De-Analysing Blizzard’s Starcraft 2 Marketplace
Earlier in 2009, Blizzard announced a non-commercial World of Warcraft add-on policy, which caused much discussion. Today at BlizzCon, Rob Pardo (illustrated) introduced the Starcraft 2 Marketplace: A future (after the game’s launch) system that would allow independent development teams to create custom “premium maps” for the game, and make money from them. That’s precisely what World of Warcraft add-on developers cannot do. So what’s changed?
Why Create a Starcraft 2 Marketplace?
Pardo stated:
“If you create a really cool map, with all original content, that’s awesome, you can put it up onto the service [Battle.net], and actually make money on your map.”
Blizzard is prepared to share a “portion” of the revenue if you create your own Intellectual Property, and don’t simply re-use their property. Seems reasonable.
The SC2 Marketplace is intended to allow parts of the mod‘ community to evolve from amateurs to professionals. “Fan made” maps were acknowledged as an important way to keep Starcraft alive – over time, players shifted from Blizzard-made maps to fan-made maps. But maps (Pardo used Warcraft 3 as an example) still tend to use Blizzard’s game assets (such as art textures), because creating original content takes a lot of effort. And passion alone does not pay the bills. By allowing map authors to earn money from popular maps, those people would be able to fund the creation of their own, original game assets.
There’s a real sense that Blizzard lost the chance to nurture and (commercially) gain from innovations within “their game engine”. Rob Pardo again:
“The Tower Defense maps came out of the Warcraft 3 community. And now you see Tower Defense in the PlayStation store…”
Earlier in the day Stompalina tweeted about the similarity between Battle.net (Blizzard’s community platform) and Steam (Valve‘s community platform). And she’s not wrong.
Both companies are unusual. They have both escaped from the traditional publisher-funded business model that underpins most major (non-casual/Flash) game development and distribution. Valve’s Steam originally gained popularity from games like Half Life, but has now become a method of distributing games written by others – everyone from small college/”garage” projects, to mainstream titles, like Total War.
Valve is already ahead of Blizzard in constructing a social-gaming platform, even though Blizzard was there first, and should understand the media better (from developing World of Warcraft). So perhaps opening up Starcraft as a semi-commercial platform for third parties is a new strategy in that race?
Why Not Create a Marketplace in Other Games?
Competition with the wider gaming industry does not explain why Blizzard are so unwilling to adopt a similar approach within their other games. Some of us (and I include myself) would like to do this within World of Warcraft. I have previously demonstrated that WoW has a huge pool of talent among its players, and that pool of talent is increasingly reluctant to work within WoW because it has become afraid to make money. Something which we now all seem agree is required to support major (time-consuming) projects.
It is possible to create original IP within WoW. Technically this would be more difficult within a MMOG, because players that don’t buy your content, still need to interact with those that do. But there are creative methods of working round those limitations.
One possibility is that Starcraft 2 is a new product, which is politically (within Blizzard’s decision-making process) and technically (programmed to be supported from the outset) far easier to impose a new strategy on. And we might eventually see a more relaxed approach in Azeroth.
My fear is that World of Warcraft is being treated differently because its brand is to valuable at this stage in its life-cycle.
Shrewd observers will note that Blizzard have started “doing the Star Wars thing” with the WoW brand: The revenue directly from the game gradually becomes less important than all the merchandise and franchise opportunities. Soft drinks and Trading Card Games were just the beginning…
The problem for “fan-based” projects is:
- Franchise and license opportunities are not available to “the little guy”. They’re not the large businesses Blizzard look for.
- If you sell a license it has to be worth something. So a “fan project” cannot co-exist with a franchised project that it (often inadvertently) conflicts with.
There have been several examples over the last year where conflict has arisen. Unfortunately, I’m not able to publicly discuss all of them. Suffice to say the legal threats are very real: Suddenly one finds one’s self liable for lost earnings of the franchisee and Blizzard. That’s almost certainly more money than you have – few people are prepared to risk bankruptcy.
On the Road to Damascus
If Blizzard have had a change of heart, will anyone trust them? Sadly the answer is yes. Not least because individuals tend to confuse the company with its products. And the corpses of all those fallen add-on developers decay fast.
A marketplace doesn’t fit Blizzard’s culture – somewhat secretive, protective, and controlling of its work. But Blizzard seem very similar to Apple. And Apple have managed to sustain a very successful iPhone store, full of applications created by independant developers. If both parties benefit, these uncomfortable partnerships can thrive.
Perhaps there is hope after all?
Postscript
The following day, in an interview with DirectTV, Rob Pardo was asked this question directly: Why Blizzard are endorsing commercial SC2 mods, while they have just outlawed commercial WoW mods? His reply was:
“We’re not making money from the people that are doing third party things for WoW. It’s not really allowed to go out and make stuff around WoW without licensing it from us. It’s really us just protecting our Intellectual Property.”
Favorite Fishing Places
This article analyses the favourite fishing locations of World of Warcraft anglers. Both where and why.
The most popular single zone is the Grizzly Hills, with Azshara’s Bay of Storms and Wintergrasp in joint second place. Reasons are split into artistic (music, scenery), emotional (relaxation, memories), practical (fish caught, convenience), and social (companions, player interaction) themes. Overall, each theme has similar importance. The article discusses the apparent contardiction between desires for solitude, and to be surrounded by life.
This is the second of several topics that explore the reasons people fish in a virtual world, ultimately drawing parallels with fishing in the physical world. Read more of this article »
Where We Fish
This article analyses where players fish in the game World of Warcraft. It reveals the role of daily quests in shaping our fishing habits, demonstrates just how popular city-fishing is, and starts to reveal why we fish. This is (hopefully) the first in a series of articles that collectively examine why people fish in this massively multiplayer online game.
The map shows number of successful fishing casts (diameter of each circle), by area. Numbers are daily totals for all United States and European realms combined, based on a sample in July 2009. Click the map for a larger view.
A successful cast is one that does not catch a junk item, which might occur if the anglers’ skill is to low. There are 14 million successful casts each day, catching 16 million fish: Some casts catch more than 1 fish. In addition, there are 4.5 million unsuccessful casts (that catch a junk item). Unsuccessful casts are not shown on the map.
“Old Azeroth” refers to the continents of Kalimdor and the Eastern Kingdoms (the pre-expansion game). Within Northrend (the main area shown on the map), casts into coastal waters are shown separately from “inland” casts in other zones.
In each area, the total number of casts is divided into 3 parts:
- Open Water (dark blue) – Casts into bodies of open water.
- Daily-Related (gold) – Casts while trying to complete a daily fishing quest. This includes all casts while trying to complete the quest, not just those that catch a quest fish.
- Pools (light blue) – Casts into schools of fish.
Northrend is the continent hosting the current game expansion, Wrath of the Lich King. The continent is home to higher-level (more veteran) players. Expect to find most fishing activity here – and we do: There are 9.3 million daily casts in Northrend – two thirds of all successful casts.
A sixth of all casts are related to the daily quests, in spite of the fact that there is just one such quest available each day (the area varies between realms, randomly each day). The Northrend fishing quests are the most popular quests in the game – completed by over 300,000 characters each day. No, really – at least before patch 3.2 was launched, which made Heroic dungeons popular again. Anglers’ might be motivated by the additional reward. Or this might suggest a far greater need to guide players. Either way, it raises some questions, such as, why is there just one fishing quest per day in the current game expansion?
Ignoring daily quest-related fishing, the most popular single location is Dalaran’s Eventide Fountain, with 1.4 million casts per day – equivalent to 1 person on each realm fishing there for 12 hours each day. The irony is that Dalaran’s Eventide Fountain is also one of the smallest body of water in the entire game. Cities account for a third of all casts – Dalaran is not the only popular city. At least half of the “Old Azeroth (Inland)” casts are casts in the waters of major cities (such as Stormwind or Orgrimmar).
So, half of all fishing activity is either directed by quests, or occurs in cities. Training (cooking and/or fishing skills) is also an important reason to fish, although it is harder to estimate how important.
Pool fishing is normally the fastest way to catch “valuable” fish. Yet only 17% of casts are from pools. Even if we look at areas with no quests and desirable “Northrend” fish, like the Grizzly Hills, half of all casts are still in open water. This isn’t the only example that suggests that anglers really are quite lazy, and don’t want to much hassle when fishing.
The remainder of this article explores some of these issues in more detail, using information about where we fish to start to explain why we fish. It also describes the method behind the numbers, with a technical appendix containing data. Read more of this article »
De-Analysing Blizzard’s Add-On Policy
Blizzard Entertainment’s new add-on policy has been discussed by everyone from Lum to Slashdot. The number of developers directly affected by the change is small, since only a few add-ons are popular enough to be considered commercial ventures. The policy is more significant because it changes a lot of established conventions, and goes to the heart of how Blizzard embraces (or increasingly, shuns) the talent within its player community. This article is an attempt to analyse the real motivations behind the policy, and highlight the apparent contradiction in policy between in-game add-ons and web-based services. Read more of this article »
Social Reconstruction of Public Transportation Information
The UK‘s local public transport data is effectively a closed dataset. The situation in the US seems similar: In spite of the benefits only a handful of agencies have released raw data freely (such as BART and TriMet on the west coast of America).
That hasn’t stopped “screen-scraping” of data or simply typing in paper timetables (from Urban Mapping to many listed here). Unfortunately, the legal basis for scraping is complex, which creates significant risks for anyone building a business. For example, earlier this year, airline Ryanair requested the removal of all their data from Skyscanner, a flight price comparison site that gathers data by scraping airlines’ websites. How many airlines would need to object to their data being scraped before a “price comparison” service becomes unusable?
User-generated mapping content is evolving, often to circumvent restrictive distribution of national mapping. Services include OpenStreetMap and the recently announced Google Map Maker.
Micro-blogging, primarily through Twitter, has started to show the potential of individual travellers to report information about their journeys: Ron Whitman‘s Commuter Feed is a good example. Tom Morris has also experimented with London Twitter feeds.
This article outlines why the “social web”/tech-entrepreneur sector may wish to stop trying to use official sources of data, and instead apply the technology it understands best: People. Read more of this article »
Paul Saffo on The Revolution After Electronics
Paul Saffo spoke to Stanford’s Media X conference on the art of predicting the future. Specifically predicting which technology will come to dominate the next decade. Paul’s talk may at first seem somewhat contradictory in nature: Demonstrating how to do it, while simultaneously showing it can’t be done. This article summarises the talk.
30 Year Cycle
Every 30-50 years a new science turns into a technology. With approximate dates:
- 1900: Chemistry
- 1930: Physics
- 1960: Electronics
- 2000: Biology
We are now on the cusp of a revolution from electronics to biology. The precise inflection point, the point of change, may not yet be clear.
Paul noted that Thomas Watson’s famous misquote, “I think there is a world market for maybe 5 computers”, was made in 1953, right on the cusp of the electronics revolution: Aside from the fact that he was talking about a specific machine, and not all computers, the quote is a good example of how it is difficult to predict the future at such points of radical change.
Forecasting the Future
The goal is not to be right, but “to be wrong and rich”: It is easy to take the view that one cannot forecast. If you do attempt to forecast you will still mostly be wrong, but the very act of trying will increase your chance of success over those that do not try.
The further away from a point in time you predict into the future, the greater the level of uncertainty. The difficulty in forecasting is finding a balance between being too narrow and too broad. Forecasting might use wildcards. The “hard part” is to be wild enough.
Typically forecasts for a new product or technology’s introduction are linear: The magnitude of the amount of use of the technology is forecast to grow steadily with time.
Reality tends to be represented as an S-shaped curve: In the early stages the magnitude of use is below the expectation generated by the linear forecast. Usage then rapidly grows, such that the actual usage rises above the prediction in the later stages. The result is that in the first part, forecasters tend to over-estimate performance, while latterly they under-estimate performance. Venture capitalists tend to have linear expectations, and so are disappointed in the early stages, while failing to see the later potential.
Robots and Inflection Points
Paul Saffo used the example of DARPA’s annual competition for robot-driven cars. In the first year only a handful of competing robot drivers made it out of the starting gate. No car completed the challenge. The next year 22 out of 25 robots got further than the leader in the first race.
The example gives a quantifiable measure of how the technology is developing, year to year.
Spotting the inflection point, the place at which real, dramatic change starts to occur, can still be hard. Sometimes it can be spotted using data which has been ignored or hidden. Sometimes it is a case of looking for what does not fit. The anonymous quote, “history doesn’t repeat itself, but sometimes it rhymes”, is apt. Look back in time as far as you look forward.
The good news is that if you miss an indicator, you still have lots of time to spot another.
Sensors
Paul contested that the last three decades had been characterised by a dramatic cheapening of a component technology, which in turn had led to the widespread use of a product:
- 1980s: Cheap processors led to the processing age. The result, widespread use of PC.
- 1990s: Cheap communications lasers led to the access age. The result was the network infrastructure to support the World Wide Web.
- 2000s: Cheap sensors are leading to the interaction age. Applications are currently missing, but widespread use of robots appears to be the future.
Biology and Electronics
Electronics is building biology, and Paul expects that eventually biology will rebuild electronics: These technologies are far from isolated.
An example of developments in electronics progressing biology can clearly be seen from work on the human genome. A well funded government-backed project was beaten by a far smaller project. The smaller project was able to successfully deploy robots, with the results that the cost of the work dropped by a factor of 10 each year. The government project had been funded based on the cost of technology at the outset, and initially failed to adequately respond fully to the changing cost structure.
The creation of the first artificial genome in January 2008 may yet prove to be the inflection point.
Trust Instincts at Your Peril
“Assume you are wrong**” (** and forecast often)
Paul used the example of the sinking of a US naval fleet near Honda, on the west coast of the United States, on 8 September 1923. The fleet had been navigating using a forecasting technique called “dead reckoning”. The coastline had a (then) new technology available to assist navigation – radio direction finding. This allowed a bearing to be given between a land station and the fleet.
The radio direction finding gave an unexpected result that did not match the forecasted position. The lead boat in the fleet concluded that their position was more favourable than anticipated (closer to their destination), and turned sharply… straight into the rocks they had been trying to avoid. The 11th boat in the fleet did not trust the judgement of the lead boat, and when the fleet turned, it hedged its bets, slowing and waiting to see what happened. It was one of only 5 ships from the fleet not to run around.
The morale of the tale: Hedge your bets, but embrace uncertainty. Or as written once on a tipping jar:
“If you fear change, leave it in here.”
Divergence of the Species
The question was asked, will biotech lead to a further aggregation of wealth? Yes. The electronics revolution had itself deepened inequality. Biotech raises a particularly ugly spectre which extends beyond wealth, to life itself. The wealthy would be likely to use their wealth to extend their lives. The ultimate outcome – species divergence. Currently the rich tend to benefit from better health care, and so extend life. But biotech is likely to create a lot more options.
Dave McClure on Social Networking and Web 2.0
Dave McClure addressed a Edinburgh Entrepreneurship Club/Edinburgh-Stanford Link event on 29 January 2008. He outlined some of the advantages of “Web 2.0″, talked extensively on the use of real-time metrics to evolve web services, developed a history of social networking websites, and highlighted the interesting aspects of Facebook. This article summarises Dave’s talk, with some additional commentary from myself.
Advantages of Web 2.0
Web 2.0 is characterised by the:
- low cost of acquiring large numbers of users,
- ability to generate revenue through advertising/e-commerce,
- use of online metrics as feedback loops in product development,
- sustainable long term profitability (at least for some).
Dave McClure did not actually try and define the term, which was probably wise. Generally the term is applied to websites and services where users collaborate or share content.
Web 2.0 has a number of advantages (although it could be argued that some of these apply to earlier iterations of the internet too):
- APIs – the ability to act as a web-based service, rather than just a “website”.
- PC-like interface, albeit still 5 years behind contemporary PC interfaces.
- RSS feeds (for data sharing) and widgets (user interfaces embedded elsewhere).
- Use of email mailing lists for retaining traffic. While email certainly isn’t a “web 2.0″ technology, his argument is that email is increasingly overlooked as a means of retaining website visitors.
- Groups of people acting as a trusted filter for information over the internet.
- Tags (to give information structure) and ratings (to make better content stand out).
- Real-time measurement systems rapidly giving feedback. Key is the immediacy of the information, and the ability to evolve the web service to reflect that.
- Ability to make money from advertising, leads and e-commerce. While true since about 1995, the web user-base is now far larger, so the potential to leverage revenue also greater.
Metrics for Startups
I believe the ability to very accurately analyse website usage, implement changes, and then analyse the results, is a key advantage of web-based services. It is an advantage often overlooked by information technology professionals and programmers. I’m not sure why – possibly because web service developers:
- don’t appreciate how hard/expensive gathering equivalent information is in other sectors of the economy, or
- are scared to make changes in case they loose business, and/or believe their initial perception of what “works” to be optimum, or
- just lack the pre-requite analytical curiosity to investigate?
Or perhaps Web 2.0 just isn’t mature enough yet for developers to have to worry too much about optimisation: A new concept for a site will probably either fail horribly or generate super-normal profits. The sector isn’t yet competing on very tight margins, where subtle optimisation can make or break profitability. Of course, optimisation of websites can deliver substantial changes in user behaviour. For example, I have found that a relatively subtle change to the position of an advert can alter the revenue generated by over 20%.
Dave McClure developed the AARRR model. AARRR segments the five stages of building a profitable user-base for a website:
- Acquisition – gaining new users from channels such as search or advertising.
- Activation – users’ first experience of the site: do they progress beyond the “landing page” they first see?
- Retention – do users come back?
- Referral – do users invite their friends to visit?
- Revenue – do all those users create a revenue stream?
For each stage, the site operator should analyse at least one metric. The table below gives some possible metrics for each stage, with a sample target conversion ratio (the proportion that reach that stage).
| Category | User Status (Test) | Conversion Target % |
|---|---|---|
| Acquisition | Visit Site – or landing page or external widget | 100% |
| Doesn’t Abandon: Views 2+ pages, stays 10+ seconds, 2+ clicks | 70% | |
| Activation | Happy 1st Visit: Views x pages, stays y seconds, z clicks | 30% |
| Email/Blog/RSS/Widget Signup – anything that could lead to a repeat visit | 5% | |
| Account Signup – includes profile data | 2% | |
| Retention | Email or RSS leading to clickthrough | 3% |
| Repeat Visitor: 3+ visits in first 30 days | 2% | |
| Referral | Refer 1+ users who visit the site | 2% |
| Refer 1+ users who activate | 1% | |
| Revenue | User generates minimum revenue | 2% |
| User generates break-even revenue | 1% |
These metrics become critical to the design of the product. Poor activation conversion ratio? Work on the landing page(s): Guess at an improvement, test it out on the site, analyse the feedback, and iterate improvements. Gradually you’ll optimise performance of the site.
I find this attempt to structure analysis and relate it back to core business performance, very interesting. However, the sample metrics can be improved on a lot, depending on the nature of the site. For example, to track virality (referral), I might watch the monthly number of del.icio.us adds, or monitor the number of new links posted on forums (Google’s Webmaster tools allow that). Tracking users all the way through the tree from arrival to revenue generation needs to done pragmatically where revenue is generated from very infrequent “big-ticket” sales: With minimal day-to-day data, it can take a long time to determine whether a change genuinely has improved long-term revenue, or whether natural fluctuations in day-to-day earnings just contrived to make it a “good day/week/month”.
Now I know this approach works, but why it works is less clear. We might like to think that we are genuinely improving the user experience, and maybe we are. However, it could be argued that merely the act of change is perceived by users as an improvement – a variation of the Hawthorne effect. The counter argument to the Hawthorne effect can be seen on sites with low proportions of repeat visitors: The majority of those experiencing the improvement will not know what was implemented before.
History of Social Networking
Dave McClure’s interpretation of the timeline of the development of social networking sites is as interesting for what it includes, as for what it omits: No Geocities; no usenet; no forums; no MUDs… The following timeline shows key services in chronological order, except without dates – all the services shown were created within the last ten years:
- Email lists (Yahoo Groups)
- 1.0 Social Networks (Friendster) – these early network established the importance of up-time (service reliability) and the ability of users to manipulate pages.
- Blogs – links between weblogs acting as networks.
- Photos and video (Flickr, YouTube) – created a sense of community, and allowed tagging/grouping of content.
- 2.0 Social Networks (LinkedIn)
- Feeds and shared social information (Upcoming.com event planner)
- Applications and widgets – the ability to embed data about a user’s friends in applications is probably “the most powerful change on the internet in the last ten years”.
- Hosted platforms (OpenSocial, Facebook) – most services are likely to allow 3rd-party developers to provide applications on their platforms.
- Vertical communities (Ning) – ultimately this may develop such that a service like Facebook acts as a repository for a user’s online identity, while specific groups of people gather on other networks.
- Availability of information – a single sign-on, with automatic data transfer between services.
The future may be “Social Prediction Networks”. This is a variation on the theme of using trusted networks to filter content: Instead of Blogging meets Search, I characterise Social Prediction Networks as Digg meets Facebook. Shrewd observers will note Facebook has already implemented Digg-like features, while simultaneously topic-specific, community-orientated Digg-clones are being launched. People gather into interest groups around a topic, and then through use of tagging and rating, the community filters content. The system effectively predicts what other people in the group will find useful. This may be an optimum approach for groups above the Dunbar number (or an equivalent number representing the maximum number of people a person can form stable relationships with).
Interesting Aspects of Facebook
Three were discussed:
- Social graph (friend list) – email and SMS (mobile phone) service providers have rich data on the frequency of communication between people, yet aren’t using this information to form social networks. Dave noted that two major email service providers, Yahoo and AOL, are currently struggling to thrive – this could be an avenue for their future development.
- Shared social activity streams – knowledge of what your friends think is important. Friends are more likely to influence you than people you do not know.
- API/Platform – dynamic behaviour and links across your social network.
Further Observations
Will growth in social networks continue? Yes – the friend list adds value to the content.
Will others compete? Probably, as a “long-tail” of networks, likely topic-specific.
Can social networks be monetarized better? Currently social networking services generate far less revenue than search services. The challenge for social networking sites is to move towards the wealthy territory of search services. At the same time, search services are moving towards becoming more like social networking sites.
How can traditional companies engage with social networking sites? Social networking sites work best for sales where a product has a strong aspect of peer pressure in the decision to buy. The most important advice is not to create a copy of a website: Instead provide less complex content that uses social networks to draw users to a website.
Applications for social networks tend to be over-complicated, normally because programmers attempt to implement functions found in software they have previously written for other platforms or websites. Generally the successful applications are very simple. Some developers have opted to break complex applications into a series of smaller applications, and use the virality of social networking sites to build traffic for one application from another.
Social network applications are exceptionally viral. They can gain users very rapidly, yet also loose users just as fast. Much of this virality comes from feeds, which typically alert friends when a user installs an application. Within a few years the feed is likely to be based on actual usage of an application.
Facebook now allows applications to be added to “fan pages” (or product pages) – so individual users need not now be forced to install an application to use it.
Those using email lists for retention are best to focus on the title of the email, and not the content. Merely make it easy to find a URL in the content. The key decision for the reader is whether to open the email. What the email says is almost irrelevant – they’ve already decided to visit the site based on the title.
