Analytics in Mobile Gaming

We helped a mobile gaming company to increase IAP, LTV, and retention using artificial intelligence-driven technology. We implemented analytics in mobile gaming, transformed big data into an appropriate format, implemented fraud detection, and applied machine learning-based tools enabling our client to predict customer lifetime value (CLV), as well as customer churn. Through Modern Data Warehouse and BI implementation, we achieved data monetization.


Optimization Of Advertisement And Customer Acquisition Budgets

The cost of acquiring new customers was tremendous and only a small group of clients made purchases inside the app.The company wants to implement data analytics and improve the performance of marketing departments which struggled with marketing expense optimization, data processing, and cost-effective data storage.

Customer Retention

Within 30 days after app installation, 90% of users stopped using the app, which means that a lot of money was spent on user acquisition but still a high number of paying users were lost. The company wants to solve the customer retention problem, optimize marketing expenses, and retain more paying users.

Unique User Preferences And Experience

Every user has different preferences and game tactics. The company wants to have an algorithm, which can predict the behavior of each individual player.


Transformation of Big Data into an appropriate format

We built a Data Lake with Data Warehouse to collect all the available data and transformed it into an appropriate format for data analysis. Collected data contains information on customers and their activity. We are now able to track each player’s progress, likes, interactions, preferences, expenses, demographics, behavior patterns, and many other data points.

Machine learning models in mobile gaming analytics

We applied machine learning models which automatically perform an intelligent analysis of each player. They automatically extract insights from new data and adapt to current behavioral patterns of customers.

Fraud detection in player acquisition

We created machine learning-driven fraud detection models that identify bots and reduce customer acquisition costs. Our model automatically delivers a report on fraudulent bot activities and it’s streams straight to the email box.

Customer lifetime value prediction (CLTV or LTV)

We built and deployed into the production LTV prediction AI model, integrated with marketing and advertising systems. It automatically predicts how much money a particular customer will spend on the application. Our client is now able to concentrate only on clients who will generate relevant revenue in the future.

Customer churn prediction

We implemented a machine learning-based customer churn prediction model that predicts which paying players are most likely to stop using the application. Now our customer is able to take action in time to retain more users. Additionally, we connected the churn prediction system with the marketing automation software and CRM systems so it automatically sends personalized offers to each customer.

Recommendation engine using machine learning

We deployed a recommendation system to increase IAP (in-app purchases). Now, the system personalizes offers and content according to each player’s preferences. That method increased customer satisfaction from the game, which directly contributed to higher business revenues.


The solution provides the organization with advanced reports and dashboards to predict customers attiring from the business. By unlocking insights, the management team can initiate the right business decisions to prevent customer churn, personalize ad offers, and increase game monetization.



Increased operational efficiency

increase graph


Increase in sales



Datapoints transformed



Hours of labour saved each month


power bi consultants
MS SQL Server developers
R programmers
oracle data warehouse implementation
continiuous integration

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