Much has been written about how artificial intelligence (AI) and machine learning target shoppers. AI and machine learning are like super-powered salespeople for retailers.
It is becoming increasingly well-understood how AI can predict what a shopper might be interested in by analyzing past purchases, browsing habits, demographics, and other factors and actions. This allows for personalized recommendations and surfaces products to consumers that they might not even have known existed but perfectly suit their needs. Machine learning can also help online stores anticipate demand, ensuring they have the proper inventory. The result is a shopping experience that feels tailored to the consumer.
When a customer searches for a car online, they will likely also start seeing ads for that exact car on Meta (Facebook and Instagram) or search results on Google. By analyzing a consumer’s online searches or past interactions (e.g., clicking on an ad, posting on a topic, or engaging with content), the AI that runs these powerful ad algorithms from Meta and Google identifies and acts upon what a consumer is looking for—featuring specific car models or financing options, for example.
But what hasn’t been talked about nearly as much is AI’s effect on the supply side of the equation. Google or Facebook will highlight and promote vehicles of interest for a particular consumer, but what isn’t as well known is the effect that AI can have in prioritizing the vehicles that need to be highlighted. Understanding metrics such as local-level inventory counts, turn rates, days-on-lot, and inventory efficiency are key in determining vehicles that need a marketing push versus others that will likely sell without it. And the constant shifts in all of those metrics make AI and machine learning essential in that prioritization. An OEM’s relative inventory and sales position can (and does) change overnight, requiring constant shifts that humans can’t possibly keep up with.
This is where Cloud Theory shines. Our proprietary AI acts upon key supply and demand data that is captured at the VIN level across virtually 100% of in-stock and in-transit inventory in real-time. In doing so, it identifies (and constantly refines) the specific VINs that need a demand boost, and appropriately includes ones in the feeds that are promoted in Google and Meta’s marketing programs.
In doing so, it ensures that precious advertising dollars are being spent on the vehicles that can best benefit from the support, directly affecting core outcomes such as increased sales or faster turn rates. Advertisers can, therefore, feel confident that they are spending their money efficiently and effectively and on the right vehicles, at the right time, in the right place.
So the next time OEMs and their advertising partners think about how AI affects change in the automotive category, they would be well served to do so on the supply side of the equation as much as they do on the demand side.
