Posted by jeremyliew in game design, game mechanics, reputation, social games, social gaming, social media.
Bokardo has an interesting interview with Bryce Glass of Yahoo, about Yahoo’s social design pattern for reputation. Building reputation systems can really help drive high quality engagement on a social media site, but is also fraught with danger and unintended consequences if not done thoughtfully:
What are the biggest hurdles in designing for reputation?
I think it’s probably the number and variety of unintended consequences that little design decisions can have further down the line. I’m fond of the article—so I cite it a lot—but Ben Brown, who founded the dating site Consumating, has a great blog-post about the ‘ill-fated points system’ that they used for that site, and the variety of… um… less-than-ideal behaviors that those incentives gave rise to. Early on, Slashdot struggled with many of these same issues, and they’ve re-jiggered their comment karma system several times through the years.
…
A big hurdle—and if you can solve this, you’re halfway there to having a well-designed and effective reputation system—is appropriately marrying the incentives that you offer your users to the appropriate set of goals that you have for your community. You want to be sure that you’re rewarding folks for behaving like good citizens, and not just rewarding them for no good reason. (Or for vague and misguided reasons like “to keep them engaged” or “so we can have a leaderboard.”)
Earlier this year, Glass gave a presentation on designing your reputation system at the IA summit outlining eleven different reputation systems:
> Named Levels
> Numbered Levels
> Identifying Labels
> Points
> Collectible Achievements
> Leaderboard
> Top X
> Temporal Awards
> Statistical Evidence
> Peer Testimonials
> User to User Awards
and how to select between them depending on questions including:
> What are your business goals? [Engagement? Promote a specific feature? Acknowledge top contributors? Increase content quality? User Retention?]
> What community spirit do you want to encourage? [Caring? Collaborative? Cordial? Competitive? Combative?] More detail on the competitive spectrum in Yahoo’s Design Pattern Library.
> What motivates your community members?
> Which entities will accrue reputation? [People? Things? Collections of Things?]
> Which inputs will you pay attention to?
> How transparent should the rules be? [More transparency is more likely to affect behavior]
He also notes that reputations should always decay over time to prevent a log jam at the top that can discourage new members and make a community appear stagnant.
Yahoo has done a nice job of categorizing some of the various reputation systems available to social architects and how to think through choosing one. I would highly recommend reading the interview and presentation and reviewing the material in the design library.
Posted by jeremyliew in blogs.
Tags: identity
I got an email today from Kyle Brady titled “Response to Your Response on My Blog” which puzzled me as I hadn’t come across his blog before. I clicked through to his post; he had been called by a recruiter about a job at my portfolio company Rockyou, turned it down and explained, in some detail, and with great enthusiasm, why.
Someone claiming to be Jeremy Liew posted a couple of comments, parts of which I’ll excerpt below:
Kyle: I think you made a poor decision that you will one day regret deeply.
I have been a venture capitalist for many years (I got my start in the early days of venture capital back in 2006) and in all these years have seen few companies with as much potential as RockYou…
We’re still not sure what the final business model for turning those widget views into cash will be, but the opportunities are endless. I came up with 3 this morning alone…
I guess you’d have been one of those people who had the opportunity to work at a great startup like Google, Webvan, Amazon or Pets.com before they went public and would be regretting that decision to this day…
You’re young so I will give you some advice: the sooner you get in on these things, the better. You get more stock options, a beter strike price and you get to be a part of a revolution at the same time…
Sleep on it tonight and tomorrow give that recruiter a call back. Tell him Jeremy Liew referred you.
You’ll thank me for it some day soon. I guarantee it.
Mahalo,
Jeremy Liew
Managing Partner
Lightspeed Venture Partners
“Investing at the Speed of Light”
*** Sent from my iPhone 3G ***
The comments linked to the Lightspeed website, and even had the right email address (as is evidenced by the fast that Kyle emailed me a very polite response, despite the nature of the comments). I’m no Steve Jobs, so I was pretty surprised to see that there is a fake Jeremy Liew out there!
Posted by jeremyliew in business models, games, games 2.0, gaming, mmorpg, virtual goods, virtual worlds.
GigaOm says that IMVU is generating over $1m/mth in revenue:
Flying under the proverbial radar for the last four years, the web-based virtual world chatroom IMVU has released new jaw-breaking data: Since April 2004, it has amassed 20 million registered accounts, with 600,000 of those active monthly users. By comparison, Second Life took five years to acquire about 550,000 active users.
The company, well known to web surfers because of its ubiquitous ads, is now earning $1 million a month in revenue, 90 percent of that from the sale of virtual currency and 10 percent from banner ads embedded in its interface, CEO Cary Rosenzweig said. That works out to about $1.66 a month per active user.
This is within the range of monthlyly ARPU for MMOGs. The article notes that Peak Concureent Uers (PCU) is around 70k, so ARPU based on PCU is >$14/mth.
IMVU sells credits to its users who use these credits to buy items from their catalog to personalize their avatars. The vast majority of the items in the catalog are user generated. Of the 20m registered users, 100k are registered to make items in the catalog, but only a fraction of these are active. Still, they have created 1.7m items, so are averaging 20+ items created per active “item maker”.
Interesting stats.
Posted by jeremyliew in business models, freemium, games, games 2.0, gaming, virtual goods.
Howard Marks spoke at Paris GDC today, and Worlds in Motion has a writeup, including quotes from Marks about key stats for Acclaim’s free to play games:
For example, profitability is often measured through average revenue per user (ARPU). “Most of the time ARPU is $30-$40 a month,” said Marks. “A month! Not just one time.”
“The next thing is percentage of uniques,” he continued, defining “uniques” as players who spend money on a given game. “We’ve found in Asia, in Korea, we’ve found that 10% of people will spend money! I think it’s great! In the United States it’s less, more like 5-10%. But we’re getting there.”
The average lifetime for a player in the free-to-play space is 3-4 months per game, less than what is generally expected for a more traditional subscription MMO.
That statistic leads to churn rate, which describes player loss per month. “It turns out you lose a lot,” admitted Marks. “You should be prepared to say, ‘I only brought in 100,000 players this month, but only 10,000 stayed.’ That’s okay! That’s okay. Some of them will come back, and you can always get more.”
He also says (according to Worlds In Motion) that Acclaim will make $30m in revenues this year and break even on their core games business, but is investing heavily in R&D. Those are great numbers, and certainly place Acclaim to the front of the pack for free to play publishers in the West.
Posted by jeremyliew in game design, game mechanics, games, games 2.0, gaming, virtual goods.
Psycochild posted on crafting from a game designers point of view last week.
He notes:
Crafting is typically broken down into the following steps:
1. Learn a recipe.
2. Collect resources.
3. Create the item.
4. Sell (or use) the item.
5. Longer term: Advance your skill
He notes the game design goals of each step:
Learn a recipe: Create achievements, appeal to socializers
Collect resources: Create demand for resources that give goals to players
Create the item: Provide inflows into the game economy
Sell (or use) the item: Create economy game
Advance your skill: Give players incentive to advance
For game designers, it is worth reading the whole thing.
Posted by jeremyliew in database, flixster, scalability.
This guest post was written by Saran Chari, CTO and co-founder of Flixster. Flixster is a Lightspeed portfolio company.
_________________________________________________
As the social web evolves and platforms like Facebook and MySpace open up to applications, many companies and developers are rushing to get distribution to their millions of users by “going viral”. For the successful applications, this can often present a problem (a high-quality one for sure) – how do you actually scale your deployment to handle that growth?
At Flixster, we’ve been riding this growth curve for 2 years now – first with our destination site itself (www.flixster.com), and subsequently on our embedded applications on Facebook and MySpace. Across our properties, we now have over 1 million users logging in each day and we are approaching our 2 billionth movie rating. Like many others, we started out with just a single virtual server in a shared hosting environment. So how did we scale to where we are today?
The Holy Grail for scaling is “pure horizontal scaling” – just add more boxes to service more users. This tends to be relatively easy at the application layer – there are a multitude of cheap and simple clustering and load balancing technologies. The data layer is typically much more difficult to scale, and is where a lot of web startups fall down. High-volume applications simply generate too much traffic for any reasonably-priced database (I’ll assume you’re probably running MySQL as we are). So what are your options?
Buy yourself some time
The overriding mantra to everything we’ve done to scale our database has been: “avoid going to disk at all costs”. Going to disk to retrieve data can be orders of magnitude worse than accessing memory. You should apply this principle at every layer of your application.
Given that, the first thing to do is to throw in a good caching layer. The easiest way to scale your database is to not access it. Caching can give you a ton of mileage, and we still spend a lot of effort optimizing our caching layers.
If you can afford it, you can also buy a bigger box (RAM being the most important thing to upgrade). “Scaling up” like this can be effective to a point, but only buys you so much time because after all, it’s still a single database.
A replication setup can also buy you some time if you have a read-intensive workload and can afford to send some queries to a slave database. This has its problems though, the biggest of which is replication lag (slaves fall behind). Ultimately, replication can also buy you some time, but for most web application workloads, replication is a tool much better suited to solving high-availability problems than it is to solving scalability ones.
It’s time to break up
Eventually, you’re going to have to find a way to “scale out” at your database layer. Split up your data into chunks. Put the chunks on separate databases. This strategy is often called “sharding” or more generally “data partitioning” (I use the two interchangeably). It works because it reduces the workload (and price tag) for each server. It’s not trivial, but it is very doable.
There is a lot of literature out there on the technical details and challenges of sharding (see the resources section). At Flixster, we’ve followed many of the strategies described by LiveJournal, Flickr and others. One of the critical things for any startup however is figuring out when to do things.
Our primary trigger for deciding to shard a given piece of data is the size of the “active” or “working” set. It all comes back to the principle of never going to disk. All of our database servers have 32GB of memory, which we give almost entirely to the MySQL process. We try to fit most, if not all, of our active data on a given server into that space.
The ratio of active / total data will vary tremendously by application (for us it seems to be in the 10-20% range). One way to figure out if your active data is saturating your available memory is to just look at cycles spent waiting for I/O on your server. This stat more than anything else we monitor drives our partitioning decisions.
The other thing we look at for a given table is the raw table size. If a table becomes too big (in terms of # of rows or total data volume) to administer – i.e. we can’t make schema changes easily – we partition it. There’s no magic threshold that fits all applications, but for us we typically decide to shard a table if we expect it to reach 30-40 million rows.
It’s certainly easier to start off with a fully sharded architecture, but most applications do not (we certainly didn’t). In fact, I’d say that if you are spending a lot of time figuring out partitioning strategies before you even have any users, you’re probably wasting development resources. So how do you actually rip the engine out of the car while it’s running? Piece by piece and very, very carefully…
Crawl, walk, run
There are a variety of partitioning strategies, which we’ve employed incrementally as we’ve grown. Here are some of the things we’ve done (in ascending order of difficulty).
Table Partitioning
If you have a large table with a relatively small “hot spot”, consider putting the active data into a separate table. You will have some additional complexity managing the flow of data from the “active” table to the “archive” table, but at least you have split the problem a bit. This is the strategy we used early on for our movie ratings table, after realizing that 90% of the queries we were writing against it were looking for data from the last 30 days.
Vertical (or feature-based) Partitioning
Your application may have features that are relatively independent. If so, you can put each feature on a separate database. Since the features are independent, separating them shouldn’t violate too many assumptions in your application.
We did this pretty early on, and have had a lot of success with this approach. For example, movie ratings are a core feature that didn’t overlap too much (data-wise) with the rest of the database. Comments are another one. We’ve followed the same strategy for several other “features” and now have six separate feature databases.
This was a major step forward for us as it split our big problems into several smaller ones. You might not need to go any further…vertical partitioning may be sufficient. But, then again, you want to grow forever, right?
Horizontal (or user-based) Partitioning
Our success on Facebook drastically increased the load on our feature databases. Even our dedicated ratings database was struggling to keep up. A few months after our Facebook application launch, we deployed our first horizontal partition, separating different users’ ratings onto different physical databases.
One of the challenges of horizontal partitioning is in rewriting your data access code to figure out which database to use. With vertical partitions it’s relatively straightforward – which feature am I coding? With user-based partitioning, the logic can get much more complex. Another challenge in horizontal partitioning is the transition from your single data source into your partitions. The data migration can be painful. Extra hardware eases much of the pain, especially coupled with replication.
Following movie ratings, we have now horizontally partitioned a handful of other tables. We’ve also doubled the size of the partition cluster itself, going from four to eight master-slave pairs. We still use our vertically-partitioned feature databases, but they are under much less stress given the load absorbed by the horizontal partitions. And we continue to partition our high-volume tables on an as-needed basis.
Finally, some tips
• Start small, and bite things off in pieces that are manageable. Massive, several-month-long re-architectures rarely work well.
• Get some advice. We spent a good amount of time gleaning wisdom from the success of others (which they were kind enough to put online for everyone!). See the Resources section.
• Pick the best approach for your specific problems (but you have to know where your problems are – monitor EVERYTHING).
• You’ll never get there if you don’t start.
Bonus tip – come work @ Flixster!
If you’re a DBA and interested in working on these kinds of problems at a company that is already operating at scale, please send us a resume: jobs – at – flixster.com. We’re also hiring Java developers.
Resources
• Unorthodox approach to database design Part1:History
• Unorthodox approach to database design Part 2:Friendster
• LiveJournal Scaling History
• www.mysqlperformanceblog.com
Posted by jeremyliew in game design, game mechanics, games, games 2.0, gaming, social games, social gaming.
One of the take aways of the social gaming summit last week was that even casual MMOGs need to focus on their hardcore players as that is their source of paying players. David Perry of Acclaim noted in one panel that the players of Dance Online monetize far better than the players of “traditional” MMOGs that Acclaim operates:
As for who’s paying, Perry (Acclaim) expected most microtransactions to come from hardcore MMORPG playerskitting out the avatars with fancy armor and such. Instead, it comes from Dance. The game is a simple dancing activity, but because users spend so much time looking at their avatars, the appearance and identity becomes even more important.
Corroborating evidence comes from this great multipage review of Audition on the Escapist. Audition is a free to play dance themed game, published by Nexon in the US.
Audition isn’t a casual game, despite the presence of numerous casual markers: short play times, transparent rules, continuous save-free play, an item-based advertising model. Although the rules can be learned in minutes, mastery requires about a month of semi-serious dedication; “pro”-level skills take significantly longer. And, like a standard fantasy-based MMO, if you drop out of regular play, you’ll return to find that all of your friends are 10 levels ahead of you and worlds ahead in ability. Your reflexes can atrophy as quickly as embouchure for a musical instrument.
When you find the beat, however, the feeling is incredible. Your keyboard becomes an instrument through which you “play” a pounding, intense rock song. When you claim the highest score, you slide into the lead dancer position, supported by the other players worshiping at the altar of your groove. Whether you’re playing backup or lead, Audition reaches deep down into the shared performance experience that has driven homo sapiens to make music and dance since the birth of the species. Beat Up, performed well, closely replicates a creative “flow” state that is almost nirvana – if you release thought and embrace the physical pulse of the music, you’re carried along in a fast and furious musical flow that you share with your fellow performers. The game’s mechanics encourage this mindset with visual cues and flourishes that reward a steady, flawless performance; you achieve “beat up” status by sustaining 100+ perfect “moves.” Once I’d tasted a little Beat Up success, there was no going back.
I am a big fan of causal MMOs branching out beyond the fantasy, FPS and sci fi themes towards genres that have potential for much broader, mass market appeal. As I’ve noted before, there is increasing evidence that women drive viral growth more than men do, so if you want to see viral growth in games, it makes sense to make games that will appeal broadly to “non gamers”. Games like Popomundo, Parking Wars and Friends For Sale all do this well. (Friends for Sale is a Lightspeed portfolio company)
Another important lesson to take away from the review is that real world context can have an impact on gameplay:
When I first started playing, I was convinced these high-level hot stepper kids had something I fundamentally didn’t. This is partially true: They did have something I didn’t, and not just endless patience or time to spare – they knew the songs.
This actually makes a significant difference in your performance. Hardcore Audition players identify desirable songs by their speed and difficulty, but a significant number of the game’s “mainstream” players actively seek music that they already listen to outside the game. (And it works both ways; players have reported purchasing music they first heard in Audition.)
…Once I started playing Audition, however, a new aural landscape opened up before me. Rather than semi-sullenly tuning out environmental music at the mall, I started to recognize bands and individual songs. And because I associated them with the feelings of accomplishment and socialization I absorbed in Audition, I actually enjoyed hearing them. Whereas I had actively disliked – to put it mildly – the repetitive beat and high-pitched vocals in Avril Lavigne’s “Girlfriend,” after mastering it in Audition I found myself tapping along with it on the radio. Audition is, among other things, an as-yet unmatched music marketing engine.
I think we’ll see more of both of these trends as social gaming continues to evolve. I’d love to hear examples from readers of games that are leading these trends.
Posted by jeremyliew in datamining.
As web based product development and game development both become more iterative, better datamining and analysis becomes more and more important. But the data generated by users behavior can be almost overwhelming. How should a startup think about getting the most insight and value from their data?
Anand Rajaraman is a co-founder of Kosmix, a Lightspeed portfolio company, and also teaches a datamining class at Stanford. He knows a thing or two about the subject, and he suggests that more data usually beats better algorithms:
Different student teams in my class adopted different approaches to the [Netflix challenge] problem, using both published algorithms and novel ideas. Of these, the results from two of the teams illustrate a broader point. Team A came up with a very sophisticated algorithm using the Netflix data. Team B used a very simple algorithm, but they added in additional data beyond the Netflix set: information about movie genres from the Internet Movie Database (IMDB). Guess which team did better?
Team B got much better results, close to the best results on the Netflix leaderboard!! I’m really happy for them, and they’re going to tune their algorithm and take a crack at the grand prize. But the bigger point is, adding more, independent data usually beats out designing ever-better algorithms to analyze an existing data set. I’m often suprised [sic] that many people in the business, and even in academia, don’t realize this.
Another fine illustration of this principle comes from Google. Most people think Google’s success is due to their brilliant algorithms, especially PageRank. In reality, the two big innovations that Larry and Sergey introduced, that really took search to the next level in 1998, were:
1. The recognition that hyperlinks were an important measure of popularity — a link to a webpage counts as a vote for it.
2. The use of anchortext (the text of hyperlinks) in the web index, giving it a weight close to the page title.
First generation search engines had used only the text of the web pages themselves. The addition of these two additional data sets — hyperlinks and anchortext — took Google’s search to the next level. The PageRank algorithm itself is a minor detail — any halfway decent algorithm that exploited this additional data would have produced roughly comparable results.
In a followup post, he notes that:
1. More data is usually better than more complex algorithms because complex algorithms don’t scale as well (computationally) and
2. More independent data is better than more of the same data, but if data was originally sparse, then more of the same data can help a lot too.
Mayank Bawa of Aster Data chimes in to say that running simple analysis over complete datasets is better than running more complex data over sampled datasets for two reasons:
1. The freedom of big data allows us to bring in related datasets that provide contextual richness.
2. Simple algorithms allow us to identify small nuances by leveraging contextual richness in the data.
In other words, since human behavior is complex, and some behavior crossmatches are rare, using a sample of data will cause some important but rare correlations to be lost into the noise.
He also points out that Google takes a similar approach to datamining.
This is good stuff.
Posted by jeremyliew in asynchronous gaming, business models, casual games, game design, game mechanics, games, games 2.0, gaming, mmorpg, social games, social gaming, user generated content, virtual goods, virtual worlds.
The Social Gaming Summit was quite a success on Friday. Over 400 attendees seemed to enjoy the sessions based on the high proportion of people in sessions (vs in the lobby) and the fact that even the last session, that ended at 6pm on Friday evening, was very well attended.
The attendee list was a good mix of game developers and publishers, with people coming from both the gaming side and the social media side. Most of the attendees with gaming backgrounds came from casual gaming, web based gaming or MMOG backgrounds. With the notable exception of EA, there were few representatives from the giants of the console space.
Although each of the topics covered different topics, it was clear that monetization was top-of-mind for all panelists as the discussion on most panels eventually turned to this issue.
I (Jeremy Liew) moderated the first session, on What Makes Games Fun, featuring game design thought leaders Amy Jo Kim, Nicole Lazarro and Ian Bogost, plus John Welch of Playfirst, the company behind one of the most popular casual game ever, Diner Dash.
The discussion was wide ranging and covered Nicole’s framework for generating emotion in games and the four types of fun and Amy Jo Kim’s five game mechanics.
There was excellent discussion about how fun, addictiveness and business models can either collide or work together, with in depth discussion of two games in particular, Pack Rat and Parking Wars.
Pack Rat was lauded as an example of a game that did a masterful job of creating addictiveness through game mechanics, and a game that had a natural digital goods/service business model baked into it. But some panelists questioned whether the “grind” without real “payoffs” at different levels could burn players out. In contrast, Diner Dash had real changes in game dynamic and strategy as players level up (e.g. when Flo gets the coffee maker at level 4, it changes the winning strategy) that made leveling up more meangingful and rewarding.
Parking Wars was pointed to as a highly social game with a genre matching to the mass market that let players “play slight variations of themselves” where they could explore slightly nefarious behavior in a safe environment. But “winning” in Parking Wars forced activity to the edges of the social network, instead of to the core, so the “points” game mechanic ended up working against the “fun”.
UPDATE: Virtual worlds has an excellent writeup of the What Makes Games Fun panel.
The second session was focused on Casual MMOs and Immersive Worlds, with Joey Seiler from Virtual World News moderating representatives NeoPets, Nexon, K2 Networks and Gaia.
One of the key questions was how to get free to play users to open their wallet. Gamasutra covered this panel in detail and noted:
Added Kim (Nexon): “A lot of people think they can make money off of casual games where people play a couple of hours a week. I don’t believe that. When people get engaged with the social experience then they’ll buy items. You need to understand the psychology.”
Reppen (Neopets) continued: “For us, it’s all about a sense of ownership that our audience has. There’s a real sense that it’s their game… The identity component to virtual worlds is so important, but there’s so many other things going on in the meta games around earning points, acquiring wealth, shopping and customizing and creating your own experiences… It’s part of a mix.”
In other words, even for casual MMOGs, you monetize the hardcore players who tie their identity into the game. Erik Bethke (GoPetsLive) said the same thing at this years GDC previously in explaining why he applies game dynamics to make virtual worlds more addictive.
UPDATE: Massively writes up the panel in Q&A style.
After lunch Andrew Chung from Lightspeed moderated a panel on Asynchronous Games on Social Networks with the CEOs of the companies behind many of the top games on Facebook, including Friends for Sale, Zombies, Vampires, Warbook, JetMan and (fluff)Friends.
Inside Social games took live notes from the panel. One interesting counterpoint in response to the question, “How do you move people down the spectrum to make them more engaged and hard core?”:
Blake (Zombies, Vampires etc)- There is always going to be some subset of your userbase that’s never going to play more than their 30 minute lunch break, because that’s all the time they have. Don’t inundate users with too much experience at the beginning, gamers hate to read, I’ve never read a game manual in my life.
Siqi (Friends For Sale)- I think there’s a lot to learn from traditional MMO design, things like levels. If you get to the next level, you get this new shiny thing. It makes the game more complicated, but it works. Our hardest core users use more synchronous features.
Shervin (Warbook, Jetman, etc) – The first generation of social games were incredibly simplistic, and the platform was so viral, that it was a lot easier for apps to grow. But it behooves all of us to invest in content. I’m staying up late at night building social games 2.0, games with richer content, deeper stories, much better user experiences. It’s going to become harder for independent developers. I can’t talk about the games we’re working on, but you can look at Playfish. Their engagement levels are high and they’re growing faster than those that have come before.
In other words, games need to be easy to learn, but hard to master.
Next up was Dean Takahashi of VentureBeat moderating a panel on User Generated Games in Social Networks and Virtual Worlds. The speakers were from IMVU, Dogster, Three Rings (Puzzle Pirates, Whirled and Bang Howdy) and Habbo.
Virtual Worlds News has coverage of the panel and noted that:
In IMVU, said Rosenzweig, creators “do what they do because it’s cool, but they like making credits” by selling the items in world. That can then be cashed out through IMVU, which leads to 90% of its revenue, taking a cut while transfering IMVU credits to real world dollars. That user attitude is true of Dogster and Catster as well–users don’t get a cut of the money generated by creating games around their items and boosting activity. They just enjoy creating and sharing.
In other words, social game players generate content for love, not for money. But if there is money there to be had, they don’t mind taking some of that too! Last month Chris Alden noted the same experience in the blog economy.
UPDATE: Worlds in Motion also has a writeup of this panel
After a short break for cookies, the attendees reconvened to hear Brandon Sheffield of Gamasutra moderate a panel about Building Communities and Social Interaction In and Around Games, featuring the leaders of Kongregate, Zynga and Addicting Games, along with noted social architect and game designer Amy Jo Kim.
The discussion centered on the desire that many users had to communicate with each other, and how games often served as an easy way to break the ice and provide topics that made it easy to start a conversation. I haven’t found any coverage of this panel online unfortunately.
The final session of the day was focused on Monetization and Business Models for Social Games. My partner Ravi Mhatre moderated the panelists, including the leaders of Mochi Media, Sparkplay Media, Stardoll and Acclaim. This was a fantastic panel. Virtual Worlds News has great coverage.
Although most of the discussion was focused on the four models of advertising, subscription, digital goods and retail, David Perry noted that there are by his count 29 business models for games.
On the mix between advertising and virtual goods, the panel mostly agreed that virtual goods was the primary revenue stream but that advertising was an important secondary stream:
“Microtransactions and advertising go perfectly togetehr,” said Miksche. “Microtransactions drive our business, but we will never have 100% of our users wanting to pay for that. Advertising is a good way to monetize that remaining X percent.”
There was some good discussion about the tension between game balance and letting players buy powerful items in the games. Several panelists noted that self expression was a key driver of virtual goods sales:
As for who’s paying, Perry (Acclaim) expected most microtransactions to come from hardcore MMORPG playerskitting out the avatars with fancy armor and such. Instead, it comes from Dance. The game is a simple dancing activity, but because users spend so much time looking at their avatars, the appearance and identity becomes even more important.
That works well for Stardoll, a fashion-themed site, especially with trends that match the real world…
“We’re One-Click Dressing,” said Miksche (Stardolls). “You come to the site and instantly start dressing. For our users, young girls, that’s very important–instant gratification.”
For those who couldn’t attend, UStream.tv hosts video from the social gaming summit.
Andrew Chen’s blog also has his takeaways from the social gaming summit.
I’ve pulled together all the coverage I could find, but if there were additional posts, please let me know in comments.
Posted by jeremyliew in conferences, digital goods, game design, game mechanics, games, games 2.0, gaming, social games, social gaming, virtual goods, virtual worlds.
I’m very excited about the Social Gaming Summit on Friday that I’m co-producing. The program looks fantastic:
10:00am What Makes Games Fun?
» Erik Bethke – CEO, GoPets
» Dr. Ian Bogost – Founding Partner, Persuasive Games
» Nicole Lazzaro – President, XEODesign, Inc.
» John Welch – Co-Founder, President & CEO, PlayFirst (Diner Dash)
» Me (Moderator)
11:00am Casual MMOs and Immersive Worlds
» Daniel James – CEO, Three Rings (Puzzle Pirates)
» Kyra Reppen – SVP and GM, NeoPets
» Min Kim – Vice President of Marketing, Nexon America
» Patrick Ford, VP Marketing and Community Development, K2 Networks
» Joey Seiler – Editor, Virtual Worlds News (Moderator)
1:30pm Asynchronous Games on Social Networks
» Siqi Chen – Founder, Serious Business (Friends for Sale)
» Blake Commagere – Founder and VP Engineering, Mogad (Monsters apps – Zombies, Vampires etc)
» Shervin Pishevar – CEO, Social Gaming Network
» Mike Sego – Developer, (fluff)Friends
» Andrew Chung – Principal, Lightspeed Venture Partners
2:30pm User Generated Games in Social Networks and Virtual Worlds
» Jeremy Monroe – Director of Business Development, Sports & Entertainment, North America, Sulake Inc. (Habbo Hotel)
» Ted Rheingold – Founder, Dogster and Catster
» Cary Rosenzweig – President and CEO, IMVU
» Craig Sherman – CEO, Gaia Online
» Dean Takahashi – Writer, Venture Beat (Moderator)
4:00pm Building Communities and Social Interaction In and Around Games
» Jim Greer – CEO, Kongregate
» Amy Jo Kim – CEO, Shufflebrain
» Mark Pincus – Founder and CEO, Zynga Game Network
» Dave Williams – SVP, Shockwave, AddictingGames
» Brandon Sheffield – Writer, Gamasutra (Moderator)
5:00pm Monetization and Business Models for Social Games
» Jameson Hsu – Co-Founder and CEO, Mochi Media
» Matt Mihaly – CEO and Creative Director, Sparkplay Media
» Mattias Miksche – CEO, Stardoll
» David Perry – CCO, Acclaim
» Ravi Mhatre – Managing Director, Lightspeed Venture Partners (Moderator)
Attendence is limited to 400 and it looks like it is going to be a full house. Hope to see some readers there!
Lightspeed Venture Partners Blog 