Key players in the automotive industry have recently adopted big data analytics tools as part of their efforts to remain competitive. When facing global threats like COVID-19 pandemic, supply chain delays and consumer shifts, having transparency and agility within their operations are imperative for automakers.
Big data comes into its own here. With many applications in the automotive sector ranging from preventing accidents to optimizing production processes, big data can provide vital support.
Predictive analytics refers to the practice of using historical data to make predictions about future trends and events, from machinery failure to economic fluctuations and beyond. Predictive analytics allows analysts to use historical information such as weather or financial records in order to anticipate future business outcomes with accuracy.
Predictive analysis uses various machine learning algorithms depending on the business application. Clustering algorithms may be employed for customer segmentation and community detection while classification/regression algorithms can help create recommendation systems.
Businesses across multiple industries are turning to predictive analytics to streamline operations, increase revenue and mitigate risk. Sephora uses predictive analytics to make product recommendations that keep its customers satisfied, while Harley-Davidson uses this technology to detect dissatisfied customers sooner so they can reach out and encourage retention. Meanwhile, The District of Columbia Water and Sewer Authority utilizes predictive analytics for optimizing repairs and reducing water loss through pipe failure prediction.
Real-time monitoring system designed to collect, process and analyze streamed car device data was recently created. This includes cloud database storage for preprocessing of data as well as machine learning algorithms to disaggregate energy disaggregation and predict energy costs in real time.
An interactive web graphical interface was also developed to visualize and analyze the data. Three common error pre-processing steps include setting not-a-number (NAN) data into zero values, dropping NAN data altogether and performing nearest neighbor data interpolation.
Connected cars collect vast amounts of data about their performance and operation, which can be used to enhance vehicle safety, mobility management and other areas. Furthermore, this data can reduce congestion by improving traffic flows and transportation systems; save lives through emergency response teams having advanced warning of crashes; provide insights into vehicle maintenance for owners to help keep vehicles running as efficiently as possible while simultaneously optimizing fleet utilization and managing maintenance costs; or simply help owners stay aware of vehicle usage trends for improved management purposes.
Customer Relationship Management (CRM)
Customer relationship management systems provide your business with a central database for organizing and automating sales activities, marketing campaigns, technical support processes and customer purchase history tracking as well as tracking customers preferences and interactions with your brand.
CRM systems collect customer information from multiple sources, including websites, telephone calls, live chat sessions, emails, mail pieces and various marketing collateral. Once this data has been compiled and made accessible to customer-facing employees via a central database system.
Traditionally, collecting and updating CRM data has fallen upon sales and marketing departments. But now more companies are creating dedicated Customer Success teams and integrating CRM capabilities into existing enterprise resource planning (ERP) software suites for easier customer data collection, analysis, and update. This allows them to better anticipate future customer needs which improves overall customer satisfaction and ultimately drives sales growth over time.
Big data refers to large, complex sets of information collected by businesses from various sources. This may include customer and operational information, vendor relationships, shipping/production schedules/finances information etc. Automakers collect this data in various ways: asking customers for loyalty card data collection purposes; monitoring social media; using GPS systems on cars/apps/cookies.
As automotive transitions to digital, big data has become an invaluable asset. Analysis and interpretation of such information offers key players in this sector with intelligence, transparency and flexibility which allows them to overcome challenges more easily while remaining competitive in their markets.
Predictive analysis and manufacturing simulations can be used to make changes to component designs before going into mass production, saving both time and money over the long term. They can also help prevent costly recalls like those experienced by General Motors who used big data analytics to reduce recalls last year.