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
Thanks for making it exceeding difficult to order by count Google Sheets!
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.
Players want to unlock content and the most efficient way to do so is to maximize how many FPS games control progression speed: SPM or score per minute. Score is usually a formula composed of objectives and kills. The key is that it’s uncapped: there’s not a fixed amount of XP up for grabs in a given match or time played (this would be a better design). The formula implies that the more “action” in a given minute of gameplay then the more score per given unit of time and the faster a player will progress. Small maps excel at encouraging this – there’s a short amount of time before you bump into an enemy or objective.
“It’s a me-a!” Mr.Utility himself. This was in his will actually.
A Quick Refresher
Pricing is tough to get right. Ideally, we’d like to select a price that maximizes profit, holding all else constant.
Slopes not drawn to scale
In the above example, we consider a single price \( P \) but we can expand this to consider the set of prices or price set that define a game or service \(P_s:{\{P_1, P_2…P_x}\}\).Again, we want to pick the set of prices that maximize revenue.
Many goods or services operate under a single fixed price. For instance, a book might cost $18—everyone who values the book $18 and above purchases it. \( revenue = n * P \) where \(n\) is the number of readers who value it $18 and above and PP is price. Simple enough, right?
Let’s add consumer surplus to the story. In the example below, this is the shaded yellow area. Some readers value the book at $30 and thus are the most profitable in the transaction ($30 – $18 = $12 economic profit). The other readers made out, but perhaps not as well.
Imagine, however, that we could charge two different prices to two different segments of readers. Say one reader valued the book at $18 and another who valued the book at $30. The trick is to ensure that the good is non-tradeable; otherwise, the customer who faces a lower price could resell to the higher-priced customer and pocket the difference.
While we could raise the book’s price to $30, we’d lose out on the $18 reader. The problem of price discrimination, the one described above, is fundamental to understanding entertainment business models.
Our Case
The consumer surplus model struggles to capture time. After paying a fixed price for the book, the reader chooses to consume it (is anyone surprised?). The reader continues to accrue utility from this consumption – the enjoyment of reading the book outweighs other activities. At some point (but not always), the accumulated utility outweighs the initial cost.
Standard Utility Curve (Reading a Book)
Let’s pretend utility accrues linearly.
It’s important to note: the reader is not “in the hole” when we see red in the above graph. The book is a sunk cost; the reader should only consume it better when it’s better than engaging in other activities. But if we extend this graph to include more Time, the curve kinks immensely.
Utility Curves: Books
There are incredibly steep diminishing returns to reading a book a second time. It’s boring. Deep diminishing returns explain why book rentals (libraries) and book reselling are so popular. Why pay a fixed price for what is usually a single-use item? Steep diminishing returns ring true for movies as well. Of all films you’ve watched, what percent have you seen a second or third time? 1%? 5%? Subscriptions and rentals make sense for this form of entertainment. But what if, instead of flattening, the utility curve grew? Enter gaming.
Utility Curves: Games & Books/Movies
Games have a unique resistance to diminishing returns. As described in Part III:
The genius of PvP (Player v Player) environments is how they necessitate the emergence of a meta-game. PvP environments resemble game theory models where it has been shown strategies evolve in an evolutionary process. In mathematics, Player vs. Environment (PvE) resembles optimization where strategies are static – one and done. Each balance change reshuffles Equilibrium in PvP environments; the search for dominant strategies in an ever-shifting equilibrium is the game itself.
The strategic evolutionary process is a near limitless piece of content to consume.
MB = MC
The efficiency of any monetization or pricing system is the degree to which it can correlate marginal cost (MC) to marginal benefit (MB). In the above examples, we fixed the price. Fixing the price makes sense, given that the utility curves flattened out. But the more the curve refuses to flatten, the most decorrelated MC to MB becomes as Time continues.
The above gets us to the emergence of DLC and MTX. Players were playing PvP titles for hundreds, if not thousands of hours. MC failed to catch up. DLC map packs like those in Call of Duty and Battlefield helped MC catch up (and grow MB!) in fixed intervals, but the correlation was still weak as Time persisted.
MTX solved for the explosion in the marginal benefit multiplayer games were providing. Unlimited or greatly exaggerated spend caps allowed players to spend closer to their MB curves than they were previously able to do so.
Utility/Price Curve: MTX & F2P Games
Price = Accrued Revenue
Software as a service (Saas) can generate similar growing utility, but they only charged a fixed price in recurring intervals. Again, this suggests that subscriptions might make sense for games. Subscriptions are better than fixed prices in correlating MB and MC, given that SaaS generates recurring homogenous LTU returns. Games, however, generate heterogeneous LTU (lifetime utility) than do many SaaS products.
We can model the heterogeneity as such:
As we consider the total Lifetime Utility generated by a standard good or game, we add up an individual’s LTU from lowest to highest LTU. If everyone valued a standard good the same, LTU would be linear. If a few players valued a game at a relativity extreme LTU, you would see a bowed curve – the high LTUs skyrocket total LTU upon addition. Look familiar? A bowed LTU curve correlates to observed LTVs in F2P games.
Does Tech leave too much LTU on the Table?
But that’s not to say all non-video games have a linear Total LTU curve. Some users value, say, Zoom more than others. As Zoom usage increases, LTU does as well, but the price does not. Zoom, therefore, fails to capture a great deal of LTU from high usage customers. MTX theory could offer a hand.
Zoom does offer tiered pricing for organizations with a higher price charged for additional features, but this doesn’t capture the high LTU users within an organization. Perhaps Zoom should get in the cosmetics business – backgrounds were a fantastic opportunity that never capitalized on.
Even at the organizational offering level, Zoom could create an additional tier that offered other features to high usage customers in the tier. Multi-track cloud recording, for instance, generates incredible value for Podcasters but costs nothing further.
The dramatically different value propositions of standard goods and games necessitate other monetization schemes. Different content economics make applying the monetization of tech companies less applicable to gaming. Instead, SaaS firms have something to learn from games.
The only Content Problem worth saving. Do it, Reed!
So maybe game companies aren’t tech companies. As much as game companies seem to borrow from tech firms, tech firms run a bigger deficit in borrowing from games. If Netflix, a subscription service with over 10,000 movies and TV shows, has its biggest competitor in a single game, Fortnite, then perhaps there’s more for tech to learn from games. And how games deal with The Content Problem is its defining characteristic. Of all forms of entertainment, games present the most compelling answer to the problem.
The Content Problem
The fundamental axiom of economics is unlimited needs and wants and only limited means to fulfill them. The parallel for entertainment might consider that core content demand nearly always outstrips supply. For example, large swaths of Game of Thrones and Harry Potter fans are underserved by a couple of books, movies, and TV seasons. Executives try to fill the void with licensing: Harry Potter backpacks, Game of Thrones beer, etc. But filling the core content demands are impossible: it takes far more than 1 hour to produce 1 hour of Game of Thrones, while the same is not valid for games.
The content consumed in a game like Overwatch or Clash Royale is the pursuit of strategy equilibrium and/or mastery of mechanics. A new unit in Clash Royale changes how players organize their decks, even if they don’t use the unit directly (they must counter it). The new units provide hundreds of new hours of content to consume relative to the near 1 man-week of labor to produce the new unit. Therefore, the marginal content output of a given member of the 16 people (!) Clash Royale team is astronomical.
The genius of PvP (Player v Player) environments is how they necessitate the emergence of a meta-game. PvP environments resemble game theory models where it has been shown strategies evolve in an evolutionary process. In mathematics, Player vs. Environment (PvE) resembles optimization where strategies are static – one and done. Each balance change reshuffles Equilibrium in PvP environments; the search for dominant strategies in an ever-shifting equilibrium is the game itself.
The marginal product of labor for a given game developer completely outclasses a given producer on a movie or TV show by virtue of the medium, not the individual. Unlike games where assets are infinitely replicable, films and TV face fixed constraints: Emilia Clarke or David Benioff can only be in a single place at a given time. They must also eat, sleep, socialize (sigh). Meanwhile, Captain Price faces no such constraints. There’s no more Game of Thrones to consume after the last episode cuts to black while there’s always another hour of Fortnite to play. How can Netflix and others adapt to the reality of these mediums?
The most straightforward strategy is a content arms race. Netflix continues to spend over $17B a year on original content while scooping up oodles of back catalog content. Of course, viewers must be interested in this content to be “effective,” and the recommendation engine plays a vital role in this. But the last episode of Stranger Things shows that the recommendation engine cannot fill the void while operating on the same indifference curve. The “more bodies” strategy to solving the content problem is expensive to execute and, as we’ll see in part 4, struggles to achieve Marginal Cost = Marginal Benefit.
Reality TV is a response: it needs fewer writers, editors, and CG to produce a given hour of content. Shows like The Amazing Race, Big Brother, and Survivor can do 20+ seasons of 22+ episodes, while Game of Thrones struggles with seven seasons of 10 episodes despite having so many more crew members. Netflix’s speed of investment here is breathtaking. But the addressable audience is more limited in scope than traditional dramas. Netflix needs a bolder evolution to combat games: TV-as-a-service.
The forgotten genre of soap opera TV provides a near-perfect blueprint. For those unfamiliar, soap operas are near year-round weekly serialized television shows. The unrelenting pace has resulted in popular series like General Hospital having 14,000+ episodes over 57 years. Netflix needs to invest in moving performances to a similar format: year-round production with weekly releases heavily. There’s always another piece of content to consume right around the corner while the back-catalog for a given show continually expands for newcomers. In many ways, this mirrors match-3 level production. The number one reason why players churn from match-3 is a lack of new levels, and a glance at community pages confirms this.
Sugar Rush or something, right?
King mitigates content churn using an increasing difficulty curve such that it takes players longer to reach content exhaustion as they progress through levels. Another strategy is also possible, however: branching narratives. Reality TV faces no such option.
Increasingly, Hollywood is shooting movies back-to-back. It’s cheaper to continue production rather than stop and go. Why not do something similar to produce more content? In this case, shoot multiple perspectives in a given series simultaneously. Lord of the Rings production operated similarly with two production crews but with a singular end product. Game of Thrones also operated with two film crews, but the end product was a single episode. Why not dedicate an hour to each perspective rather than splice the two in a single episode? A multi-perspective production multiplies a 10 episode season to 30 while holding down cost. Netflix can’t solve The Content Problem, but it can mitigate it.
Interestingly, Youtube has solved most of this problem via a two-sided marketplace. The smattering of volume helps the supply-side problem even if a particular creator has a finite number of videos (remember, you can still play a given game for an unlimited amount of time without “running out” of content). Youtube has encouraged users to subscribe to many different creators, accounting for regular release cadence.
Diminishing returns for linear content are incredibly steep; few users will watch a film or movie more than once. Increasing and prolonging the LTV of a viewer is most elastic with more content: a costly proposition. To compete with games, TV, and movies need far more supplies. If technology and business models can change innovative products rather than be a vehicle for them, now has never been a better time to explore changes in storytelling.
Ran Mo retraces the incredible fast follow of the auto-chess genre. Before you could blink an eye, what was a mod in Dota 2 (which itself was a mod) had spun off into three incarnations. The first was from the mod maker, Drodo Studios’ plainly labeled Auto Chess, the second was Valve’s Underlords, and the third was Riot’s Team Fight Tactics. It’s not clear any of them have done “well.” Indeed, none of them are making any real money as there’s little to purchase amongst any of them.
Valve’s Underlords PSU Declines
In near free-fall since launch
But if there is a leader, it’s Riot’s Team Fight Tactics (TFT) by content cadence (from a player count perspective, it’s unclear if TFT is doing something meaningful). How do we explain the last entrant now leading the pack? Ran seems to argue for the power of the platform:
“Underneath the hood of League of Legends is a deep set of systems and tools: messaging and voice chat systems, social systems, matchmaking, internal development tools, event systems, cloud-based server infrastructure, post-game data analytics, and so on. These tools enhanced play experiences and supported the continuous release of contents, events, and updates.”
Ran goes further, explaining that “We’re likely entering a new wave of consolidation today, with new moats centering on live-service ecosystems […].”
I could not disagree more. Not simply on Riot’s platform power, but the wider value of game platforms altogether. Game platforms have, and will always be, valueless: there is too much game specificity and velocity for meaningful platform features to scale. “[…] messaging and voice chat systems, social systems, matchmaking, internal development I could not disagree more. Not simply on Riot’s platform power, but the broader value of game platforms altogether. Game platforms have, and will always be, valueless: there is too much game specificity and velocity for meaningful platform features to scale. “[…] messaging and voice chat systems, social systems, matchmaking, internal development tools”? While standard and required for live service, they are not particularly “deep.” If these features form a moat, then it’s the equivalent of a kiddie pool defending Versailles. The ever-growing number of PC game launchers, all of whom do these exact things, suggests as much. The primary value of game “platforms” is discovery and perhaps security.
The deeper platform features of Steam (Marketplace and Workshop) have failed to see widespread adoption. Other platforms haven’t even tried building anything beyond a friends list and chat. None of them can justify a 30% cut, and for many games, “multi-tenancy” or listing on multiple platforms is the clear revenue-maximizing strategy. Of the top 20 Steam games, only 35% non-Valve games use either Marketplace or Workshop. Furthermore, 59% are multi-tenant or are on other PC launchers. Expect this to increase as Steam degrades as the sole game discovery launcher. Activision realized this long ago when they shifted Call of Duty to Blizzard.net and away from Steam. Blizzard titles anchor the launcher with eyeballs but do little feature work. It’s 2020, and Blizzard still region locks many titles, meaning switching from NA to EU servers entails losing all progression for some titles.
Other firms take game platforms to eat at certain parts of the game “supply” chain. In addition to a given piece of technology working for games, these firms hope the tech will scale outside of games as well. This is an executive fetish that rarely, if ever, pans out. Machine Zone (MZ) is the latest failed entrant into “but we’re really a tech company”. Both Cognant, its internal ad-buying platform, and Satori, its cloud platform, went up in flames. MZ ended up selling to AppLovin at 10% of its peak value. The jury’s out if Improbable, the latest “but we’re really a tech company” can dig out of $85M losses. A more considerable hope is Unity, but even then, only 8% of the 716 customers to spend $100,000 or more have been non-gaming firms—games as a technology an executive fetish that rarely, if ever, pans out. Epic’s PR team was out in full force trouting The Mandalorian using Unreal, but indeed this is a mingy share of Epic’s revenue. Games have similar challenges to tech firms, but tech firms rarely have identical challenges to games (all squares are rectangles, but not all rectangles are squares). A common theme of this blog reiterated in this series is that games are a distinct and unique medium. The faster we embrace this, the faster the industry can evolve.
While game companies may never be tech companies, there may be a small place for game technology companies. Unreal and Unity are real and are here to stay – they solve complex and prevalent problems game makers face. If the games industry grows, the total addressable market for game tech solutions grows as well. Is there room for more game technology companies beyond Unreal and Unity? The previously mentioned Improbable seems to think so, but little additional evidence exists.
There’s more to be said for a given game as a platform, but the evidence is scant even then. Publishers haven’t been able to retain the value of extensions within a given title (Auto Chess splitting from DOTA 2) or outside of it (MOBAs splitting from Warcraft III and Heroes of the Storm). The simple ability for players to build content within games hasn’t precisely accelerated either. If anything, modding has become more challenging, not easier since its ascent in the early 90s. Can we think of a single major 2020 release to embrace mods? Much was made of Halo 3’s Forge, but we’ve seen few copycats since its release in 2007. If we consider a corollary to Bill Gates’ definition of a platform, it might go something like this: “A game is a game platform when the playtime of platform content eclipses the main game.” In this view, there hasn’t been a modern game as a platform. A softer version might consider a game as a platform when “the playtime of additional modes eclipses the main game.” Despite a much lower bar, it’s not clear that Fortnite, with its concerts and social spaces, has reached it either.
We’re left with Roblox, a trustworthy game platform. Roblox provides valuable tools that make it easy for developers to create compelling games and provide for discovery. But the jury is out to consider this the future of games, especially with its elder sibling, Manticore, is going nowhere fast.
RPGs vary from CCGs, which vary from FPSes. FPSes might need to solve 64 player servers, while CCGs may need transfer markets and RPGs need deep customization systems. This specificity of these challenges shrinks the total addressable market for the tech solutions game firms have devised. Central platform features haven’t adapted to a particular game design (and the speed at which it changes). As a result, we’ve rarely seen game companies make a successful transition to a tech firm.
It’s a billionaire bonanza, but from single to multi-digit billionaires.
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 learnfrom games (the content problem & MC = MB).
Networking Effects
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).
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