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[0:00]Hello, my name is Paul, and I'm a senior solution architect at Google, and I work with gaming customers. Today, I want to talk to you about a new solution that we've been working on, which is a full stack game analytics on Google Cloud. The problem is that games produce massive amounts of data, and this data comes from many different places, such as in-game telemetry, add attribution platforms, and add monetization platforms. Collecting all this data into one place and then performing analytics on top of it can be quite a challenge for gaming companies. Our solution helps address this by providing a framework of how to ingest this data, how to store it, and how to perform analytics on top of it. We start with the data sources. So these are your game clients, your game servers, and all third-party platforms that you might be using. This data is then ingested into Google Cloud via a variety of services, such as Firebase, Cloud Logging, and Google Cloud's data transfer service. Once the data lands in Google Cloud, it is then processed by Dataflow and stored in BigQuery, our enterprise data warehouse. From BigQuery, you can then perform analytics via services such as Looker and Google Data Studio. You can also export this data to advertising platforms such as Google Ads and Facebook Ads to perform remarketing campaigns. Our solution provides templates for all of these components, making it very easy for gaming companies to deploy it and then customize it to their needs. We've also worked with customers to come up with a gaming specific data model that helps with analytics for all of your important gaming metrics. The solution is comprised of three main components. The first is the data ingestion, which takes all of your data, all of your first party and third-party data sources, and lands them into Google Cloud. The second component is the data processing component, and this component takes the raw data, processes it, and stores it in BigQuery. The third component is the data consumption component, which focuses on various ways in which you can perform analytics on top of the data that's stored in BigQuery. Now, let's take a deep dive into each one of these components. The data ingestion component helps you get all of your gaming data into Google Cloud. If you're using Firebase, you can use the out-of-the-box integration that Firebase has with BigQuery to get all of your Firebase data into BigQuery. If you're using other third-party services, such as add attribution platforms or add monetization platforms, you can use Google Cloud's data transfer service to land all of that data into Google Cloud. If you have your own custom logs, you can use Google Cloud Logging to ingest those into Google Cloud. Once all this data lands in Google Cloud, it's typically stored in Google Cloud Storage. The data processing component takes all of your raw data that's stored in Cloud Storage, processes it using Dataflow, and then lands it into BigQuery. We provide templates for the data flow jobs, making it easy to deploy these jobs and then customize them to your needs. We've also worked with our customers to come up with a gaming specific data model. This data model is based on the Google Analytics 4 event structure. It contains various tables such as dimension tables and fact tables, and it helps you perform analytics on all your important gaming metrics. The data consumption component focuses on various ways in which you can perform analytics on top of the data in BigQuery. The first way is by using Looker, which is Google Cloud's business intelligence platform. We provide templates for Looker blocks, making it very easy for you to deploy Looker and then customize it to your needs. We also have Google Data Studio templates that you can use to perform analytics. You can also use BigQuery ML, which allows you to perform machine learning on your data directly in BigQuery. Last but not least, you can also export this data to various advertising platforms such as Google Ads and Facebook Ads to perform remarketing campaigns. This solution helps with various gaming use cases. First, it helps with player LTV prediction. This helps you understand the long-term value of your players and then allows you to identify which players are likely to churn. It also helps with identifying players that are likely to pay. We also help with segmenting your players so you can perform cohort analysis and then understand various things such as retention and monetization per cohort. You can also use this data to perform ad spend optimization, which helps with optimizing your return on ad spend. This helps answer questions such as which ad network should you choose and how much should you bid on that ad network. We also help with anomaly detection, which can help with identifying fraud and then alerting you when there are anomalies in your data. The solution provides templates for all of these various use cases, making it very easy to deploy these and then customize them to your needs. So in summary, we start with the data sources. We then ingest them into Google Cloud and land them into Cloud Storage. Once they're in Cloud Storage, they're processed by Dataflow and stored in BigQuery. From BigQuery, you can then perform analytics via Looker and Google Data Studio. You can also perform machine learning on your data using BigQuery ML, and then you can export this data to add platforms to perform remarketing campaigns. We provide templates for all of these components, making it very easy for you to deploy these and then customize them to your needs. We've also worked with customers to come up with a gaming specific data model that's very comprehensive and helps with all your analytics needs. If you have any questions, please reach out to your Google Cloud account team. Thank you.