Auto Trader's Digital Transformation: Leveraging Data with Looker and Google Cloud

Auto Trader Group plc is a British automotive classified advertising business. It specializes in new and second-hand automotive sales, including cars sold by private sellers and trade dealers. Auto Trader is the UK’s largest digital automotive marketplace. Originally a print magazine, today Auto Trader is one of the UK’s largest websites. As of January 2020, Auto Trader was the UK’s 16th most visited website, with an average of 50 million sessions per month and 4,000 website interactions per second. The company’s successful transformation from print to digital reflects a general trend in the automotive sector and in retail as a whole, driven by shifts in both technology and culture.

Auto Trader, the UK’s largest digital automotive marketplace, faced a significant challenge when transitioning from print to digital. The company wanted to empower its employees to make data-driven decisions and provide insights to their retailers and consumers. The shift to digital generated a massive amount of data from various online interactions, which everyone, from internal teams to automotive retailers to consumers, was eager to access and use for better decision-making. In 2018, Auto Trader embarked on a journey to modernize their tech stack, centralize data, and provide self-service access to trusted metrics. The company needed to satisfy the data demands of both their internal stakeholders and external users. Retailers wanted to understand how much their cars were worth to consumers and how their ads were performing. Consumers wanted to find the right products by being served up ads relevant to their search. However, the existing data management system became a bottleneck, and new issues arose around how to make personally identifiable information (PII) secure and compliant.

Auto Trader decided to modernize and move their data stack to the cloud. They envisioned a solution that would empower internal users to access and use data to make better decisions and provide external retailers with the insights they were craving, while also offering intelligent recommendations to customers. Auto Trader built a modern multicloud data architecture that would meet everyone’s needs. At its core, Auto Trader uses BigQuery as their modern data warehouse for performance and scalability. Other tools include Apache Kafka for managing streaming data, Snowplow for consolidating event data, and Apache Airflow for orchestrating and managing DAGs (directed acyclic graphs). The analytics stack consisting of Looker and Databricks sits on top of these layers. Looker enables analysts to do deep exploration into the data and build dashboards that can be accessed and analyzed company-wide. The data that runs through Looker and Databricks then goes through internally defined zones, so it can be tracked through different steps of refinement and for quality. Data ultimately ends up in the “trusted zone,” which is available to everyone and used internally, as well as exported to Auto Trader’s data-driven online products.

• The implementation of Looker and the shift to a cloud-based data architecture transformed Auto Trader into a data-driven organization. The company was able to empower its employees to make data-driven decisions and provide insights to their retailers and consumers. The Looker deployment spurred a surge of internal interest and transformed Auto Trader into a data-driven organization. To further encourage a data culture, Auto Trader set up a data academy for its employees with the purpose of elevating skill sets. The company also launched an internal Looker Champion program to bring awareness of resources across the organization. The successful cloud-based architecture in place that pulls data from multiple sources, Auto Trader squads are continually learning and adjusting their best practices as they find creative ways to use Looker and other tools to increase business value to both automobile buyers and sellers.

• Auto Trader was able to better manage their extensive amount of data from online traffic and channels.

• Provided internal and external users with the access and insights they craved.

• Met increased security and privacy requirements.