Data at the Wheel - Analytics In the Auto Industry - Sciera
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Data at the Wheel – Analytics In the Auto Industry

The headlines tell us that the automobile industry has been a leading adopter of IoT technology and has very willingly jumped on the digitization bandwagon. Increasing consumer demands for mobility and round-the-clock connectivity has overhauled “business as usual” in the automotive industry. In fact, vehicles are now being presented to consumers as an amalgamation of services and products. A number of use-cases are emerging for the application of innovative technology-led solutions.

  • The industry itself is plagued with hyper-competition and analytics can provide insights and give companies a competitive advantage.
  • The major elements of an automobile company’s value chain are vendor management, manufacturing, sales, and after-sales service. Supply chain analytics and vendor performance analytics are helping organizations optimize the procurement process as well as the supply chain.
  • Operational analytics is helping make shop floors leaner and more productive.
  • Predictive maintenance and market analytics help them in targeting the correct customer at the correct time.

Technology is also helping transform automobiles and the user experience. Telematics has rapidly become the norm in the automobile industry. Telematics enables vehicles to communicate with each other as well as communicate with other end-users.

  • Telematics is already being used to optimize routes, on the basis of traffic congestion and the shortest possible route available.
  • With telematics, you can monitor the major components of your vehicle and send notification and alerts in case if any needs maintenance or prayer.
  • Speed, turn, stop related data with many other data-points are being analyzed in real-time with machine learning to get an insight into the driving patterns of users. This is helping drive greater efficiencies in insurance premium definition.
  • In other areas, drivers are the most important resource for a logistics company and their safety can be improved by identifying and retraining the most errant drivers.

Currently, 60 – 70% of the vehicles being sold already have telematics capability ingrained into them. By 2022, that number will increase to 75%.

Car dealers are the consumer face of the automobile company and are under tremendous pressure to maintain healthy profit margins. Of course, most of their supply chain and pricing is dictated by the OEMs they represent.

  • In such a scenario, leveraging analytics to track several key KPIs can help them increase their profits. Sales overview, Sales by model and brands, salesperson, inventory planning, etc. can help them identify loopholes and plug them.
  • Sales by a salesperson can help them identify which salesman needs more training to increase his/her productivity.
  • Many customer and sales are lost due to unavailability of parts. Having metrics for tracking and analyzing inventory will not only reduce the inventory-carrying cost but also ensure availability when customers ask for it.

Many dealers also run significant service center operations. After-sales service is a key contributor to the profitability of their business apart from being an essential ingredient in overall customer satisfaction. In the consumer’s mind, after-sales service plays an important role in building brand equity. Many dealers have access to telematics data. They can leverage that to know which component needs maintenance or replacement and when to replace it. This also enables them to make sure the part is available before the customer walks in. Many premium brands have started a concierge service where they can pick up and drop the vehicle at the client’s doorstep after servicing. This experience can be driven much more efficiently with analytics.

It is very important for both dealers and auto majors to stay on top of the customer and market data. This enables them to understand the potential of each segment, improve customer experience, and also cross or upsell. It is possible to plug in competition data into building your strategies. A robust practice of analytics can help them do so by merging and analyzing data from disparate sources.

Let’s now turn to something which everyone in Silicon Valley is talking about, i.e. autonomous vehicles. Call them that or driverless vehicles or self – driving cars this is said to be the wave of the future. Even though autonomous vehicles are yet to take the market by storm, they are set to be game changers. The whole concept of driverless cars is an embodiment of the deep application of data analytics in the automotive field. Driverless cars need to identify the objects ahead of them. They draw in tremendous amounts of data from numerous sensors and other sources. Analyzing them in real-time to drive action is an application of deep learning. The vehicle needs to understand which route to take and of course be mindful of safety. All this is accomplished through AI and analytics. Driverless cars constantly interact with the telematics interface and also with other vehicles to make traveling from point A to Point B seamless and hassle-free.

This may just be the tip of the analytics use-case iceberg. Innovative use-cases are becoming visible everywhere. One is letting customers design their vehicles using AR and then analyzing which features are being used the most to simplify vehicle design. Data analytics is also being used to crunch through options to come up with the most streamlined design with the best feature-portfolio possible.

In the digital age, the success of a brand or an organization depends on how well it can read its customer and generate insights which will differentiate it from the others. Data analytics can be a boon in such a scenario for the automotive industry to drive just such a competitive advantage. The aim is a powerful product amped-up with a unique user experience. And data will take the wheel on that journey.