Predictive Data Analysis for XtreamService
Use past customer behavior to predict their future actions – and profit from them.
People are predictable, but not always logical. The math of simple equity mining can identify customers in a position to buy, but not necessarily tell you who is most likely to buy, or what you should sell them.
Similarly, your DMS data tells you a customer’s every past transaction, but it doesn’t give you explanations for their behavior.
We use aggregated population data and our own research to group similar customers together. These groups become behavioral and demographic profiles that apply specifically to the automotive industry and help predict car-buying behavior.
By identifying which profiles your customers belong to, you can better target your marketing and increase profits. You gain a better understanding of which types of campaigns, deal proposals, and possible up-sells they’re likely to respond to. More importantly, you know how to meet their needs.
Deal within 12 Months
Traditionally, dealers have been reluctant to try to make a second sale to the same customer in less than a year. Try comparing the customer’s current vehicle to his profile information. A surprising number of people purchase again in less than 6 months after they realize their first choice didn’t meet their needs. You can step in and help them find one that does.
New to Used
A common assumption is that someone who has driven a new car would never purchase used. There are two main reasons this people will do just that: age and income. Low income buyers ineligible for prime advertised rates get stuck in long terms and high rates. Many of them settle for small vehicles because it’s all they can afford new. As they age and grow to resent such a tiny vehicle, a bigger used model with features they are missing becomes tempting. Your predictive data will tell you which customers fit this profile.
It’s time to use predictive analytics to start capitalizing on leads that mere data mining or equity analysis would have overlooked altogether.