UGC platforms have hailed the rise of the Creator Economy™. Roblox, TikTok, and Youtube have democratized the creation of content, abstracting the costs of getting content to market. But we’ve assumed UGC cuts out content gatekeepers in favor of entrepreneurs. Everyone gets a warm glow when “the little guy wins”. And by all means, this appears to be true! Creators can single handily craft and distribute TikTok videos in seconds, not days or months. The barriers to Roblox creation are higher, but it’s a far cry from the rigamarole of traditional game publishing. The effects of UGC are profound: despite easing requirements with Early Access, Steam hosts 50,000 games to Roblox’s 40 million. It’s such a gap I made this handy chart to underscore the difference:Continue reading “The Creator Economy is for Anyone but not Everyone”
In 1931, American economist Harold Hotelling published the seminal paper The Economics of Exhaustible Resources. Harold described a problem many firms face: how much of a non-renewable resource should they sell at any given time? This problem is more obvious when thinking about managing an oil supply, but just as relevant when considering how to manage match-3 levels.Continue reading “The Environmental Economics Approach to Liveops Content Management”
Modern live-service games have self-segmented in genres: match-3, 4x, collection RPG, battle royale, etc. We know these genres evolve and start to incorporate new mechanics. Over time, these mechanics become standard genre fare. For example, invest-n-express titles like Gardenscapes are an outgrowth of the match-3 genre, adding collection mechanics to the core match base. In HD, we’ve seen innovations like Apex Legends’ revive mechanic modified in Warzone’s Gulag – players fight for revival in a 1v1 mosh pit. But how could we better understand why game genres change rather than simply observing them change? I argue Thomas Kuhn can help.Continue reading “Why Do Game Genres Evolve? A Kuhnian Explanation”
I recently came across this Tweet:
I love the way Ran writes https://t.co/3XUxfsXjDL— Growf (@pinchedforgrowf) December 22, 2020
The article’s writer, Ran Mo, is a Lead Product Manager at EA according to his Linkedin while the quote Tweeter is a fellow product manager. Dive a bit deeper and the retweets are all from fellow VCs or product people (guess there’s something to this).
This isn’t exactly uncommon. The Deconstructor of Fun podcast is hosted by three Product Managers and guests frequently come from a similar ilk. A scroll through the last 20 DoF blog boosts a breakdown dominated by PMs.
DoF Posts by Job Discipline
Games are at the cleavage of art and science, so why are PMs the only ones with something to say about it? The alternative voices we do have, Eric Seufert (UA), Alexandre Macmillan (Analytics), and Javier Barnes (Design), only take a couple of sentences of digestion to realize the dramatically different way they frame and discuss problems. Their pieces tend to have more backbone or a strong theory that underlies an empirical observation. I’m a fan of this approach.
PMs are driven by their social caste, mainly moving up it. Networking is crucial to this, an insight that seems to go over the head of analysts and designers (at our own peril). The PM hierarchy is reflected in the “up or out mentality” re-enforced at tech and gaming firms. A scroll through PM Linkedin and you’ll see the following ladder:
None of these motivations discredit, in any way, the strength of the ideas expressed by PMs. Or the fact they actually take the time to express them. But it does help explain why they can feel hollow at times, trying to fit a socio-political mold rather than a genuine expression. This is reflected in how many game PMs will depart for higher paying tech PM jobs in the Valley or to fellow gaming firms for title bumps. And there’s nothing necessarily wrong with that.
If I had a plea, it would be for all game disciplines to write vigorously. Write everything you know to be true and let’s hash it out. The game craft is too important to be dominated by one discipline. We should all be thinking hard about these problems.
Ran Mo and Joseph Kim (@jokim1) argue for looking at games from a Silicon Valley perspective. The usual three make an appearance: moats, networking effects, and platforms. Moreover, while I thought it had died out after the launch of Halo 3, there continues to exist an inferiority complex amongst game makers. Games never seem to get the mainstream or broader tech circle legitimacy many think they deserve. Despite operating in the Valley and major tech hubs, game companies do not reach the crazy evaluations of FAANG (ATVI has a market cap of $63B, while Facebook sits at $215B). Furthermore, public intellectuals like Tyler Cowen or Ben Thompson rarely discuss games as a share of their commentary (not the case with Matthew Ball – but maybe he drinks too much kool-aid). Even internally, game firms seem to be chasing subscriptions off the heels of Netflix and Spotify.
The fundamental problem comes from not respecting or understanding games as a distinct How to Build the Amazon of Game Companies mainly describes why game companies will not be Amazon. The fundamental problem comes from not respecting or understanding games as a different and unique medium separate from linear content. In many ways, Kim’s piece wants to make this point but does not go for the kill by the end of the article..
I’m writing a some posts to address and expound on this. In two parts (networking I’m writing a two-sided series to address and expound on this. In two parts (networking effects & platform power), I’ll examine why game companies fall short of traditional tech companies. Then, with another two parts, I’ll address what tech companies have to learn from games (the content problem & MC = MB).
Networking effects describe the positive externalities from the n+1 user to a service. Every time someone joins Facebook, the benefit increases in value as others can interact with that person. Gyms, for instance, work in the opposite direction: every member who joins a gym occupies a fixed amount of capital and decreases the value to each other member.
Fawning over networking effects comes from the path dependency inherent in the model: more users leads to more users. What does VC not want self-perpetuating growth? However, as Margolis and Liebowitz argued during the Microsoft case:
Although these simple numerical and algebraic examples appear both logically sound and structurally uncontroversial, these examples entail severe restrictions. The logic underlying path dependence is seductive but incomplete. […] Given that the theoretical claim that can be made for path dependency should be understood as only a demonstration of possibility, the case for path dependence becomes an empirical one.
It is not that networking effects are not real, but they are not as powerful as they were first made out to be. After all, networking effects could not save Friendster or Myspace, and as we will see, they mean little for particular games.
At a certain scale, diminishing returns decay positive network effects to zero. The 2nd user who joined Facebook was far more beneficial to the 1st user than the 100th million users who joined. While not directly comparable, Google’s Chief Economist, Hal Varian, makes this point regarding the predictive accuracy of models with additional data.
And we can create a similar arbitrary model for a particular game: diminishing network effect value for the marginal user that inevitably results in an asymptotic total network effect value.
Not all games face the same curve. A game like Hearthstone, with 1v1 play, has far less to gain from an additional user than, say, League of Legends, which has 5v5 and many ranked segments. More users reduce matchmaking times, potential latency, and thickening skill distribution (higher P, you will be matched against similar skills). The fewer segments (modes, ranks, etc.), the less powerful networking effects are, and the quicker the marginal value curve depresses. Cross-play doubled down on this by removing platform segmentation. Nevertheless, even for League, the networking effect power is infinitesimally small at scale; the significant gains are eaten with a relatively low user count.
Games do not have the “sticky” elements of networking effects. Synchronous consumption is another example. In any real-time game, a network effect is delivered when you play with friends. At the same time, something like Instagram Stories is consumed whenever the user pleases. It is not clear that having a friend play the same game I do is beneficial unless we play simultaneously.
Schelling’s Nobel Prize-winning work on tipping is a more apt model for describing many games today. Consider a group of players all playing the same game. These players are partially driven to play this particular game to be “in the know” or a part of the pop-culture conversation. Each of these players has a given threshold for defecting to a new game based on the share of public discussion consumed by the game. Players defect if the game declines as a share of the conversation; they leave as the game goes from 100% of the public conversion to 99%. More will leave at 99% and more still at 98%, continuing a downward spiral into a new equilibrium.
Of course, the inverse is true as well. Some might join a game if its share of public conversation goes from 0% to 1%, and even more would participate if it went from, say, 20% to 30%. A new game release can set off this “tipping” chain reaction in players. Look no further than the migration of Fortnite players into Warzone. The power of tipping is as strong as “public conversation” is as player motivation. Unfortunately for developers, this means instability in the long-run capitalization of viral game hits.
I have avoided addressing two-sided marketplaces as they will more neatly fit into the next part: platform power (or lack thereof).
The ASA has banned misleading ads from Playrix’s Gardenscapes. Running the same creative in user acquisition hits diminishing returns fairly quickly as the creative “clears the market” for users attracted to that creative. To broaden appeal, why not simply advertise the game as existing in a entirely different genre? This opens up a whole new segment and drops CPIs significantly. Of course, advertising gameplay that doesn’t exist surely means these users will exhibit extremely poor KPIs. However, there’s a broader implication to these ads and one that harms the entire game industry.
The harm is described in Akerlof’s Nobel Prize winning paper on car lemons:
Akerlof’s original example of the purchase of a used car noted that the potential buyer of a used car cannot easily ascertain the true value of the vehicle. Therefore, they may be willing to pay no more than an average price, which they perceive as somewhere between a bargain price and a premium price. Adopting such a stance may at first appear to offer the buyer some degree of financial protection from the risk of buying a lemon. Akerlof pointed out, however, that this stance actually favors the seller, since receiving an average price for a lemon would still be more than the seller could get if the buyer had the knowledge that the car was a lemon. Ironically, the lemons problem creates a disadvantage for the seller of a premium vehicle, since the potential buyer’s asymmetric information, and the resulting fear of getting stuck with a lemon, means that they are not willing to offer a premium price for a vehicle of superior value.
If users start to have an expectation that a given game ad does not truthly describe the game in question then they’ll be less likely to click. This means higher UA costs even if your firm does not engage in these type of ads.
A similar problem is starting to creep up in PvP games. Developers have started to be confronted with the uncomfortable reality that PvP is a zero-sum game: for someone to win, someone else has to lose. And of course, when players cannot progress they churn. Supercell has heavily introduced bots in Clash Royale as a response.
Of course, players cannot identify if they are indeed playing a bot or a real player. This steals one of the compelling aspects, if not the compelling aspect, of PvP away from players: outsmarting another individual. But like the lemon problem above, if this trend continues then players will start to question if they’ve dominated a real opponent even if the particular game doesn’t use bots. Games that use unmarked bots start an industry expectation that diminishes the experience for all.
There’s a good moral rule here to help us and it’s from an 18th century philosopher called Immanuel Kant. Kant advocates for something called the Categorical Imperative. This claims that if we were to universalize a given action and it would result in a “contradiction” then that action is immoral. Consider lying: if everyone were to lie then the world would not function, therefore lying is immoral. Or consider being lazy: if everyone were lazy then nothing would get done. It’s a no-holds-barred approach to consider moral action, but thankfully we’ll use it a much more narrow scope.
If unmarked bots were to be universalized, PvP games would be irrevocably harmed. If misleading ads were to be universalized, then players would stop clicking on them all together. Both of these situations violate the Categorical Imperative and align with outcomes that benefit the entire industry. The Categorical Imperative makes for a simple and rule based approach to consider the “greyer” parts of developer action.
There’s a compelling aspect to achieving group oriented goals: being apart of something larger than yourself. Lots of F2P developers harp on the importance of social features. Yet the social experience in many games is abysmal. Lots of teammates or clanmates don’t seem interested in participating instead preferring to “free-ride”, putting forward little effort but getting the fruits of the team reward. Mancur Olson’s foundational work, The Logic of Collective Action, describes how this problem manifests in the public sphere (sometimes literally in the case of electric scooters). Game designers have a much easier time aligning individual and clan incentives than public officials yet they sometimes miss easy wins. How can we make the clan experience better then it might otherwise be?
In Clash Royale, clans advance a boat against rival clans. Advancing the boat depends on individual clanmates playing games everyday (and winning). The more clanmates play consistently, the more the boat advances and the better the rewards the clan will receive. But for many clanmates playing everyday requires a great of effort, why not let others earn the rewards for you?
The problem is severe in Battlefield where “PTFO” or “Play the Fucking Objective” is standard nomenclature. Players often won’t engage in activities that benefit the team (capturing flags), instead preferring to pursue their own objectives (generally: shoot players as fast as possible).
A given player faces two potential payoff schedules when considering to allocate effort to the clan. There’s the expected payoff with no effort (the probability that the clan/team will win if the given player did nothing) as well the probability that the clan will win if the player puts forth effort. We can model this as such:
is the probability of winning the clan event given give the effort of a given player or rather the additive probability of this given player participating.
While R is the reward from winning.
This problem exacerbates as team size grows: the efficacy of a given player varies inversely with the number of teammates. This makes intuitive sense: in Battlefield, a player in 2 versus 2 match has a greater impact on the outcome then a player in a 32 versus 32 player match. The incentive to free-ride rises as the number of teammates or clanmates rises. Weakness hides in numbers.
We’ve also ignored the game-theory dynamics of this problem for simplicity, but it’s worth mentioning. If I know my other teammates are not going to put forth effort, why should I? This leads to Nash equilibriums where clans have almost no activity.
How can we overcome the free-rider problem and ensure that all teammates put forth effort? The highest cost-benefit feature is simply better monitoring tools. In many clan or team based games, clan leaders face asymmetric information: they simply can’t identify the players that do not put forth effort. A simple measure of activity (last login) or games played in the last week goes a long way to kicking out free-riders. We might also consider a joint-production function. In Battlefield or Clash Royale each player would receive a score based on their effort or contribution to team advancement, if the team wins they receive a multiplier on this score. Such a system would have two benefits: it would more closely align individual effort with individual outcome (reap what you sow), and it would increase the benefit for high performing clan members to engage in monitoring. For example, a high performing member might have $20 in contributions with a 2x multiplier or $40 for winning compared to a low performing member with $5 in contributions and therefore $10 for winning. In real terms, the high performing member has an even greater incentive to encourage low performers to put forth effort.
There’s a lot to be said for social shaming as well. While it hasn’t been effective for zero effort participants, there’s evidence it might help players on the margin. A push notification demonstrating that your clans needs you or perhaps better yet, a system where your clanmates can send you push notifications is a compelling way to push players into action.
Perhaps the greatest miss I see is not in clan monitoring (kicking out free-riders), but in self-selection to begin with. Clans are generally pareto efficient for players meaning that there’s zero cost and only benefit to joining one. Players then generally look for near max-size clans as they maximize the clan’s probability of winning a reward and thus the players. Reducing search costs by recommending (or restricting) clans based on device language, location, and some measure of progression maturity makes all players better off.
It’s hard for social monetization opportunities to take-off if team based activities suck. We still have a long way to go to fix top of the funnel problems. Afterall, teamwork makes the dreamwork.
“We want to be more data driven” or “We want to create a stronger data culture” are common organizational refrains. Supposedly, having more data or data playing a larger role in the decision making process is profitable. It’s weird because I haven’t seen any research to suggest this is the case. In firms like Facebook, it’s obvious as more data improves ad personalization and thus revenue. But this is data as a engineering project rather then a tool in the decision making process. Firms want to make better decisions with data. This is a misidentification of the value chain. Data isn’t that helpful if it’s not packaged with empiricism, an epistemological way of acquiring knowledge.
To even get off the ground analyzing data, we need theory of measurement. What should we track, given limited engineering resources and raising storage costs? Claiming we should track, say payments and logins, at the exclusion of audio volume, implies a cost-benefit value ranking. Why are payment and login more value to track? The theory is that understanding payments and logins will unlock more insight then volume as volume plays a less significant role in the app. Is this true? Hopefully, the institution has the intuition or previously collected to knowledge to make an educated guess. Firms have discovered that refining this knowledge can make their bets more likely to succeed. As it so happens the West has created the best knowledge refinement process in the history of humankind: the scientific method.
Data or more broadly, empiricism, is a key part of the scientific method as it expands the sample size of a test beyond antidotal evidence. Doing this at scale, as well as the methodology of running true experiments, A/B tests, means that knowledge is more valid (less likely the result of antidotal evidence) and stable. Firms can now learn.
Arguing to be data-driven or informed misplaces the value in the supply chain. We need to more explicit in this endeavor – it’s not about data, it’s about science.
In a textbook neoclassical experiment, Levitt alters the quantity of Candy Crush hard currency at a given price point. While economists generally think of price variation as the way of deriving demand curves, quantity variations are just as legitimate a tool.
Despite a sample size of over 15 million and a wide range of quantity convexity (80% variation across variants), all quantity discounting schemes produced similar revenue. Levitt concludes by commenting,
“…varying quantity discounts across an extremely wide range had almost no profit impact in the short term.”
The interesting and little explored result indicates that,
…almost all of the impact of the price changes was among those already making a purchase; radical price reductions induced almost no new customers to buy…
This suggests free to play games are made up of two groups of users: purchasers and non-purchasers. This means the decision of becoming a customer is exogenous, there is no ability to convert non-customers to customers i.e. this is decided outside of the game. Put another way, non-customers are perfectly price inelastic and customers are perfectly price elastic. Indeed, industry research collaborate this.2
- Interesting, but is it actionable?
Were this to hold, it suggests a number of results. The first is that product manager’s ability to monetize non-customers (99%~ of users) will not come from IAP, but rather other forms. This may help explain why F2P ad revenue and incentivized video continues to show YoY growth.3 4
Furthermore, product managers should consider experiments exploring the maxima point of ad frequency. Given that there’s a trade-off between retention and ad-frequency there exists an optimal ad frequency point.
With little chance of non-customers converting to customers, product managers should worry less about increased ad frequency turning off potential customers.
The final result suggests the ROI of trying to raise the LTV of customers exceeds that of trying to raise the new customer creation rate. Product managers should develop roadmaps in accordance.