Harking all the way back to early November 2009, the late Ralph Paglia (considered the godfather of automotive Internet) and I were in the upstairs bar of Tootsie’s in Nashville Tennessee, deep in conversation as to where the industry (and its technology) needed to go. In town for the fall Digital Dealer Conference, we were contemplating about prediction tech long before automotive AI was the mainstay topic it is today. Here, 15 years later, our industry still isn’t near where we were hoping this tech would be.
Granted, prediction tech insofar as lead scoring, was still well on its way back in 2009. My DealerKnows partner, Bill Playford, himself was on the bleeding edge of that. Tech could predict the likelihood a prospect would buy a vehicle by analyzing countless data points based upon the customer’s information alone. Looking to current day, A.I. (artificial intelligence – words used loosely by automotive standards) have ramped this up with everything from in-market shoppers, multipoint attribution info, customer journey data, buying signals, and more. However, that is not what Ralph and I were yearning for.
As we talked through the prediction tech available to us back in that crowded honkytonk, I stated it simply wasn’t enough. I said what CRM and DMS companies needed to do was assist in the sales process. I wanted them to utilize all historical sales and service data existing within them, based upon each rep’s previous experience, coupling it with the prospect information, and direct inbound leads and calls to the agent who has the highest probability to sell them. This is where the rubber truly meets the road. Here we are in 2024 and we haven’t solved it.
Determining the likelihood a customer will buy based solely upon their information takes away the belief that the dealership and its employees play any role in the customer’s decision. The store’s make-up, the unique characteristics of its employees, and its historical sales data should be able to predict who on a sales floor is most equipped to sell them. By examining every prior sale each agent has had, the make/model/trims they historically sell the most, overlapping hometowns/place of residences/previous employers/familial situations as that of the customer could help build rapport and influence decisions. THIS is the type of prediction tech that CRM and DMS’s should attempt to integrate into their tools. Artificial intelligence or not, these are the type of advancements that we shouldn’t be waiting 15 years on.
So I call out to CRM and DMS companies to look more intuitively at the data that resides inside your tools, leverage all the customer/salesperson/sales/vehicle data that exists, and use these predictive analytics to steer the right opportunities into the hands of the person best suited to sell them. The idea that our team members and the insights from experiences with past customers/sales plays zero role in the propensity for a customer to buy from a dealership is unacceptable to me. I still believe this is a people business. Ralph Paglia did too. We just need the prediction tech to improve it.
Technology should assist in sales, but I still believe it takes great people to deliver a worthy experience. Read my blog about where A.I. fails and people succeed.
