How to Leverage Machine Learning to Predict Car Shopping Prospects and Timing

Applying models created from data gathered over time, we can see who is in the market for a car passively and actively. Custom models can be created from sales data and current or past customer interactions.

Amy:

So there are two things that we can do with our machine learning platform to help people. The first is, we do look at that historical data and the location data, your mobile location data, to see where people are shopping in advance, and what they end up buying.

And with that, we create a model. And we can say, OK, you know, applying this model, what do all these people have in common? And applying this model to our entire 250 million household dataset, which people can we isolate that are most likely to be in market for a car in the next 3 to 6 months?

And that goes right back to getting to them early. And if you know about it before they know about it, it’s going to be a model prediction, right? And once we’ve done that, it’s very easy to find those people in your market area and reach out to them. Very easy.

The other thing that we can do is we can create a custom model for an individual brand or dealership by taking their sales data and doing analysis on their specific customers. And then creating a custom model for them again, so that they can target lookalikes in their market area, who are going to be most likely to buy from them.


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