Data-driven solutions for Gaming Industry

Our analytics mean better monetization, more effective UA optimization, and more accurate business decisions.

Let’s talk
Data-driven solutions for Gaming Industry

We empower leaders:

Turn player data into
higher revenue and retention

Maximize profits from every campaign – intelligent LTV and ROAS predictions allow you to optimize user acquisition spending, increasing your return on investment.

Make smarter decisions on iOS – SKAdNetwork-based reports and probabilistic modeling enable accurate estimation of long-term ROI, giving you a more complete picture of campaign effectiveness.

Increase engagement and monetization – dynamic user segmentation and player-tailored IAP personalization ensure that everyone finds an offer tailored to their needs.

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Use data to grow your business

Free consultation

We have proven solutions
for your business challenges

ROAS predictions

ROAS predictions

ROAS predictions

Increase your return on investment in user acquisition. Our long-term ROAS predictions will show you which channels and campaigns are truly generating profit, enabling optimal budget allocation.

User acquisition reporting for iOS

User acquisition reporting for iOS

User acquisition reporting for iOS

Maximize ROAS despite limited attribution. Our models use SKAd data and analyze user segment behavior to provide a source of truth for iOS, showing the full long-term picture in a single tool.

Personalization
user experience

Personalization
user experience

Personalization
user experience

Increase conversions by personalizing the user experience. Precise user segmentation allows you to adjust the difficulty level and present personalized offers, creating an engaging experience and maximizing revenue.

Product Reports

Product Reports

Product Reports

Optimize retention and monetization with Product Reports. Analyze A/B test results at the UA channel, country, and user segment levels. Balance game mechanics and game economy based on precise data.

We share our
knowledge and experience

Your data holds great potential.

Ask us how to make the most of it


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    Why should choose Alterdata?

    We combine expert experience, extensive technical knowledge, and a flexible approach to collaboration to create data solutions that are truly tailored to your organization’s needs.

    Comprehensive End-to-End Implementation

    We manage the entire process: from consulting and technology selection, through data warehouse construction, to the development, maintenance, and optimization of solutions. This ensures that our clients receive consistent support at every stage of their data-related work, without having to coordinate multiple independent vendors.

    Data Expert Team

    We bring together the expertise of data engineers, analysts, data scientists, IT architects, and business consultants to address both technological and business needs. Our team helps translate an organization’s goals into concrete solutions that effectively support decision-making and business growth.

    Technology Neutrality

    We choose tools based on the goal, not the other way around. We work with popular cloud and analytics technologies, including Google Cloud, Azure, AWS, Snowflake, Databricks, Power BI, Tableau, and Looker. Thanks to our extensive knowledge of these tools, we recommend the solutions best suited to the client’s situation, rather than pushing a single technology.

    Flexible Model of Collaboration

    We offer support exactly when you need it, ranging from individual specialists to a Data Team as a Service model, without the need to build a full in-house team. This allows you to quickly expand your organization’s capabilities and leverage expert knowledge in a way that aligns with your current needs.

    Business-Specific Solutions

    We design services and architecture tailored to specific requirements, budgets, industries, company sizes, and business objectives. We treat each implementation as a unique case to ensure that the technology supports the processes, workflows, and priorities of the organization in question.

    Secure Architecture

    We create scalable, secure solutions designed to support organizational growth, handle increasing data volumes, and facilitate migration to modern cloud environments. We ensure access control, stability, and scalability so that the data platform can grow alongside your business.

    Tech stack: the foundation of
    our work

    Discover the tools and technologies that power the solutions created by Alterdata.

    Data lakes and lakehouses ETL/ELT pipelines and data streaming Serverless services Cloud Data Warehousing Data transformation tools Business Intelligence Data automation and orchestration ML & AI
    Data lakes and lakehouses
    Function

    Google Cloud Storage enables data storage in the cloud and provides high performance, offering flexible management of large datasets. It ensures easy data access and supports advanced analytics.

    Function

    Azure Data Lake Storage is a service for storing and analyzing structured and unstructured data in the cloud, created by Microsoft. Data Lake Storage is scalable and supports various data formats.

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    Amazon S3 is a cloud service for securely storing data with virtually unlimited scalability. It is efficient, ensures consistency, and provides easy access to data.

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    Databricks is a cloud-based analytics platform that combines data engineering, data analysis, machine learning, and predictive models. It processes large datasets with high efficiency.

    Function

    Microsoft Fabric is an integrated analytics environment that combines various tools such as Power BI, Data Factory, and Synapse. The platform supports the entire data lifecycle, including integration, processing, analysis, and visualization of results.

    Function

    Google BigLake is a service that combines the features of both data warehouses and data lakes, making it easier to manage data in various formats and locations. It also allows processing large datasets without the need to move them between systems.

    ETL/ELT pipelines and data streaming
    Function

    Google Cloud Dataflow is a data processing service based on Apache Beam. It supports distributed data processing in real-time and advanced analytics.

    Function

    Azure Data Factory is a cloud-based data integration service that automates data flows and orchestrates processing tasks. It enables seamless integration of data from both cloud and on-premises sources for processing within a single environment.

    Function

    Apache Kafka processes real-time data streams and supports the management of large volumes of data from various sources. It enables the analysis of events immediately after they occur.

    Function

    Pub/Sub is used for messaging between applications, real-time data stream processing, analysis, and message queue creation. It integrates well with microservices and event-driven architectures (EDA).

    Serverless services
    Function

    Google Cloud Run supports containerized applications in a scalable and automated way, optimizing costs and resources. It allows flexible and efficient management of cloud applications, reducing the workload.

    Function

    Azure Functions is another serverless solution that runs code in response to events, eliminating the need for server management. Its other advantages include the ability to automate processes and integrate various services.

    Function

    AWS Lambda is an event-driven, serverless Function as a Service (FaaS) that enables automatic execution of code in response to events. It allows running applications without server infrastructure.

    Function

    Azure App Service is a cloud platform used for running web and mobile applications. It offers automatic resource scaling and integration with DevOps tools (e.g., GitHub, Azure DevOps).

    Cloud Data Warehousing
    Function

    Snowflake is a platform that enables the storage, processing, and analysis of large datasets in the cloud. It is easily scalable, efficient, and ensures consistency as well as easy access to data.

    Function

    Amazon Redshift is a cloud data warehouse that enables fast processing and analysis of large datasets. Redshift also offers the creation of complex analyses and real-time data reporting.

    Function

    BigQuery is a scalable data analysis platform from Google Cloud. It enables fast processing of large datasets, analytics, and advanced reporting. It simplifies data access through integration with various data sources.

    Function

    Azure Synapse Analytics is a platform that combines data warehousing, big data processing, and real-time analytics. It enables complex analyses on large volumes of data.

    Data transformation tools
    Function

    Data Build Tool simplifies data transformation and modeling directly in databases. It allows creating complex structures, automating processes, and managing data models in SQL.

    Function

    Dataform is part of the Google Cloud Platform, automating data transformation in BigQuery using SQL query language. It supports serverless data stream orchestration and enables collaborative work with data.

    Function

    Pandas is a data structure and analytical tool library in Python. It is useful for data manipulation and analysis. Pandas is used particularly in statistics and machine learning.

    Function

    PySpark is an API for Apache Spark that allows processing large amounts of data in a distributed environment, in real-time. This tool is easy to use and versatile in its functionality.

    Business Intelligence
    Function

    Looker Studio is a tool used for exploring and advanced data visualization from various sources, in the form of clear reports, charts, and interactive dashboards. It facilitates data sharing and supports simultaneous collaboration among multiple users, without the need for coding.

    Function

    Tableau, an application from Salesforce, is a versatile tool for data analysis and visualization, ideal for those seeking intuitive solutions. It is valued for its visualizations of spatial and geographical data, quick trend identification, and data analysis accuracy.

    Function

    Power BI, Microsoft’s Business Intelligence platform, efficiently transforms large volumes of data into clear, interactive dashboards and accessible reports. It easily integrates with various data sources and monitors KPIs in real-time.

    Function

    Looker is a cloud-based Business Intelligence and data analytics platform that enables data exploration, sharing, and visualization while supporting decision-making processes. Looker also leverages machine learning to automate processes and generate predictions.

    Data automation and orchestration
    Function

    Terraform is an open-source tool that allows for infrastructure management as code, as well as the automatic creation and updating of cloud resources. It supports efficient infrastructure control, minimizes the risk of errors, and ensures transparency and repeatability of processes.

    Function

    GCP Workflows automates workflows in the cloud and simplifies the management of processes connecting Google Cloud services. This tool saves time by avoiding the duplication of tasks, improves work quality by eliminating errors, and enables efficient resource management.

    Function

    Apache Airflow manages workflows, enabling scheduling, monitoring, and automation of ETL processes and other analytical tasks. It also provides access to the status of completed and ongoing tasks, as well as insights into their execution logs.

    Function

    Rundeck is an open-source automation tool that enables scheduling, managing, and executing tasks on servers. It allows for quick response to events and supports the optimization of administrative tasks.

    ML & AI
    Function

    Python is a programming language, also used for machine learning, with libraries dedicated to machine learning (e.g., TensorFlow and scikit-learn). It is used for creating and testing machine learning models.

    Function

    BigQuery ML allows the creation of machine learning models directly within Google’s data warehouse using only SQL. It provides a fast time-to-market, is cost-effective, and enables rapid iterative work.

    Function

    R is a programming language primarily used for statistical calculations, data analysis, and visualization, but it also has modules for training and testing machine learning models. It enables rapid prototyping and deployment of machine learning.

    Function

    Vertex AI is used for deploying, testing, and managing machine learning models. It also includes pre-built models prepared and trained by Google, such as Gemini. Vertex AI also supports custom models from TensorFlow, PyTorch, and other popular frameworks.

    FAQ

    1. What areas of gaming data does Alterdata specialize in?

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    We help gaming companies analyze data related to User Acquisition, ROAS, LTV, monetization, player behavior, and in-game economy. We combine marketing, product, and financial data into one unified analytics ecosystem.

    2. Can Alterdata integrate data from tools like Adjust, Firebase, or AppLovin?

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    Yes. We have extensive experience integrating data from UA, analytics, and ad platforms such as Adjust, Firebase, AppLovin, Google Ads, and BigQuery, creating a single source of truth for decision-making.

    3. How does Alterdata help improve ROAS and marketing performance?

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    We use advanced analytics and predictive models to optimize budget allocation, forecast ROAS and LTV, and enable faster, data-driven decisions in user acquisition campaigns. Our gaming projects have delivered significant performance improvements.

    4. Can Alterdata analyze player behavior and game economy?

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    Absolutely. We build custom BI systems and dashboards that track the full player journey, identify drop-off points, and provide insights into monetization and game economy balance—helping teams make informed product and business decisions.

    5. Are Alterdata solutions suitable for indie studios and large publishers?

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    Yes. We work with indie game studios as well as larger gaming companies and publishers. Each solution is tailored to the client’s scale, data maturity, and business goals.

    6. How soon can we see results from working with Alterdata?

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    Initial results—such as better data visibility, automated reporting, and clearer campaign insights—are often visible within weeks. Long-term, clients gain a solid data foundation for scaling marketing and game development.