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See How Automotive Industry Uses Analytics to Solve Business Problems With Case Studies

Big Data or Analytics in Automative Industry

Imagine the perfect automobile ever. It maneuvers itself on the road even when you are sleeping, stops by at your preferred patisserie for your favorite dessert, and wakes you up just in time for a quick touch-up before you step out of the car. If this is the stuff from a Batman movie, it is coming to life one step at a time. The automotive industry has seen rapid development over the last decade, thanks to big data analytics. Whether it is enhancing vehicle safety with cognitive IoT, revolutionary changes in the transportation and locomotive services and functions, decreasing repair costs and increasing uptime with predictive analysis, or driverless cars, the technologies of the digital economy are making a huge dent in the progress of the automobile domain. That presents an ocean of new opportunities for professionals in the domain to upskill and capitalize on this growing trend. Here is how some of their counterparts at leading firms are using analytics and technology to transform their contribution and careers in the automotive industry:

Examples of How Automotive Industry Using Analytics to solve problems

  1. Audi partnered with Adobe to provide a seamlessly consistent brand experience – The Audi website goes far beyond a place to simply communicate corporate information. Instead, it is a destination site for long-time Audi fans and new consumers, offering visitors a complete 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. Audi’s main challenges were: maintaining, brand consistency across 93 websites in 40 languages, empowering local web teams and dealers to develop websites efficiently, and integrating applications for seamless online customer experiences. Using Adobe analytics solutions, Adobe Experience Manager, and Adobe Marketing Cloud, they were able to provide a seamlessly consistent brand experience on all their websites worldwide. Read this interesting case study here to know the full details.
  2. Jaguar Land Rover speeds validation up to 90% – One of the primary goals of Jaguar Land Rover plc another automotive industry example was to quickly and cost-effectively deliver vehicle features that meet unique marketplace demands. The organization also needed to effectively maintain a greater range of features options and combinations from which customers could choose. Jaguar Land Rover implements a suite of IBM® Rational® software to create a new requirements management and modeling system. Read how they reduced the time required to fully validate software for a given permutation by up to 90 percent while achieving dramatic savings in terms of staff hours and effort. 
  3. Top 10 Automotive Manufacturer Makes the Business Case for OpenStack – A white paper about how a top 10 car manufacturer used OpenStack to create an enterprise-wide private cloud to support a big data initiative, it explores why it was important for a top 10 automotive manufacturer to use “Data as a Service Around the World.” The company had to be ready to analyze data from its product, as well as non-product sources, such as the servicing departments to drive quality. Read here to know how they excelled in the 6 categories of Open Innovation, Data Analysis, Shared Data, Agility, Real Time, and Costs.
  4. Volkswagen partnered with automotiveMastermind on predictive analysis to boost sales and customer retention ratesThrough a partnership with automotiveMastermind, Volkswagen has integrated predictive analytics into their sales process. VW dealers use their new partners’ proprietary behavioural analytics and marketing automation ability to increase store sales and customer retention rates. automotiveMastermind’s technology takes thousands of data points from dealer management systems and combines it with Big Data such as social media profiles, financial records, product and consumer lifecycle information and socio-demographics. The data is then distilled down to one simple number between zero to 100. The number demonstrates to the dealer a ranking in the behaviour prediction of potential customers. Besides, automotiveMastermind also delivers customer-specific talking points and highly personalized marketing campaigns right to the dealers desktop to increase the probability of a customer visiting a dealer. Read how automotiveMastermind transform the buying/selling process using Behavioral Prediction Technology.

         
    Here is a video explaining what is Predictive Analysis from the ground up.

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Sources: Adobe, IBM, Openstack.

Swati Aggarwal

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