TELEMATICS, IOT AND BIG DATA:

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1 TELEMATICS, IOT AND BIG DATA: Driving The Automotive Industry Forward ENHANCING CUSTOMER EXPERIENCE Data from IOT enables you to a proactive and predictively manage vehicle maintenance and help reduce warranty costs Taking safety, quality and personalised driving experiences to the next level + Vehicle telematics collect a wealth of data in motion. The need to harness this data and gain competitive advantage. + BIG DATA BRINGS NEW VALUE TO AUTOMOTIVE TELEMATICS

2 INTRODUCTION Businesses of all kinds are entering a new era of computing that is customer-centric and fuelled by big data. The big data era is characterised by the growth of social media, an explosion of mobile devices and a physical world being outfitted with millions of networked sensors connected through the Internet. These factors have resulted in an unprecedented growth of all types and volumes of data available to businesses. The automotive industry is no exception. In fact, it is estimated that the auto industry will be the second-largest generator of data by This estimate is not surprising, considering that some plug-in hybrid vehicles generate 25 GB of data in just one hour. connected vehicle, predictive asset optimisation, actionable customer insight and data warehouse optimisation. This paper examines some of these use cases and showcases how Penser Analytics s o l u t i o n s c a n h e l p a u t o m o t i v e manufacturers, suppliers and dealers use big data to increase competitive advantage and business success. While gathering vehicle information may not be new, harnessing it at today s volumes and velocities, and integrating it with information about a car s operating environment at a given moment in time, is revolutionary. For the first time, data from cars, surrounding vehicles and other external conditions can be pooled to give drivers real-time information about where they are going and how best to get there safely as well as deliver a new level of value to automakers, partners, suppliers and to cities looking to operate increasingly efficient transportation systems. While big data affects nearly every industry, specific market forces are driving change in the automotive industry. Automotive companies can use big data and analytics to help realise these business imperatives. Penser Analytics has built solutions based on key industry use cases, including the

3 Big Data Brings New Value To Telematics Automakers are investing in big data and analytics technology that helps capture and analyse telematics information in real time to: Improve vehicle safety and reliability through analysis of system and component performance in the context of real-world usage patterns and environmental conditions Big data can help automakers, suppliers and dealers better understand the conditions that lead to component failures, excessive wear and/or usage patterns. With this insight, they can proactively and predictively manage maintenance schedules, help improve the design of future vehicles and Reduce warranty costs by detecting and remediating defects before they become a safety threat Information about the performance of vehicle systems (brakes, air bag deployments, stability/ traction control and so on) is captured through telematics, sensors and embedded software, that can be integrated with data from outside the vehicle to provide unique context to the captured event. Revolutionise the root-cause diagnosis of product defects by making it less resource-intensive, and adding the information about the surrounding environment at the time of failure. Big data and analytics help make the concept of a connected vehicle a reality, enabling collection of information about drivers that advances safety and the driving experience. Connecting the vehicle to its surroundings through the cloud also facilitates development of assisted and autonomous driving.

4 Analytics Infused After Sales Service Vehicle maintenance schedules are based on average performance and routine schedules instead of actual use patterns and other unique conditions. Utilising big data and analytics, automakers, suppliers and dealers can not only gather more information about how vehicles, systems and components perform under real-world conditions, but also analyse it to answer specific questions. The analysis might indicate safety hazards or details that could help them design better end products. They may also gain insight into how particular braking and acceleration patterns affect performance; redesign the console based on high numbers of complaints about the placement of air conditioning controls; or combine geographic, weather and service records to look for repair trends in extremely cold environments. By better utilising all available data, auto industry organisations can: C r e a t e c u s t o m i s e d, p r e d i c t i v e maintenance schedules that help customers avoid costly unscheduled maintenance and breakdowns, thereby generating customer loyalty and goodwill Gain greater insight into vehicle systems p e r f o r m a n c e u n d e r r e a l d r i v i n g conditions, ultimately enabling them to build safer, more reliable cars Pool data across vehicle models, subassemblies and components to improve quality, increase service efficiency a n d o f f e r f e e d b a c k t o p r o d u c t development For example, with the consent of drivers, the system can collect data from its cars in nearreal time and build models using a powerful analytical platform. The analysis can identify which operating factors, such as road conditions, charging patterns and trip length, have the greatest impact on battery life. Further analysis can help the automaker predict when batteries need replacing, so it can alert owners before their batteries fail boosting customer satisfaction. Deploying Big Data And Analytics Platform From Penser Analytics Sherlock from Penser Analytics, a platform for big data and analytics, aimed at helping automakers capture and analyse telematics information in real time through the cloud, enabling them to offer enhanced driving experiences and improve driving safety directly through the vehicle or through customised, mobile offerings. Sherlock is based on our patent pending revolutionary database management system, combined with our cutting edge realtime predictive analytics solution. Sherlock comes with Powerful predictive capabilities that enable automakers to use current data to anticipate future events and trends, such as mechanical failures or customer behaviour. an integrated, enterprise-class, Hadoop-based solution for managing and analysing large amounts of raw data derived from an ever-growing variety of sources.

5 Redefining Vehicle Buying The customers today feel the need of a vehicle because of factors like growing family and upholding social status among various other factors. They typically follow what is called Customer Decision Journey (CDJ). Very often the buying process starts with online research in a quest to find the right vehicle. Third party websites (automobile news, car comparison sites etc.) are the most relied upon online sources for this information. Offline sources include tips from the current vehicle owners, expert opinions, books and magazines etc. Test drives further assist the prospective buyers to narrow down to the right vehicle, however they are forced to undergo a sales pressure environment. After the decision to buy a right vehicle is arrived at, the customer visits multiple dealerships in search of the vehicle at the best price. Customers often undergo heavy and painful negotiations with the sales personnel during this process. The disconnect between the information available online and at the dealership adds on to the agony. Even after having finalised the deal, a customer has to then go through the time consuming F&I (Finance and Insurance) process to get the appropriate financing for the vehicle. Problems in customer decision journey of purchasing a vehicle 65% of Millennials would prefer purchasing without negotiating with a sales person (Source) 92% of car buyers don't trust the sales person (Source) 72% are more likely to visit another dealership on the same day. 57% visit for a better price. (Source) 52% of Millennials say that if they had a bad experience with a dealership, they would never consider it again (Source) Valuable insight can be gained by combining external data such as social media content with internal data such as part and product information, customer information, s and call-centre interactions. Deep analysis of customer demographics, transactions and clickstream data can allow companies to create new segmentation that enables marketing to make more personalised offers that yield more sales. Automobile manufacturer can also gain insight into which products are trending up, helping to anticipate a potential increase in demand. And knowing where the customer is located at a given time enables the delivery of offers when the customer is most receptive.

6 Leveraging Predictive Analytics Manufacturing companies frequently use predictive analytics to monitor and maintain in-plant assets for better availability, utilisation and performance. For today s automotive industry, predictive analytics can be extended beyond in-plant equipment to include the vehicle itself, both pre-sale and post-sale. Information from predictive analytics can be used to provide a feedback loop that enables carmakers and dealers to optimise quality and supply-chain processes while minimising warranty costs. However, organisations struggle to take advantage of this big data opportunity because they lack the capability to rapidly analyse data from the myriad of sources available today. Sherlock, a Penser Analytics solution, integrates industry, services, software and research expertise to augment traditional analytics with big data capabilities. Combined with user-friendly industry standard dashboards, Sherlock enables automotive companies to analyse data from available sources with low latency. By leveraging advanced analytics capabilities, companies can improve visibility into their operations and optimise asset health and yield. For example, OEMs can analyse years of vehicle operating data combined with publicly available data to identify potential leading indicators of vehicle wear, and then anticipate parts and service demand. Sherlock allows organisations to collect and integrate structured, unstructured and streaming data, including: Data from sensors and actuators on plant equipment and vehicle systems Image, video and graphic data across various types and formats Maintenance and engineering information such as enterprise asset management data Call-Centre information logs, voice and audio records, and text In-service usage data from service providers and third-party partners Geospatial and temporal information, like GPS, weather and environmental data Social Media interactions with present and potential customers Automotive organisations can use Penser Analytics Solutions to analyse the root causes of failure, determine the leading indicators of failure and assess the risk of failure and outage versus preventative maintenance. By detecting anomalies and p a t t e r n s i n e q u i p m e n t a n d p a r t s performance, companies can optimise the supply chain and ensure that parts suppliers can meet anticipated demand, helping to increase customer satisfaction. Potential business benefits provided by Sherlock, include reduced capital and o p e rating expenditure s, i m p ro v e d production yield, improved customer satisfaction and the ability to curb warranty costs by detecting and correcting problems early. Companies can also maximise asset value by reducing equipment downtime and outages. TO LEARN MORE ABOUT OUR SOLUTIONS FOR AUTOMOTIVE INDUSTRY Contact us at: T: E: cs@penseranalytics.com Visit our Website: