Predictive Analytics Will Make Auto’s Future More Profitable & Efficient
If you haven’t already, take some time to check out the Automotive Intelligence Summit Agenda here!
The Automotive Intelligence Summit focuses on the future of the automotive sphere through eight different lenses we call the AIS Core Topics: Connected Mobility, Fintech Solutions, Transportation Research, Economic Forecasts, Predictive Analytics, Mergers & Acquisitions, Compliance & Regulations and Investment Capital. In today’s blog post, we’re highlighting the topic of Predictive Analytics.
Predictive analytics will smooth over so many pain points throughout the automotive industry. Combining data, statistics, model simulations and artificial intelligence to make predictions about the future, predictive analytics can help foresee changes in vehicle supply, demand, financial markets and other seemingly chaotic systems. With predictive analytics, you aren’t just reacting to changes as they occur; the hope is to anticipate developments in finance or the auto industry and equip yourself with strategies to weather those changes or, even better, profit from them.
Here are a few workshops on the AIS agenda this summer that will address Predictive Analytics…
Wouldn’t you like to know which customers you can convert the second they enter your showroom? Artificial intelligence holds a key to that reality. LotLinx President Eric Brown insists machine-learning AI will open a new era of selling cars, and with his session, “How AI Can Identify Imminent Purchase Intent,” he’ll do his best to convince you of that, too. Artificial intelligence optimizes to the dealer’s marketing and promotional environment against their available inventory. Furthermore, by flipping the paradigm on digital marketing strategies, Brown contends you can more easily pinpoint “needles in the haystack” and sell them the car they want and need. This optimization results in significant increased levels of marketing proficiency, which equals less spend for the dealer and higher returns on capital. Furthermore, with AI, you have little to lose and a lot to gain — Brown suggests a hike in sales velocity by as much as 78% and 80% cost reduction when AI correctly identifies buyer intent.
Along similar lines as Brown, Amy Hughes and Matthew Kolodziej — both of Experian’s dealer intelligence team — contend that knowing the online activities of “high-value users” can help you sell more cars with just a bit of additional tweaking. In their joint presentation, “Unlocking the Mystery: How Sales-Based Attribution Transforms a Dealer’s Bottom Line,” Hughes and Kolodziej will divulge the findings of a study observing how analytics can improve dealership performance, as well as point out which marketing activities most effectively lead directly to more sales.
Consumers are not one-dimensional, and neither are their credit profiles. As such, auto applicants should not be viewed only through traditional credit data but also alternative data. In a nutshell, that represents the crux of Equifax Vice President of Auto Data & Analytics Peter Oburu’s argument in his presentation, “How to Use Alternative Data to Impact Profitability.” Oburu will discuss why alternative credit-data sources, such as phone and cable TV payment histories, are gaining greater acceptance among automotive lenders to improve everyday decision-making, increase profitability by maximizing opportunity in the auto lending market and make better risk assessment and improve decision-making and lending.
Some dealers and lenders may ask themselves, “Can vehicle information help assess the creditworthiness of borrowers? What vehicle types are associated with better credit performance? How economically significant is vehicle information?” If you find yourself wondering about similar issues, you can’t miss Moody’s Analytics Lead Auto Economist Michael Vogan present “How Vehicle Information Informs Credit Risk Measures.” In this session, Vogan will describe how vehicle information, including residual price forecasts, lifts the ability of traditional credit scores to classify borrowers from most likely to default to least likely.
Do you have any questions about predictive analytics? Ask them in the comments below! That’s what we’re about with AIS… For all your questions, all the right answers.
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