The weatherman with a good forecast used to be a car salesman’s best friend. Personally, I’ve added information technology guru to the list of top friends, and here’s why: to avoid being data rich and insight-poor.
With terabytes of consumer audience data now available at your fingertips, this isn’t a place you want to be stuck—stymied by more information that you can effectively use. There’s an old saying in advertising that goes ‘half your money is wasted— you just don’t know which half.’ Therefore, we’re using data to be much more efficient on that ad spend to refine ads and to laser-target.
You’ll hear plenty of marketers claim they have “Big Data” and that it influences their campaigns. And we make that claim at Nissan too, but our goals tend to be vastly more complex than many.
The challenge? Nissan does not just go out and buy consumer data to inform our marketing campaigns. We integrate multifaceted data sets generated internally along with third-party consumer research data. It allows us to reach auto shoppers during multiple phases of the shopping process.
Nissan’s insights power campaigns to drive sales across a portfolio of 20 vehicles in more than 200 markets among more than 1,000 dealers.
Nissan vehicles are known for innovation that excites. We have labored to put that brand promise to work for us with technology insights too. Leveraging a host of tech innovations, Nissan became much smarter about refining its advertising to make the most compelling offers influenced by multiple variables—from consumer demographics to geographic location to local, seasonal weather.
New technology also allowed us to align audience data with the consumer’s shopping process. Through careful testing and optimization, Nissan developed a highly effective digital marketing program that promotes a site experience, which led to a higher purchase rate.
Prior to these developments, Tier 2 digital marketing campaigns were inconsistent. They were supported heavily in some months but lacked support in others. Nissan had recently created a Tier 2 co-op program combining its marketing dollars with those of dealers. This resulted in individual digital media campaigns for more than 200 “designated market areas” (DMAs). These campaigns featured local offers because offers by model and DMA were different.
Nissan had a database housing all offers by model and DMA, which could only be accessed manually, requiring the agency to build individual ad units. This approach resulted in thousands of ads being assembled one-by-one and trafficked each month to all media vendors. As a result, most digital campaigns could not go live until the 15th of the month as it simply was not possible to generate all the individual ads needed to power such a campaign. This “on and off” presence prevented Nissan from consistently engaging with automotive shoppers and presented the potential for significant missed opportunities at the beginning of each month. To complicate this problem further, offers would change during the month, causing thousands of creative revisions.
So Nissan had a couple of major challenges—figuring out how to build digital creative more efficiently and how to make ads as compelling as possible by integrating first- and third-party data.
Nissan developed a dynamic ad server connected to the pre-existing offer feed, allowing it to update content in real-time. With access to a database of model images and ad copy, the system could now build the creative needed on the fly. This dynamic ad builder (DAB) would finally allow campaigns to start earlier in every DMA and ensure up-to-date offers. Furthermore,— the concept could expand beyond standard desktop digital creative to power digital video, mobile and tablet ads.
With the Tier 2 website and dynamic digital creative, now we could build a much better mouse trap. Finally, a live 24/7/365 campaign was an option. We developed plans to engage automotive in-market shoppers with model-appropriate creative every day of the year ensuring Nissan was always part of their consideration set.
Better yet—we could much more effectively buy an audience instead of just buying a banner ad. For example, I could focus on soccer moms aged 25-54 who would be served an ad specific to their geographic area with appeals designed for them. Then, based on performance of that ad, we could refine it. If one combination of appeals didn’t work, that soccer mom didn’t get served the same ad again. Instead, our ad builder would try another combination. The key to this success was leveraging both third-party and Nissan data on auto shoppers.
Looking first at third-party data, Nissan chose audiences ranging from individuals who had been actively researching an automotive purchase to those whose lease was about to expire. For first-party data, Nissan leveraged how users interacted with its own set of websites. The combination allowed building out a comprehensive program to reach out to them again.
In fact, the dynamic ad builder in some cases taught us things that countered conventional wisdom. For example, by not advertising a lease rate to a certain demographic, the ad builder showed us that in some lease-heavy markets, doing so was a positive step, regardless of the price.
As the adage goes, it’s cheaper to keep consumers than find new ones. So we were able to target the lowest-hanging fruit first—folks who already had some level of interest in Nissan and were looking to buy a vehicle. With this new approach, Nissan was able to purchase digital media impressions against consumers who had visited a certain model but had not interacted with any additional content on the site such as building a car or looking at dealer inventory. By identifying these consumers and pulling them back to the site, Nissan could continue communicating and keep their models top of mind while refining ad appeals.
This approach used audience data to identify consumers and then implement a “programmatic media model” that only bought digital media inventory that the in-market shoppers viewed.
The beauty of the strategy was the efficiency. We actually forced a number of our advertising partners to scale up their game and changed the way media sellers approached us. To ensure efficiency and optimization of our dynamic ads that kept fine-tuning themselves to grab the attention of our audience, we set a monetary threshold goal per action with our media partners. We made those actions retail-driven so that it was incumbent for the media outlet to ensure the ads were selfoptimizing and getting the viewers to take some action on our site—whether that was viewing dealer inventory or building a car on our site.
Unlike other media that operate under a model where content is purchased based on its propensity to deliver auto shoppers, this audience-powered programmatic solution ensures minimal waste with advertising only appearing to active buyers..
Since Nissan first rolled out the optimization program in 2012, its ability to turn auto shoppers into Nissan shoppers has continued to improve. Performance is evaluated based on users’ interaction with Nissan shopping tools and identifies the digital media that led the consumer to them. This provides efficiency metric and provides a guideline to ensure that the most compelling offers and media channels are being used.
The approach also allows us to generate “report cards” for each vendor so both parties know how they’re doing. In fact, it’s accomplished in real-time so even if there’s a glitch in the system that stops the ad optimizing process, we get a red flag and can divert our spend from the vendor until the issue is corrected.
In this environment that’s naturally more competitive and complex than ever, being able to better leverage these insights is a major advantage.