Big Data - An Automotive Outlook Graeme Banister, Frost & Sullivan The Hague 12 th September 2013
Table of Contents Frost & Sullivan Overview 3 Big Data Basics 6 Big Data & The Automotive Ecosystem 9 Big Data Implications for FIA Member Clubs 13 2
Frost & Sullivan Overview 3
Our Industry Coverage Automo&ve ¡ & ¡ Transporta&on ¡ Aerospace & Defense Measurement & Consumer Information & Instrumentation Technologies Communication Technologies Automotive & Energy & Power Environment & Building Healthcare Transportation Systems Technologies Minerals & Mining Chemicals, Materials Electronics & Industrial Automation & Food Security & Process Control 4
Our Automotive & Transportation Practice Mobility Automotive Rail & Public Logistics & Infrastructure Transport Supply Chain § Urbanisation § Connectivity § Rolling Stock (Light § Urban Logistics § Intelligent Transport § Car Sharing § Powertrain Rail, Metro, § Intermodal System (V2X, traffic § Mobility Integrator § Chassis MainLine, High § New Business mgt, congestion § New Mobility § Safety & ADAS Speed Rail) Models charging, tolling, § Inter-modality § Electric Vehicles § Infrastructure § High Speed parking, etc.) § IT Mobility § Aftermarket & (signalling, track, Logistics § IT Integration § Urban Mobility & Distribution station) § Courier, Express § Rail Infrastructure a mix of relevant § Vehicle Interior § Bus & BRT and Parcel § Road Infrastructure studies from other systems for § Vehicle Technology § 3PL & 4PL § Sea Ports areas passenger, (Powertrain, Interior, § IT Logistics commercial & PI, AFS) off-road vehicles § Maintenance 5
Big Data Basics 6
Big Data Characteristics What is It? The 3 V’s Business Questions • Unstructured • Volume data • What to Keep? • Variety • Sophisticated Analytics • Where’s the • Velocity required to Value? handle 7
Big Data – A Big Deal? 8
Big Data & The Automotive Ecosystem 9
Big Data Business Cases - Big data to help tap synergies between multiple eco system partners aiding new business use cases Digital Retailing Retail inventory management 60% leads for car sales are Inventory planning based on digital leads ; cars driven by people living offline auto data for digital around retail outlets ad targeting Warranty and Traffic management recall costs and implementation 2 – 3 % reduction in a 2-3 Smarter approach in billion dollar warranty reducing city’s traffic bill congestion using ITS City infrastructure Diagnostic and repair optimization and time management development Decreasing potholes in city’s Reduction in diagnostic by 30-40 % using apps, time by ~70% and average improving public sector repair time by ~ 25% infrastructure facilities 10
Key Challenges for Big Data Implementation Harnessing relevant and prioritized vehicle and user data are key answers to industry challenges Understanding the Big Data: Relevant & customer from the prioritized information- web (car vs. lifestyle What data you process preferences) – and what data you don’t Customer Analytics and CRM Shortage of skill set The need for better data for data analytics and quality - high data transfer data governance – cost per vehicle for Data Scientists downloading information Data privacy issues on Whose benefitting from the type of data being the ecosystem – How to shared – government monetize data and share limitations and driver value concerns 11
Examples of Big-Data Features and Services Automotive companies are working on big data in siloes, need is to get a centralized big data strategy to push more innovation in this space OEM OEM OEM Connected Services Fleet Related Warranty & Providers Services Product Planning Marketing Aftersales/Dealers Component Failure Dynamic Parts Targeted Digital Traffic Management Fleet Optimization Prediction Pricing Marketing Optimizing Vehicle Predicting Recall Social Media Usage Road Infrastructure Dynamic Route Performance Scenarios Analytics + Public Transport Planning Apps & HMI Usage Proactive Brand Loyalty Multimodal Journey Freight Pricing Planning Analytics Diagnostics Analytics Cross Brand Feature Demand by Disaster Driver Behavior Used Car Valuation Ownership Regions Management Analysis Analytics Demand Sensing – Parts Inventory Production EV Related Services Asset Tracking Deals & Rebates Management Scheduling Crowdsourced Feature Packaging Service Contracts Product Feature Traffic + Parking + Prognostics (Option/Std) Upselling Campaigning Weather Tailored Auto Eco-Driving + Financing Driver Training Usage-Based Insurance Current Services which will benefit Forward Looking Innovative Services from Big Data 12
Big Data Implications for FIA Member Clubs 13
Key Opportunities for FIA Member Clubs 2 3 1 Customer Proactive Driver Retention / Brand Loyalty Diagnostics Safety Three Key Areas of Opportunity to exploit by harnessing Big Data 14
Volvo Cars Case Study To understand mechanical performances of Volvo‘s vehicles under actual Market driving conditions . Legacy data warehouse systems could not integrate Challenge diagnostic readout data with design and warranty information Solution Impact Ø Teradata’s system increased raw data Ø Created an immediate cost reduction availability from 364 GB to 1.7 TB for Volvo's impact analysis showed returns on initial analysts with access to performance project costs of 135 percent exhaustive analytics Ø Increased precision in warranty Ø Teradata fused product design, warranty and reimbursement , compared mechanical diagnostic readout data onto a data failures with geography based conditions warehouse and driving patterns Ø Volvo can now access a single data set for Ø Increased capability to diagnose, design product design, manufacturing, quality and manufacturing problems within current assurance, and warranty - reducing production run response time and faster decision making Frost & Sullivan anticipates significant cost savings will be generated by companies creating Big Data partnerships to transform warranty / breakdown service 15
Hertz Case Study To improve customer service and brand loyalty by better understanding Market and responding to information returned via customer communication Challenge channels (internet, mobile, social, SMS) Solution Impact Ø Hertz collated and understood customer Ø Data processing has become centralized , sentiment surveys by centralizing data previously customer satisfaction surveys collection process were looked into distinctly at Hertz’s 8600 locations Ø The partnership with IBM has enabled Hertz to understand and analyze unstructured Ø Radically reduced response time now feedback data from their “Premium” members allows Hertz to gauge and understand insights that was previously not available. Ø Hertz’s analysis and response time was halved enabling them to provide real time Ø Example: Hertz identified delays at specific feedback increasing customer satisfaction times of day in Philadelphia & so adjusted staffing levels to negate the issue Frost & Sullivan forecasts significant investment by automotive businesses into Big Data partnerships to identify customer preferences, enhance service and improve brand loyalty 16
Current Roadside Assistance Experience Vehicle Customer Customer Breakdown Satisfaction Contact • At Home • Variable based • Verify Issue on ability to • On Road • Initiate Service locate & fix 17
Future Roadside Assistance Experience Vehicle Customer Customer Breakdown Satisfaction Contact • Early Warning • Tailored • Initiate and Service • Solution Guide Service Processing Delivery 18
Thank You! Graeme Banister Consulting Director , Automotive & Transportation Direct: +44 207 915 7807 Mobile: +44 7889 029279 Email: graeme.banister@frost.com 19
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