Contents

1. Introduction

Every month, lots of people on the internet like to engage in online fights about whether their favourite game made more money than someone else’s favourite game.

This is a PvP event: player versus player online combat where people fight each other for what is basically just internet bragging rights.

And where there are strong and regularly occurring opinions on the internet, you can guarantee that content creators are soon to follow to use this to create content.

It’s (mostly) harmless fun. But the analysis involved can be… questionable at times.

But can you really blame people when they don’t know better?

There is a lack of good resources to understand revenue analysis using market intelligence. Being upset at this would be like being upset that people were bad at saving for retirement if the only education available was /r/WallStreetBets.¹

I feel strongly about helping people get better. So let’s do something about this. Let’s get better together at understanding SensorTower data, why it’s useful, and how to use it.

The focus of this essay is about SensorTower. However, the insights and conclusions apply to all forms of market intelligence more broadly.

2. Let’s talk about market intelligence

2a. Is SensorTower wrong and does it use made up numbers?

Yes.

2b. Wait, so everyone is just using factually incorrect data to argue / kick / scream / yell / vomit / [insert verb] at each other?

Yes.

2c. So how is SensorTower still in business when they sell made up data?

2ci. Firstly, there are levels to how “wrong” data can be

For example, let’s say you needed to know how big the US population was.

You could for example just make up a number and say “Ehhhh a billion sounds big so let’s go with that.” This is, of course, a very wrong number.

You could also decide to say²:

Well the US has 50 states and each state probably has 2 large cities;
A large city has maybe 3M people on average;
So the total population of the US is $50 \times 2 \times 3M = 300M people$

Now, this is also clearly wrong. For example, many US states have more than 2 cities. And what about all the people who don’t live in cities? Are we just pretending they don’t exist?

And yet… the US Census Bureau says the US population is currently 334M. So our estimate is actually pretty usable for basic calculations even if it is wrong.

This is what it means to have different “levels of wrongness”.

Numbers can be wrong, but still practical and usable.³ As long as the data is “good enough” to be usable, then you can use it so long as you appreciate its limitations.

2cii. Secondly, you are not supposed to just use SensorTower’s numbers directly

Even if you aren’t sophisticated enough to correct SensorTower’s flaws (e.g. lack of data, lack of people to work on this task, not worth it to actually bother, etc.) the data is still valuable.

But the same way you wouldn’t claim that the US population is exactly 300M, you shouldn’t claim that any specific game’s revenue is exactly whatever SensorTower reports it as.

The point of 3rd party data sources such as Nielsen, Alexa, Forrester, etc. is to provide a consistent methodology to aggregate hard to measure data over time to analyze trends and movements.

The movement and trends are far more critical versus the actual underlying number itself.

For example, it’s not important if SensorTower tells you that the player count for Blue Archive was 2.1M or 2.2M players. What matters is how this compares to other periods in time.

Has this number been trending up / down in the past 3 months or is the player count stable?
How does the player count compare to the prior year during important events such as Anniversaries?
Is the proportion of players by country shifting over time (e.g. increasingly JP / CN focused vs increasing international presence)

So if Blue Archive’s player count jumped from 2.1M to 5.3M year-on-year, then you can reasonably argue that the game has grown. If it shifted from 2.1M to 2.2M, then you can reasonably argue that the player base is stable.

The fact that 3rd party data sources use a consistent methodology means that the numbers they report are systematically wrong.

It is because these numbers are systematically wrong that we can perform trend analysis and can be comfortable despite the fact we know the numbers are all “wrong”. As I said in 2ci., numbers can be wrong but still practical and useful.

2ciii. Finally, you are not the customer for SensorTower

The average person does not buy expensive data feeds from market intelligence companies. Companies and data analysts buy data feeds from market intelligence companies. And a good team of data analysts at a competitive intelligence department can do a lot with even incomplete and “wrong” data.

Let’s say for example you are the Head of Data Analytics at a major gaming company. Let’s call this imaginary company YoHoMi.⁴

YoHoMi gives you a research budget and you buy a massive pile of data from SensorTower. As the Head of Data at YoHoMi, you notice that SensorTower is wrong. This is because SensorTower doesn’t actually know YoHoMi’s “true” revenue.

But YOU know what the “true” value is: You work at YoHoMi after all! So just call your friend in Finance and ask for it!

With enough data, you can probably reverse engineer why SensorTower is wrong and correct the flaws⁵ This obviously helps a ton in learning what the measurement and methodology flaws are. And… here comes the catch: Remember that SensorTower applies the same flawed methodology to everyone!

So once you know how to correct SensorTower’s flaws, you can now reverse engineer all the “true” values for all your competitors.

Oh. Oh ho ho.

So with enough work, you as the Head of Data at YoHoMi every month can now legally buy and reverse engineer the revenue of all of your competitors without needing to commit crimes like breaking into their offices and kidnapping your competitor’s CFO.⁶

This is part of why 3rd party data sources can be highly valued by companies: Market intelligence is difficult to obtain. A provider that can provide you with enough information to generate your own more accurate intelligence is valuable even if their data is “wrong”.

3. Let’s talk about monthly SensorTower PvP

I will be frank. Most of the discussion around SensorTower revenue online is terrible. It is like watching MMO trash mobs flail against other trash mobs.

What I want to do in this section is help you better understand how to approach analyzing revenue data. This applies both for the monthly PvP as well as how to think about analyzing revenue in a real marketing or revenue analytics job.

I can’t promise I can turn you into a revenue analysis raid boss. But at least you can be a lvl 35 Boss instead of a lvl 1 Crook.⁷

For all of the following analysis, data is from SensorTower data (pulled in September 2024). China Android revenue has been estimated as 1.5x iOS.

3a. Analyzing new launches such as Wuthering Waves (WuWa) or Zenless Zone Zero (ZZZ)

3ai. Track revenue trends to understand revenue stability

Looking at the monthly revenue numbers for new games is a popular hobby for people.

It is common knowledge that most games often have a significant “pop” at launch, and that future revenue often does not achieve the same heights as the initial launch. So people will often look to see where revenue settles longer term.

However, looking at monthly numbers alone is misleading.

Gacha game monetization is often driven by limited time purchases, which is often released at a fixed rate over time following a game’s launch.

The date the game launches and the timing of new content releases is not required to follow the Gregorian calendar. This means that direct monthly revenue analysis can be deeply deceptive.

It is also more helpful to understand how volatile player spending behaviour is. Volatile player spending implies some combination of factors such as player churn and lack of product-market fit. Volatile revenue also increases risk for developers and reduces the ability to plan ahead.

What you ideally would like to understand is:

How consistent and predictable is player spending behaviour?
How high does player spending peak when new content is released?

So let’s have a look at that then. Here’s the daily revenue trends for a selection of games for the first 180 days after launch. All values are calculated on a 7-day rolling average and rebased to 100 to facilitate direct comparisons across game titles regardless of the absolute $ revenue values.

This is pretty messy. So let’s go through this a few games at a time.

Blue Archive and Nikke offer a good example of the generic curves you might expect to see for a generic game:

There’s a hard pop at initial launch;
Decline in revenue as players churn off the game since it’s not suitable for them; and
Revenue settles into a semi-predictable cycle with a few hard spikes when especially popular content releases happen (such as Swimsuit Hina for Blue Archive JP or Viper for Nikke) that get close or exceed original launch revenue.

You can compare this to a game such as Tower of Fantasy to see what an inability to develop cyclical spending behaviour can look like.

Mihoyo is highly interesting for several reasons. What stands out the most to you when you see this graph compared to all of the previous graphs?⁸

Firstly, Mihoyo’s revenue cycles are incredibly stable and predictable. This is ideal for stable budget planning and investment decisions. It also reflects a very strong IP loyalty and attach rate with players.

Secondly, Genshin Impact is one of the few games where player interest did not appear to significantly decline during the first 6 months. If anything, having multiple continuous content releases that exceeded the initial launch peak is incredible.

Thirdly, future games such as Honkai Star Rail (HSR) and ZZZ do not appear to have the initial hump that is common to most game launches. The ability to cross-advertise within Mihoyo’s existing customers meant that these games drove massive immediate Day 1 adoption rather than taking several days to gain traction.⁹

While the revenue peaks in future cycles are lower than Genshin, this is mostly due to the outsized Day 1 launch effects from internal promotion to existing Mihoyo customers.

There will likely also have been higher churn from players who did not like the new game genres. Analyzing the exact numerical values of these peaks is therefore not meaningful.

What is important to take away here is that HSR eventually settled into a stable and predictable cyclical pattern. This revenue reliability is critical in establishing a margin of safety for on-going business operations.

Note that this did not happen instantaneously. Looking at the first four banner cycles for HSR alone would imply that revenue is continuously declining. This is why a 6-month or longer time period is better to establish a firm trend.

This is also why I caution against casual online analysis that draws spurious conclusions about individual game level performance using only monthly data over a short period of time.

For example, ZZZ first month data would capture the first two cycles, but the second month’s data would only capture the third cycle and miss the fourth. This means that you would draw incorrect conclusions about the game’s revenue performance.

Likewise, I would not rush to make conclusions about WuWa based on the revenue data alone. While the peaks are apparently declining over time, it is still too early to draw any substantial conclusions.¹⁰ What is most critical is to see whether over the next 90 days or so, the game can achieve a stable and predictable revenue cycle or if revenue will continue to be volatile.

3aii. Country-driven revenue

It is also helpful to understand which markets are the most important for a new game. This is because the largest markets will likely have an outsized impact on feedback for a game’s development.

Some key observations that should be flagged:

Mihoyo’s titles have 60-70% of their revenue from China. WuWa very noticeably does not even reach 50% and is about 15% lower;
APAC also has an outsized impact on WuWa, and driven specifically by South Korea, which is actually a larger market than the USA for WuWa;
Japan is 21% of ZZZ revenue versus 14-15% for the other games, but the share of revenue from USA and EMEA is lower;
Fate/Grand Order makes nearly all of its money from Japan and China alone (about 90%). Therefore almost all other regions are irrelevant for it.

These region-level differences are also important because the revenue potential of customers in each region is different.

Analysis of revenue differences by region, and which regions are the most valuable, will be covered in Section 3d.

3b. Analyzing long-running games such as Fate/Grand Order (FGO)

For long-running games, we are mainly interested in understanding how game revenue has evolved over time. FGO is one of the longest running games, so let’s use this as our example.

We know that FGO is heavily driven by Japan spending, so we can split the data by Japan vs non-Japan revenue. Here’s our first cut with an all-history view:

This is messy, but we can start to see some seasonality and trends in the data:

The primary major revenue months for FGO are around Christmas / New Year (Dec / Jan) and the Anniversary (July / August);
FGO annual revenue peaked around 2018/2019. It has been continually declining ever since;
While other games benefited from COVID-related increases in digital spending during the pandemic, FGO decline accelerated over this period instead; and
FGO still makes more money on average per month than most gacha games.

None of this will come as surprising news for FGO players¹¹. We can however delve a bit deeper in understanding the anatomy of what FGO’s slow gradual decline looks like.

Let’s look at a comparison of FGO’s revenue by year stacked against each other so we can compare performance by month:

This is pretty hard to read. So let’s break this up into two eras: One for 2015 to 2019, and one for 2019 to present day.

The progression over time is Black (2015) → Dark Blue (2016) → Light Blue (2017) → Dark Yellow (2018) → Light Yellow (2019)

Very roughly speaking, FGO’s performance in each month is broadly speaking better than the prior year for almost every single month. You can see things start to slip in 2019 however, and total revenue is very slightly down versus 2018.

We see almost the exact opposite pattern from 2019 onwards.

The progression over time is Light Yellow (2019) → Black (2020) → Purple (2021) → Mauve (2022) → Dark Orange (2023) → Red (2024)

We can see here that, broadly speaking, each successive year is lower in revenue compared to the prior year.

The FGO developers have not shown the consistent capabilities or capacity to develop new innovative gameplay systems. Their monetization methods also heavily depend on the New Year GSSR Campaign and Anniversary releases to stimulate spending.

As such, the primary lever they have left to address revenue decline is squeezing the players harder. And so we get announcements such as the NP8 announcement this year.

3c. Analyzing companies with portfolios such as Mihoyo

Companies with a portfolio of games should operate differently from companies with a single blockbuster hit title.

The primary benefits of having a portfolio of IP are:

Greater ability to segment your customers and provide them with the specific gaming experience they want and thereby retaining them as customers or inducing increased spending;
Increased revenue from multiple IPs rather than a singular IP; and
Reducing volatility by spreading risk across multiple IPs rather than having concentrated risk in one IP.

I have previously talked about how Mihoyo is organizing their content releases across their games to prevent direct competition between their games.¹²

This is why comparing Genshin vs HSR revenue or their revenue ranking is rather meaningless.

It doesn’t matter if Genshin revenue declined and HSR increased in any given month (or vice versa) if that is exactly what Mihoyo planned to happen in the first place to prevent cross-game competition!¹³ ¹⁴

So we need to measure companies with portfolios differently from other companies. Here are two approaches you can take.

3ci. Lesson 1: Evaluate portfolios on total revenue and not individual components

Because the revenue split across Mihoyo’s games are somewhat artificially constructed by Mihoyo, analysis should be performed for Mihoyo at an aggregate level.

So let’s do that. Here is the monthly revenue of Mihoyo’s games across their full lifetime:

What can we learn from this?

Launching HSR has improved Mihoyo’s revenue stability by providing a higher level of base support;
- Previously, monthly revenue was capable of falling below $100M but no longer does so post-HSR launch;
- The lowest post-HSR launch total revenue has fallen to is $113M¹⁵;
- Providing this base level of financial stability gives Mihoyo significant downside protection, which allows it to plan ahead and make riskier business decisions knowing it has a stronger margin of safety;
Mihoyo’s average revenue has also increased which means that HSR was accretive to their business;
- The average monthly revenue pre-HSR launch was $160M.¹⁶ Post-HSR launch, the average monthly revenue has increased to $200M;
- A Welch’s t-test for two data sets of pre vs post-HSR launch monthly revenue provides a p-value of 0.02, which also allows us to reject the null hypothesis that the two populations have the same mean value for α = 0.05¹⁷

Adding the total values across Mihoyo games is therefore the bare minimum any commentary of Mihoyo’s performance requires. Any commentary that fails to do this should be immediately viewed with hostile suspicion.

This does feel somewhat unsatisfying however. So what if… we could go even further beyond?

3cii. Lesson 2: Calculate the Share of Wallet of your customers

Ultimately, Mihoyo does not care if you spend $50 on Genshin or $50 on HSR or $50 on ZZZ. They do care if you spend $50 on something not Mihoyo.¹⁸

As such, what you really want to measure is Share of Wallet. As the name suggests, Share of Wallet is how much of someone’s overall spending you capture.

The basic methodology is as follows:

Calculate the average income that is available for your customers to spend;
- This can be either based on their post-tax income, or a segment of their post-tax income;
- For example, a fashion company might care about the average spend per person on clothing and footwear specifically rather than total income;
- Average post-tax income is the easiest to obtain, but is less granular. Ideally you want to use category specific expenditure data, but this may not always be available;
Multiply the average income for spending per customer by the total customer base you have. This is the total “wallet” your customers have available to spend;
Divide your revenue by the theoretical wallet available. This is your “share” of the wallet;
So for example:
- If the average customer has $100 to spend on clothes each month;
- You have 1,000 customers;
- Your monthly revenue is $40,000;
- Then your share of wallet is 40%.

This is a very helpful metric to track because it reflects the priority that your customers place on you as well as adapting to broader economic changes.

For example, Japan is a major market for gacha games. Real wages in Japan have also been declining for 26 straight months. The previous record was a 23 month long period in 2007 to 2009, just after the financial crisis. It would make sense if consumer spending in Japan might decrease during this period.

So let’s say that an average Japanese consumer used to spend $50 a month on your game, but now spends $30. Does this reflect a problem with your game? A basic analysis of only revenue would say yes.

But let’s say the average person’s entertainment budget shrank from $100 to $50 a month due economic pressure. This person went from spending 50% of their entertainment budget on your game to spending 60% of their entertainment budget on your game. Despite having less money, they chose to prioritize your game over other choices.

Using the share of wallet metric therefore reveals that your game is actually succeeding!

This is why a basic reading of revenue numbers can be incredibly deceptive and more sophisticated methods are needed.

A share of wallet analysis is also helpful when you run a portfolio business.

Your first product or service will capture a large share of wallet. However, each incremental product or service will only capture an incremental marginal share of wallet. What you care about is understanding what the marginal changes are, and how customer behaviour changes as they are exposed to each additional product or service.

This approach can be applied for both business-to-business (B2B) and business-to-customer (B2C or retail) companies. The broad principles and approach are the same although there may be industry specific differences.

So let’s do a basic and simple calculation for Mihoyo to demonstrate this approach.¹⁹ For this example, I will be using the China / Japan / US markets²⁰ and using publicly available economic data only.²¹

Share of wallet is rebalanced to 100 to show proportional trends across each country. Player overlap between Genshin Impact, Honkai Star Rail, and ZZZ is assumed to be 50% with 30% F2P population.

Several key trends worth noting include:

Chinese data is highly seasonal in the government statistics. This could be due to several factors such as changes in consumer activity during Chinese New Year, the influence of rural migrant workers, etc. Therefore, trends should be viewed in aggregate as a time series against prior the equivalent prior 12 month period;
Overall share of wallet continues to broadly remain above initial launch levels of spending, and remains either at or above 100 for the major markets;
- This implies that out of the players who did not churn off Mihoyo IPs after release, their interest in these IPs remains strong and then continue both to staying highly engaged and to spending on these IPs;
Total share of wallet (i.e. spend per player as a proportion of their total consumer expenditure) after launch is decreasing over time;
- This is adjusted for player count since it represents spend per active player. Therefore it is not due to factors such as churn but reflects consumer desires to spend money;
- Many of these reasons can be due to macroeconomic factors such as post-COVID adjustments where consumers deprioritized digital spending²², inflationary pressures²³, etc.
- There are also satiety factors which I described in my previous essay on monetization²⁴;
Spend increases slightly and stabilizes after the release of HSR in May 2023;
- Share of wallet does not return to prior levels, likely heavily influenced due to the macroeconomic factors previously mentioned;
- The US is the weakest of the 3 major markets with share of wallet fluctuating around the original Sept 2020 levels;
- Share of wallet in Japan has remained steady despite the squeeze on real incomes and spending capacity, which is a positive sign for revenue durability in a critical market;
- This may be a reflection of the difference in gaming habits in America, as well as HSR’s greater F2P friendly focus causing retention of lower spending players;
- There is a lack of demographic data to comment on whether growth in younger aged players (who have less disposale income) is also influencing these trends²⁵.

Obviously if you actually worked at Mihoyo, you would be able to perform this analysis at a much greater level of detail. For example, you could:

Analyze differences in willingness to spend based on precise geographical locations rather than at an overall country level to support highly specific advertisement targeting decisions;
Use much more granular consumer spending data directly from, say, credit card and payment processing companies themselves to understand what the “true” post-tax income for your customers actually is;
- e.g. What is the spending as a proportion of a consumer’s entertainment budget rather than their overall consumer spending to remove inflation as a factor?
Analyze the share of wallet for whale players and determine the triggers that are more likely to encourage spending on Constellations;
Analyze customer loyalty to your IP (e.g. If share of wallet is flat while consumer expenditure is decreasing, it reflects strong brand loyalty); and
Analyze how each additional product changes the retention of customers, such as redirecting churn by funneling customers into an alternative game so you can continue to retain them.

3d. Let’s talk about app download and active users data

What doesn’t get talked about as much, but should, is the app download and active user data. This is less sexy than arguing about money, but is critical to understanding the health of a game.

3di. Player value varies both by region and by game

Different games attract different types of players. By understanding the demographics of the player base, we can understand and then try to predict the future financial state of a game.

Here is a overview of app downloads for various games by region:

You will notice that the proportions in this graph differ significantly from those shown earlier in Section 3aii, where I showed revenue by Region. I will reproduce that graph below for ease of comparison.

This is why earlier on I said that the revenue potential of customers in each country is different.

We can explicitly quantify this using the Revenue per Download (RPD) metric. Here is the different RPD across these games and regions²⁶:

Region	Genshin Impact	Honkai Star Rail	Zenless Zone Zero	Wuthering Waves	Fate/Grand Order
Japan	103	148	46	53	328
HKTW	40	67	30	34	95
China	20	39	35	11	148
USA	17	25	8	22	89
APAC (exc. CNJP)	4	7	9	21	28
EMEA	4	5	2	8	83
Americas (exc. US)	2	3	3	5	24

August 2024 Revenue per Download (Data Source: SensorTower)

A few key observations:

There are major differences by region:
- Japan is the highest spending region on a per download basis due a range of factors such as familiarity and normalization of gacha mechanics. This makes it a critical market to succeed in;
- Hong Kong and Taiwan are strong markets, but the total population in these regions is low;
- EMEA and broader Americas can have individual whales, but on the whole has lower revenue per download compared to APAC despite having higher income per capita on average;
- If you are wondering why EMEA and Amercias excluding USA tend to be deprioritized for collaboration or marketing events, this is a helpful clue as to why;
As mentioned in the earlier section on portfolio dynamics, it is inadvisable to read too heavily into a single snapshot of spend within Mihoyo games (such as Genshin vs HSR) due to intra-portfolio competition being fully controlled by Mihoyo itself;
- One notable exception is ZZZ. ZZZ’s attach rate to the core Mihoyo audience appears to be less sticky than HSR;
- ZZZ should also be seen as an attempt to extract additional marginal spending from the core Mihoyo audience, rather than a fully fledged standalone product;
FGO has significant brand equity thanks to its broader tie in with the Fate franchise, leading to higher spend per download;
- It is also a reflection of the fact that if you start playing FGO today, you probably know what you’re getting into. So there is selection bias that will skew the denominator for the RPD calculation;
WuWa has significantly lower RPD than its direct competitor Genshin even removing launch download impacts which skew the numbers;
- It is highly likely that this is driven by WuWa’s positioning as a direct Genshin competitor;
- The types of individuals who will abandon a fully functioning game with no major defects are generally players who have low attachment to the game to begin with;
- As I wrote in my previous essay on corporate decision making, “It takes a lot for a whale to walk about from $’000s or more sunk into an account. While exceptions may apply, if a whale chooses to quit and accept sunk cost then this is likely due to a problem that having more players cannot directly fix”;
- This means that out of the players WuWa was able to capture from Genshin’s core audience, most of them will be lower spending players and will comprise a lower proportion of whales²⁷;
- WuWa’s higher average RPD in APAC is a reflection of its stronger player base in Korea.

3dii. Trying to project future trends

Let’s try and do something fun. What can we do if we combine RPD data with the raw download data?²⁸ ²⁹ ³⁰

Please note that the following commentary is going to be much more speculative in nature.

Let’s start with the download by region for each of the games we looked for RPD.

Some of this is not surprising (e.g. FGO having the lowest downloads, WuWa having >50% of downloads in China despite <50% revenue from China indicating its revenue weakness in that country).

However, the Mihoyo download numbers are interesting.

These download statistics are for August 2024, which is the premier release of Patch 5.0 and Natlan. So your first instinct is that the Genshin numbers are heavily inflated.

But no, they’re not:

This is quite interesting. Because outside of the initial release hyper for other Mihoyo games, Genshin consistently achieves 1.5 to 2x in downloads compared to the other games. And as far as I am aware, Mihoyo’s doesn’t overbias their marketing spend on Genshin versus their other games either.

So what’s going on?

Genshin is likely at the stage where it will maintain a consistent cultural impact in the gaming / anime space. It is functionally “too big to fail”.

This is supported by cultural phenomena such as a dominating presence at conventions and community anime / gaming events³¹.

If so, what are some fun things we can do about this from a business strategy and planning perspective?

The primary benefit of running a portfolio entertainment business is that there is “something for everyone”;
- This is part of the rationale for Disney’s long list of acquisitions. The goal is to build an ecosystem where you never have to engage with non-Disney IP ever again no matter what your personal taste is;
- It also means that families can all enjoy Disney products from their Disney+ subscription. Children can enjoy children’s programming or rewatch Frozen for the 74th time, millennial adults can watch the latest Marvel or Star Wars TV spin-off, boomers can watch Grey’s Anatomy or Desperate Housewives;
This “something for everyone” approach to portfolio building is what drives the motivation for Mihoyo to create more games across a range of genres;
- Part of this strategy is driven by the fact that Genshin is just not the ideal game for many people. It may be a fun game, but not the ideal game someone wants;
- Providing a range of alternatives allows for these individuals to churn off of Genshin and into another game within Mihoyo’s portfolio rather than into a competitor’s product;
- For example, HSR and ZZZ are more willing to cover darker / mature / popular culture / niche themes compared to Genshin;
The risk to this strategy is that a portfolio approach in entertainment requires dealing with the attention economy problem;
- This is covered in my separate essay on the AAA gaming industry;
- One of the key ideas is that people can only spend money on things they are aware of and have an attachment to. So if you don’t even have someone’s attention to begin with, you disqualify yourself from even asking them for money;
Under this approach, the business strategy should be to:
- 1) Maximize the total number of players who spend their entertainment “inside the Hoyoverse”;
- 2) Provide a large variety of IP that supports the needs of the player base and their interests;
- 3) Create as many touch points between the entire portfolio that monopolize the attention of your players and restrict the ability for competitors to erode your player base;
So how do you get people into your portfolio?
- Here, Genshin is clearly the primary entry point for people into the Hoyoverse;
- Once within the Hoyoverse, you can cross-advertise either through formal marketing channels to a captive player base, or through “cultural osmosis” as players naturally interact with other players;
- If you assume half of the HSR downloads per month are due to Genshin players onboarding via intra-portfolio dynamics, then about 30% of new Genshin players per month are also converting into HSR players;
- This helps offset natural churn from these properties to maintain overall player numbers;
You can see the impact of Mihoyo’s ability to funnel it massive player base into new titles through the revenue and download statistics;
- ZZZ’s initial launch downloads for example are higher than that of HSR’s, reflecting the incremental growth in Mihoyo’s player count;
- Also remember in Section 3ai that the initial 180 day revenue graph showed that HSR and ZZZ didn’t have the initial “pop” after initial Day 1 launch that nearly all other games have;
- Instead, HSR and ZZZ had a massive and immediate Day 1 revenue spike. There was no need to build up support and interest during the first week of launch. Players had already been instantly directed to the new Mihoyo property ahead of time and were ready to spend immediately;
One prediction is that Mihoyo will lean more heavily into this and try to push Genshin more aggressively as an entry point into the portfolio;
- This includes new content and development time spent on features relevant to new and / or returning players;
- For example, Mihoyo games have a “Returner” event, which provides bonuses for players who have stopped playing for at least 14 days to encourage them to come back;
- This addresses a player behaviour where players may play Genshin at the start of a Patch to see the new content and Events, and then quit after a week or two of gameplay;
- However, Genshin has introduced a new feature where players who have not been playing for 45 days to come back and enjoy a bonus reward equivalent to 10 gacha pulls;
- This feature is addressed a longer-term churned players and is not present in other Mihoyo games at time of writing;
A more open and directly “newbie-friendly” approach focusing on Genshin as a primary broad-audience entry point into the Hoyoverse however will place immense pressure on Wuthering Waves and any other future Genshin-style games;
- The RPD and download data implies³² that Genshin players have high switching costs and are mostly uninterested in picking up a game that is very similar;
- Genshin targeting a broader audience that has not yet onboarded onto a Genshin-style game also then restricts the future player base for competitors;
- Mihoyo can afford to optimize Genshin around new player acquisition and increase the value proposition because value extraction can occur across the Mihoyo portfolio;
Longer term, Mihoyo’s portfolio approach can theoretically allow for an “omnichannel approach” to customer engagement;
- A key issue with maintaining a large customer base is preventing erosion said customer base;
- For entertainment, the key asset you want to monopolize is attention;
- A tactic that is used in the broader entertainment industry is allowing their IP to bleed into all aspects of your daily life;
- For example, movie and TV entertainment tries to cross mediums through the use of merchandising, brand deals, theme parks, etc. to provide additional touch points to engage with the IP;
- Attempts by major gaming companies to cross the omnichannel barrier have often been highly limited and unfocused, restricted to simple tasks such as basic official merchandising or brand deals such as Halo Mountain Dew;
- However, the next evolution should be brand and IP acts as statements of value, identity, and expression;
A marketing and culture strategy build around this might focus on:
- Consistent and wide-scale merchandising across multiple regions to provide additional channels for monetization and engagement with customers;
- Seeding creative activities and programs around artistic creation that generate content to saturate attention spans and crowds out competition from eroding your customer base;
- Position the brand of each product in the portfolio to provide values and aspirations for individuals to project identities upon;
- Engaging in more brand licensing deals as an alternative mechanism to monetize a large player base without direct value extraction from the players themselves;
- A “lifecycle plan” where players have clear products and services allowing them to naturally transition and stay within the Mihoyo portfolio as they grow out of existing product / service consumption³³;

Essentially, it’s a plan to grow a Chinese version of Disney through a games-focused approach.

4. Conclusion

The next time you see a monthly PvP event or need to analyze data at your job, remember the following lessons:

Section 2: Does SensorTower or any other market intelligence provider wrong and use made up numbers?

Yes, the data is wrong;
No, it doesn’t matter;
Focus on insights you can generate based on trends if you can’t trust exact numbers;
If you work at a job where you use market intelligence, see if you have the power to make adjustments and reverse engineer the “truth” to have an advantage over your competitors;

Section 3: Techniques to analyze data

New product and service launches should be analyzed not on a pure revenue basis, but on the ability to stabilize and generate reliable recurring revenue;
Portfolio companies should be assessed on overall portfolio performance;
- Any commentary that fails to do this should be immediately viewed with hostile suspicion;
Share of wallet is difficult to calculate but is highly recommended for companies or individuals with sophisticated analysis capabilities;
Advanced commentary and future predictions aren’t reliable unless they also discuss:
- Country exposure: This sets the limits of your revenue earning potential;
- Download / RPD: Provides insights into the spending behaviour of customers and the change in the customer base over time, both of which will influence future revenue.

1
Please do not use /r/WallStreetBets as retirement investing advice.
2
Eagle-eyed viewers may recognize this as Fermi estimation
3
Also, some things just can’t actually be measured without getting into deep philosophical rabbit holes. For example, did you know that it’s impossible to accurately measure the coastline of a country?
4
My lawyers say I have to tell you that any resemblance to real persons or other real-life entities is purely coincidental. All characters and other entities appearing here are fictitious. Any resemblance to real persons, dead or alive, or other real-life entities, past or present, is purely coincidental.
5
.For some market intelligence companies, if you spend enough money and are an important enough customer, you can even interview their analysts. This way you learn private and confidential information about how their methodology works.
6
Your larger competitors can probably also reverse engineer your revenue as well if they know how!
7
This is how ~~mafia~~ memes work right?
8
Random fun fact: The sudden and unusual small bump in revenue for Genshin on Day 138 is the impact of the start of Chinese New Year on 12th Feb 2021, affecting the overall total revenue for the entire game.
9
This has the effect of causing the lines to crash immediately from Day 1 onwards due to the outsized effect of the Day 1 launch and leads to lower future peaks.
10
Especially since the most recent cycle is depressed due to giving away a free character, Xiangli Yao.
11
For example, the onboarding problems, lack of gameplay updates, etc. contribute to a stagnant gameplay loop and playerbase.
12
When one game runs a highly desirable, powerful, and high profile character, the other games will feature less popular characters to prevent competition. In my previous essay on Mihoyo’s monetization I gave the example of Acheron vs Chiori, but other examples such as Huohuo vs Furina also exist.
13
In theory, the same principles would apply for other companies with a portfolio of IP, not just in the gaming industry. The key is that the IP must be meaningful competitors to each other, and the portfolio owner is sophisticated enough to deconflict their IP.
14
Disney is an excellent example of a strong portfolio because their mass acquisition of IP and strong Marvel franchise led to a dominant position in film. During 2010 to 2019, Disney films were the number one highest grossing movies in all but two years. This includes a truly remarkable 2016 where Disney swept the top 5 highest grossing movies that year, of which 4 of them earned over $1B. To further emphasize just how crazy Disney’s strength has been in movies, since 2010 to date only 47 films have grossed over $1B. Disney distributed 26 of these 47. They even had a 25% stake in Spider-Man: No Way Home so that’s 27 they had a financial stake in. It’s truly a disgusting level of performance.
15
Note that this low revenue coincides with Black Myth: Wukong’s release. Given Mihoyo’s high reliance on the domestic Chinese market, it makes sense to try and deconflict with other major competitors where possible and save “blockbuster” releases for alternative months., and in general the support level for revenue appears to be around $150M.
16
I have excluded the September 2020 revenue as it is an outlier due to the partial month. This would have changed the average by about $5M.
17
I have used a basic t-test here since it is taught in many beginner statistics classes and therefore will be familiar to the average reader. However, there are obviously more robust approaches that can be taken. In particular, a time series approach allows for forecasting and inference accounting for stationarity / seasonality / autocorrelation / etc.
18
This does not have to be because of a rival game. At a fundamental level, all categories of entertainment all compete with each other. The main issue is that for the vast majority of game companies, this is a philosophical debate rather than a tactical business issue. After all, how do you influence someone to choose gaming rather than other categories of entertainment such as watching Netflix or learning to paint? This is something that can be tackled as part of a marketing strategy but is a topic to discuss at another time.
19
Please note that for the purpose of these calculations, I will be making a lot of broad assumptions. My goal is not to provide a definitive answer for how much Mihoyo’s actual share of wallet is. Rather, my goal is to teach you the principle and concept for how to perform such a calculation either for online PvP purposes or for you to apply in your own job.
20
Since they comprise approx 80+% of total revenue across Mihoyo games
21
Data sources: SensorTower, National Bureau of Statistics of China, Japan Cabinet Office, U.S. Bureau of Economic Analysis, Yahoo Finance.
22
e.g. Going to a Taylor Swift concert or taking overseas vacations instead of spending on online games due to being forced to stay indoors
23
Especially the aggressive spike in inflation in 2022
24
e.g. Why do you need yet another Pyro carry if you already have several others?
25
Since these trends are based on national consumer expenditure averages, they are sensitive to changes in demographics. Ideally you would want to do a share of wallet analysis across different demographics to adjust for this factor.
26
To adjust for RPD statistic being skewed by the large volume of downloads during the initial release week, I have taken the RPD for these games for August 2024 only
27
Annecdotally, even when Genshin whales who did pick up WuWa, they chose not to spend money on it and instead treat the game as a time filler in between Genshin content. Anecdote is not data, but an interesting story to understand player motivation and behaviour.
28
Functionally what I am doing is using RPD as a poor proxy for Customer Lifetime Value. CLTV is the ideal approach to analyzing your customers if this can be done, but for obvious reasons is not possible to compute as a random blogger. RPD is the next best alternative, even though it has significant flaws.
29
Downloads are being used as a way to represent the “sales pipeline” of players into the game. I don’t have reliable statistics on how downloads convert into net new players and the incidence of conversion into playing players so there are significant limitations here.
30
Why do this analysis even though there are so many caveats and assumptions that need to be required? For one, it’s fun. But even if the exact numbers are incorrect, we can still get a directional sense of trends. While RPD is a poor proxy for CLTV for example, we would expect them to be positively correlated. This allows us to at least try to make predictions at a high level.
31
[citation needed]
32
But does not prove, which is why I say this section is very speculative
33
The same way that a child can grow up watching nothing but Disney products and services their entire life

MoonBear Musings

Yes, Sensor Tower is wrong. No, it doesn’t matter.