The automotive industry is undergoing a tremendous transformation, thanks to globalization, increasing competition, changing customer preferences, and with the rapid pace of technology innovations. From autonomous self-driving vehicles, applications to sensors, AI and big data analytics, this sector is experiencing unlimited possibilities for harnessing the data in different ways.
Today, using analytics, information management, quantitative techniques, and statistical models, automobile companies are able to get better insights from the data for understanding customer behavior and targeting this segment effectively.
The Role of Big Analytics in the Automotive space
- A recent study by Strategy Analytics conducted for Intel has indicated that the autonomous vehicle market is set to grow from $800 billion in 2035 to around $7 trillion in 2050 using connected technologies.
- Big data forms the basis of all these applications as vast amounts of data is being collected through remote sensors, which is analyzed and utilized for transforming the automotive industry, boost automation, and increase self-sufficiency. In the very near future, data analytics is likely to the key driving force behind automotive innovation and growth in this space.
- The evolution of data analytics in the automotive industry is likely to have a significant impact where cars can communicate, collaborate information as well as navigate without the need of human intervention, while producing high amounts of data.
- The use of big data analytics is also helping in significant cost reduction for the automakers by assisting them in exploring new methods and using materials which are likely to provide great benefits.
- Big data provides automakers a deeper understanding of their customers by collating millions of data points. Automakers are able to derive valuable insights for creating targeted marketing campaigns to save them more money and provide greater brand exposure.
How various auto companies are using data analytics to reap the best benefits?
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 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 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 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, 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, 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.
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.