Automotive automobile analytics great learning

The union of the automotive industry and smart technology has changed the way adventurers seek thrill and how even the most-technologically challenged drivers navigate. The automobile industry has seen rapid development over the last decade, thanks to big data analytics. Big data is helping the automotive industry advance further in a number of ways- by enhancing vehicle safety with cognitive IoT, decreasing repair costs or increasing uptime with predictive analysis and much more.
This digital revolution in the automotive industry presents an ocean of new opportunities for professionals in the domain to upskill and capitalize on this growing trend. Here are a few areas where the automotive industry is using big data analytics to get to the next level:

How Automotive Industry is Developing with the help of Analytics

  1. Changing the Retailing Norms – Digital economy is challenging the retail norms followed by automotive companies. Automakers now offer 24/7 connectivity, expansion in the number of access channels and a seamless digital experience across all engagement channels. For example, Audi partnered with Adobe to provide a seamlessly consistent brand experience. Instead of the Audi website being a place to only communicate corporate information, it is now a destination for long-time Audi fans and new consumers – offering visitors a complete new brand experience with current news, links to dealers, vehicle guides, and the always-popular Audi Configurator – an interactive application that enables customers to design their own cars. According to Deloitte, “the role of the traditional dealer is being questioned and innovative sales processes are being trialed by some automakers keen to exploit opportunities to differentiate and complement existing retail experiences on offer.”
  2. Predictive Analysis for Customer Satisfaction and Building Smart Cities – Cars contain 50 or more sensors which collect data on speed, emissions, fuel consumption, usage data for resources, and security. All these data can be used to find patterns and resolve quality issues either in the nick of time or prevent them from happening altogether. Analytics is being used to increase both customer satisfaction and quality management at a cost-effective level. Product recall is a commonplace menace for the automotive industry that forecasting tools and predictive analysis are actively combating to mitigate risks of product recalls. Progressive firms are also using predictive analytics in collaboration with the government to predict and identify high congestion zones based on data collected from automobiles for town planning and building smart cities. Urban city problems like effective traffic management, distribution of resources, and environmental issues can all be tackled with combining insights from automotive data and other sources such as satellite, cellphone, GPS data, etc.
  3. Ensuring Apt INsurance Premiums with Telematics – Telematics is enabling automotive manufacturers, sellers, and insurers to gather information from different geographical locations and usage patterns. OEMs (Original Equipment Manufacturers) track their customers even after the sale is completed. The data generated by sensor-driven cars about driving behaviour, speed, braking habits, turning styles, acceleration, and abidance with the traffic rules in a country is then used to create driver profiles. Insurance companies set premiums based on these driver profiles to set higher premium for high-risk drivers and lower for good drivers. The days of reckless driving are long past us!
  4. Big Data Leading to Bigger Boom in the F1 Circuits – Big data analytics has taken the F1 racing teams to the next level. High-speed racing amalgamated with data science is providing a new high-tech metric to measure performance by using data points about tire pressure, braking patterns around corners, fuel burn efficiency, acceleration time, etc. Offline data centres are being set up for every team, that provide real-time on-track data to boost performance and fix glitches. According to Dataiku, “In 2015, racing teams at the U.S. Grand Prix collected over 243 TB of data, all of which was cleaned, formatted, and analysed off-site so that teams could make the appropriate changes on-site.”
  5. Optimisation Using Prescriptive analytics – IBM states, “Predictive analytics helps companies understand the drivers behind customer buying patterns to anticipate which products customers want, how many they want and when. Prescriptive analytics, on the other hand, optimises production planning, scheduling, inventory and supply chain logistics to meet business requirements. Through a combination of mathematical algorithms, machine learning and artificial intelligence, a prescriptive analytics solution can recommend the optimal action plan likely to drive specific business outcomes.” Marketing Mix Analytics is being used in various sectors such as banking and retail but the automobile industry is catching up soon. Advanced analytics allows automotive companies to identify trending features, items, and customisation options like an automatic gearbox or a specific modification or colour. Companies then use these analytical reports to forecast demand at a granular level.
  6. Automobile Financing – Auto-finance companies gather massive amounts of customer data. With this data, companies are analyzing customer financial history and preferences. By clubbing this analysis with demographics and geography, companies are now able to propose ideal personalized finanacial schemes according to customer requirements. Differentiated services from auto-finance companies generate more leads in business and keep their customers away from fraudulent and the defaulters.

    Here is a video explaining what is Big Data from the ground up.



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