While smartphones and tablets become more and more popular, the gaming trend tilts to mobile games. Even though some gamers are resisting to embrace this new gaming era, statics shows that the mobile gaming market is now generating more than 50 percent of the global video game revenue.
In this project, I want to visualize the top 3 markets in global mobile gaming markets: US, Japan, and China. Also, I want to focus on the US market. The project ended up producing a poster with 2 sections: Global Market Analysis and US Strategy mobile games.
Data Source & Methods:
Data Plan & Methods
Since the performance and the revenue are highly related to the business strategy, it is difficult to find free dataset for the project. In this project, I’ll use the apple-app-store-strategy-games datasets on Kaggle. This dataset includes the necessary information about mobile games.
I also scrape down the data on Thinkgaming.com, which provides the rankings of the 1-day iPhone game revenue for free, and plan to use the dataset to sketch a rough shape of global sales. I use the top-100-grossing games list to create similarity compare. I also play with a dataset on this Japanese-based website which records and estimates monthly revenue of popular mobile apps and make a cross-comparison. The dataset on Kaggle contains multi-value columns, I used Python to clean it up and came up with counts of languages and genres.
After re-shape the dataset, I made a map with Gephi. Gephi is software for network analyzing, but I use it to create a more straightforward relationship map. The map shows the interaction and money flow between apps and markets. The Global Node presents the global ranking. Due to the high similarity, Gephi colors Japan and Global the same.
I tried to produce a graph that also presents the actual economic size of each app and market in it. However, there were too many uncertain elements as over-simplified estimations. Alternatively, I found this project on the internet.
The project was created by Veli-Pekka Julkunen. This project supported me with my plan that the Top 3 Markets are not only different — they are very different. Below is the world market map Julkunen created using the same method I used, but including more data of other minor markets.
For the analysis of the strategy games, I made multiple charts on Tableau and select some of the interesting truths to put on my poster. For example, here are survival pie charts of strategy games in US iOS app stores. It’s not important what is the precise number of each year, so I did not label them.
Surprisingly, there are not many games being equally popular in the global market. Even though popular mobile games tend to be translated, it is difficult to gain lots of money from all of the larger markets. It is surprising because I have some beliefs that the distance of the entertainment cultures have been closer in these years. ( For example, Avengers: End Game dominates globally… almost)
There are more and more game apps every year. The sizes of the apps are also increasing, both the median and the average of the size ( of US strategy games in the dataset ) is showing high similarity to Moore’s Rule. The indication is that the size of the games is growing with the growth of hardware. I’m interested in the causal relationship between them.
3 audiences viewed my poster and gave comments before I completed this project. One of the viewers is an expert on mobile games; the others are either having no idea of the US market or living in the US but do not play mobile games. 3 of them can understand the poster well. They cannot understand the map at first glance but successfully get the idea by a guess. I made color and layout adjustments based on their advice.
There are charts I removed at this stage due to the user tests. For example, the heat map below is meant to describe the benefit of each market. However, the Global Market is confusing because it should be the add-up of all markets. I noticed that in the very last stages of the user test. While I removed the Global elements I noticed that the relationship map itself contains this part of data, so I removed it from the poster.
Result & Reflection:
The poster printed in A2 size. A .png version is below.
Conducting data visualization in the area which I’m interested in but have never explored is very exciting. In addition to the datasets in my data plan, I also reviewed existed projects on the internet. The mobile gaming market is a very competitive and energetic area.
Some websites provide information collecting, analysis, and visualization as the primary service to support the mobile gaming industry. During the data searching process, I felt inspired by annual reports ( free version) of these websites. Seeing information visualization in real life is a beautiful simulation for my learning experience.
Although most of the data related to this fast-growing industry tend to be expensive for an individual researcher, there are ways to get an estimated value of it. My estimation is less considerable for such a large dataset I chose. In the next step of this project, I’d want to build a more reliable way to estimate the value of the (range of) revenue on apps.
I also want to analysis the elements of popular games of Top 100 grossing apps in larger markets and global markets. It’s difficult to label the features myself, but I think crowdsource mode would be the right choice on this task.