Data Analysis in the Automotive Industry
The interconnected relationship between data and the automotive industry is becoming increasingly transparent and essential.
Especially in recent years, with the rapid advancement of technology, increasingly niche, and higher customer expectations, supply chain improvements, and global regulations, automakers have started to redefine how they use data intelligence and technology to improve the effectiveness and efficiency of how they operate.
As the automotive industry continues to face challenges from competitors, a saturated market, consumer shifts, and volatility, it’s important that they have transparent and detailed information to make strategic business decisions.
With the help of big data and data analysis, automakers can widen the products and services they provide, deliver more value to customers, and remain agile and scalable regardless of global challenges or market pressures.
How are data analytics used in the automotive industry?
Key players in the automotive industry use data analytics to improve performance, monitor relationships with suppliers, develop customer relationships, and reduce operational costs.
Throughout the automotive lifecycle, many processes and people collaborate to design and deliver products and services that are reliable and cost efficient and have the features that customers want to see. These processes involve many pieces of information and datasets that must be analyzed. This helps the industry develop smarter, more connected cars for customers and increase sales and marketing to improve the customer journey and increase company profits and operations.
Data analytics is the backbone of compiling and understanding the complex datasets to derive actionable strategies, and because there is so much data collection involved, the auto industry has truly turned into a data-driven industry in recent years.
Here are some ways data analytics are used in the automotive industry:
- Development and production
Data scientists and engineers analyze large volumes of information and complex data points to improve their development and production processes. For example, car manufacturers use data analytics tools to test a combination of components to determine the most fuel-efficient and best-performing car models. This helps the design process, as the manufacturer can select the most aerodynamic models designed to be cost and fuel-efficient for the customer.
Manufacturers also use data analytics tools to predict potential issues with a vehicle. For example, sensors installed in vehicles have predictive abilities to detect when potential problems might arise. As a result, they can resolve the issues, recall defective products, and advise clients on repairs before they become more serious problems.
- Sales and marketing
From a sales and marketing perspective, data analytics tools play a huge role in the engagement and quality of car dealerships and manufacturers’ relationships with their customers. Fostering good customer relationships is critical for automakers because it creates trust and loyalty, growing their market and customer base.
Predictive analytics help automakers personalize the customer experience and better target the pain points of individual customers. For example, data analytics can show customers’ demographics, preferences, and budgets and guide service offerings tailored to customers’ specific situations. Automakers can also use data analytics tools to better enable their dealerships’ aftersales offerings, such as vehicle service, parts and accessories.
How big data can be used in automotive
Big data describes complex, large datasets that continuously grow exponentially over time. In the automotive industry, big data exists in supply chain management, financing, predictive analysis, and design and production.
Each connected car and automotive process produces data constantly, which means automakers have access to an entire fleet of helpful information and datasets. For example, big data can be integrated into sound supply chain management practices to monitor their stability and overall efficiency. Automakers can compare products required based on the materials and suppliers available to use reliable components and ensure on-time delivery of finished products.
Big data can also be used within the car to connect users with information like safety alerts and automakers with real-time vehicle conditions. With connectivity from inside and outside the vehicle, automakers can see trends and make improvements to vehicle models.
One of the most important uses of big data in the automotive industry is predictive analysis. It helps automakers predict customer problems in advance and develop actionable plans to remedy these issues. Not only does it ensure better reliability of vehicles, but it also dramatically improves customer satisfaction and cost control.
Why is data important in the automotive industry?
Data provides essential information that allows automakers to benchmark their performance and make strategic business decisions. Data analytics help plan for delivering better customer engagement initiatives, cost reduction efforts, reliable car designs, higher customer retention rates, and agility and scalability in a competitive market. It structures the evidence that automakers need to understand the effectiveness of their decisions and operations.