### A Simple Model of Cosmetics and Why They’re Hard to Sustain

In Six common mistakes when moving to live-service games and free-to-play, Ben Cousins argues that cosmetic-only monetization is a mistake:

The games that make billions from cosmetic-only economies typically only succeed because of the sheer numbers of players. On a per-user basis they actually have very poor monetization, relative to games that use more aggressive methods. This is because for a multiplayer game that is built from the ground-up to be about dominating other players, the proportion of the audience who are interested in self-expression via cosmetics is rather small.

He’s right. Traditional HD developers choose cosmetics because there are no core design implications. Cosmetics can be layered into nearly any game at any stage of production. But for it to “work” massive scale is needed and even then it’s risky. It’s no wonder that very few mobile games in the top 100 grossing that use cosmetics only monetization. But I also think Ben misidentifies the challenge of cosmetics. They are certainly not about self-expression (at least the successful ones). In fact, Ben is right: male-centric multiplayer games are about domination but this is why cosmetics are viable.

Ironically enough, this is best summed up by piece on “cam girl” economics:

Men want a few things, and probably one of the biggest is winning a competition.

You see, you’re not just trying to get a guy to pay you – you’re trying to get a guy to pay you in front of a bunch of other guys. This is a super key. A man wants to feel attention from an attractive women on him, and this is made even more satisfying when it’s to the exclusion of those around him. He is showing off his power by buying your happiness.

High-level cosmetics in multiplayer games often signal domination whether or not the cosmetic is attached to skill. Apex is particularly effective at this. Consider low and high level banners:

The stat tracker element directly shows a player’s time commitment to the game. We can also see the more exotic colors and shapes communicating danger. Sort of like the poisonous dart frogs: cute, but deadly.

And finisher animations are particularly humminating since both the player performing the finisher and the one being finished must watch.

None of this should be particularly surprising, we are political beings after all.

While these might be good observations, we need a falsifiable hypothesis of cosmetics. This allow us to make more predictive statements about how a new cosmetic will or will not sell. And to an economist this means a model!

## The Model

Consider a basic model of cosmetic demand:

$$D_i = (C_i{_j} -\mu{_j})P_jT_j$$

Where:

$$\bullet$$ $$Di$$ is demand for the $$i$$th cosmetic $$\\$$ $$\bullet$$ $$(C_i{_j} -\mu{_j})$$ is the level of differentiation of the cosmetic from the average cosmetic in circulation at that given cosmetic vector $$j$$. Just think of $$j$$ as any customizable "slot". This benchmarks the cosmetic against its closest comparables in the same way you wouldn't benchmark a goalie against a midfielder. $$\\$$ $$\bullet$$ $$P_j$$ is the prominence of the cosmetic vector or how "featured" the cosmetic is in-game (a watch versus an entire costume) $$\\$$ $$\bullet$$ $$T_j$$ is the amount of time the cosmetic vector is featured on screen, both for the owner and others $$\\$$ There's a lot to tease out of this model, but much of it is beyond the scope of this post. Let's instead focus in on the inflation problem or what's suggested by $$(C_i{_j} -\mu{_j})$$.
$$\\$$ There are two elements: the cosmetics level of the game and player's individual cosmetics level. Let's imagine a player starting a game at time $$t_1$$ and with cosmetic $$C_1$$. During this period, the player derives a certain amount of cosmetic utility, $$u_1$$. After earning a level, a player may unlock a higher rarity cosmetic ($$C_2$$) and choose to equip it. They move to $$u_2$$ at $$t_2$$ as a result.

## Player Cosmetic Utility Choice Model: Getting a Better Cosmetic

Now, let's consider what happens when a player unlocks another cosmetic ($$C_3$$), but this time the cosmetic is of lower rarity. Because $$u_2$$ > $$u_3$$ the player does not equip it.

## Player Cosmetic Utility Choice Model: Getting a Worse Cosmetic

Players are benchmarking a given cosmetic against the currently equipped cosmetic in the same slot. Overtime players earn/purchase cosmetics that give them higher and higher utility. This makes it harder and harder to sell cosmetics - each one must make the player better off then the one before it. Every player in the game is going through this journey, and as such the average cosmetic level rises, just as we modeled in $$\mu{_j}$$. And when this rises differentiation falls and with it quantity demanded. That's a long winded way of saying that cosmetic inflation hurts monetization.

## Horizontal or Vertical Progression

Progression is a powerful toolkit, even for cosmetics. One way to combat the inflation problem is essentially print more money. And by this I mean introduce higher and higher rarity. Riot, for example, introduced Ultimate II skins (3250 RP).

Alternatively, the horizontal way to attack this problem is to add cosmetic vectors. Dota 2 has mastered this with things like chat lines and ping cosmetics as customizable vectors. Of course, this too will face challenges are players collect more and more items in the given cosmetic vector.

Cosmetics are a hard road to follow and inflation is always chasing developers. It's important to think carefully about mitigation strategies and how to grow cosmetic vectors overtime as you would any other element of a live-service.

### The Economics of Battle Pass are Broken. Let’s Fix It.

Monetization’s modern paradigm is defined by a direct store and battle pass (BP). After years (and ongoing) criticism of loot boxes, Fortnite re-wrote the rulebook in a way that seems to make both developers and players happy. However, it’s important to consider that at sufficient scale any monetization scheme looks like a winner. It’s unclear if Fortnite is a winner because of the pass or despite it. For instance, the collapse of Clash Royale’s monetization can be partly traced to the introduction of its own pass.

Reports of the death of loot boxes have been greatly exaggerated as well. Of top 10 grossing free-to-play games, 8 sold loot boxes in some form. Of the top 10 premium games, 4 did. It’s going to take more work to dislodge the loot box paradigm.

And it’s understandable. If a developer isn’t going to reach Fortnite scale, battle pass isn’t a sufficient monetization solution. But it doesn’t have to be this way. We’re in the early innings of BP; by breaking down the model we can pivot the pass from an engagement to monetization driver.

## The Model

We can understand BP spend depth by benchmarking it against an $$average\; daily\; monetization\;cap$$. The core challenge with the pass is the relatively small spend cap. To start, consider a pass with a fixed entry cost,$$fc$$, and $$N$$ tiers at a given price $$y_i$$. $$\\{total\;monetization\;cap}=\sum_{i=1}^N {(y_i)} +fc$$
In part, the maximum spend is limited by the cadence of the pass. $$d$$ is the pass length, in days. Dividing by this gives us the average daily monetization cap (ADMC).
$$\\ {average\;daily\;monetization\;cap} = \frac {\displaystyle \sum_{i=1}^N {(y_i)} +fc} {d}$$

We now have a model to compare different passes. Let’s examine Fortnite, Valorent, Call of Duty Warzone and Dota 2.

Each game has a widely different approach. Some go for more tiers and longer length while others go for less tiers and shorter season length. While not accounted for above, it is also important to adjust for pass frequency over a year long period. Dota 2 has a 200% increase in ADMC over Fortnite, but Valve only releases the pass once a year. Warzone, on the other hand, maintains a 100 tier season over an 8 week span consistently. This would average 5-6 passes a year. Nonetheless, battle pass presents far less spend depth over other monetization vectors. In a loot box system, the spend cap is the average price price to unlock all content. In a direct store, the cap is correlated to the total sum of prices divided by the rotation cadence. In almost all games this puts ADMC north of ~$20. However, we’ve still failed to account for opportunity costs to paint a complete profit maximizing picture. ## The Pivot ### Marginal Pricing BP tier pricing is plagued by inconsistency. Most games use exponential time to complete curves, with each level taking more XP (and therefore time) to complete then the prior one. Here’s Fortnite chapter one, season four as an example: And unlike RPGs, XP earn rate does not generally increase overtime. Yet, tier price is constant at$1.50 despite increasing XP requirements and constant earn rates. This is a rather odd proposition as it suggests that the usual $1.50 tier price forgoes a differing amount time depending on the tier number. By dividing by average time to earn we can understand the cost of an hour forgone per tier number. For example, tier 100 may take 6.5 hours to earn, and with a price of$1.50 this means the player pays $0.23 per hour if they choose to purchase the tier ($1.50/6.5hr to earn). Tier 1 only takes 12 minutes, suggesting a hourly price of $120 ($1.50/0.125hr to earn)!

This incentivises players to withhold tier spending until the end of the season.
More bang per tier buck. Yet players who make it to the end of the season are the most price inelastic! Why not vary tier price to maintain a constant benefit? Here’s what this’d look like assuming $1.50 buys 6,000 XP instead of a fixed tier: Tier 1 would cost$0.10 since it forgoes very little time while tier 99 would be $9.00 since it forgoes over 6 hours. I chose 6,000 XP to illustrate, but any amount can be chosen as the constant. At around 7,000 XP per$1.50, marginal tier pricing produces a greater spend depth then constant tier pricing (99 tiers at $1.50 =$148.50 spend cap).

This also combats the sunk cost problem. If a player is in the middle of a long tier, the price to complete the tier remains constant in hard currency. Purchasing a tier doesn’t always boost a player to the same “spot” in the next tier. In that case, it’s easy to feel like the early XP earned in the long tier is “wasted” if the player is considering purchasing the tier outright.

Setting the optimal XP/tier price is an exercise in price elasticity. If we set a tier price to say, $10,000, almost no player would buy tier. On the other hand, a price of$0.01 would produce very little revenue. Therefore, there exists a revenue maximizing amount of XP at a given price. While we may not know this exact number, it’s likely to be higher then current prices may suggest.

### Tier UX

Moving to marginal tier pricing puts much more pressure on the UX wrapper. Frustratingly, despite exponential time to complete, nearly every pass displays the distance between levels as constant. All of these levels in Apex look just as easy/hard as one another:

There are two solves for this: (1) alter tier distance (increase spacing between levels consistent with XP required) and/or (2) hard level labeling.

First used by King’s groundbreaking experimentation team, hard level labeling simply labels a label as hard (as long as it actually is). This encourages booster spend by a significant margin, as it tells players it’s optimal spend in the level. This has spread to other match-3 titles and now we find super hard levels.

Simply labeling a BP tier as hard should produce the same effect. This becomes a bit trickier for exponential curves as all the hard levels are backloaded. Shifting to an s-curve design avoids this as well as being a more equitable distribution of rewards.

### More Tiers or Less Days

A simple way to increase ADMC is increase the cadence of the pass. Instead of 12 weeks to complete, refresh the pass after 8 weeks. In Fortnite’s case this would increase ADMC by ~50% ($1.90 to$2.86). We can model the marginal effects of changing the pass refresh rate:

Adding more tiers produces a similar effect but in reverse. Instead of 100 tiers, why not Dota’s 2,000? Cost and smart use of content plays a strong role.

### Alter Cost Profile

Shortening cadence or adding tiers runs into supply side problems. There ain’t no such thing as free content. Furthermore, it’s wise to consider the vector with the highest return per price of content: does adding an Epic outfit to the direct store produce more marginal revenue then adding it to the BP? But even before answering that question we need to consider the cost of producing items all together. Despite Epic employing hundred of employees and engaging in outsourcing, ~12% of tiers in the pass are “costless”. This means they consist of either currency or boosters rather than distinct items.

Having cheap items altogether is another avenue. Apex has mastered this. Consider the stat tracker: it’s simply a tally of a particular stat. Or loading screens: an old piece of 2D art. This type of content is extremely cheap to produce. The foundational conversations of a game economy should include content vector specifications.

Valve has employed clever use of changing colors on cosmetics and Fortnite adopted something similar in their new crystal levels.

## Where To

MTX design has evolved and there’s no reason to think BP won’t do the same. At the end of the day, it’s a mechanic not a destiny. In future posts, I’ll expand on pivoting the pass even more. This is just an appetizer.

### Why Do Game Genres Evolve? A Kuhnian Explanation

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. And overtime, these mechanics become standard fare for the genre. For instance, invest-n-express titles like Gardenscapes are an outgrowth of the match-3 genre that adds collection mechanics on top. In HD, we’ve seen innovations like Apex Legends’ revive mechanic modified in Warzone’s Gulag. But how could we better understand why game genres change rather than observing they simply do? I argue that Thomas Kuhn can help.

From the Stanford Encyclopedia of Philosophy:

Thomas Samuel Kuhn (1922–1996) is one of the most influential philosophers of science of the twentieth century, perhaps the most influential. His 1962 book The Structure of Scientific Revolutions is one of the most cited academic books of all time. Kuhn’s contribution to the philosophy of science marked not only a break with several key positivist doctrines, but also inaugurated a new style of philosophy of science that brought it closer to the history of science.

Kuhn’s task in the Structure of Scientific Revolutions is to explain the process of scientific change. He proposes that at a given time, in a given field, there exists a “paradigm”. Paradigms serve two functions: a framework for asking questions and the tools to solve them. It’s useful to think of genres in the same way: they are frameworks for answering key questions to engage a particular Bartle-type. MMOs have different mechanics and frameworks to engage MMO players whereas match-3 games have their own mechanics to engage match-3 players. But that’s a levels explanation not a change explanation. Paradigms shift when the current paradigm cannot sufficiently account for key emerging questions. The complete and utter destruction of the RTS genre to MOBAs serves as a great example. Consider the RTS genre as a best practice guide to answering the following question: what mechanics do I use to create deep, long-session strategic gameplay?

Company of Heroes and Starcraft had sufficient answers to these questions but MOBAs challenged those answers. Are complex multi-unit controls necessary to creating that sort of gameplay? MOBAs told us that, no, multi-unit controls are not necessary. Can teamwork create ever deeper strategic experiences? Yes, says MOBAs.

It’s almost too perfect a parable when we further consider that MOBAs were literally an extension of RTS. The original DOTA or Defense of the Ancients was a Warcraft III mod. This is the exact type of model drift Kuhn describes.

Next, Kuhn would predict, comes a model crisis. As MOBAs started to sipcion players from RTS, the genre has a chance to strike back and incorporate these elements. From a Kuhnian perspective, failure to do so would result in it’s death. Incorporation was attempted, but resoundly rejected. Dawn of War III tried to add strong hero units with deep in-session progression. Relic misunderstood the questions MOBAs were trying to answer and copied the wrong elements. Perhaps more importantly, RTS never had a strong free-to-play answer, Starcraft was too little too late. Kuhn would call the period of conflict “revolutionary science” and the MOBA victory a paradigm shift.

## Dawn of War II and DOTA 2 Steam PSU

However, it’s not always the case that incorporation will fail. Hearthstone rapidly created Battlegrounds to repel the threat of Auto-Battlers. It seems to have worked, but the lack of monetization in Battlegrounds has lead to year-over-year revenue declines.

The Kuhnian model suggests framing a given game under a “game theory”. What is the game trying to answer in the genre it’s fight for? What are the existing answers (if any)? Why is it worth answering? This is extremely useful for teams to consider. Generally, pitches I’ve seen describe the game’s vision rather then the game as a back and forth conversation with market participants.

Tech has had a better understanding of the paradigm process. The near frictionless mass distribution of software means the paradigm process moves at light speed. Clubhouse generated huge mindshare, now everyone from Slack to Linkedin is trying their hand at audio. We saw the same movement with “stories”, a original Snapshot feature copied by Instagram and Twitter. Time will tell if their incorporation process answers the same questions Clubhouse has.

In the 60+ years since The Structure of Scientific Revolutions, a lot of Kuhn’s original ideas have been challenged. Despite this, Kuhn provides a rich model to think about why some game genres live and others die.

### Some questions for Metaverse(rs)

The “metaverse” discussion seems more about cultural “in-group” signaling then a thorough exploration of an idea. It’s frustrating. Rather than talking about “what the metaverse will look like” instead we should examine the forecast of a “metaverse” all together.

Most commonly, I’ve heard it referred to the idea that the future of games is a single shared 3D world with a variety of experiences driven by user-generated content. Roblox is most often associated with the term. Yet, what evidence is there that games are converging on a single shared 3D world?

Roblox and Fortnite are the best and biggest data points. Roblox is a successful game-as-a-platform unlike LittleBigPlanet or Halo’s the Forge. Those titles and ones before them are best thought of as mod platforms, wherein players are creating extensions of the core experiences rather then entirely new ones. On the other hand, Roblox allows for vastly different experiences, from FPS to RPG. Roblox also allows creators to earn cash in a straightforward and consistent way, uncommon for mod platforms. Roblox gets the “game-with-a-game” billing rather then marketplace because since it’s held together by a thin layer of: a launcher, avatar system and unified virtual currency (Robux).

Roblox has undoubtedly been a massive hit with over 40M DAU, but it’s only captured a specific age demo. As players mature, they fall off the platform. This isn’t the direction you’d like LTV curves to take. According to their S-1 filing, 67% of users are under the age of 16 while 14% of Roblox’s users are over 25 years old. Roblox’s inability move up the age ladder is largely attributed to its childish cosmetics and unpolished playgrounds.

The more devolution Roblox gives to developers to solve these problems the less it means to be a “Roblox game” since there’s less in common between each title. Devolve enough and you’re just Xbox Live. We’re starting to see evidence of this as developers can override the avatar system.

But poking and prodding the definition of metaverse reveals a series of questions. Why isn’t something like Xbox Live a metaverse (persistent avatar, friends list, social hangout)?

• What are the player benefits of a 3D shared world rather than a 2D menu?
• Why did PS Home fail?
• Is Second Life a metaverse?
• If so, why did Second Life fail?
• What observable things will happen in the next 2, 5 and 10 years that suggest we’re on track for a metaverse?
• How do these things differ from more UGC game platforms?

The metaverse is a forecast. Proponents should be more forthcoming in laying down testable hypotheses.

### Is PS+ A Good Idea? I Analyzed 200+ Game to Find Out.

Psyonix credits PS+ with vaulting Rocket League to success. Mediatonic decided to follow suit with Fall Guys. And after its success, Destruction All-Stars delayed their launch to be included in the program. Does PS+ deserve all credit it’s been given? There are some stark trade-offs worth examining.

Prima facie, PS+ faces an uphill battle – developers receive near $0 for offering up their games. Additionally, any users brought in on PS+ incur additional server costs for the developer. Should developers really be so willing to throw their titles at the service? The service charges players ~$5 monthly to receive two random games. These are full entitlements, not subscriptions. If a player leaves PS+ they still own the games. The billed benefits of the program are:

(1) the additional sales generated from other platforms during the PS+ period
(2) the “momentum” generated after the PS+ period to additional sales on Playstation
(3) MTX revenue from additional players

I wanted to examine expected PS+ player volume as well as sales uplift afterwords. Examining cross-platform effects are possible if the game is cross listed on Steam, but I’m lazy. And I have written enough code. So I’ve posted the spaghetti code for others to do so (and spot my errors!).

## The Dataset

gamestat uses the Playstation API to estimate player counts. This is particularly apt for PS+ analysis as it excludes players who “claim” a game without playing it at least once. The dataset only includes “played entitlements”. We’ll cross-reference this against a list of PS+ PS4 games. Occasionally there are regional differences but I selected the North America list for simplicity and personal bias. Remasters or collections are attributed to the base titles and are not parceled out in API data.

## The Growth of PS+

The subscription has grown overtime, consistently attracting more and more downloads for the average title. Fall Guys didn’t just outperform prior titles, it obliterated past comparisons.

### PS+ Played Entitlements Per Title

In 2019, PS+ exploded with the median title having 2.46M downloads compared to 2018’s 665k. This only increased in 2020. Some of this is probably fueled by Sony dropping the number of monthly titles from 6 to 2 in February 2018 (thereby increasing download concentration) as well as picking titles with stronger Metacritic scores.

Additionally, the service moved away from indie darlings towards titles with more publisher heft. Past year’s Call of Duties have found a second life in the program. This success has only attracted more “old” AAA titles to the program. We also find titles previously released on other platforms choosing to launch on Playstation via PS+ such as Grow Home and Super Meat Boy.

### Weeks After First Playstation Launch to PS+ Inclusion

But heighted downloads are not enough, developers need revenue upside especially when considering Sony limits the ability of PS+ launched titles to appear on other platforms. We need to consider increases in PC sales and post PS+ uplift in PlayStation sales.

## Is There Post-PS+ Uplift?

At quick glance, Fall Guys shows a shape decline in entitlements after the PS+ program ends. That makes sense when a title goes from $0 to$20 for a large swath of players. However, there is a “handover” month in September that looks highly correlated with the prior month’s PS+ numbers.

### Fall Guys ‘Played’ Entitlements

On the other hand, the effect is only predictive of sales in the waning days of the PS+ period wherein downloads numbers are smallest. Additionally, the lift is short-lived (~2-3 weeks after the PS+ period ends).

If we examine all titles, we find a PS+ “hump”: entitlements increase during the PS+ period and then decline.

### Monthly New Entitlements Per Title: Before, During & After PS+

For a clearer picture, let’s connect entitlements 1 month before and after PS+ for those games with a non-PS+ launch period (i.e. excluding Fall Guys). We should see upward sloping curves if there’s a post-PS+ lift and indeed that is the case.

### Monthly New Entitlements Per Title: 1 Month Before & After PS+

Even after all those free entitlements, there’s a still a good deal of players who purchase the game.

The mean uplift was +277K entitlements when comparing the pre-PS+ month and post-PS+ month. However, there’s a whopping standard deviation of +286K entitlements, with a median uplift of 160k+ entitlements.

Not all PS+ uplifts are created equal it appears. The biggest winners were already big franchises, further riding off of brand awareness and possibly some networking effects with so many newly added players. NBA 2K 2016 was the top earner, with over +1M entitlements compared to it’s pre-PS+ haul.

What’s not accounted for are players that downloaded the game during PS+ and only first played after the PS+ period. I’m going to guess and say that accounts for 20% of the entitlements.

We also need to consider that many games pair price sales during and after the PS+ period. I’d reckon a $30 average selling price of which$20 goes to the publisher after platform margins are accounted for. For every 100k units, this works out to $1.6M net marginal revenue before severs costs are subtracted or MTX is added. PC uplift isn’t accounted for and neither is the opportunity cost of having to be PlayStation exclusive for a period of time. The best I can tell, PS+ revenue uplift isn’t lifechanging (know your OCOR!) and the scale of the uplift is correlated with how well you were doing anyways. That said, I can’t find a decrease in post-PS+ sales and that’s good for developers nervous about giving away their game for free. ### Is There An Actual Case for Cyberpunk’s Delay? Cyperpunk 2077 launched and it turns out the PS4 and Xbox One versions of the game were riddled with bugs. This has lead to an avalanche of omniscient pundits declaring “I told ya so!”. My personally favorite roast in this Miyamoto meme. Rushing development feeds into narratives around greedy firms. “If only they didn’t want so much money!” Much of this banter is comprised of cheap shots devoid of making real claims about what CDPR should have done. Should the game have been delayed an additional 4 months? 6 months? And if so, why? If the board really didn’t understand the scope of the bugs then the question turns to the organizational design CDPR. What organizational breakdowns led the lack of information the board had about the bugs in game. Were QA leaders not empowered to speak up or not trusted? These are much tougher questions to answer. After all, as Pixar is fond of saying, “[Games] don’t get finished, they just get released”. The key question is when to release. There will always be bugs and there will always be new features to add. Ultimately, release timing is a cost/benefit decision. Relative to the additional development cost what increase in sales would we expect from a delay? Do we have ever higher margins from a 4 or 6 month delay? To be clear, Cyberpunk is already outselling all other CDPR games, hardley “one of the most visible disasters in the history of video games“. What further increase would analysts expect with what additional delay? ### Why Do Only Product Managers Write About Games? I recently came across this Tweet: 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. ### Why Do FPS Players Like Small Maps? It’s the incentives, stupid. 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. FPS players like small maps because they function as costless XP boosts. Nuketown will be making its 5th appearance in the CoD title with Cold War. ### Game Companies Aren’t Tech Companies Part IV: MB = MC ## A Quick Refresher Pricing is tough to get right. Ideally, we’d like to select a given price such that revenue is maximized (consider revenue as profit), holding all else constant. 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 max 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 $$P$$ 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.

But what if there was a way to charge the reader who valued the book at $30 exactly$30 and the reader who value the book at $18 exactly$18? While we could raise the price of the book 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 accrued utility outweighs the initial cost.

### Standard Utility Curve (Reading a Book)

It’s import 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 if doing so is better then 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 sort of boring. This is why book rentals (libraries) and book reselling are so popular. Why pay a fixed price for what is usually a single-use item? This rings 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 off 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:

PvP environments [in games] necessitate strategies that evolve in an evolutionary process. This means equilibrium in PvP environments is constantly reshuffled with each balance change; 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 with which it can correlate marginal cost (MC) to marginal benefit (MB). In the above examples, price was fixed. This made sense given that the utility curves flattened out. But the more the curve refuses to flatten, the most discorrelated MC to MB becomes as Time continues.

This 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 to a closer to their MB curves they were previously able to do so.

### Utility/Price Curve: MTX & F2P Games

Software as a service (Saas) is able to generate similar growing utility, but they only charged a fixed price in recurring intervals. Again, this suggests that subscriptions might make sense for games. Games, however, generate even more heterogeneous LTU (lifetime utility) then do many SaaS products. This suggests subscriptions are better than fixed prices in correlating MB and MC, but weaker then MTX systems. 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 as they are added. Look familiar? This is exactly how observe 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. Clearly, some users value say, Zoom, more than others. As Zoom usage increases, LTU does as well, but 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 gets in the cosmetics business – backgrounds were an amazing opportunity that never got capitalized on.

Even at the organizational offering level, Zoom could create an additional tier that offered additional features to high usage customers in the tier. Multi-track cloud recording, for instance, generates incredible value for Podcasters but costs nothing additional.

The dramatically different value propositions of standard goods and games necessitate different monetization schemes. This makes applying the monetization of tech companies less applicable to gaming, but perhaps SaaS as a service firms have something to learn from games.

Gaming Companies Aren’t Tech Companies!

### Game Companies Aren’t Tech Companies Part III: The Content Problem

So maybe game companies aren’t tech companies. But as much as game companies seem to borrow from tech firms there’s even more to be said for the opposite. 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 it’s 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 that there are 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 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 it not true for games.

Consider the following:

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). This can 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 person (!) Clash Royale team is astronomical.

The genius of PvP (Player v Player) environments is how they necessitate the emergence of a meta-game. In mathematics, Player vs Environment (PvE) resembles the field of optimization where strategies are static – one and done. PvP environments, however, resemble game theory models where it has been shown strategies evolve in an evolutionary process. This means equilibrium in PvP environments is constantly reshuffled with each balance change; 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, movies 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 for it to be “effective” and the recommendation engine plays a strong role in this. But the the last episode of Stranger Things is just that, the reco engine cannot find 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: less writers, editors, and CG needed 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 7 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 then 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 heavily invest in moving shows to a similar formats: year-round production with weekly releases. There’s always another piece of content to consume right around the corner while the back-catalog for a given show is continually expanding 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 quick glance at community pages confirms this.

King mitigates this increasing difficulty for example. This increases the time to completion but could also result in churn. Reality TV faces no such option. Another strategy is also possible however: branching narratives.

Increasingly, Hollywood is shooting movies back-to-back. It’s cheaper to continue production rather then 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 in a similar way with two production crews, but with a singular end product. Game of Thrones also operated in this way from a production standpoint, but again the end product was single episode. Why not dedicate an hour to each perspective? This easily 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 sheer smattering of volume helps the supply-side problem even if a particular creator has a finite number of video (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 so regular release cadence is also accounted for as well.

Diminishing returns for linear content are extremely steep, few users will watch a film or movie more than once. Deepening the LTV of a viewer primarily come through more linear content: an extremely expensive proposition. To compete with games, TV and movies need far more supply. If technology and business models can really change creative product rather than be a vehicle for it, now has never been a better time to explore changes in storytelling.

Part IV