Location analytics is a big thing in General Motors these days. A spatial analysis of the data available with the company showed that their customers will drive two hours to buy a car. However, they may not be ready to drive down so far for a servicing. Also, customers will be ready to drive this far, bypassing a more convenient dealership, if they know they can save $500.
Location analytics is a big thing in General Motors these days. A spatial analysis of the data available with the company showed that their customers will drive two hours to buy a car. However, they may not be ready to drive down so far for a servicing. Also, customers will be ready to drive this far, bypassing a more convenient dealership, if they know they can save $500. According to Bruce Wong, an advanced network analytics manager at General Motors, location analytics has helped the company trim down their marketing to target only those households that buy new cars rather than simply hitting the general public with ads.
Location analytics is a small but critical component of big data deployment in the automobile segment. In an industry that deals with several thousand different spares, thousands of dealerships and millions of customers, identifying the right budget, location to target and audience to market to can mean a saving of millions of dollars. Consequently, automobile companies now deploy big data analytics in every stage of production and marketing their products.
Product Design : Companies gather real-world data from customers who have cumulatively driven billions of miles to understand safety factors, performance issues and design requirements that influence designs of newer models of the vehicle.
Supply Chain : Sourcing components from various OEMs and suppliers is a process riddled with inefficiencies. Big data analytics help manufacturers analyze the various suppliers based on their cost, proximity, reliability and quality and help arrive at a decision through a sound scientific process.
Marketing : As in the case of General Motors, the various analytical tools built with big data help marketers identify the right audience to go after, thus saving millions of dollars that may otherwise be wasted on audiences that were never your target to begin with.
Aftermarket : Companies like eEuroparts.com specialize in genuine replacement and performance parts online, ranging from new BMW parts to providing almost-extinct SAAB parts. These companies often deal with thousands of components from different manufacturers. Big data helps these companies predict the demand for specific components across the various geographies – this enables better logistics and sourcing processes that brings about an increase of several thousand dollars, if not millions, in the bottom line.
Financing : Automobiles are often bought through debt and financing options. Big data helps in understanding the market for every model, the geography they target, the demography of the ideal customers to help launch financing schemes that will not be a one-size-fits-all but rather heed to the specific requirements of a particular customer.
While these opportunities make big data an extremely important part of the present-day automobile business, bigger things are yet to come. With autonomous cars (also called self-driven cars) expected to become available commercially in less than a decade and with over 10 million of them likely to be on American roads by 2032, there is a massive opportunity waiting for big data in the automobile industry. Each of these new cars come equipped with multiple sensors and satellite navigation data which means billions of data points to play with. The possible ways one could use this massive volume of data to derive meaningful information is only left to imagination.
What are some big data opportunities in automobile that you see has not been utilized yet?
