How Auto Trader is democratizing high-quality, first-party behavioral data with Snowplow

By pairing Snowplow, Braze, and a reverse ETL tool, the automotive marketplace has created a composable CDP that empowers multiple teams to do more with data.

A couple looking at a car in a dealership


Auto Trader is the UK’s leading automotive marketplace, listing approximately 430,000 vehicles each month. Founded as a local classified magazine in the 1970s, this pioneering British company has evolved in line with changing consumer habits—since 2013, they’ve operated exclusively as a digital marketplace. 

This transition has brought considerable success. Auto Trader’s digital platforms attract around 63 million visits per month, generating 588 million minutes of engaged time and ultimately leading to more people buying and selling vehicles. 

While many factors have contributed to this success, Auto Trader’s use of behavioral data has been fundamental. The Data and Platform team, led by Darren Haken, has leveraged best-in-class tools from across the modern data stack to build a scalable, flexible platform that enables different areas of the business to perform more effectively. 

Here’s how they’re democratizing behavioral data across the organization and fueling rapid growth. 


For Auto Trader’s data team, the first step in improving its digital platforms was to capture an accurate impression of a core business metric called the ‘Full Page Ad-view’ (FPA). This metric, once considered the single-most important measurement of success for the company, indicated the potential size of the buying audience and was communicated to the business on a monthly basis. 

Reliably tracking FPAs, however, proved to be a challenge. 

The data team had been using a combination of a homebuilt pipeline and Google Analytics to capture FPAs. While this met their immediate needs, it lacked rich event semantics. Instead of representing events in the language of the business (for example, whether users registered interest in a car), it relied on the generic language of traditional web analytics such as button clicks or form filling. It measured FPAs as pageviews, and didn’t capture engagement with new features like enhanced thumbnails. 

The team needed a more flexible solution to future-proof their data collection strategy. While they had the expertise to build this internally, they felt their attention would be better spent working on more strategic projects. With this in mind, they turned to Snowplow Behavioral Data Platform

Auto Trader loves open source technologies. Snowplow is an open source technology—we didn’t see the value of managing it ourselves, but we like the fact that we can contribute code.”  



By using Snowplow to measure engagement with FPAs (on both the client and server side), Auto Trader’s engineering team increased trust in the behavioral data they created. Stakeholders from across the business could see, in granular detail, how users were interacting with FPAs, and use this information to inform strategic decisions. 

The work of the engineering team didn’t go unnoticed. More departments recognized the huge potential of using high-quality behavioral data to drive their initiatives—and the User Tracking team (which provides tracking as a data product) was now considered an enabling function across the organization.

Auto Trader’s marketing team, in particular, saw the benefits of gaining a deeper, richer understanding of both buyers and sellers across their platforms. To enable them to ‘do more’ with data, the team decided to build a composable CDP combining Snowplow for data creation, BigQuery for transformation, a reverse ETL tool, and Braze for Lifecycle Marketing. 

For Auto Trader, this ‘unbundled’ setup enabled limitless creativity. Compared to an off-the-shelf CDP, their composable CDP didn’t require significant capital and resource investment (which typically takes months to implement), or lead to restrictive vendor lock-in. 

The composable CDP empowers Auto Trader’s marketing team to gain a deeper understanding of prospects, which they can use to tailor marketing activities. Thanks to the joint Snowplow, BigQuery, and Braze solution, the team no longer has to build and maintain custom pipelines, and the marketing team can self-serve data.

I’ve never seen engineers and marketing people talk to each other as much as they are right now. All of our objectives are now aligned on shared outcomes. Access to first-party data has made our data engineers have more empathy for our marketers and our marketers are now more willing to provide insight into the campaigns they are running.”



As collaboration between Auto Trader’s engineering team and the rest of the company increases, their composable CDP will meet evolving needs. Using a suite of best-in-class tools, they’re aiming to: 

  • Build a real-time personalization engine powered by behavioral data and audience segments
  • Gain a holistic view of marketing performance by joining marketing data with transactional data
  • Further democratize their data assets through improved self-service

As the feedback loop continues to develop between Snowplow and the reverse ETL tool, Auto Trader’s customer experience will only continue to expand and improve. Using BigQuery to do machine learning on the data Snowplow creates will not only improve the data models that Auto Trader builds, but it will also open up an entirely new door for marketing activation.

We’ve just touched the surface with our reverse ETL tool, Snowplow, and Braze. I am super excited to see what’s possible.”


How you can get started with Snowplow

To learn more about how Snowplow can empower your organization with behavioral data creation, book in a chat with our team today. Alternatively, Try Snowplow is our free, easy-to-use version of our technology, which allows you to create your own behavioral data in under 30 mins.

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