This post is contributed by Maureen Moran, Group Product Manager, at Epsilon
With over 17 million cars sold annually in the United States, it’s clear that consumers are continuing to invest in the purchase of cars. Amidst the changing automotive space, as consumers make different mobility decisions, now more than ever marketers in the automotive segment need to understand the behaviors of consumers to intelligently market to them. But it’s not only about understanding their behavior, it’s having the intel to predict future purchases.
Having a comprehensive and accurate view of a consumer to drive decisions is a key to marketing success for automotive brands. It’s more important than ever to be able to target marketing efforts not just to consumers who might be interested in a certain vehicle, make or model, but rather to consumers who are most likely to be interested in a certain vehicle. The best way to do that is to use past performance to predict future behavior. Data modeling with predictive analytics is the key and extremely successful when used in Propensity Models. It’s about using actionable data to help predict what a consumer will likely do next in order to help drive incremental sales.
Predictive analytics is transforming the way automotive brands and marketers engage with customers, and these solutions are now within dealers’ reach. Propensity to buy models are designed to articulate which consumers are likely to make their purchase and can be used to create a more targeted and more cost effective marketing campaign. Having an idea of who is ready to buy and who is not helps determine the appropriate offer. Those that are likely to buy won’t necessarily need high discounts while consumers who are not likely to buy may need a more aggressive offer or promotion.
To truly understand purchase behavior, consider automotive propensities such as likely to buy a domestic vehicle, propensity to buy an economy car or likely to lease a vehicle to incorporate into your marketing strategy.
For example, likely to buy domestic vehicle identifies drivers who are likely to purchase a domestic vehicle made in the United States. Pairing this model with a vast array of data elements such as make, model and year provides deeper insight into customer preferences and helps drive more personalized offers.
In contrast, the likely to buy import vehicle identifies drivers who are likely to buy import vehicles, vehicles manufactured by companies with headquarters outside of the U.S. These households may also be likely to own at least one low-end import vehicle already and skew younger than non-import buyers.
Leveraging automotive selects like vehicle make/model/year and current market value, as well as Epsilon’s TotalSource Plus demographic and lifestyle data, will help you craft more relevant offers.
Marketers’ needs for new data offerings continue to evolve as more advance marketing efforts are required. It’s important to be nimble, and ready to adapt to the changing needs of consumers. Having a framework and a process for identifying propensities based on demand is key.
To learn more, download this brochure on targeted propensities for the automotive industry: http://engage.epsilon.com/targeted-propensity-models-for-auto