Online gaming is the world’s most popular form of entertainment, easily dwarfing film, television, and even sports. Valued at $162.32 billion in 2020, the global gaming industry is predicted to approach $300 billion over the next five years.
Gaming is a solitary activity, which is why so many people turned to it during COVID-19 lockdowns. However, it also involves nearly constant interaction with other players and the game environment itself, generating nearly unfathomable quantities of valuable business data that game developers can use for both advertising and internal market research. With players worldwide engaged 24 hours a day, 7 days a week, game developers have nearly infinite opportunities to entice their customers to purchase in-game assets, sell advertising to third parties, conduct A/B testing on new features, and directly collect other business intelligence that could be used to improve the in-game experience and build player loyalty.
However, this data is of no value without the proper tools to parse and analyze it. Google Cloud Platform’s smart analytics solutions for the gaming industry enable developers to glean the actionable information they need to enhance existing titles, launch new products, and improve their in-game advertising revenues.
1. Accelerate time to insights with a modern data warehouse
BigQuery, Google’s serverless data warehouse solution, was originally designed for internal use within Google, so it’s capable of executing even the most complex queries, on the largest data sets, at planet-size scale. With a 99.99% SLA, BigQuery uses automatic resource provisioning on a multi-tenant distributed architecture, enabling analysts to execute queries on petabyte-sized data sets easily and quickly.
Bust down silos and unify your data with BigQuery Omni, Google’s multi-cloud analytics solution. BigQuery Omni lets users access and analyze data on AWS (with support for Microsoft Azure in preview), without having to move or copy datasets — and deal with latency and egress fees.
BigQuery’s user-friendly graphical interface and seamless integration with popular business intelligence (BI) tools like Looker mean that your analysts can spend their time analyzing data instead of fumbling with complicated software.
BigQuery is budget-friendly too, with a three-year TCO that’s up to 34% lower than cloud data warehouse alternatives.
2. Leverage real-time streaming analysis for real-time response
A leader in Forrester’s 2019 streaming data analytics wave report, Google Cloud empowers game developers to obtain insights from large-scale, real-time data streams as soon as they’re generated. Because GCP’s solutions automate and abstract resource provisioning, streaming analytics are accessible to both data analysts and data engineers.
- Use Pub/Sub to ingest and analyze hundreds of millions of events per second from applications or devices nearly anywhere in the world.
- Use BigQuery’s streaming API to directly stream millions of events per second into your data warehouse for SQL-based analysis.
- Deploy more responsive, efficient, and supportable streaming pipelines with Dataflow, Google’s fully managed, unified batch processing and streaming analytics service.
- Use Confluent Cloud and Cloud Dataproc to bridge, integrate, or extend existing on-prem and cloud streaming solutions, such as Apache Kafka and Apache Spark, with GCP’s next-gen analytics tools.
- Replicate data streams from Oracle and MySQL databases to BigQuery and other GCP services in real-time with Datastream, Google’s serverless Change Data Capture (CDC) and replication service, now available in preview.
- Leverage AI for real-time personalization, anomaly detection, and predictive maintenance scenarios using Google’s advanced AI Platform.
3. Level up your analytics game with Looker
Looker, Google’s browser-based BI tool, integrates seamlessly with BigQuery and offers customized visuals, collaborative dashboards, and a single source of truth for key performance indicators (KPIs). Looker’s simple GUI makes it easy for anyone to create and share stunning, insightful visualizations on the metrics that matter, including installs, session length, monthly active users (MAUs), daily active users (DAUs), drop-off rates, and more.
- Centralized governance tools ensure that KPIs are standardized across games, ensuring that team members are collaborating with consistent and accurate numbers. Over 50 measures can be combined with hundreds of dimensions, enabling analysts to ask any question of their data.
- Collate data throughout players’ entire lifecycle, from acquisition to retention and monetization, in one place, and dive deeper into what drives them. How long did newbies play during their first session? What were drop-offs doing before they left the game?
- Calculate daily ROAS (return on ad spend) targets to optimize spend on campaigns, see revenue before a user has fully matured to understand LTV, and focus time and dollars on the segments that are monetizing best.
- Identify Whales and Minnows, then use custom segmentation tools to better understand what drives them.
4. Build, train, and deploy ML models at scale
Historically, data scientists had to piece together ML point solutions manually, making model development and experimentation so time-consuming that very few models made it into production. To solve this problem, Google Cloud introduced Vertex AI, which collates all Google Cloud services for building ML under one unified UI and API.
Vertex AI allows data scientists, analysts, and developers alike to access the same AI toolkit that Google uses internally, including computer vision, language, and conversation and structured data, all continuously enhanced by Google Research. In addition to simplifying and accelerating the process of moving ML models from experimentation to production, Vertex AI makes it easier to spot patterns and anomalies, allowing for more accurate predictions and better decision-making. Vertex AI also makes it possible for developers and data analysts to update ML models regularly to meet fast-changing needs in a highly dynamic market.
Users who lack data science backgrounds can use AutoML to build models quickly and train them without having to code, taking advantage of Vertex AI’s pre-trained APIs for computer vision, language, structured data, and conversation. For more advanced users who want to dig in and do custom tooling, Vertex AI requires nearly 80% fewer lines of code to train a model with custom libraries than competitive platforms.
Let SADA help you power up your analytics game
As a trusted Google Cloud Premier Partner, SADA has an extensive track record of helping organizations harness the power of Google Cloud to solve their biggest challenges. After partnering with SADA to migrate to GCP, FUN-GI, a game studio, can now gather valuable data and run customized business intelligence analysis. The FUN-GI team was immediately impressed by GCP’s ease of deployment and scaling:
“We went from thousands of players and a lot of issues during soft launch, to a global launch on GCP with 11 million downloads while maintaining excellent data integrity and server performance. We don’t have to worry about scaling; GCP auto-scales. It just works.”Alfred Fung, CEO, FUN-GI
We also helped FlowPlay, a company that develops community-based virtual worlds, leverage Google Cloud to maintain the highest standards for system uptime, latency, and performance, all at a cost that keeps the company profitable. After migrating to GCP, FlowPlay’s traffic increased by 50%, but their system response speed doubled, significantly enhancing performance and improving the in-game experience.
We can do the same for you. We’ll help you develop an easy-to-understand, apples-to-apples, quantifiable comparison of your current infrastructure to GCP, so that you can provide your stakeholders with the detailed analyses they need to make a data-driven decision. Contact us today for a free, no-obligation GCP migration assessment.