The CarStory Insights app bases its predictions on inventory data and consumer demand as well as vehicles’ features and pricing.
Vast, a company that pairs big data with artificial intelligence, once asked dealers to tell them about the one thing they wish they knew about their inventory.
Dealers had a clear wish: They wanted to know when their vehicles would sell.
So Vast, of Austin, Texas, followed through with an inventory management app launched in September called CarStory Insights, which uses predictive analytics to determine how long used vehicles will remain on the lot.
The app bases its predictions on inventory data that the company says encompasses around 90 percent of the used vehicles on dealership lots today. The technology gives dealerships windows during which it expects vehicles to sell based on factors such as features. It also takes into account consumer demand in local markets, and can rank vehicles by when the app believes they will sell.
“We go down to the feature level when we analyze these cars. We identify local competition,” Chad Bockius, Vast’s chief product officer, told Automotive News. “We have an algorithm that allows us to identify vehicles that are most similar. We analyze the competition the same way we analyze your vehicle. We look at recent sales history. We try to get a sense of how quickly those cars are moving compared to the rest of the market. Looking at the features of those cars, which elements are driving them to move more quickly or slowly?”
Bockius compared CarStory Insights to IBM’s Watson, an artificial intelligence platform that once competed on the TV show “Jeopardy.” For each question, Watson generated three answers with varying degrees of confidence — and even answered incorrectly sometimes.
Bockius said Insights takes a similar approach. The platform will generate predictions for vehicles with considerable data available in certain markets. For rarer vehicles that are sparse in a particular market, Insights will remain silent because the amount of data is insufficient to make a strong prediction.
The app will judge how a price change and even color will affect sales.
“We know what features drive churn. You might have a vehicle not moving very quickly and part of the reason is you only have five or 10 features that are affecting churn,” Bockius said. “Next thing we look at is color. You might have a blue Jeep Grand Cherokee when people want white, black and red. That’s all based on our analytics.”
Harry Haber, used-car manager for Capistrano Mazda and Capistrano Volkswagen in Southern California, uses Insights as a supplement to the vAuto inventory management solution.
Sometimes the tool will recommend a price that Haber considers too low, but that isn’t the app’s fault. In these cases, Haber said, he realizes that the store may not have had the right game plan for the vehicle.
“With the fluctuation of the used-car market, the margins are so compressed sometimes. CarStory tells me I should sell this car for this [price] based on equipment and what the market-day supply is. Sometimes, it’s hard to price it down that low,” Haber said. “Was it a bad acquisition? I don’t know. Maybe I shouldn’t have bought that car. Maybe, if I took it on a trade-in, I should’ve wholesaled the car instead of putting it on the front line.”
Haber said he had never had a platform with predictive capabilities. Early on, there was a little skepticism about the predictions, but Haber said the app has earned his trust.
Haber said: “In the beginning, you start getting these sales alerts. You say, ‘I don’t know about that.’ Then I double check and say ‘That’s probably right on.’ ”