Get your detective’s hat on and whip out your magnifying glass. We’ve now bundled all of our DataSuite products under one plan, one price. All to make it simpler and easier to level up your analytics for your games and crew.
Without breaking the bank, you can now grab every feature we have in our DataSuite plan for one cost. You’ll be able to query your raw data, pump this into your tools, easily spot trends or investigate bugs across your portfolio, and run advanced A/B tests on all of your games.
So in this article, we’re going to chat about what exactly those features are, why we’ve made this change, and how you can use DataSuite to solve problems for your games, faster.
Five products wrapped in one
There’s a lot you can do with DataSuite. And after chatting with our customers, we figured that there’s no real one-size-fits-all approach to their analytics. Every studio and publisher’s setup and structure are unique, with different teams, resources, and needs.
It made sense to bring them all together. It’s now a lot simpler to figure out what exact tools you need and predict how much it’ll cost your studio. And it means any new features we roll out, you’ll have access to right off the bat.
There are five products listed in our DataSuite. Here’s what they are and what they do:
- Metrics API: Fetch your metrics for all of your games – You can build custom dashboards showing just the metrics you care about. So you can compare all of your game’s metrics and performance at a glance, making it easier to spot trends or bugs across your entire portfolio.
- Player Warehouse: The home of your data – Our very own prebuilt data warehouse that you can rent out. We bring in your data from our GameAnalytics tool into our Player Warehouse. So your data analysts can find quick insights about your games on the fly and run advanced tests on groups of players. (No engineers required, and saving you months of development time.)
- Raw Export: All of your data, served raw – You can send your GameAnalytics data programmatically (in JSON format) into your own data warehouse or data lake every day. And it’s much easier to chop this up and mix it with any other data sources you have.
- Event Export: Pull out just your events – Similar to our Raw Export, this is your data trimmed down to the event level. So rather than completely raw, you’ll see data for every single event in your games. You’ll have rows for the events each day, and every row has even more data behind it.
- Data Visualization (coming soon): Transform your data into easy-to-read visuals – We’re still working on the kinks for this. But soon, you’ll be able to use our own data viz tool for your data inside our player warehouse.
Let’s put this into practice
You own the fictional publisher, RedLobster Publishing. You launch 20 hyper-casual games a month and work with 30 different studios and developers. And now, you’re looking to do more with your data.
This is where DataSuite comes in. (Keep in mind, we’re focusing on one main example. There’s still plenty more that you can do.)
Metrics API alerts you when there’s a problem
Your data engineer notices that players are starting to drop off for one of your games. You’ve already used the Metrics API to build a custom dashboard and monitor the health of all of their games in realtime, so you’ll always see when anything suspicious happens.
Player Warehouse helps your data analysts do initial research
Day to day, your data analysts might be running quick queries to test their theories or get more insights from the data (before investing time with the raw data and potentially bringing in any data engineers). In this case, they would use the Player Warehouse to find out where players are dropping off in-game and maybe compare this with data from different sources.
After running some queries, it looks like players are leaving after level three. And there was an ad event tied to this level, too.
Now, your data analyst has two theories which they need to figure out:
- the level is too hard
- players saw too many ads.
Our Player Warehouse can help identify this problem, but the ad data only shows so much (it doesn’t have timestamps). Without digging any further, we can’t know which came first: the ad or the level becoming too hard. So we don’t know which event caused the player to leave. Thankfully, you could take a few routes to figure this out.
Route one: Run A/B tests on set groups
A simple way would be to set up two groups to test. One, where you show the ad at this stage, but make the level easier. And the second is to remove the ad but keep the level the same difficulty.
Run this A/B test for a few days, and see which one performs better in the Player Warehouse. Roll out the winning version to the rest of the game. Job done.
Route two: Use event export to access those timestamps
You can use Event Export to bring in event-level data, with timestamps, to your own data warehouse. Working with the data engineer, your data analyst could run advanced queries and filter through single events in the game. Combine this with the ad data, and you can find out exactly what happened first and whether or not they left because there were too many ads or a tricky level.
The difference here is we, GameAnalytics, define the schema for Event Export. This means we still work with the data and set the columns for your event-level data (rather than sending empty and customizable columns, like in our raw export). But for the example we’re chatting about, this route would work perfectly.
Route three: Use Raw Export to get the complete picture
OK. So you’ve analyzed the data. But you still can’t figure out why the player left. Now’s when you might turn to our raw export.
This is where you can be more thorough with your investigation. Grab the raw numbers – all that unaggregated data – and combine it with everything else you have. You’ll be able to run more custom queries to find any hidden issues. (So say, after some digging, you find out it wasn’t anything to do with the ads or level difficulty. But instead, there was a bug in the latest release of your game.)
Your data analyst would have to work with the data engineering team, and you’d use your own data warehouse or data lake to query the raw data. Which can sometimes take a bit longer and relies on more teams. But your raw data can help you understand so much more about your game.
Level up your analytics
There are plenty of ways to use DataSuite. We recently helped TapNation increase their LTV to 100% Day 30. They now collect and analyze data across every single game in their large portfolio, which they use when running A/B tests. And with this data, not only have they been able to make faster decisions, their product team has increased the average lifetime value of their games by 50% within six months of releasing a new title.
We hope this has been a helpful introduction to what our DataSuite can do for you. If you’d like to learn more about how you can use these features, or even how to get started, then get in touch. We’d love to have a chat.
