How Machine Learning and AI are Boosting Marketing Efficiency and Slashing Waste

machine learning

In the not-so-good old days, connecting with in-market car shoppers was a guessing game, at best. Dealers selected the media channels they thought might have the best chances of attracting serious buyers, often relying on no more than past experience, instinct, and maybe a few long-standing relationships with media reps.

That antiquated process was a lot like searching for a needle in a haystack. And dealers frequently came up empty-handed, except for a hand full of hay, while wasting plenty of money along the way.

Today, artificial intelligence and machine learning have changed the game. At the risk of torturing the metaphor, a dealer can now skip the haystack (in this case, a big, generic audience) and go straight to the needles (in-market shoppers looking for specific VINs).  

Today’s machine learning platforms can create those lucrative, all-needle audiences quickly and efficiently, precisely targeting active shoppers with hyper-relevant messaging. Which means dealers can set aside the uncertainty, eliminate the wasted ad spend, and connect directly with shoppers they know are looking for a specific VIN they have in their inventory, right now.

First, some definitions:

While the terms are often used interchangeably today, there is a difference.

Artificial intelligence (AI) is the ability of a machine or system to perform tasks that typically require human intelligence, such as reasoning, generalizing, and making decisions. (Fun fact: while the term has certainly become part of the vernacular in recent years, it was actually coined by a group of computer scientists at Dartmouth in 1956.)

Machine learning, which grew out of AI, uses algorithms to parse and analyze data, finds patterns in the results, and utilizes those patterns to inform decisions and take action. In contrast with AI, machine learning doesn’t require programming (by humans) before it can act.

The rise of the machines.

AI and machine learning are occupying an increasingly important space in the toolkits of marketers everywhere. According to the “State of Marketing” report issued by Salesforce Research, 51% of marketing leaders are already using AI. Of those, 57% say it’s absolutely or very essential in helping their company create 1-to-1 marketing across all customer touchpoints.

Putting patterns to work.

A machine learning marketing platform is on a constant quest to identify patterns in an online shopper’s behavior. It finds those patterns by gathering and analyzing data from multiple sources, such as search behavior. From the resulting analysis, the platform can discern what type of experience and result the consumer is expecting.

Once a machine learning platform has made that determination, it can then provide what it’s “learned” the shopper is looking for, such as detailed information on a specific vehicle. By meeting those needs, dealers are better able to take advantage of micro-moments – the multiple, pivotal points in a shopper’s journey where decisions are made. Winning that sequence of micro-moments completes the connection between the shopper and the dealer.

The new normal.

In the past, seeing this kind of personalized, specific messaging without overtly requesting it might have unsettled many consumers. But Amazon, Netflix, and other businesses thrive (and, some might say, conquer,) by preemptively offering customers more of what they’ve already confirmed they like.

Today, it’s an experience consumers are more than comfortable with. In fact, it’s what they expect.

Targeting, and retargeting, with precision.

Now that algorithms are able to process extensive stores of data to determine which communications will be most effective with a specific shopper at a particular stage in the purchase journey, they’re able to deliver the right message, to the right person, at the right moment, across the purchase journey. Over time, the process becomes “smarter,” continually optimizing to achieve better results, more efficiently.

Machine learning can also help “win back” potential customers who have not yet converted. AI-assisted bidding enables marketers to prioritize self-declared likely buyers with aggressive retargeting efforts, across the web and across devices, with the messages predicted to be most compelling.

Machine learning and AI, made accessible.

Early on, LotLinx understood the power of machine learning and AI to help dealers implement marketing strategies that align directly with their business goals. Our technologies detect low-funnel shoppers who have indicated their interest in a specific car, down to the make, model, year, and even geographic location. The platform can then determine the right time and right place to present that shopper with a VIN-specific ad. From there, the shopper can click through to the vehicle details page, right on the dealer’s website. All that’s left is to guide the sale to completion.

Dealers using LotLinx technologies are proving, on a daily basis, the ability of machine learning and AI to boost engagement, increase ROI, and minimize wasted ad spend.

The freedom to focus.

When dealers choose to put machine learning and AI to work for their marketing strategies, they can then simply specify their inventory turn strategy, and allow technology to take care of the execution. That means they’re better able to focus on managing their inventory and optimizing the sales process. (It also means they can shut the door on that horde of media reps outside their office.)

The future looks bright. And automated.

Once the domain of computer scientists and researchers, the power of machine learning and artificial intelligence is now bringing countless benefits to professionals of all kinds – and that certainly includes automotive marketers. If you haven’t yet explored this powerful tool, now is the time.

Once you’ve experienced the effectiveness and efficiencies these technologies can generate, not to mention the money they can save you, you’ll never look back. (And you certainly won’t miss the haystack.)