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User-Level Data and how it can change your business.
September 08, 2017

User-Level Data and how it can change your business.

Anonymous | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Treasure Data

We use Treasure Data to track data about in-game events in our games. Our monetization and data teams analyze the data we record and use it to make business decisions for future growth of the products. We also are using it as a replacement to Flurry Analytics, because it allows us to analyze data at the user level, rather than an aggregate.
  • User Level data tracking gives us the ability to figure out what the users do in our app, including their playstyle flows.
  • Treasure Data allows us to track users across multiple games, to see if our Cross Promotion between our game is successful.
  • Treasure Data queries have allowed our QA team to be a part of the data verification process, much more so than when we used Flurry Analytics.
  • Website navigation is a little difficult for users who check on it once a month, because it seems like the website UI keeps changing very rapidly.
  • Some of the search features on the portal are less than ideal and should be improved.
  • Maybe additional support for queueing queries.
  • We are better able to track users across our different games.
  • We are able to better test our in-game tracking events.
  • We are able to categorize different user groups and make business decisions our the different group's behaviors
  • We are able to track our users across our different games, enabling us to measure the success of our Cross Promotion efforts.
  • Because we have Android & iOS versions of our games, we are able to track mirrored in-game events and compare users' behaviors between Android groups and iOS groups.
Treasure Data allows us to look at the user-level data, rather than simply an aggregate of an in-game event that we track. Because we have user-level data access, we are able to better categorize users into different groups, which allows us to monitor how a new feature is being used by the different user groups.
Treasure Data is great for finding out what an individual's behavior is like within our application. Especially figuring out if a user has triggered specific in-game events that we are tracking. However, it is more difficult to use when trying to get a "quick look" at an app feature's overall usage (not single user specific). This is where Flurry's aggregate model is more useful.