Game Intelligence is Sensor Tower’s dedicated product for mobile game market analysis. Measure the size of the mobile game market over time by Downloads, Revenue, RPD, or ARPDAU broken out by Sensor Tower’s Game Descriptors.

Game Intelligence can answer questions like, “Has Hypercasual stolen market share from Puzzle games?” or “Which art style attracts the highest spending players to Strategy games?”

It also has navigation, Custom Columns, and Custom Field tagging features that are helpful for detailed competitive analysis or M&A research.

Game Intelligence has the typical Sensor Tower selectors for App Store, Google Play, or Unified, plus Date Range, Device, and Country / Region filters. You can additionally break down the dataset by any of the Game Descriptor Tags:

You can also enable these tags for other products besides Game Intelligence by turning on the toggle in the UI Settings page. Learn more about Game Descriptors.

Game Summary charts all apps in the Games category, but only breaks them out by the subcategories supported on the App Store and Play Store. Game Intelligence only includes games tagged by Sensor Tower’s team of analysts, but broken out with much greater detail and accuracy. Our Game Descriptor tags focus on the biggest games since 2017, and covers the majority of the market by downloads, and the vast majority of the market by revenue.

Class, Genre, and Sub-genre are represented in one consolidated piece of UI called Genre Taxonomy. The Product Model filter allows you to identify the core business strategy of a game, including how a game generates revenue and the costs to acquire and retain players. Theme also has a nested structure to group themes that typically appeal to similar audiences, like Combat (Military and violent themes), Nurture (domestic and home-making themes) or Kids (cute animals and School themes).

For a full list and definitions of our Game Descriptor tags see our Game Taxonomy Definitions resource.

Game Intelligence measures the total amount that any of these specific Game Descriptors are having on the overall market. Understand and filter each of these Game Descriptors to understand the best trends in gaming.

You can also use the Bubble Chart plot type to visualize and explore correlations between three metrics at a time. The x-axis, y-axis, and bubble radius are all configurable to plot Downloads, Revenue, RPD, or ARPDAU.

To go another layer deeper, click on a row in the table to expand it and see the games that are included from the filters you’ve chosen that contribute the most to the overall totals. From here you can:

• Configure Custom Columns to help see other attributes of those games, like their publisher country, if the game is an Editor’s Choice, or your own data that you’ve uploaded as a custom field.
• Select games and click View Estimates to chart the performance of individual titles in App Analysis.
• Choose Publisher as the entity type, and migrate those publishers to the Publisher Breakdown feature to understand the publishers operating those games.

Publisher totals on Game Intelligence are affected by the filters you’ve chosen, but if you navigate selected publishers to the Publisher Breakdown view, the game descriptor filters will not be respected. So if you’ve filtered for only RPGs on Game Intelligence, then select a publisher that operates some RPGs and some Action games, and migrate that publisher to Publisher Breakdown, you’ll see totals for all apps operated by that publisher.

Use Cases

Device

If you filter by the Geolocation AR genre (dominated by Pokemon Go) and pivot by Device, you’ll see that most downloads are on iOS, but looking at Revenue, iPad underperforms for this kind of outdoor experience.
See in Game Intel →

Category / Region

If you select the Category breakdown and look at Worldwide and then Asia, you’ll see that in Asia, Mid-Core games overperform in RPD much more than that of any other region.
See in Game Intel →

Genre

Choose the Genre breakdown, then select All-Time and Worldwide, and measure by Downloads, and you can chart the dramatic rise of the Hypercasual genre, which has moved from the 5th position to the dominant #1 biggest Genre by downloads. Since Hypercasual monetizes primarily through advertising, it underperforms on Revenue.
See in Game Intel →

Sub-Genre

Sub-genre is most helpful when comparing similar or competitive kinds of games. You could use it to compare the RPD of various RPG and Strategy sub-genres with valuable long-term audiences. Comparing Turn-Based RPG, MMORPG, Squad RPG, and MOBA, the MMORPG games have the best per-download monetization, with MOBA coming in last.
See in Game Intel →

Product Model

A game's Product Model is its core business strategy. You can use this data to answer fundamental financial questions around how a game generates revenue and the costs to acquire and retain players. Each Product Model represents a distinct business strategy to find and sustain profitability. For example, you can compare the downloads and revenue of mid-core, hybridcasual and casual in action games over time and pinpoint exactly when the hybridcasual model overtook the casual model.
See in Game Intel →

Monetization

Monetization features tagged are extremely important to understand how a game product is making money out of players. You can use this filter to understand with more granularity how genres, for example, are generating money. You can review the connections between genres like RPGs utilizing much more Gacha, and almost no AdRemoval as an example.
See in Game Intel →

Meta features

Meta features are fundamental to understanding how a game retains players long-term. ‘Meta’ refers to a system or game structure that is parallel to the core gameplay loop and grants players a long-term objective to pursue. Most metas revolve around creating layers of progression to keep players engaged with the core loop forever. For example, 70% of top grossing games worldwide make use of a Character Collection meta. 
See in Game Intel →

CSV Export

The CSV Export feature lets you export data based on your chosen filter criteria and selected breakdown type. The selected filters and the breakdown type chosen in the left navigation bar affect the CSV output. Currently, you can only view data aggregated by one breakdown type at a time.

Within the CSV, you can break down data by any of the following:

• Taxonomy Class
• Taxonomy Genre
• Taxonomy Subgenre
• Product Model
• Country
• Setting
• Theme
• Art Style
• Camera

Each row of data in the CSV corresponds with an aggregation of the selected breakdown type and granularity across the date range. Additionally, the data within each row respect the filters selected in the UI, such as the game intel filters and the store. Depending on the store selected, users can view iOS, Android, or Unified download and revenue estimates for each row.

As an example, if Theme is the selected breakdown, and five themes are selected in the corresponding filter, then we will see rows for each of the five themes for each period of granularity (day, week, month, quarter), spanning the date range.