The video game industry is a large and rapidly growing market, totaling $108.9B in revenues in 2017. Moreover, a dominant trend that has emerged over the last decade is a significant increase in the proportion of people playing video games on mobile platforms such as iOS and Android: in 2017, 42% of the video game market was attributed to mobile games played on a smartphone or tablet. Popular mobile games yield substantial daily revenues, with Fortnite, one of the highest grossing iOS games, generating over $1M of daily revenue. Angry Birds, costing only $140k to develop, generated $70M of revenue in its first year. However, due to the same financial upside based on the large number of users and relatively low entry barriers, the mobile gaming segment is quite competitive with a large number of firms of varying sizes.
In this competitive space, few developers have been able to consistently produce commercially successful new content. We believe this is at least partially attributable to inabilities to generate and commercialize successful creative content. Two examples of this are Rovio and Supercell. After significant successes with Angry Birds and Clash of Clans, respectively, both firms have struggled to maintain user bases and revenue for these games and introduce new franchises with the same high level of commercial success.
Companies who develop and publish video games require a continuous source of unique and compelling content in the form of new game concepts, franchises, features, and expansions for existing games to remain competitive in the global video game industry. Given that firm resources are limited in scope and scale, the firm’s organic creative resources and development capabilities will always be unable to globally maximize the scope and scale of the firm’s creative ideation. However, simultaneously, there is some level of latent creative ideation resident within the “crowd” of the online gaming community, where a diverse set of creatively-inclined individuals may be interested in providing creative ideas for these purposes in return for some reward. Yet this “crowd” has neither the programming, development, and distribution resources of a video game company, nor the resolve and inclination of a commercial entity – both of these elements are required to move creative ideas from raw ideation to commercialized content.
In general, the current development process for the majority of game designers involves creating an initial game idea from within the firm and then putting it through various rounds of testing for commercial promise. Firms generally develop the idea into a game, play-test the game in-house amongst employees, and then move to a limited release in a specific market or markets before global release. Firms make decisions to continue or discontinue development and release at various points throughout this process based on a variety of factors such as reception in the test market and size of the potential user base. As mentioned above, this in-house, organic process relies on a limited and costly resource base (the firm’s employees) for the generation and vetting of nearly all creative content, features, expansions, add-ons, themes, and more.
DevGame will create value in the mobile gaming market by incorporating a crowdsourcing and machine learning feedback loop to decrease the cost of content creation and increase responsiveness to the tastes of the market. More specifically, DevGame will develop mobile platform video games with content selected and optimized by supervised machine learning, based on current and historical video game success. The games will receive regular content updates, where new content is sourced and selected from a combination of crowdsourcing and machine learning in a constant feedback process.
The machine learning algorithms will be designed to measure video game success as a function of a video game’s features or content. “Success,” in this regard, can also be defined as a function of numerous performance metrics, for example, revenue, number of users, number of new downloads, rate of new downloads, average user rating, and expert rating, to name a few. The initial tasks, therefore, are obtaining quantified video game features and performance data, the latter of which is readily available.
Quantifying Video Game Features
The first task is to aggregate and quantify video game features, both in relative and absolute terms. In order to accomplish this goal, DevGame will use a supervised latent dirichlet allocation (LDA) process to “scrape” common features from expert reviews on video game websites. The quantification of these features will occur in two distinct phases, basic and advanced. In the basic phase, the same LDA process will provide a “weight” to the feature for a specific game from a specific website, providing a relative comparison between games, and an aggregate value when the entire set (or subset) is taken into account. The advanced phase of feature quantification will use convoluted neural networks (CNN) to analyze videos of games being played, where images can be mapped to the features generated by the LDA process. As a result, the advanced phase will produce a far more sophisticated and objective measure of the feature list than is achievable through LDA alone.
The Creative Ensemble
With the features and performance metrics quantified, the “success” algorithm will generate features or a combination of features that can be used as the starting point for DevGame’s creative department. In this sense, DevGame is not eliminating the necessary human component of creativity; instead, DevGame is using machine learning to narrow the infinite list of possible content for the creative department based on a more accurate understanding of user preferences. This process will improve game throughput, reduce the variation of game success, and improve the product for customers.
The above process outlines the starting point for a game, where a fixed subset of features will be selected for a particular game type (eg, first person shooter), resulting in the release of the initial version, known as the “sandbox.” Users will download and play the game for a short period of time (eg, one week), before DevGame updates the game with the list of the next set of features to be implemented. This set of (initially) 3 features will be selected by the algorithm as the next “best” set of features to maximize game success based on the current version of the sandbox. Of the set of 3 features, 2 will be crowdsourced and crowd-selected, while the third feature will be chosen by the algorithm based on the outcome of the 2 crowdsourced features. For crowdsourced content, users will be randomly divided into two groups. The first group will provide the content for feature #1, while the second group will rank the content provided by the first group. The same process, with roles reversed, will apply for feature #2. Once the crowdsourced features are selected, the algorithm will supply the quantified feature #3, based on the new current state of the sandbox. DevGame will then incorporate all ideas into the next release of the game, updating the sandbox, and starting the feedback process over.
The demonstration for DevGame needs to answer a short list of questions:
- Can a supervised LDA process produce usable common features across video games?
- Do these features serve as meaningful indicators of game performance?
- Can we incentivize and produce meaningful, crowdsourced content?
As such, we would generate a short list of iOS games and review websites from which to scrape and quantify game features. Based on the small sample size, the early list may require meaningful supervision in order to aggregate generated topics into usable features. Once this task is accomplished, it will be a task of modeling the effects of the features on the aforementioned game performance metrics.
With respect to ensuring a productive, crowdsourced process, numerous applications exist that demonstrate such value; the online community Steam already serves as a usable reference point for generating crowdsourced content in a video game setting. The platform has seen the positive and negative aspects of linking developers with users for game selection and content sourcing, highlighting the need for a structured process.
The first step of the pilot program will be to outsource construction and maintenance of the algorithm to a small set of data scientists from which to develop the sandbox. Once this effort is complete, the initial creative sandbox will be generated via storyboard using DevGame’s creative resources. This concept will then be realized into an iOS game through a contract development team, who would also be retained for the short duration of pilot for the game update process. Finally, the game, including the crowdsourced feedback loop, will be tested in a limited market that is particularly suited for video games and creative content (eg, a university or a small number of universities).
The initial game release will be a free app with in-game purchases. The primary need for funding is the initial game development, which industry estimates place at $150,000. At an average revenue per user of $5.00, which is in line with industry averages, the breakeven number of users would be just 30,000, which is a readily attainable goal. The mobile gaming market had 2.8 Billion monthly active users at the end of 2016. If DevGame reached just 1% of the number of users reached by a single prominent competitor, it could conservatively achieve $24M in annual revenue.
We believe that a combination of selective crowdsourcing and machine learning algorithms can be applied to the game development process such that developers are able to more efficiently produce content that contains both the necessary conditions present in successful games and innovative content generated from critical analysis of the games. Through the use of algorithmic tools, we will be able to analyze what features and content correlate to different measures of success within a game and then release that content to optimize revenues for a game.
- Patrick Rice
- Samuel Spletzer
- Matt Nadherny
- Thomas DeSouza