Automotive dealerships are now trying to please consumers whose expectations for a great customer experience are set on non-automotive sites such as Amazon, Netflix, Spotify, and Tinder. These online destinations have successfully responded to a new breed of consumer who demands a more personalized shopping experience. They’ve flourished in part by using machine learning to understand customers’ wants and needs and to provide smarter product recommendations. Customers expect automotive sites to match the experience they’re getting outside the industry.
And automotive has a lot of catching up to do.
The New Era of Personalization
Shoppers have had their expectations raised by personalization and will respond favorably when their expectations are met: 76 percent of consumers surveyed by Cars.com said they purchase products based on personalized recommendations either half or most of the time.[i]
But personalization is not the norm in our industry. Too often we’re leading the conversation with customers by talking about the product, not the person who is about to make the second-most expensive purchase of their life. For example, even though seven out of 10 consumers are undecided about make and model when they shop for a new car[ii], nearly all online car search experiences force people to select make or model as the initial step in their journey — instead of first learning about the shopper and offering intelligent suggestions based on those learnings.
No wonder automotive shoppers would rather go to the DMV, clean toilets, have an extended phone conversation with their mother-in-law, or get stuck on jury duty than shop for a car.[iii]
How Automotive Can Catch Up
Automotive brands can catch up in a few important ways:
Learn from the Leaders
Automotive dealerships need to look outside the industry to learn best practices. For example:
- Spotify[iv] and Netflix[v] famously apply machine learning to recommend songs and movies based on customers’ preferences matched against the interests of other customers with similar tastes. Spotify sifts through listening data – both yours and the people you follow – to recommend playlists that create true music discovery rather than simply replicate what you’ve been listening to already.
- Dating site Tinder[vi] matches people with other people by first asking members to set up personal profiles and then suggesting matches to them. Tinder refines recommendations based on each person’s feedback.
These businesses are succeeding by using machine learning, a form of artificial intelligence in which computers train themselves to make smarter decisions. With machine learning, a site goes beyond making superficial product recommendations based on your purchasing behavior. Machine learning can help dealerships in a number of ways, for instance:
- Automotive websites can offer smarter, more personal product recommendations to each shopper based on their browsing behavior and information that shoppers are willing to share about their personal lifestyles (e.g., whether they commute, love music, or live in an urban area).
- Dealerships can make recommendations that might not have been obvious to the shopper just like Spotify suggests an artist you might not have heard of but who is close enough to your tastes to interest you.
- Dealerships can make search and retargeting investments more personalized as your machine-learning-enabled CRM tools comb vast sets of user data.
At Cars.com, we’re taking our own advice. We recently launched a fundamental change to our site that re-imagines the car shopping experience. The new Cars.com Matchmaking Experience uses machine learning to give consumers personalized vehicle recommendations based on their lifestyle preferences. The more Cars.com learns about a person’s interests, the smarter and more personal the recommendations become.
We give our visitors the option to create personal profiles that build upon their lifestyle interests and needs. From there, the site takes user preferences, combined with our 20 years of vehicle and consumer data, as well as sentiment analysis, to give shoppers a targeted list of cars. Site visitors can swipe left or right to dismiss or favor the choices we provide. Based on shoppers’ choices, the site applies a proprietary machine learning algorithm to make smarter recommendations until the shopper finds the car of their dreams.
With Matchmaking, we’re delivering to dealerships not only more qualified leads on the lot but a more engaged customer online. A pilot of Matchmaking Experience has resulted in a 752 percent increase in profile creation on the site, 87 percent increase in return visitors, 225 percent increase in email leads, and two times the number of page views per visitor versus the traditional search experience.[vii]
Personalize the Sales Process
Salespeople have always been at the forefront of personalization. Great salespeople know how to ask the right questions about a shopper’s own wants and needs to recommend the perfect match whether they’re selling cars or mobile phones. Dealerships now have the tools for consumers to personalize their shopping experience by selecting the salespeople they want to work with, too. Doing so makes a dealership more responsive. About 97 percent of car buyers prefer to select a salesperson before they visit a dealership.[viii]
For example, Salesperson Connect™ is a feature that connects shoppers to a salesperson before ever visiting a dealership, just like someone using Uber or Lyft can select their drivers. The salesperson profiles include customer ratings, background about a salesperson’s interests, and other information that makes the visit to an automotive dealer more personal. (You can read more about those crucial elements in this blog post from DealerRater.)
The advent of personalized shopping is one of the change catalysts that dealerships must adapt to in order to thrive. Watch our blog for more insight into how dealerships can win by adapting to change catalysts.
[i] Cars.com consumer survey, 2018.
[ii] Cars.com consumer metrics, 2018.
[iii] Cars.com consumer survey, 2018.
[iv] Forbes, “How Did Spotify Get So Good at Machine Learning?” February 20, 2017.
[v] Netflix Technology Blog, “Using Machine Learning to Improve Streaming Quality at Netflix,” March 22, 2018.
[vi] The Date Mix, “How Does Tinder Work: A Beginner’s Guide,” June 11, 2018.
[vii] Cars.com Internal Data, Matchmaking Pilot scaled to 50% audience, July 5, 2018-July 19, 2018.
[viii] DealerRater, “Car Shoppers Are Judging You,” January 2017.