A tight grip on big data is helping Auto Trader to move with its customers along the car buying journey and to improve the quality of advertising at each stage of the procurement process.

Lara Izlan, director of commercial platforms and operations at Auto Trader, who manages advertising platforms and data analytics for the organisation, says her role is to think about how the firm can use first-party data on car buyers to create smarter insights on user behaviour and to create better commercial advertising solutions for major brands.

“The user perspective – and in our case, that’s the person who’s buying a car – is key,” says Izlan, who spoke with Essential Retail at the recent Adobe Customer Experience Forum in London. “Advertising is very complex and there are lots of challenges for organisations in the sector and for brands who are looking to buy space. One of my objectives is to improve the experience – and data plays a key role.”

Using all the information to hand

When it comes to audience targeting, Izlan and her colleagues are keen to use information to understand the user journey in more detail. She says the car buying process can take several months. All customers, regardless of the item they are buying, will be in a different mind-set at the end of the purchasing process than at the beginning, yet few firms – including retailers – focus on the nuances of this progression.

“The journey isn’t linear – customers take a complex route,” says Izlan. “Information points exist at every stage and, when they’re presented with new knowledge, customers take a step back and re-evaluate. That iterative journey presents a challenge, but also an opportunity. Successful companies will walk with the customer on this complex journey.” 

Auto Trader was keen to exploit its data and to help customers along their car-buying journey. Izlan and her team implemented Adobe Audience Manager to aggregate information and used an advanced algorithm to create targeted advertising campaigns. By providing better information to customers, the firm also helped major car brands to increase the value received from their on-site advertising.

Too static

Izlan says the firm was previously capturing the activities of users at a point in time, such as those searching for a specific vehicle. The approach felt too static and did not include additional information on the user, such as other vehicles of interest and details on their stage in the purchasing process, from initial research to final purchase.

“Car buyers do not make static decisions – they have a lifestyle and they need a vehicle that feels right,” says Izlan. By using big data and an advanced algorithm, Auto Trader has been able to isolate user behaviours, analyse stages of the car-buying process and suggest to vehicle manufactures the propensity of individuals to make a purchase. The firm then serves advertising to users based on the identified stage of procurement.

“You can’t judge the success of a campaign just by the number of clicks,” says Izlan, reflecting on the success of the approach and the key lessons for retailers. “Too many organisations focus on targeting and don’t really think about the message they should be putting in front of users. A discount offer, for example, will not be right for every customer. We’ve seen that users who are halfway through the process are more interested in the features of a car.”

Izlan joined the company in 2015 and enjoys her role. “I’m an analyst at heart, so having a huge amount of first-party data to play with has been incredible,” she says. “I enjoy the challenge of trying to make sense of this data and to build new things that will ensure our advertising ecosystem works even better. I’m also a commercial person and I like to find solutions that move this industry forwards. I want us to be more effective, more efficient and more user-centric in our approach.”

Izlan says her team is also motivated by the potential of machine learning and how the technology could potentially be used to create better advertising solutions for big car brands. “It’s still early days,” she says, referring to developments in this area. “You don’t want to jump in and use AI all over the place – it is a complex technology and we need to figure out where its application makes the most sense for the business.”