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Thursday 28 February 2013

What if movies were designed for free-to-play?

This tweet from Ben Cousins over at ngmoco (looking forward to The Drowning!) got me thinking:

In a response, I said that's true if you consider the entirety of the movie industry (where some people buy everything they watch, while some pirate it - and yet another group pirates the movies and buys a lot of movie-related merchandise), but that on the level of any one movie, if they're analyzed from a free-to-play angle, they're terrible businesses.

I guess that obligates me to write something about how would a free-to-play movie work. Not being very well versed in the details of how movies get produced today, I guess I'm either way off in the deep end, or in an advantageous position to speculate about it. Take your pick, shoot me down in the comments. It's entirely possible it's not even possible to make every movie work as a standalone free-to-play (in which case we're back to something like Netflix as the freemium business model for movies), but since we did figure it out for games, why shouldn't we try to figure it out for movies?

What's a good free-to-play product design like? A quick summary:

  • A basic version of the product should be available for free. If someone's motivated enough, they should be able to enjoy the full experience without opening their wallet, but they'll have to contribute in some other way. Pirated movies don't count, that's not contributing. Ad support is a weak solution - better than nothing, though.
  • "Basic" doesn't mean low quality, because the free product should be as engaging as the premium version. A low-rez online clip doesn't cut it, it just drives people back to piracy.
  • The bar to spending money should be really, really low and well incentivized. An Amazon $0.99 rental for 24h counts, iTunes store $15 download does not. The incentives still need work, though - ease of access, good recommendations, easy streaming to the big screen are a good start.
  • The upper limit to how much one customer can spend on the product should be high enough to be practically unlimited. Spending more should always result in some additional marginal value.
  • High value customers come in two shapes: those who buy something really expensive once (such as a collector's edition, like Ben was linking to), or those who keep spending, again and again.

In some markets and for some movies, the industry does manage to capture the middle. However, these are not optional points for a free-to-play design. You have to consider all of them, or you're turning away customers. A free-to-play design typically expects a few percent of the audience to pay for their experience.

On the level of basic free edition, the easy suggestion to make is to have each movie be viewable from its own site in exchange for a Facebook Like or a retweet. By doing that, free viewers are contributing viral visibility to the product. As I mentioned above, this should not be a crappy low-rez edition, but a real, enjoyable stream. Done this way, movies would have to have stable, long-term addresses, rather than the marketing campaign sites they now have, but that would be a good thing. Free-to-play is a lot about the long tail, in both volume and time.

That site can sell offline copies as DVD or BluRay for someone who (for whatever their reasons) can't or doesn't want to stream. That may be quaint, but hey, people still buy vinyl, too. It can also rent the movie for streaming to something else than a computer. Clearly, there would need to be several incentives for someone to want to contribute a couple of bucks for the regular edition of the movie, and this is probably the hardest thing to get right. Eg, you could charge for pause function, but that would be a pretty dick move, likely to drive away people who would otherwise enjoy the experience. Perhaps the free edition should only come to play a month after release, until which streaming always costs.

Stuff like comment tracks, making of, etc can be a paid extra. They're made for true fans, and true fans are by definition willing to pay for the work. Some of that stuff can reasonably be priced much higher than it typically is, today.

Selling merchandize and collectors editions are obviously something the site should feature. It should also have exclusive items, such as limited edition access to the production crew. Just look at any of several successful Kickstarter campaigns to see what might a $5000 edition of a movie be packaged with. Today's featured documentary on Kickstarter about the Arab Spring has 10 premier night tickets next to the crew for that price, and another reward for double that (check it out yourself). The Kickstarter rewards are time-limited, but a free-to-play movie should have similar items available for fans throughout its distribution lifetime. They will need to be refreshed. Free-to-play is a service, not a product.

A re-watch would need to have some special features for it, all of which could be paid stuff. This would benefit some movies much more than others - and create an incentive for artists to create more movies like that. I wouldn't mind!

Now, I haven't even tried to run any numbers on this thought experiment, and I don't know where to pull the reference data. According to Box Office Mojo, last year's top grossing movie was The Avengers at $623M US, and on position #100 was The Five Year Engagement at $29M US box office revenue. The same site estimates US ticket prices today at $8.05, so that would mean 78 million US viewers for Avengers and 3.6 million for "5 year". However, those figures probably do not include rentals or online, and almost certainly do not include merchandize, which I would guess is a substantial extra for Avengers (and included in my suggested model above), so basing any comparisons on those data points would be very flawed.

A blockbuster film like Avengers collects most of its revenue very close to the release date, but other movies, like the perennial favorites Sound of Music, The Wizard of Oz and It's a Good Life, or somewhat more recent examples like Pulp Fiction, Inception or The Fight Club would keep racking views and revenue for years, even decades. So, would the Avengers ever get its current revenue as free-to-play? Perhaps not. Would Five Year Engagement? I don't see why not. Would Pulp Fiction or Fight Club, neither of which apparently make it to the all-time top 200 grossing movies on Box Office Mojo be able to generate a billion dollars off their engaged fan base over time? Of course they would.

Are you prepared to deal with a negative test result?

Someone recently asked me, whether he should do a limited-market test launch for a product he knows isn't finished yet, in order to learn more from actual users. A worthy goal, of course. Perhaps you're considering the same thing. I have, many times. Before you decide either way, consider the following:

What do you expect to learn? If you need to see people using your product, throwing it on the App Store won't help you achieve that objective. Better you go a nearby meetup of people you'd like to see using your product, introduce yourself and ask them to test it while you're looking. If you can't take the product with you or need to have an entire team experience the end-user feedback first hand, invite 10 people over for some pizza (either to your office or some more cozy environment) and record the event on video. I promise you, it'll be an eye-opening experience.

Do you have a hypothesis you're trying to verify? Can you state how you're verifying it? Can you state a test which would prove your hypothesis is false? Are those tests something that you can implement and measure over a launch? Awesome! If not, then you need to think harder and probably identify some other way of gaining the insight you're looking for.

Are you just trying to gather experience of something not directly related to the product itself? Such as, you don't yet know first hand how to manage an App Store launch. Well, you could launch your baby -- or you could quickly create another product with which to learn what you needed to learn. This tactic has the added benefit that such side-products are typically simpler, so they're easier to analyze for the understanding you're after.

But most importantly, what will you do if your test comes back with a negative result? Far too often, this hasn't been given any consideration, and when it does happen (as it typically does, if you haven't thought through the process), the response is "oh, the test failed, never mind, we'll just go ahead anyway". Unfortunately, in most such situations, it was not the test which failed. Rather, it successfully proved that the hypothesis being tested was incorrect. This is a completely different thing, and going ahead without changes would be a mistake after such a result. You have to be prepared to make the hard decisions. For example, Supercell killed Battle Buddies after their test launch showed it would not convert enough people.

You should test often and early. You should gather market data to support significant further investments of time or resources to any development you're undertaking. But you should also be prepared to take any necessary actions if the tests you're running show that your assumptions were incorrect, and the product doesn't work the way you intended. Those are not easy decisions to take, if you're invested into the product, as most creators would be. Think it through. A launch is not a test.

Tuesday 5 February 2013

Arbitrage as a game mechanic

Reading this rather amazing story about cross-border arbitrage, I could not help but think about how it applies to game design.

Here's how the arbitrage math adds up. The ferry costs approximately $275 round trip, and gas is about $8 a gallon in Sweden, which, if we assume our car gets around 30 miles per gallon, gives us $435 in expenses. Throw in food, lodging, and other miscellaneous costs, and the total should come in around $600 or so. Remember, diapers costs more than twice as much in Lithuania as they do in Norway, so we only need to buy that much to break even.

If in the real world it's possible to entice enough entrepreneurial activity from a neighboring country to make the supermarkets of south Norway run out of diapers, imagine how powerful arbitrage opportunities are for game design. It can do everything:

  • Increase play frequency, as you need to come often to exploit recurring opportunities
  • Drive explorative gameplay, as more and more players search for new kinds of arbitrage
  • Incent specialization, because to exploit arbitrage, you need to focus on a particular activity
  • Drive expected lifetime up, as leaving the game means leaving value on the table
  • Drive lifetime value up, because in a free-to-play game, longer play time means more opportunities to buy
  • Drive virality up, because players have incentive to find both supply and demand for their particular arbitrage skill

Many of these factors apply even to a single-player game that simulates market activites. Look no further than the classics of market games, David Braben's Elite (1984) (or Star Trader, which preceded it by a cool 10 years). However, the forces really come to forefront when applied to a social game where the arbitrages don't need even need to be programmed in, as long as the design doesn't eliminate their possibility. Players will probably discover them.

That doesn't mean it's trivial to fully exploit that capability, though. For example, I don't think we ever really explored the arbitrage mechanics fully in Habbo Hotel, even though the system is full of player to player trading, rare items, well-hidden nooks and crannies, and whatnot. The most important feature missing in Habbo Hotel is rich support for specialization. RPG style games bring specialization through character classes and skills, resource management games through directing players to invest their earned resources in a particular type of activity, and so forth. The game mechanic should reward specializing, by making it possible for a player highly capable in a particular section of the gameplay to trade that capability with others for the skills or resources provided by another type of specialization. Don't reward being a generalist, or allow maximizing all stats.

Thursday 1 March 2012

Increase engagement with social analytics

Last week I discussed segmentation as a method for identifying and differentiating customers for their specific service needs. Whether used for young cohort's introductory period service, high-value segments special treatment, or to identify the group on a transitionary path to high value and help accelerate that process, segmentation is a very versatile tool for business and product optimization. It can be approached with many techniques and I'll go on to more implementation details on those. But first, an introduction to the next topic after segmentation: social metrics.

While social behavior is not historically strongly featured by many products in either the gaming space or in the wider scope of freemium products, your customers and users are people, and thus they will have social interaction with others you can benefit from. If you can capture any of that activity in your product measurement, it can serve as a very valuable basis for in-depth analytics. Today, I will focus on those products and services in which their audience can interact among each other - that is, there is some sort of easily measured, directly connected community.

Any such product will probably have user segments such as:

  • new users who would benefit from seeing good examples of effective use of the product, guidance on the first steps, or some other introduction beyond what the product can do automatically or what your sales or support staff can scale to
  • enthusiasts who would like nothing better than to help the first group
  • direct revenue contributors who either have a lot of disposable income, or otherwise find your service so valuable to them that they'll be happy to buy a lot of premium features or content
  • people who, though they're not top customers themselves, find innovative ways to use premium features for extra value
  • people who are widely appreciated by the community for their contributions, "have good karma"
  • people whose influence within the community is on the whole negative due to disruptive behavior

and many, many others. Two of these groups are easy to identify simply based on their own history, I'm sure you'll recognize which two. The other four are determined largely by their interaction with the rest of the community and other users' reaction to their activities. How do you find them? This is a rapidly evolving field of analytics with constantly growing pool of theoretical approaches and practical tools, and can look daunting at first. The good news, there are many practical tools already, and while theoretical background helps, the first steps aren't too hard to make.

You'll need to develop some simple way to identify interaction. The traditional way to begin is to define a "buddy list" of some sort similar to Facebook friends network, Twitter following, or a simple email address book. However, I find a more "casual" approach of quantifying interactions works better for analytics. Enumerate comments, time in the same "space", exposure to the same content, common play time, or whatever works for your product. At the simplest level, this will be a list of "user A, user B, scale of interaction" stored somewhere in your logs or a metrics database. This is already a very good baseline. With the addition of time/calendar, you'll be able to measure the ebb and flow of social activities, but even that isn't strictly necessary.

Up to data set of about 100k users and half a million connections or so, you'll be able to do a lot of analysis just on your laptop. Grab such a data dump and a tool called Gephi and you're just minutes away from fun stuff like visualizing whether connections are uniformly defined or clustered into smaller, relatively separate groups (I bet you'll find the latter - social networks are practically always have this "small world" property). This alone, even though it isn't an ongoing, easily comparable metric, will be very informative for your product design and community interaction.

In terms of metrics and connected actions, here's a high-level overview of some of the more simple-to-implement things:

  • highly connected users are a great seed for new features or content, because they can spread messages fast and giving them early access will make them more engaged. While in theory you'd want to reach people "in between" clusters, the top connected people are an easy, surprisingly well functioning substitute.
  • those same people with a large number of connections are also critical hubs in the community, and you should protect them well, jumping in fast if they have problems. This is independent of their individual LTV, because they may well be the connection between high-value customers.
  • high clustering coefficient will indicate a robust network, so you should aim to build one and increase that metric. Try introducing less-connected (including new) people to existing clusters, not simply to random other users. A cluster, of course, is a set of people who all have connections to most others in the cluster (i.e., a high local clustering coefficient).
  • Once someone already has a reasonable number of semi-stable relationships (such as, 4-8 people they've interacted with more than once or twice), it's time to start introducing more variance, such as connecting them to someone who's distant in the existing graph. Most of these introductions are unlikely to stick, but the ones that do will improve the entire community a great deal.
  • if you can quantify the importance of the connections, e.g. by measuring the time or number of interactions, you can further identify the top influencers apart from the overall most connected people.
  • finally, when you combine these basic social graph metrics to the other user lifetime data I discussed previously, you'll get a whole new view into how to find crucial user segments and predict their future behavior. This merged analysis will give you measurable improvement far faster than burying yourself into advanced theories of social models, so take the low-hanging fruits first.

That's it for yet another introductory post. Time for feedback: what other analytics areas would you like to see high-level explanations about, or would you rather see this series dive into the implementation details on some particular area? Do let me know, either via comments here, or by a tweet.

Tuesday 21 February 2012

There's no such thing as an average free-to-player

A quick recap: in part 1 of this series, I outlined a couple of basic ways to define a customer acquisition funnel and explained how it falls short when measuring freemium products, in particularly free-to-play. In part 2, I continued to explain two alternative measurement models for free-to-play and focused on Lifetime Value (LTV) as a key metric. A core feature emerged: the spread of possible LTV through the audience is immense, ranging from 0 to, depending on product, hundreds, perhaps even thousands of euros. 

This finding isn't limited to just money spending, but is seen over all kinds of behavior, and is well documented for social participation as the 90-9-1 rule. From a measurement point of view, one of the most overlooked aspects is how it destroys averages as a representative tool. At the risk of stating the obvious, an example below.

When 90% of a freemium product players choose the free version and 10% choose to pay, the average LTV is obviously just 1/10th of the average spending of the paid customers. However, when there's not just a variety of price points, but in fact a scale of spending connected to consumption (or if we're valuing something else than spending, such as time), the top 1% is likely to spend 10x or more than the next 9%. Simple math will show you that the top 1% would be more valuable than the rest of the audience in total, as illustrated here:

The average? 0.19. Now, can you identify the group that is represented by "average spending of 0.19" in the above example? Of course you can't - there is no such group. Averages work fine when what you're measuring follows some approximation of a standard distribution (e.g., heights of people), but they break down with other kinds of quantities. Very crucially, they break down on behavioral and social metrics. Philip Ball's book Critical Mass goes to some length on the history of these measurements, if you're interested in that.

Instead of measuring an average, you should identify your critical threshold levels. Those might be the actions or value separating the 90% and 9% value players, and equally, 9% and 1%. Alternatively, you might already have a good idea of your per-user costs and how much a customer needs to spend to be profitable. Measure how many of your audience are above that level. Identify and name them, if you can. Certainly try to remember them over time to address them individually. This goes deeper than simply "managing whales", to use the casino term. Yes, the top 1% are valuable and important to special case, but it's equally if not more important to determine what are the right strategies for developing more paying customers from the 90% majority.

This is why it's important to measure everything. If you only measure payment, the 90% majority will be invisible to your metrics, and it's usually very hard to identify ahead of time which other measurements are important for identifying the activities that lead to spending. Instrumenting your systems to collect events on all kinds of activities on a per-user basis (rather than just system-level aggregates) enables a data mining approach to the problem. Collect the events, aggregate them across time for each player (computing additional metrics, when appropriate), and then identify which pre-purchase activities separate those players who convert to paying from those in the same cohort who do not. There are several strategies for this, from decision trees to Bayesian filtering to all kinds of dimensionality reduction algorithms. The tools are already pretty approachable, even in open source, whether as GUIs like Weka, in R, or with Big Data solutions like Apache Mahout, which works on top of Hadoop.

Essentially, this approach will surface a customer acquisition funnel akin to what I described earlier, but using the raw measurement data. It will probably reveal things about your product and its audience you would not have identified otherwise, and allow you to optimize the product for higher conversions. The next step in this direction is to replace the "is a customer" criteria above with the measured per-player LTV value. Now, instead of a funnel, you will reveal a number of correlations between types of engagement and purchase behavior, and will be able to further optimize for high LTV. Good results depend on having a rich profile of players across their lifetimes. A daily summary of all the various activities in a wide table with a column for each activity, and a row per player per day is a great source for this analysis.

Friday 10 February 2012

Developing metrics for free-to-play products

In my previous post, I outlined a few ways in which a "sales funnel" KPI model changes between different businesses, and argued that it really doesn't serve a free-to-play business well. Today, I'll summarize a few ways in which a free-to-play model can be measured effectively.

Free-to-play is a games industry term, but the model is a bit more general. In effect, this model is one where a free product or service exists not only as a trial step on the way to converting a paying customer, but can serve both the user as well as the business without a direct customer relationship, for example by increasing the scale of the service, making more content available. From a revenue standpoint, a free-to-play service is structured to sell small add-ons or premium services to the users on repeat basis - in the games space, typically in individual transactions ranging from a few cents to a couple of dollars in value.

As I wrote in the previous article, it's this repeated small transaction feature which makes conversion funnels of limited value to free-to-play models. Profitable business depends on building customer value over a longer lifetime (LTV), and thus retention and repeat purchase become more important attributes and measurements. Here is where things become interesting, and common methodologies diverge between platforms.

Facebook games have standardized on measuring number and growth of daily active users (DAU), engagement rate (measured as % of monthly users on average day, ie DAU/MAU), and the average daily revenue per user (ARPDAU). These are good metrics, primarily because they are very simple to define, measure and compare. However, they also have significant weaknesses. DAU/MAU is hard to interpret as it pushed up by high retention but down by high growth, yet both are desirable. Digital Chocolate's Trip Hawkins has written numerous posts about this, I recommend reading them. ARPDAU, on the other hand, hides a very subtle, but crucially important fact regarding the business - because there is no standard price point, LTV will range from zero to possibly very high values, and an average value will bear no reflection on either the median nor the mode value. This is, of course, the Long Tail like Pareto distribution in action. Why does this matter? Well, because without understanding the impact of the extreme ends of the LTV range to the total, investments will be impossible to target, implications of changes impossible to predict, as Soren Johnson describes in an anecdote about Battlefield Heroes ("Trust the Community?").

Another way of structuring the metrics is to look at measured cohort lifetimes, sizes and lifetime values. Typically, cohorts will be defined by their registration/install/join date. This practice is very instructive and permits in-depth analysis and conclusions on performance improvement: are the people who first joined our service or installed our product last week more or less likely to stay active and turn into paying users than the people four weeks ago? Did our changes to the product help? Assuming you trust that later cohorts will behave similarly to earlier ones, you can also use the earlier cohorts' overall and long term performance to predict future performance of currently new users. The weakness of this model relates to the rapidly increasing number of metrics, as every performance indicator is repeated for every cohort. Aggregation becomes crucial. Should you aggregate data older than a few months all-in-one? Does your business exhibit seasonality, so that you should compare this New Year cohort to the one last year, rather than to the one from December? In addition, we have not yet done anything here to address the fallacy of averages.

The average problem can be tackled to some degree by further increasing the number of cohorts over some other feature than the join date, such as the source by which they arrived, their geographic location, or some demographic data we may have of them. This will let us understand that French gamers will spend more money than those from Mexico, or that Facebook users are less likely to buy business services than those from LinkedIn. This information comes at a further cost in ballooning the number of metrics, and will ultimately require automating significant parts of the comparison analysis, sorting data to top-and-bottom percentiles, and highlighting changes in cohort behavior.

Up until now, all the metrics discussed have been simple aggregations of per-user data into predefined break-down segments. While I've introduced a few metrics which can take some practice to learn to read, the implementations of these measurements are relatively trivial - only the comparison automation and highlight summaries might require non-trivial development work. Engineering investments may already be fairly substantial, depending on the traffic numbers and the amount of collected data, but the work is fairly straightforward. In the next installation, I will discuss what happens when we start to break the aggregates down using something else than pre-determined cohort features.

Tuesday 7 February 2012

Metrics, funnels, KPIs - a comparative introduction

I know, I know - startup metrics have been preached about for years by luminaries like Mark Suster, Dave McClure, Eric Ries and many, many others. The field is full of great companies and tools like KISSmetrics, ChartBeat, Google Analytics, to name but three (and do a great disservice to many others). Companies like Facebook and Zynga collect and analyze oodles (that's a technical term) of data on their traffic, customers and products, and have built multi-billion dollar business on metrics. Surely everything is done already, and everyone not only knows that metrics matter, but also how to select the right metrics and implement a robust metrics process? There's nothing to see here, move along... or is there?

Metrics depend on your business as much as your business depends on them. No, more, in fact. It is possible (though hard) to build a decent, if not awesome business purely on intuition, but it is not possible to define metrics without understanding the business. Applying the wrong metrics is a disaster waiting to happen. In fact, in some ways this makes building a robust metrics platform more difficult than building the product it's supposed to measure. Metrics can't exist ahead of the product, but are needed from the beginning. Sure, with experience you will learn to pick plausible candidates for KPIs, and may even have tools ready for applying them to new products, but details change, and sometimes, with those details, the quality of the metrics changes dramatically. This is obviously true between industries like retail vs entertainment, but it's also true between companies working in the same industry.

This is a big part of why metrics aren't a solution to lack of direction. They can be a part of a solution in that well-chosen metrics will make progress or lack of it obvious, and may even provide clear, actionable targets for developing the business. Someone still needs to have an idea of what to do, and that insight feeds back into all parts: product, operations, measurement. I've never liked the phrase "metrics driven business" for this reason. Metrics don't do any driving. They're the instrumentation to tell you whether you're still on a road or what your speed is. You still have to decide whether that's the right road to be on, and whether you should be moving faster, or perhaps at times slower.

What to do, then? Well, understanding the differences helps. Lets start with a commonly applied metrics model, the sales funnel.

In a business-to-business, face to face sales driven business, a traditional funnel may begin with identifying potential customer opportunity, then measuring the number of contacts, leads, proposals, negotiations, orders, deliveries and invoices. A well managed business will focus on qualified customers and look for repeat transactions, as the cost per opportunity will likely be lower, and the revenue per order may be higher, leading to greater profitability. They will also look at the success rate between the steps of the funnel, trying to improve the probability of developing an opportunity into a first order.

This model is often adapted to retail: advertising, foot traffic, product presentation, purchase decision. For some businesses that's it - others will need to manage the delivery of the product, and may see further opportunities in service, cross-sales, or otherwise. Online retail businesses measure every step in much greater detail, simply because it is easier to do so. Large retail chains emulate that measurement with very sophisticated foot traffic measurement systems. But even in its simplest form, while the shape is similar, the steps of the funnel are very different.

Online businesses have developed a variety of business models, among which two large categories are very common: advertising and freemium.

The advertising-funded two sided market model is two different funnels: a visibility - traffic - engaged traffic - repeat traffic page view model, and a more traditional sales funnel for the advertisers, though even that one has been, through automation, turned to look more like an online retail model than what advertising industry is used to. This model is further enhanced by traffic segmentation and intent analysis, allowing targeted advertising and a real-time direct marketing product, the sales funnel of which is even less familiar to the sales funnel I described at the beginning.

Freemium isn't even one business model: a B2B service with a tiered product offering and a free time- or feature-limited trial product may ultimately use the traditional sales model, only so that the opportunity to prospect part of the model is fully automated. Often it's entirely automated to the point a customer never needs to (or perhaps even wants to, assuming a simple enough product) talk to a sales rep. Still, the basic structure holds: some of the prospective leads turn into customers, and carry the business forward. Free service, be it for trial only or for the starter-level segment, is a marketing cost and a leads-qualifier, enabling a smaller sales force.

On the other hand, the free-plus-microtransactions model, one which we pioneered with Habbo Hotel, and has since been used to great success by many, including Zynga, can certainly be described as a funnel, but to measure it with one requires significant violence to many details. The most important of these is that because individual transactions are typically of very low value, building a profitable business on top of a model which aims for, and measures one sale per customer is practically impossible. This class of business doesn't just benefit from repeat customers, it requires them. Hence, a free-plus (or, as it is called in the games industry, free-to-play) business model must replace counting a "new customer" metric or measuring individual transaction value with the measurement of customer lifetime value. Not just measuring it on average, but trying to predict it individually - both to try to develop 0- or low-value users (oh, how I hate the word) to higher value by giving them better value or experience, and by identifying the high value customers to serve and pamper them to the best of the company's ability (within reason and profitability, of course). And once you switch the way you value revenue, you really need to switch the way you measure things pre-revenue.

Funnels change. There are business models where funnels really can't provide the most instructive KPIs, even though they still may be conceptually helpful in describing the business. As this post is getting long, more on the details of KPIs of free-to-play in the next episode.

Sunday 16 November 2008

Chris Anderson on freemium conversion

Chris Anderson, author of The Long Tail, uses free-to-play web games as a case study on conversion rates for freemium products. I wrote about the conversion and monetization rates in this world two months ago as a followup to my GCDC presentation from last summer. I can't really think of a better example of freemium model than Habbo - a freely accessible service with high engagement and a large audience really gets to utilize and showcase the model at its very peak. The only thing missing is even easier micropayment models. We'd love to use the iTunes store for selling Habbo items, for example.

Thursday 16 October 2008

Splitting the virtual worlds market to segments

IMVU founder Eric Ries commented on Virtual Goods Summit and suggested that virtual worlds can be divvied up along three axes of UGC/first-party, subscription/pay-for-stuff, and economy/gameplay focus. This is certainly one good way of thinking about the focus decisions needed when designing and developing a product in this market, but personally, I think this model, along with others I've seen and played with myself, suffers from a few key weaknesses that arise from the need to simplify things. I'm not saying the model can't help put things in order, just that there's more to finding the right solutions than this. Lets go with the great blogger tradition of point-for-point response.

UGC vs first-party

It's amazing Sulka didn't comment on this: UGC is not just about letting users upload pictures or items to a world. More to the point, Habbo certainly is not first-party content focused. Yes, all our furni is designed and developed by our own teams, and we don't enable user uploads. But at the same time, over 90% of all of the activities in Habbo emerge from the community - users take what we've made, and do their own things with it. Most of what's going on, we had no idea would happen.

Eric says IMVU's efforts to enable UGC dwarf those to create their own first-party catalog. Well, so do ours, despite his classification of Habbo being first-party content focused. Every feature, every furni, every activity, every news item receives more thought on "how do we support users to go to their own directions here?" than "what do we want this to be about?". Plus the significant fraction of our work that has absolute no effort to produce content attached to it, and is fully focused on player activities.

Lets just use the old, tired LEGO analogy here. How much of LEGO is first-party content? Just enough to get the imagination of the players going so they can create something of their own. Anything more would be too much, and this applies to any VW that can call itself "social" - and none that isn't social isn't going to be interesting. Trying to make a useful UGC split for any purpose other than copyright infringement monitoring is a red herring, and even for that one purpose it's not very likely to be useful due to other moderation requirements.

Subscription or pay-for-stuff

This is one of the stronger arguments, if only just because those are the business models the industry has latched on to. They're certainly not the only possibilities though, nor are they alternatives to each other. Eric's points about the strenghts and weaknesses are good - but you can benefit from both at the same time, and support the weaknesses of one model with the strenghts of the other. This is certainly an area where we have a lot of experience, over 8 years of it, and I don't think we've gotten very far yet..

Economy or gameplay

Eric used the word "merchandising" instead of economy, and I think that's the crucial over-simplification that leads to thinking that pay-for-stuff games and worlds are just about cross-selling opportunities best left to a competent marketing department to handle. I'm wondering whether he's simplifying the choice to make it easier to explain, or purposefully misleading someone on what's crucial to think about, or whether our friends at IMVU simply haven't realized this yet: the first-hand sales are a small fraction of the total trade in an item-based game, and the gameplay balance is just as critical here as it is in a game built out of designer-created quests and gameplay mechanics. What's more, because its emergent behaviour, it's nearly impossible to predict, and very difficult to measure, model and understand. Yet that's exactly what's required in order to succeed.

I hope that explains why I choose to call it economy-driven rather than merchandising.

PS. Browsing around Eric's blog a bit further, this article is a gem

Friday 5 September 2008

The sweet spot in free-to-play, pay-for-stuff market

I've been talking recently about a few particularities in the business models based on end-user micropayments that have created lots of followup discussion and questions. So much, in fact, that I decided it's time to try to explain one crucial and somewhat counter-intuitive detail in writing for later reference.

First, a bit of background: this information is based on my work with Habbo over the last 5 years, and is half learned from experience, half based on theoretical models built from that experience. I'm sharing this with the world because while it's been an interesting ride to build an online social game with an end-user business model, breaking pretty much every conventional rule in the process ("games have to have objectives", "there is no profit in micropayments", and so on), it's still better for our business if people understand why it works. If this allows a competitor to fix a problem in their product and get off the ground, so be it - there's plenty of growth to go around here, and failures don't help anyone. As a disclaimer, the numbers I'm discussing here have no relation to Habbo, though the basic observations certainly apply.

Let's start with an obvious statement and follow it up with something less obvious: Everyone wants to maximize revenue per player. However, in a free-to-play environment, where the majority of players do not contribute direct revenue, the right tool for the job is not to try to extract the maximum amount of money from those who do pay - rather, to increase the number of players buying anything at all - even if it's just $1 over their entire lifetime. In other words, it's good to have a lot of very low individual value players.

To explain it in detail, lets look at two assumptions behind a flexible pricing business model: first, that the number of customers grows as the cost of goods drops, and second, that the maximum consumption is unrelated to the minimum. There is no average customer who would spend more than half of others, and less than half of the rest. If there were, the picture of that customer base would look something like the image here, and it's pretty strange looking, wouldn't you say? You've probably seen pictures resembling this one where they don't start from the dominating $0 value point - that's the normal distribution.

The first assumption really is very simple: more people are willing to buy a product at a lower price. This is true for most goods, with some notable exceptions in the luxury goods market, where the perception and desirability of a product goes up with its price. However, it is difficult to create a mass-market luxury item, and those do tend to be cheap (and small).

The second is perhaps slightly more involved especially if one is used to thinking of fixed-price models such as one-time purchase of a boxed product or monthly subscriptions, both of which are difficult to scale up on a revenue per customer basis, so scaling them down is highly undesirable as well. However, it's more clear, if not obvious, by looking at other consumer goods - whether tangible such as drink- and foodstuff or intangible like movies, music and other entertainment. Buying these once certainly does not exclude further sales of the same product to the same customer - rather, it's a strong indicator of sales potential!

The free-to-play, pay-for-stuff model follows both of these assumptions. Cheap purchase price attracts more customers out of the existing free users, and transactional item-based sales allows repeat purchases of theoretically unlimited amount. Those who are willing to buy more will do so, up to some practical maximum of consumable goods and discretionary spending.

In this environment, focusing on higher-paying customers makes sense only if the number of customers drops by less than half when the revenue per customer doubles. Again, with the exception of some luxury goods segments, this rarely happens. Think about it: how many chocolate bars of standard quality would you expect to sell for $1? How about for $2? More or less than half? How about for $10 for the exact same package? I'd wager chocolate bars sell at least 10x better at the price of $1 than at the price of $10 each, and the increase of customer base more than covers the lower per-unit revenue.

This is a simple exhibit of power-law market dynamics, and is easiest observed when looked at through a logarithmic chart. Readers of books like The Long Tail or Critical Mass should not be surprised. There's a twist through - because this starts from zero gains (at the free players), the exponential behaviour follows a different path in the beginning. This model also turns Pareto's Law on its head - due to the (in my experience) relatively high exponent, the highest total value is at the lowest end of the spending.

Now, of course there is a minimum profitable price for a bar of chocolate that does not become near-$0 even at very high volumes, unlike purely digital products, so increasing chocolate-sales revenue by dropping prices does not necessarily increase profits, and I'm completely ignoring the effects of packaging and marketing on the perceived value of items. For digital sales, where packaging is more flexible and material costs are effectively non-existent, we still have to consider not-unsubstantial fixed development costs, a certain amount of costs associated to servers and bandwidth, some transaction-related pricing friction, and so forth, but certainly the minimum value (and price) of one unit of digital sales can be driven much lower than a bar of chocolate.