Automotive analytics drives consumer-focused industry

The automotive industry and analytics have always worked in tandem, and now with a shift in focus from product to consumer prompted by market changes and economic downturn.

Automotive organizations have always relied on analytics to drive their products, but with the proliferation of data and the changing customer landscape, analytics use in the industry has shifted.

From marketing to insurance to maintenance, analytics adoption has helped the automotive industry develop rapidly across the board. And automotive analytics helps the industry stay focused on consumers.

The shift in perspective

Automotive organizations are pushing their analytical capacity and working on their maturity. The market conditions have changed, and competition has only gotten fiercer while data availability has dramatically increased.

"Broadly over the last 20 years or so, there has been a shift from being product-centric to being very consumer-centric in most of my automotive clients," said Ashwin Patil, managing director at Deloitte Analytics. 

Customer-first is the name of the analytical game for the automotive industry -- mining customer data to discover habits and trends. With data-driven target marketing and personalized offers, it's easier for automakers to put the right vehicles in front of your audience.

"What has changed -- and then how I see the pervasiveness grow -- is the ability to have that data broadly available for everybody so analytics is pervasive today, in the automotive space -- the maturity level for that is what is being challenged every day," Patil said.

Maturity in marketing for the automotive industry means using data to monitor competition and industry trends that drive purchases. At the top of an automotive enterprise, that means using purchase data to further new products. On the retail end, it means dealerships stocking their inventories with the cars people in the area want to buy.

Great Recession reset

The Great Recession between 2007 and 2009 and the resulting effect on the industry forced large automakers to restructure and look inward. The changes implemented during the following years have shaped the way these enterprises approach analytics.

"All of a sudden, companies were focused on 'What do my consumers need and how do I best serve those needs?'" Patil said.

As the economy declined and consumers became more conservative, sales on high-profit vehicles such as trucks and SUVs declined, and automakers found themselves facing disaster. With predictive analytics, automakers can prepare for those kinds of changes in consumer buying habits.


With semi-autonomous vehicles on the road and more connected devices within vehicles, the automotive industry is rife with available data. This has only increased in time with the greater complexity of vehicles.

"The ability that technology gave them to be able to mine large amounts of data and drive intelligence out of it has just steadily increased," Patil said. "And it's still increasing in that space."

More data has given automakers more information to base their decisions on and permitted their changes. The automotive industry is now able to track consumer behavior better.

"The amount of knowledge available, the amount of reach available, the amount of competitive intelligence that's available to consumers today has shifted very drastically," Patil said.

Sensor data from systems like OnStar or vehicle plugins like Snapshot from Progressive provide data that can improve customer experience by offering monetary incentives to customers who drive safely. Data captured in these ways can also be used to monitor traffic trends and help local governments improve traffic management and infrastructure, such as filling in potholes around the city. 

Customer-centric automotive analytics also involves predictive maintenance. Automakers can use data to predict and prevent or streamline product recalls before regulation authorities get involved. Predictive maintenance in vehicles can also alert drivers to issues with their vehicle before it becomes a major problem, saving customers money and improving their experience with the product.

Changing focus effects

Automotive analytics is part of each step of the product lifecycle. Information gained from consumer purchases allows for product changes to match the market's needs and wants. This gives automakers the ability to shape their entire process to keep up with a fluid market.

Analytics of consumer trends and changes in the industry can help automakers identify purchase patterns and optimize vehicle manufacturing. Advanced analytics can also help automakers mitigate risks in the supply chain to avoid shortages proactively.

Analytics at the end of the cycle affect the decisions that automakers make within their design department and how they shape their product line. Whether a driver relies on an infotainment system or never uses it, that is factored in for the next model.

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