Analytics

Combining Online and Offline Data in Automotive – Part One

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As technology advances, new ways to gather customer data and new ways to use that data in marketing and customer outreach have emerged. This has created a new need for many companies in various industries. The need to combine offline data with online data in accurate and intelligent ways, allowing the organization to gather more customer intel and do a better job of using that data to reach the right consumers at the right time. This is especially true in the automotive industry, which has many more types of customer interactions and touch-points than the average industry. Auto companies are gathering first-party data online from visitors to their websites, their apps, and from those exposed to their online advertising. They are collecting offline data from customers at dealerships and any live events (auto shows, etc..). They are collecting in-vehicle data while drivers operate the vehicle, both data from the car’s mechanics/functionality and from the drivers’ use/interactions with vehicle features and apps. Finally, they are also ingesting second and third-party data from partners and data sellers. This could be income and financial data from insurance companies, banks, and other financial institutions. It could also be general industry data such as syndicate studies and surveys from J.D. Power, Oracle Data Cloud, IHS or Cox Automotive, just to name a few.

Setting the current data landscape in Automotive

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You can see how this can get complicated quickly, making it difficult to accurately collect and merge this data in ways that are useful in making business decisions. Currently, there aren’t any auto manufacturers that are paving the way and standing head and shoulders above the competition in this arena. All auto companies are collecting all the data I mentioned above, and some are moving forward and making improvements in different ways. But, all of them struggle to accurately merge online and offline data, making sure the user behind the online device is the same person who filled out the form at the event, and the same consumer who took their vehicle to their local dealership for service. Additionally, even after figuring out how to combine online and offline data, they then struggle with using this data across business units to provide a consistent marketing message and a consistent consumer experience. Much of the struggle comes from lack of communication across business units, as teams within these units don’t know how other units are using the same data, how each one of them plays a specific role in the consumer journey, and that they are only as good as the weakest link.

A trusted digital partner is the key to success

To combat the issue of actually combining online and offline data, auto companies need to have strong technology and digital transformation partners that can help them correctly implement the software and digital platforms needed, as well as identify the needed talent/skillset to maintain these new systems. To tackle the issues around using the data, after correctly combining it, these auto companies need to consider implementing some kind of data center of excellence or competency center. This center of excellence should include leaders from the auto company’s internal business units, from the digital transformation partner, and from the ad agency/marketing service providers. Getting all of these stakeholders working together is important to the success of this endeavor. Successfully combining your online and offline data in automotive can put you ahead of the competition and can potentially allow you to gain deeper consumer insights, allow you to better target individuals with personalized content and provide a much more consistent consumer experience across all consumer touch-points.

Now that we have discussed the need for combining online and offline data in part one of this blog series, we will get into some specifics around the tools needed to collect and combine data in part two. We will also talk about some simple, but important, examples of combining online and offline data-sets in automotive. Check back next week for part two.

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