games that you see?
The last couple of years have seen an explosion of innovation on the web that has broadly been labeled Web 2.0. There has been a lot of debate about what exactly constitutes web 2.0 but what hasn’t received as much attention has been what changes have enabled these Web 2.0 companies to arise – what is different now from the mid 90s and Web 1.0. I think the change can be summarized in one (somewhat clumsy) word: Variablization.
Variablized Development Costs
In the 90s we used software development models, primarily waterfall models, where a usable product wasn’t available until close to the end of the development period. Most code was written from scratch, with little reuse or public domain code, and large teams were necessary.
With the popularization of agile programming methodologies, widespread use of open source software, greater ability to use offshore development resources on a consulting basis, and a culture of “open beta”, the costs of developing a website or web service have become both lower and more variable. Ideas that look promising but fail to capture user interest in beta can be identified much earlier and at much lower cost, and resources can be shifted to more promising avenues.
Variablized Content Costs
In the 90s almost all content was created by professional editors and writers, employed by companies. To launch, they had to create a critical mass of content, which cost a certain amount.
Recently, with lower expectations out of beta products, the widespread adoption of user generated content and emerging best practices in how to use user generated content, the costs of content creation have dropped dramatically and become variable.
Variablized Marketing Costs
In the 90s, there were only two ways to get a large number of users. The first was offline marketing – the famous Pets.com superbowl ad approach. Expensive, and with a high minimum level of spend required to break through the clutter. We all know how that worked out.
Overture and Google have changed that landscape. Their CPC model means that you can spend as much as you choose to gain new users, and that your marketing spend can be completely variable.
Variablized Distribution Costs
The second way to get a large number of users in the 90s was to get a distribution deal with one of the big portals – AOL, Yahoo or MSN. In those days, this was the only way to reach a large number of internet users effectively, and you typically had to sign up for a multi year, multi million dollar deal to do it.
The vast majority of Web 2.0 companies rely on advertising as their business model. I think this is because advertising is the one business model that has become variable (relative to the 90s). Back then, to sell online advertising, you both needed to have substantial scale, and you needed to have your own sales force.
Today, thanks to ad networks and CPC contextual targeting (not just Google’s adsense, but also Quigo, Yahoo’s Publisher Network and others), even the smallest of websites can start earning advertising revenue.
There have not been equivalent innovations for subscription and ecommerce business models, and as a result, we’ve seen far fewer web 2.0 companies that use those models.
Conclusion
These changes in cost structure are a useful lens through which to view the current startup environment. It’s been said before that it is cheaper to build a company than ever before. While that is partially true, it is not the whole story. Digg has raised over $10m, Youtube over $12m, Photobucket and Rockyou (a Lightspeed company) over $15m, and Facebook has raised over $275m. (With the exception of Facebook) while these are lower than the amounts raised by companies in the 90s, they are still large numbers. Variablization of costs only makes costs go away when usage is low. In other words, while it still takes money to succeed, it is cheaper to fail than ever before.
Luckily for VCs like me, that means that successful companies will still need to raise money!
1. Engage Quickly – (my interpretation: provide value within 30 seconds)
2. Mimic Look and Feel – (make your widget look like the page it is in)
3. Enable Self Expression – (let people personalize their widgets)
4. Make it Dynamic – (keep showing new stuff)
5. Expose Friend Activity – (show what friends are doing)
6. Browse the Graph – (let people explore their friends and friends of friends)
7. Drive Communication – (provide commenting features)
8. Build Communities – (expose different axes of similarity)
9. Solve Real World Tasks – (leverage people’s social connections to solve real problems)
I recently met the CEO of a company who claim to be one of the most popular social networks in Turkey with several million monthly visitors from Turkey. This happened by accident – the founders are Americans who have no prior connection to Turkey.
This is just one of many examples of how difficult it can be to predict or control the growth of viral social media. Google’s Orkut, is a better known example – a social network started by a Turkish engineer working in the US that now dominates in Brazil and India. Friendster and hi5 fall into this bucket as well. As I’ve noted before, the online advertising market in the US is bigger than that in the rest of the world combined. The senior management of these companies know this, and all would love to see more US traffic, but it is now beyond their control.
The reason is the mathematics of viral growth. If the viral coefficient (the number of additional members a new member brings) in a population is less than one, it grows but eventually hits a ceiling. But if the viral coefficient is greater than one, it grows unbounded. Although your social media property may start in many different populations, it will come to be dominated by those with the highest viral coefficients.
This is best demonstrated by example. Consider a new social media property with 30 members, 10 each from three distinct populations; call them Pinks, Purples and Greens. Suppose that by luck and because of the initial users, the viral growth coefficient for the Pinks is 0.6, for the Purples is 0.9 and for the Greens is 1.2. Watch what happens to the populations over time:
All three groups are initially equally represented, But already by time period 4 the population is more than 50% Green. By time period 10 it is more than 75% Green and probably considered a “Green social network”. By time period 16 less than one member in 10 is not Green. For Green now substitute whatever national, language based, religious, racial or other demographic grouping that you choose.
These evolutions can happen very fast since a time period is the time it takes for a new member to invite more members. 2-8 weeks might be a reasonable assumption.
This example is vastly simplified, of course. Viral coefficients vary over time within and between groups. Viral coefficients also don’t vary quite as much between groups; the same underlying feature set is being exposed to all groups. But it illustrates the point that randomness can play a significant role in the eventual makeup of a social media property’s user base.
I posted about a similar finding in May, how you don’t necessarily get the wisdom of crowds, but sometimes just the crowdiness of crowds. It was based on a NY Time’s article that showed how randomness can have a big impact on the most popular songs for a crowd when popularity information is public.
Wired’s August edition has a good article about how newspapers are putting their readers to work which is worth reading. But one section that jumped out at me has broader applicability:
A GetPublished! button features prominently on many Enquirer Web pages, and the submissions land in Parker’s queue. They almost never resemble anything commonly considered journalism.
“It used to read, ‘Be a Citizen Journalist,'” Parker says. “And no one ever clicked on it. Then we called it ‘Neighbor to Neighbor,’ and still nothing. For some reason, ‘Get Published’ was the magic phrase.”
Changing copy can make a huge difference in your level of user interaction.
Direct marketers have known about the importance of good copy for years. Good email marketers constantly test and refine subject lines to improve open rates. Many best practices in email marketing subject lines have evolved that are often directly applicable to other areas. The same is true of the best lead generation businesses who are constantly tweaking their landing page copy and form-fill flow to maximize the completion rate.
Copy can improve interaction rates in media businesses as well as these more transactional examples. Another example is in social media optimization (an element of search engine optimization). For example, this list of the top 101 advertising headlines ever written (top 10 excerpted here):
1. They laughed when I sat down at the piano – but when I started to play!
2. They grinned when the waiter spoke to me in French – but their laughter changed to amazement
at my reply.
3. Do you make these mistakes in English?
4. Can You Spot These 10 Decorating Sins?
5. How a “fool stunt” made me a star salesman
6. How a strange accident saved me from baldness
7. Who else wants a screen star figure?
8. Who else wants a lighter cake – in half the mixing time?
9. Free to brides – $2 to others
10. Free to high school teachers – $6 to others
Email virality and other forms of viral marketing can also often be tuned and improved through better copy. When dealing with email invitations from friends, more social messages may be more effective than the hard-sell/call-to-action type copy of the examples linked to above. For example, Flixster (a Lightspeed portfolio company) asks in the email subject:
In both cases the actual copy is important, but more important is the fact that the companies constantly A:B test their copy to optimize and improve their conversion rates.
The takeaway here is that, although technology startups full of top notch engineers often look to improving the product to improve user interaction rates, sometimes something as simple as a change in words can have much the same result.
Marketers are always looking for new and innovative ways to break through the clutter and get their advertising message across. This is true across all media. In TV, some advertisers are looking beyond the 30 second spot to sponsor a whole show, do product placement and even user generated ads on TV. In print, some advertisers are looking beyond standard 4 color full page ads and have experimented with buying out a whole issue, and even scratch and sniff ads. But in traditional media, as you can tell from the links- these events are rare enough to be newsworthy.
In online, advertisers look beyond standard ad units (buttons and banners) as a matter of course. Everyone wants to do something innovative, whether it be widgets, viral video, sponsorships, takeovers or immersive campaigns.
I’ve recently seen a few examples of this increase in innovation in online creative. At Widgetcon earlier this month, one of the panels was Widget Marketing in the Media Mix, where reps from boutique online agencies Denuo + Droga5, crayon, Organic and Fleischman-Hillard discussed their views on widget marketing. One of the most telling quotes from the panel was from Chad Stoller of Organic who said, “If you can count it then it’s not innovation, it’s not new. You have to be just as creative with how you measure something, as creative as you are with your execution.”
This novelty-seeking mindset is increasingly typical of the new, online-focused, boutique agencies. They are all looking to demonstrate their creativity and thought leadership. A couple of weeks ago New York magazine had a great article about the new guerrilla ad agencies and the viral ad campaigns that they are creating. It highlights one of the most successful viral marketing campaign to date, a viral video where Marc Ecko purportedly tags Air Force One with their “Still Free” graffiti logo.
Apparently, “including media coverage” the video has had 115m impressions. Despite its “homemade” feel, the video cost nearly $400k to shoot ($150k went towards repainting an old 747 to make it look like Air Force One). That implies a CPM of around $3.50 on production costs alone – pretty good for online video advertising if these were the only costs.
The demand for new media creative talent to make these innovative campaigns has increased salaries, and hence costs, across the board:
Salaries for digital creative directors rose 60 percent nationwide in 2006, from an average of $115,000 to $185,000, according to a survey by the recruiter TalentZoo. “We’re trying to hire two people right now,” says Charles Rosen, “and we cannot find them.” Rosen is one of the co-founders of Amalgamated, which started with six people in 2003 and has just hired its 40th employee. “People who were juniors when we left Cliff Freeman, where some of us used to work, want $250,000 or $300,000 now. ”
These increased costs are not confined to the startup boutique agencies either. At GM Planworks (the division of Starcom MediaVest dedicated to managing General Motor’s $3.2bn ad spend) the number of employees has grown from around 200 in 2000 when the group was formed to about 500 today, primarily driven by the increase in people dedicated to new media. Yet GM spent only 10-15% of its ad budget online last year. The cost of online creative and production as a percentage of ad spend is much higher than it is for traditional media, in large part because of the urge to do more innovative creative executions online.
Often advertisers and agencies don’t have the in-house capabilities to even do the creative for the “something new” that they want in new media. At the Ypulse Teen Mashup conference last week, Craig Sherman (CEO of Gaia) spoke about Gaia’s recent immersive campaign for Scion which will allow Gaia users to buy Scions, trick them out and race them. It’s an exciting embedded advertising campaign, but a significant custom integration effort on Gaia’s part.
Similarly some big advertisers have started to launch widget marketing campaigns through partnering with Clearspring. But it’s been up to Clearspring to supply the professional services to make the widgets; the advertisers don’t have that skill in house.
This custom work isn’t something that only startups have to do either. Even Yahoo and AOL have teams within their ad sales groups dedicated to doing custom creative for special sponsorships and promotions that involve non standard ad units. Often this creative work isn’t charged for but is “eaten” by the portal to sell the whole advertising package.
The quest for innovation in online advertising comes with higher production and creative costs. As the online ad industry matures, I would expect it to evolve to look more like print, TV and other more mature ad markets. In these more mature markets the majority of campaigns are more standards based and less customized, and the costs of production and creative come down as a proportion of the overall marketing budget.
I’d be interested to hear about readers’ experiences with highly customized campaigns and their costs and effectiveness.
I’ve been traveling a bit this week, speaking at Widgetcon on Wednesday and at Community Next on Saturday. Both panels were on the topic of viral marketing; at Widgetcon with a focus on how brands can use widgets for marketing, and at Community Next with a focus on how to measure viral campaigns.
Dave McClure moderated the panel at Community Next and conducted an interesting experiment with the audience that really encapsulates some of the key lessons of viral marketing. He seeded two memes into the audience. One person was asked to start saying “meep” repeatedly. Another group of five people were asked to put their hand onto another person. The idea was to see which memes spread furthest in the audience.
The “Meeper” juiced up the visibility of his meme by adding an element of clapping as well (“meep”, clap, “meep”, clap etc), and walking up and down the front of the stage. Initially maybe 10 people near the Meeper started to meep as well (and clap – more clapping than meeping actually) but this eventually died down as it failed to get picked up more broadly. The initial early adopters started to feel self conscious when no one followed them, and stopped meeping.
At this point, the people on stage still had no idea what the second meme was until Dave asked how many people were touching someone else. About a third of the audience, maybe 50 people, raised their hand. Although it initially lagged, the second meme had far outpenetrated the first.
Although a somewhat artificial experiment, Dave managed to demonstrate a number of the key lessons about viral marketing in a very clever way:
1. A “high visibility” app can get quick pickup among early adopters very quickly. “High visibility” can be caused by a high invite rate, inviters who invite a lot of people on average, or simply something that is extremely visible and obvious (e.g. music on your profile page, or some guy walking up and down the stage clapping and saying meep).
2. High visibility can cut both ways. New users who are seeking social proof can see who is adopting, and decide whether or not they are “like me”.
3. Early adopters can also be early abandoners and not representative of the broader population. (see Josh’s classic post on the 53,651)
4. Product matters. While a highly viral app can get distribution quickly, if the uninstall rate is high then it never gets beyond a certain size. While virality dictates the speed of growth, uninstall rate (typically a function of product quality) dictates saturation size, which in many cases is a more important business driver.
5. It helps to start virality from a larger base. Viral marketing is a probability driven game, and if you don’t have enough initial seeds then a failure of virality from any one seed can stop all growth immediately; with more seeds you have more “shots on goal”.
A lot of the same lessons came out of the panel discussion at Widgetcon. There was a real focus on asking what should be the right metrics for measuring “success” for a widget based marketing campaign. CPM and pixel count (728×90 etc) didn’t seem to be the best way to measure and sell advertising when users voluntarily affiliated themselves with a brand. Echoing lesson #4 above (Product matters), most of the panel came back to engagement with the widget as the key metric for success. If the widget isn’t good, users won’t engage.
As the industry’s attention turns to the tactics of viral growth (whether through email, cross sell, calls to action, optimization of color, font, copy and position etc) it’s a good reminder that, as has always been the case, product matters more than marketing (whether viral or not).
This discussion is all useful qualitatively. However, it’s also helpful to take a “first principles” look at how to quantitatively value the user of a facebook app. I’m going to assume a media business model for now, although some digital goods facebook apps have been launched as well. I know of at least three companies with apps generating over 100m PV/month across their user base (not widget impressions), so there does seem to be the potential to build media businesses off of the Facebook platform. Lets take a look at how we can calculate value:
Value of a Facebook app user
= RPM x lifetime “pageviews” generated by user and subsequent invitees
= RPM x lifetime “pageviews” generated by user x virality factor
= RPM x “pageviews” per user per month / monthly churn rate x virality factor
(note that I use the term pageviews although they are really iframe views; the important factor is that the app developer can insert an IAB standard ad unit. )
Similarly, value goes up as PV/user/month goes up. This argues that apps with high ongoing engagement (ie some aspect of ongoing utility) will be more valuable. Some apps (including some of the most popular ones such as Top Friends and Horoscope) don’t generate very many pageviews at all because all the value is delivered in the widget on the profile page, so few iframe pageviews get delivered. These fall mostly into the self expression or communication categories. Apps such as iLike on the other hand, generate a lot of ongoing engagement and PVs.
Value goes down as monthly churn goes up. One of the factors that reduces churn and increases “stickiness” of an app is how much “archive” value is built on top of the app. The more you commit to adding information to an app, the stickier that app will become. Pets is a great example of this – as you “level up” your pet and get more equipment for it, you become less and less likely to get rid of it. Fortune Cookie is an example of an app where there is little archive value and its easy to either switch out to a new app or get rid of it altogether.
Finally, value goes up as virality goes up. Although Facebook has turned down the virality of apps recently, certain apps, primarily those with a communication and self expression component, tend to be more viral in nature. Lance and Jia of Rockyou (a Lightspeed portfolio company) did a good interview with Venturewire where they talked about how to get viral on Facebook.
All Facebook apps are not created equal on these four dimensions. If you’re building a Facebook app, it’s worth while thinking about your app using this framework to figure out how important it can be to your business. I’d love to hear from developers of Facebook apps to hear what they think about this framework, and how their app measures up against it.
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