Alternative Data in the Gaming Industry: A Case Study

DataScrum
DataScrum
Oct 14 · 4 min read

An overview of alternative data and its application to the gaming industry by Chris Schon

The video game industry is booming. In the first week of October, Activision basked in the glory of a record 100 million mobile downloads of their new first-person shooter game, COD: Mobile. The industry has earned more than the movie and music industry combined for the past 8 years and shows no signs of slowing down. This rise can be accredited to a combination of increased production value, rapid release cycles, expanding market demographics and a shift towards lucrative free-to-play and subscription engagement models. Putting it more simply, the gaming world is becoming immersed in mobile content, micro-transactions, and Hollywood-like releases. With this, masses of data is produced by the players and fans, meaning the opportunity for us to mine that data for market insights.

So what is alternative data, and why is it important? In finance, alternative data as defined in this blog post, is data used by investors to evaluate a company or investment that is not within their traditional data sources (financial statements, SEC filings, management presentations, press releases, etc.). In the gaming industry, the opportunity for alternative data to provide market insights is huge. An alternative data trading strategy would involve combining credit card transactions, web and app activity, player usage and streaming statistics, and social media sentiment to give a holistic view of company and market performance. Institutional investors can then gain an edge on the market by understanding the correlations with key activity and sales performance indicators for the developers. EA, Ubisoft and Activision all explicitly state in their 2018 annual reports that delivering mobile games is a large part of their revenue strategy going forward.

“Players are engaging with games in more ways than ever before. We’re committed to meeting them where they are with a broad portfolio of amazing new games and live services, choice of engagement models including free-to-play and subscriptions, and new opportunities to play, compete and watch.” EA CEO Andrew Wilson

Here are the key alternative data sources when analysing the gaming industry:

  • Mobile activity data for app performance.
  • Credit card transactions for sales.
  • Web click and social media sentiment data for reaction to releases and announcements.
  • Steam player data for raw player usage performance.
  • Location & event data for understanding interest in gaming events.
Alternative data in gaming

With this data, we can answer questions like:

  • Are game developers delivering on their mobile strategy?
  • How does web & app traffic correlate with stock prices and earnings?
  • Are in-game sales performing?
  • What is the level of interest and general sentiment around the companies’ major releases?

Example: Mining Alpha with SimilarWeb Data

During DataScrum’s inaugural alternative data hackathon, I got my hands on a large sample of SimilarWeb’s data. SimilarWeb collects daily usage statistics on millions of websites and apps. By filtering for Electronic Arts (EA) owned websites and apps, I built a monthly trading signal for their stock based on 4 key metrics:

  • Total visits
  • Daily active users
  • Total installs
  • Total downloads

I built a decision algorithm to long/short the stock based on the performance of each of these metrics compared to previous months. The results were solid; a 23.5% return over 3 years compared to a 21.9% purely long strategy.

Building a trading strategy purely based on these metrics is ill-advised, but this proved the ease of integration of SimilarWeb’s service into a trading pipeline, as well as the granularity of their data. I also aligned spikes in website visits and app downloads with important game announcements. Understanding these numbers can without a doubt provide an indication to the performance of the companies’ key game releases each year.

Many thanks to the DataScrum team for organising the hackathon — check them out here. And to SimilarWeb, you can find more about their investor solutions here.


Chris is a Data Scientist at Applied Data Science, a consultancy that develops innovative data science solutions for clients. Check out their website here.

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