Roamer Games is the new kid on the game development block. They are all about creating a mid-core strategy game that would give you the thrill of Civilization in just a five-minute gaming session. Early in the production, their team figured that to create a hit title, they needed to understand players’ behavior on a granular level. That’s where GameAnalytics’ data tools came into the picture. This case study breaks down how Roamer Games used GameAnalytics combined with AI technology to level up their game development.
Understanding Roamer Games’ Needs and Challenges
The game Roamer Games had in mind was a cross-platform but mobile-first marvel. Think iOS, Android, and WebGL. It’s a blend of Civilization and Clash Royale – a strategy with a dash of action.
The studio needed the lowdown on how gamers played their game right from the early stages of development. They wanted to know everything: retention, how long gamers played, how often, where they dropped out, and even what they thought of the first-time user experience. And later, they became set on understanding how players who used Real-time PVP feature compared to those who skipped it.
Roamer Games uses the Unity engine for developing their game. And before integrating GameAnalytics, they also used Unity’s analytics. While it was initially sufficient, they quickly realized they needed more. After reviewing and comparing different analytic tools, they decided to run with GameAnalytics. David Smit, CPO of the studio, comments:
WebGL was a major driver for our choice of analytics provider. That and the ease of setup early in the product development process made it a clear choice to go for GameAnalytics. Their data warehouse offered us a platform to go deep and granular with the data, while the dashboard gave us quick insight into the most important daily metrics.
Lastly, he appreciated the quick integration:
Setup was surprisingly easy. Install the SDK, create a game on the Dashboard, and link the two. Now you are good to go.
Combining GameAnalytics and Artificial Intelligence
The dashboard was the go-to for Roamer Games. It gave them the initial insights they needed every day. They even saved common queries to track player progress, where players dropped off, and Cohort data to understand how players hung around.
The data export options from GameAnalytics allowed Roamer Games to go even deeper with their data, unlocking player and event-level insights. The data tools that GameAnalytics offer are incredibly powerful. We now have access to an enormous amount of data through BigQuery. It requires some good knowledge of SQL, but it’s the way to go if you want to dig deep into the product and what happens to these users. Once you understand the different databases and workflow, you can get incredibly granular and hone in on any specific point in the game.”
Since the studio does not have a dedicated in-house data scientist, the team didn’t shy away from using AI – specifically ChatGPT – to form and optimize complex queries. They simply specified the data sets and tables available and asked ChatGPT to write the queries. The team was aware of occasional hallucinations and inconsistencies, and when they found some, they asked for a revision, and the chat would fix things for them.
Here is an example of a prompt Roamer Games used to build a query:
This is Table X about the player state: *paste the structure of that data set and table*.
This is Table Y about the daily checkpoints: *paste the structure of that data set and table*.
Give me a query that returns the following information, split by build, of the last six months.
This is a query generated by ChatGPT:
However, BigQuery returned an error as it tried to grab a field ‘Build‘ that does not exist in the design_event table (highlighted in red). On top of that, the warehouse attempted to split by build while already filtering by a specific build, leading to a meaningless split.
The team simply prompted ChatGPT with the error: “error: Unrecognized name: build at [5:7]”, and AI provided an improved query. Now, Roamer Games received a meaningful query:
Optimizing the Game with Key Findings
Here is what Roamer Games learned about its game after integrating with GameAnalytics. The studio discovered that players who dived into PVP had a 50% better Day 1 retention. This allowed Roamer Games to understand the value of the feature in their product very early.
Plus, they figured that most players picked to play as Vikings. This led the studio to prioritize not only the starter pack but early unit creation in general. The team made sure that they focused on creating a whole set of crowd-pleasing units from the Viking era first.
Last but not least, they also noticed gamers were spending more time playing the game:
We measured progression between levels a lot. The number of core games someone played was the main indicator of their progression. We found that after level 3 or 5, players dropped out in large numbers. Thanks to this insight, we could easily pinpoint the issue and quickly improve the balancing of these levels.
Discover, improve, optimize
Roamer Games leveraged GameAnalytics to delve into player behavior and optimize game features. Despite not appointing a dedicated data scientist, the studio was able to translate data from Raw Export to meaningful queries using artificial intelligence and further gain valuable insights via the BigQuery warehouse. As a result, the studio improved the game’s starter pack and other units by prioritizing Viking characters, enhanced level progression, and created data-driven, player-centric gaming experiences.