How to Increase ROAS from 8% to 41%? CC Games Case Study

CC Games, a 20-person gaming company based in Warsaw, started its journey in 2015 when CEO Łukasz created a simple checker game, “Warcaby,” as a programming exercise. 

This game quickly surpassed expectations and set the stage for a larger venture. Now, CC Games is a significant player in the mobile gaming market, with two major titles, “Checkers” and “Chess,” amassing over 100 million downloads globally.

Navigating Through the Red Ocean of Mobile Gaming

In the fiercely competitive Red Ocean of the mobile gaming market, CC Games acquires most users through User Acquisition (UA) efforts. 

In 2021, 80% of their players came from paid campaigns, which increased to 94% by 2023. UA became the company’s strategic and financial pillar.

Before approaching us, CC Games had been running various paid campaigns, targeting individual player behaviors ranging from game installation to playing a certain number of games.

However, the CEO delved deeper into the purchase path and realized that focusing solely on these isolated characteristics might not be the most efficient strategy. 

He pondered a pivotal question: What if, instead of casting nets for the small fish in the ocean, they combined several key characteristics and focused their efforts on hunting the whales? 

The Whale Hunting Strategy

CC Games sought to enhance the profitability of their marketing campaigns by targeting their most valuable users. 

The company wanted to understand the behaviors of the top 5% of players who generate the most money in the game to allocate their marketing budget toward them. 

So, instead of focusing on quantity, they preferred to target quality.

And our mission was to equip them with the tools necessary for the hunt.

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Embarking on the Whale Hunt

Our role was to identify events in the early stages of a user’s interaction with the app, which would indicate whether they would become highly valuable in the long run.

This was achieved in five steps:

  1. Exploratory Data Analysis: Initially, we analyzed data from user journey events to understand player behaviors and preferences.
  1. Defining KPIs: Next, the focus was placed on key performance indicators like retention, in-app purchases, and rewarded ads to determine ‘best’ or ‘good’ user profiles.
  1. Time Frame Definition: Then we established critical time frames for collecting data from these events and observing the KPIs.
  1. Building the Model: Employing logistic regression, we correlated the event data with long-term results, identifying the likelihood of different KPIs being met by specific user actions.
  1. Creating a Proxy Event for Google Ads: Finally, an artificial event, combining various successful user actions, was crafted for Google Ads campaigns to target potential high-value users — the whales.

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Better (and Cheaper) Data Measurement

Additionally, as we began testing our initial whale-hunting campaigns, CC Games realized the need to improve their User Acquisition (UA) performance measurement (ROAS). 

Until then, they had been using AppsFlyer – a comprehensive and very expensive Mobile Measurement Platform (MMP) tool for measuring marketing expenses.

However, considering they were sourcing users from a single channel, they sought a simpler and more cost-effective solution that would allow them to accurately measure marketing expenditures and ROI.

The Google Cloud Above The Ocean

To achieve our objectives, we developed the Product Metrics Report, a specialized analytical tool based on the Google Cloud Platform.

Why Google Cloud?

We chose it primarily because CC already used Firebase to collect player data. It tracks user activities and provides valuable insights like revenue, marketing campaign effectiveness, and user details based on country and operating system.

Integration with Google Ads

Firebase seamlessly integrates with Google Ads, another tool used by CC Games. This integration is particularly useful for understanding the performance of marketing campaigns. 

Conveniently, Firebase can automatically link users to specific campaigns, particularly those originating from Google Ads.

Automating Data Transfer to BigQuery

For Firebase and Google Ads data, we’ve established an automatic transfer system to BigQuery, a powerful analytics and data warehouse tool from Google.

Leveraging BigQuery’s Machine Learning

BigQuery comes with machine learning capabilities. This feature simplifies complex tasks like building predictive models (for instance, for whale hunting), as we can access necessary algorithms directly within BigQuery, avoiding external tools.

Addressing iOS User Data with Cloud Function

However, Firebase and Google Ads are less effective for iOS user data. To overcome this, we used data from AppsFlyer, but since direct transfer to BigQuery isn’t feasible, we initially stored this data in Google Cloud Storage in parquet format. 

We then used a custom Python script developed with Google Cloud Function to transfer this data to BigQuery. This process is regularly scheduled through Google Cloud Scheduler.

On top of that, to avoid the high costs of AppsFlyer, we limited its use to only iOS data, removing Android data.

Data Processing in BigQuery

And once we have data in BigQuery, we carry out a series of steps to organize and refine it. For this, we use SQL, which helps get the data ready for reports. 

We set up a schedule that automatically runs these steps regularly to keep everything up-to-date. This ensures that CC’s reports are always based on the latest information.

As CC Games ventured into whale hunting, they also aimed to enhance their ability to measure UA performance (ROAS). 

Traditionally reliant on AppsFlyer, a comprehensive but expensive MMP tool, they needed a more straightforward, cost-effective solution for accurately measuring marketing spend and ROI.

We proposed the Product Metrics Report, an analytical tool based on the Google Cloud Platform.

Why Google Cloud?

Firebase Data Integration: Leveraging Firebase, which automatically attributes users to specific marketing campaigns, particularly those from Google Ads, was crucial as CC Games primarily used Google Ads for UA.

Seamless BigQuery Integration: Firebase’s native integration with BigQuery, a powerful analytics and data warehouse tool equipped with machine learning capabilities, significantly streamlined our whale hunting process. 

This allowed us to avoid external tools for model building, such as Python or R, keeping all machine learning processes within the Google ecosystem. 

Finishing the Fishing

We accurately calculated all the necessary KPIs using the Google Cloud environment, facilitating automatic data transfer between Firebase, Google Ads, and BigQuery.

This strategy negated the need for third-party tools for campaign effectiveness, data analysis, and predictive modeling.

CC Games now receives straightforward, insightful reports with multiple KPIs, including the vital ROAS metric. 

And speaking of ROAS…

The newly developed events identified ‘good users’ early, resulting in Google Ads campaigns that delivered a fivefold ROI increase compared to installation-focused campaigns.

The new targeting model is scalable and adaptable, too, allowing CC Games to test different markets, considering the diverse purchasing behaviors of players from various countries.

  • Simplified, clear reporting
  • Consolidated business analytics
  • Cost-effective, precise ROAS measurement
  • 5x ROAS increase compared to installation campaigns

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