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Tesla could give established automakers lessons on how to maximize big data. "They are already more advanced. They are already an OEM 2.0," says Matthias Bentenrieder, who is a partner at consultancy Oliver Wyman.
He says that Tesla has a better understanding than its competitors of how to use big data to do things such as enhance its relationship with customers, monitor the performance of its vehicles and determine which promotions really work with car buyers.
Amazon and Google also are experts on knowing their customers’ preferences and how to make offers to them that will generate additional sales and a stronger relationship. However, automakers are not good at this and this weakness could hurt them.
"In five to 10 years if they are not world class in this they will be in trouble," Bentenrieder says. "Amazon and Google are world class experts in this and once the automakers let them in they could be pushed out."
Today, most automakers have no direct relationship with their customers and they are struggling to determine how to use the data that they have.
One automaker working to change this is BMW with its i subbrand. Customers for the i8 and i3 have a direct relationship with BMW, which gives the German automaker a better understanding of how its products and services are being used.
Another area where automakers can build closer ties with customers is via car-sharing programs such as Daimler’s Car2Go scheme and BMW’s DriveNow program. In both instances the automakers are gaining valuable information about their customers. They could use this information to determine which car-sharing customers might be most interested in offers to purchase a car of their own.
Bentenrieder says the key to making the most out of big data is to simplify.
"Start by thinking about what you want to achieve and then identify what data you need to achieve this,” he recommends.
It sounds easy but Bentenrieder says automakers often face a hard choice of either investing in something like a new crossover that should pay dividends within a couple years or putting a large chunk of money into the people and tools needed to win with big data despite knowing that it might take five years or longer before they see any financial payback.