Automotive dealerships are no longer competing with other dealerships to win customers in the digital world. The real competition is coming from sites outside automotive such as Amazon, Netflix, Spotify, and Tinder, which have created new standards of personalization for shoppers. These destinations use 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. Machine Learning Raises the Bar Machine learning is a form of artificial intelligence in which computers train themselves to make smarter decisions. The self-learning comes from reading vast amounts of data (usually too vast for people to analyze quickly and accurately). With machine learning, a site goes beyond making superficial product recommendations based on your purchasing behavior and the purchasing habits of people like you. Sites learn from your likes and dislikes and from your lifestyle interests and behaviors to make personalized recommendations that you might not have thought of yourself. Shoppers have had their expectations raised by personalization and will respond favorably when their expectations are met. Not surprisingly, 76 percent of consumers surveyed by Cars.com said they purchase products based on personalized recommendations either half or most of the time. But this kind of personalization is not the norm in automotive. 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 To catch up, automotive brands need to learn from the leaders outside of the industry. 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. Machine learning can make an automotive website 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). And with machine learning, a site 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. It can also help determine the right salesperson at the dealership to connect you with by matching your persona and behaviors. Or a dealership’s search and retargeting investments can become more personalized as your machine-learning-enabled CRM tools comb vast sets of user data. Cars.com Matchmaking 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. Maybe you’re a daily commuter who prefers style, comfort, and smartphone connectivity. Or perhaps you are a sun lover who likes to hit the beach. We help you create a precise profile either way. 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. Our understanding of how all shoppers browse our site makes it possible for us to suggest vehicles that are related to a shopper’s preferences even if the suggestion is not a direct hit – just to give shoppers some unexpected ideas. For example, we might suggest a particular sedan to someone who selects SUVs as their ideal match if our data suggests that SUV lovers have also searched for a specific make and model of a large sedan. A Personalization Strategy Matchmaking is central to Cars.com’s strategy to transform car shopping and selling through personalization. Within the last year, for example, Cars.com also launched: Salesperson Connect™, a feature that connects shoppers to a salesperson before ever visiting a dealership. Hot Car, which uses a uses a proprietary machine learning algorithm to identify which vehicles on Cars.com are most likely to sell quickly. Best Match, a sorting feature that gives shoppers relevant vehicle search recommendations. 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] We’re just beginning to tap into the power of machine learning and artificial intelligence. What we consider personal today will likely be superseded by an even better experience as machine learning evolves. The time is now to start learning. 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. 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