Big Data and Analytics in The Automotive Industry
The automotive industry continues to face a growing number of challenges and pressures, including cost pressure, competition, globalization, market shifts, and volatility. At the same time, big data and analytics today offer previously unthinkable possibilities for tackling these pressures and many other challenges automakers are facing. Analytics is a powerful tool. However, applying analytics effectively requires knowledge and experience beyond statistics, operations, or information technology. Analytics done right requires a comprehensive set of capabilities that intersect and integrate with multiple functions and skill teams across an enterprise. With that as the backdrop, our global team of automotive specialists have authored this collection of articles, "Big Data and Analytics in The Automotive Industry", with the hopes of sharing what possibilities analytics offers automakers and what you should consider when designing an analytics-driven initiative.
With increasingly better possibilities of data analysis, predictive analytics is developing into a powerful tool facilitating an enormous boost in forecasting efficiency as well as operations and performance. However, the task lies in automakers’ ability to make sense of giant quantities of readily available knowledge and experience data. Analytics allows this information to be merged regardless if the information is “machine-readable” datasets or unstructured data such as videos, sound recordings, or texts. The followings are the main takeaways from this collection.
- With the explosive growth of datasets, Deloitte believes that the focus of big data analysis is not obtaining more data but through data analytics to discover the underlying issue and resolve the problem deriving from business objectives. When constructing big data applications, enterprises need to base on their business and IT strategies to design a top-down framework, with the collaborative support from IT, business, and algorithm experts.
- With increasing customer dependence on social media and the Internet as a research and communication tool, car manufacturers today may want to rethink and evolve how they engage buyers throughout the sales and ownership cycles. Analytics and information management can provide the means to tackle unique set of questions for customer data from the primary stakeholders (operational, managerial, or executive) groups. While a critical step in making customer data useful is customer segmentation, we believe effective customer segmentation starts with the end in mind and requires careful research design to produce actionable insights.
- The customer retention (CR) rate finds its place as a key business performance metric. To optimize CR efforts holistically, OEMs require an in-depth understanding of customers’ behavior to define the most engaging customer experiences. At Deloitte, we drive value through our four wall breakthrough approach, namely Purposeful Planning, Analytics Framework, Modelling Capabilities, Engagement for Actions, which makes analytics relevant to the real world and deliver more meaningful results.
- Competition amongst OEMs is increasing as they fight for brand strength, segment share, and profitable growth. Dynamic market conditions have also dramatically increased the complexity of marketing and sales planning. OEMs need to have a deeper understanding of markets and customers, which can then be factored into marketing spend management and decision-making more effectively. In our experience, the use of a consistent set of metrics to guide incentive program development can help OEMs improve strategic planning and increase return on VME spend. And critical to the strategic planning and performance management processes is ongoing marketing mix analytics (MMX) that identify and measure incentive programs that drive growth more efficiently.
- The areas identified by some automakers as the most concerning areas of risk all require strategic forethought and long-term planning if they are to be successful in both mitigating supply chain risk and managing risk for reward. In our experience, those automakers that are able to effectively manage risk across multiple tiers of the supply chain will gain a competitive advantage and be better able to drive growth. Core to achieving those objectives is leveraging the vast amount of data automakers have available and applying advanced supply chain analytics to both mitigate supply chain risk and manage risk for reward.
- Predictive quality analytics capabilities available today allow quality issues to be detected and resolved very early on, thereby facilitating improved abilities for quality management teams to manage against both customer satisfaction and cost control concerns. Mature analytics systems can today process large quantities of data and offer various analysis methods, thus identifying potential faults in advance and providing opportunities to develop suitable counteractive repairs. The introduction of such predictive quality analytics systems will dramatically alter recall management, leading to considerable increases in efficiencies, and result in massive savings in warranty costs.
- Automakers are under a great deal of pressure from shareholders, regulators, and consumers to evolve their product quality, safety and recall management capabilities. In our experience, leading capabilities spanning process improvement, operational strategy, advanced analytics and visualization, and finance can enable automakers to better manage quality and safety issues and recalls.