“Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Maxwell Knight, vice president of marketing science services at Turn.
In the automotive industry, branding and direct-response campaigns are almost always divided into national and regional tiers. While this makes sense for achieving the different goals of a national brand and a local dealership, the result can be a fragmented consumer experience.
It appears the industry is ready for a change. Nearly 90% of automakers and dealers in a recent Ernst & Young report said automotive can no longer rely on traditional sales tactics for growth.
Moving to a new, more dynamic understanding of customer experience and engagement – and evolving traditional attribution models in the process – starts with addressing the two tiers of automotive marketing and using customer data in a decidedly cross-tier way.
One traditional approach to marketing spend is to identify customer behavior based on search and clicks, and then programmatically place ads reflecting the products consumers seek out online.
In the automotive industry’s two-tiered marketing ecosystem, however, the line between online behavior and marketing response is structurally complex. Tier 1, the national tier, has focused on brand engagement and using data analytics to build models around customer behavior. Meanwhile, Tier 2 regional marketing has focused on driving buyers to local dealers for test drives and to close the deal.
The disadvantage inherent in this tiered system is that regional marketers could well benefit from insights based on national-level analytics. At the same time, Tier 1 could better direct its programs with access to consumer data at a regional level.
The outcome of bridging the tiers would be a wider understanding of consumers’ experiences across the entire car-buying life cycle. I call this customer-state data.
Customer-state data can amplify the efforts of both Tier 1 and Tier 2 in automotive. For example, an automotive company can establish a cross feed of anonymized data attached to geography that is shared between tiers and teams via a centralized data management platform (DMP).
Tier 1 can identify customer activity around national-level content in terms of video consumption and subsequent competitor research. It defines the customer state from this data.
Tier 2 responds with its own region-specific data, showing Tier 1 that potential buyers representing the identified customer state have recently visited local dealer websites. Regional data also shows consumers that have explored certain models of car during these visits.
Tier 1 may trigger national-level ads in the region but it also works with Tier 2 to formulate a more experience-focused regional strategy. Rather than duplicate regional ads for the same car model, Tier 2 produces relevant data-driven messaging about financing, for example.
These cross-tiered applications can extend as deeply into the ecosystem as a given car lot. Dealers may identify financing as the best next step based on customer-state data. They can then put that insight to work with signage and special offers.
When it comes to attribution for automotive, the industry doesn’t need to try and attribute every single banner, search click, video ad and native experience all the way down to a car moving off the lot. With customer-state data, the question becomes whether marketing is driving the right types of engagement.
Insights from customer-state data help marketing satisfy the consumer’s desire for right-time and right-place experiences, rather than the more traditional concept of simply relying on algorithm-driven right-time and right-place ads.
In the end, especially for lower segments of the sales funnel, customers know what they’ve experienced so far and are primed to respond to experience-specific marketing. Customer-state data represents the future of that dynamic, bridging tiers to better inform campaigns and driving the consumer to buy that next car.