Let’s look at some of the big brands in the automotive space are effectively utilizing big data analytics to meet some of the key challenges.

Volvo

Volvo is one of the biggest brand names in the automobile industry, which has adopted data analytics to glean valuable insights through its data sets derived through vibrations, temperature, and pressure sensors in their cars. Volvo has integrated telematics solutions in over 25,000 trucks. The telematics devices constantly monitor critical faults and using predictive modeling, the system provides an early warning sign of the problem to the driver and fleet manager. Such remote diagnostics has helped Volvo in bringing down the diagnostic time by 70% and reduce the repair times by more than 20%.

Apart from this, data analytics has also played a key role in enhancing the decision-making process for the enterprise – Volvo uses data analytics for improving the design and quality of its vehicles and for providing greater customer satisfaction.

BMW

BMW is renowned for producing some of the most hi-tech cars and sells over 2.5 million cars around the world. This automobile leader’s business model relies on big data for its core processes right from design, engineering, support, production, sales along with customer assistance. Using some of the most innovative technologies such as AI, predictive data analytics, BMW has been focusing on building the future cars which will be fully driverless and it is confident of achieving the “level 5 autonomy state” by 2021 – a vehicle which can be driven without any human intervention on the roads.

BMW has also tied up with a location data service provider, HERE, to collect data to educate and assist the next generation of consumers ready to experience the self-driving cars. The video from onboard cameras, machine-related data including braking force, wiper, headlight usage and GPS information is being collected and fed into HERE systems for route planning and mapping to assist the vehicles and train and acclimatize them to different traffic conditions and be prepared to become completely autonomous.

Volkswagen

Volkswagen is one of the leaders with a track record of producing high-quality cars in the market and has been able to see some great results by combining predictive analytics into its sales activities. By using behavioral analytics and prediction analysis, Volkswagen is able to provide their dealerships with increased opportunities for boosting their sales and improving customer retention.

With the use of proprietary technologies, thousands of data points are captured through the dealer management systems which are combined with big data comprising of social media profiles, product, consumer lifecycle, financial records, etc. This helps in arriving at the ‘Behavior Prediction Score’ which is a ranking that helps in revealing the number of customers that are likely to buy to the dealers.

General Motors

General Motors, one of the biggest car makers in the world, uses big data to create 360-degree customer profiling for sales predictions and also uses Geographic Information Systems and data analytics for boosting the dealership performance. GM shares the spatial analytics data with its dealers and helps them better understand their customers. GM also heavily focuses on personalized marketing by integrating spatial data analytics with detailed demographics and customer-specific information and focuses on highly targeted marketing campaigns.

Tesla

Tesla, the pioneer in electric vehicles, claims to be a technology company as much as an auto manufacturing company. It does not come as a surprise that Tesla has been banking on data gathering and data analysis way before its competitors. All Tesla vehicles send data to the cloud. In 2014, through this data gathering, monitoring, and analysis, Tesla was able to detect the problem of overheating of certain engine components and it was able to “automatically repaid” every vehicle using a software patch.

Conclusion

Data analytics is leading to a complete transformation of the automotive industry. It is allowing manufacturers to capture data effectively through multiple sources and map these data sets to specific business contexts to boost their revenues and improve customer experiences.