GM Uses ArcGIS to Predict Customer Demand
From electric cars to heavy-duty full-size trucks, General Motors (GM) provides a complete range of vehicles to meets the needs and expectations of drivers on a global scale. GM’s eight distinctive automotive brands include Chevrolet, Buick, GMC, Cadillac, Holden, Baojun, Wuling, and Jiefang.
GM has undergone a top-to-bottom transformation to position itself for the next 100 years. Under Chief Information Officer Randy Mott, GM’s Innovation Centers are driving greater information efficiency, resulting in faster decisions and a shorter time to market. The focus driving GM’s IT transformation is to provide better insight into customer needs and wants in a simpler and quicker manner.
To improve their dealers’ understanding of the market, GM is implementing a site analytics tool across its dealer network. The tool uses Esri ArcGIS technology to combine US Census tract data with demographic-based consumer profiles, available through the ArcGIS platform. GM’s dealer network planners can now rely on intelligent maps of their data to understand network and site performance and to develop short and long-term strategic plans.
With the new site analytics tool, dealers can visualize the car-buying habits of people in their regions via interactive maps. These maps allow users better understand demographic shifts within each dealership’s territory to identify the best mix of vehicles to offer consumers. Keeping an eye on population changes also helps GM determine when to establish a new dealership to meet demand in an underserved area.
Using ArcGIS-based location analytics, dealers can identify exact locations where potential customers live and how far they are willing to drive to buy a car or to have a car serviced. GM decision makers can also see where dealer territories overlap which helps the company reassess site locations and better serve the market.
Given a successful dealership location, GM can find the best locations for a new dealership ranked by similarity to the successful dealership in terms of population, demographics, and market potential. GM can also use spatial interaction gravity models, travel distances, and inventory supply to predict demand or cannibalization between dealerships. By understanding the marketplace and accurately predicting demand, GM is able to improve business decisions.
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Through GIS, we can bring data down to a level where we can look at it via maps and apply geo-analytics to understand the spatial relationships causing the real problems. Having the capability of geo-analytics will help all of GM, including our deal.
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