市場調査レポート

自動車データの収益化:2015年

The Automotive Data Monetization Report 2015-2016

発行 TU Automotive 商品コード 336964
出版日 ページ情報 英文 58 Pages; 14 Figures
即納可能
価格
本日の銀行送金レート: 1USD=102.18円で換算しております。
Back to Top
自動車データの収益化:2015年 The Automotive Data Monetization Report 2015-2016
出版日: 2015年08月31日 ページ情報: 英文 58 Pages; 14 Figures
概要

自動車が回収するデータの量は飛躍的に増加しており、このデータをいかに有効利用するかが自動車業界の次の課題となっています。

当レポートでは、自動車が回収する各種データの有効活用法について調査し、データの有効利用および収益化のビジネスモデル、提携関係の例、ケーススタディ、他業界に学ぶ教訓などをまとめています。

第1章 イントロダクション

  • 自動車メーカーの支出の拡大
  • 集約データの所有
  • 複雑なデータの収益化
  • 市場機会の収益化

第2章 自動車の接続サービスとデータビジネスの基礎

  • 自動車データのバリューチェーンとフロー
  • 効果的なデータ管理・データ収益化の重要性
  • 自動車事業者にとっての現在の戦略実行の重要性
  • 自動車データの収益化の動向
  • 地域的差異

第3章 商業・提携モデル

  • エコシステムマップ
  • 提携モデル

第4章 商業モデルのオプション

  • 無償組込みクライアント
  • 組込みクライアントのライセンス料
  • 無償サービス
  • サービス料:月・年間
  • 利用機能あたりの料金

第5章 障壁と課題

  • データの法的責任
  • ブランドイメージ
  • データプライバシー

第6章 ケーススタディのカテゴリー

  • 車内マーケティング&広告
  • CRM (Customer Relationship Management)
  • 運転者支援 / 交通管理
  • 車両の健全性/VRM (Vehicle Relationship Management)
  • 車両設計
  • アフターサービス/パーツビジネス
  • 自動車保険・UBI (Usage Based Insurance)
  • インフォテインメント

第7章 ケーススタディ

  • Aupeo
  • Hortonworks
  • INRIX
  • 日産自動車
  • Parkopedia
  • ParkTag
  • Tera Data
  • Vinli
  • WirelessCar
  • Xtime
    • ビジネスモデル
    • 教訓
    • 分析・コメント、など

第8章 注目の利用例

  • 送料管理のためのテレマティクス
  • 内部データの活用
  • 自動車の評価とプライシング
  • パーツ管理
  • データの集約と仲介

第9章 魅力的なB2Bの機会

第10章 他の産業から学ぶ教訓

  • Eコマース
  • モバイルソーシャルメディア

第11章 最終分析・コメント

第12章 調査手法

第13章 略語

第14章 文献

このページに掲載されている内容は最新版と異なる場合があります。詳細はお問い合わせください。

目次

Industry Overview

The volume of data being generated and collected from cars is growing exponentially. How to capitalize on this data is the next challenge the automotive industry has to face. This report looks into the opportunities to monetise data through an overview of use-cases and case studies. The report also maps the monetization eco-system, presenting partnership options of how data analytics companies and content companies can work with OEMs.

Key Takeaways

Key topics include:

  • Industry Case Studies: Gain key industry insight with 10 case studies exploring the different approaches to monetizing data and pitfalls to avoid
  • Business Models: What are the most compelling data use cases? A look at in-car marketing and advertising to auto-insurance to vehicle design
  • Partnership Options: Discover how companies are working together to derive increased revenue from car data
  • Revenue Potential: FInd out how industry players are leveraging and capitalizing on vehicle data for new services and creating new revenue streams

Your Key Questions Answered

Data Use Cases:

  • What are the most compelling data use cases?
  • What are the challenges and opportunities in each case?

Use cases analysed include:

  • In-car marketing and advertising
  • Customer Relationship Management (CRM)
  • Vehicle Health/Vehicle Relationship Management (VRM)
  • Vehicle Design
  • After Sales/Parts Business
  • Auto Insurance
  • Infotainment

Partnership Options:

  • How does the automotive industry respond to this huge amount of data?
  • What does the monetization eco-system look like? What types of companies are involved in the process, and what are their services?
  • What do they provide etc.?
  • Who can you work with and how? What are the compelling B2B opportunities? How can data analytics companies and content companies work with OEMs? Do OEMs outsource/go in house?
  • What are benefits and drawbacks of each option?

Contributors

  • Aupeo! Holger Weiss, CEO Aupeo!
  • Hortonworks Dan Daogaru, General Manager IoT, Hortonworks
  • ParkTAG Silvan Rath, CEO, ParkTAG
  • Inrix Mark Prendergast, Product Management, INRIX
  • Vinli Mark Haidar, Founder & CEO, Vin.li
  • Parkopedia Hans Puvogel, COO, Parkopedia
  • Xtime Chris Ice, VP Product Marketing and Jim Roche, Senior VP for Marketing and Managed Services, XTime
  • Nissan Toshiro Muramatsu, Director Vehicle Information Technogy Division, Nissan Motor
  • Volvo / Wireless Car - Greg Geiselhart, Director of Sales,Wireless Car and Robert Valton, Innovation Manager, Volvo
  • Teradata Starsensor Technology, Torbjorn Rosenquist, International Automotive Practice Lead, Teradata

Table of Contents

  • List of Figures
  • Key terms
  • Executive summary
  • Business models
  • Compelling B2B opportunities
  • Partnerships
  • Barriers and issues
  • Balancing benefits and risk
  • Protecting brand image
  • Lessons learned

1. Introduction

  • 1.1. Increased spending by automakers
  • 1.2. Ownership of aggregated data
  • 1.3. Data monetization is complex
  • 1.4. Capitalizing åon the opportunity

2. Automotive connected services and data business basics

  • 2.1. Automotive data value chain and flow
  • 2.2. Why efficient data management and data monetization are important
  • 2.3. Why it is crucial for automotive players to put a strategy in place now
  • 2.4. Trends in automotive data monetization
  • 2.5. Regional differences

3. Commercial and partnership models

  • 3.1. Ecosystem map
  • 3.2. Partnership models

4. Commercial model options

  • 4.1. Free embedded client
  • 4.2. License fee for an embedded client
  • 4.3. Free service
  • 4.4. Monthly or annual fee for service
  • 4.5. Fee per feature use (metered)
  • 4.6. Mixed

5. Barriers and issues

  • 5.1. Data liability
  • 5.2. Brand image
  • 5.3. Data privacy

6. Case study categories

  • 6.1. In-car marketing and advertising
  • 6.2. Customer Relationship Management (CRM)
  • 6.3. Driver assistance / traffic management
  • 6.4. Vehicle health/Vehicle Relationship Management (VRM)
  • 6.5. Vehicle design
  • 6.6. After-sales/parts business...
  • 6.7. Auto insurance / Usage Based Insurance (UBI)
  • 6.8. Infotainment

7. Case studies

  • 7.1. Aupeo: CRM disguised as infotainment
    • 7.1.1. Business model
    • 7.1.2. Partnerships
    • 7.1.3. Regional differences
    • 7.1.4. Barriers and issues...
    • 7.1.5. Analysis and commentary
  • 7.2. Hortonworks: Big data analytics across the automotive value chain
    • 7.2.1. Business model
    • 7.2.2. Analysis and commentary
  • 7.3. INRIX: Synthesizing big data to drive insights on traffic
    • 7.3.1. Business model
    • 7.3.2. Partnerships
    • 7.3.3. Analysis and commentary
  • 7.4. Nissan Motor Corporation: Real world data to accelerate market acceptance of EVs
    • 7.4.1. Business model
    • 7.4.2. Regional differences and partnering
    • 7.4.3. Analysis and commentary
  • 7.5. Parkopedia: Real-time predictive parking information
    • 7.5.1. Business model
    • 7.5.2. Analysis and conclusion
  • 7.6. ParkTag: “Everything is about conversion and the power of bridging activities.”
    • 7.6.1. Business model
    • 7.6.2. Lessons learned
    • 7.6.3. Analysis and commentary
  • 7.7. Teradata: Integrated data to increase value through analytics
    • 7.7.1. Business model
    • 7.7.2. Partnerships
    • 7.7.3. Lessons learned
    • 7.7.4. Analysis and commentary
  • 7.8. Vinli: App store for the car....
    • 7.8.1. Business model
    • 7.8.2. Partnerships
    • 7.8.3. Analysis and commentary
  • 7.9. WirelessCar: Everything from marketing to brand loyalty to internal value is obtained via data. .
    • 7.9.1. Business model
    • 7.9.2. Regional differences and partnering
    • 7.9.3. Barriers and issues...
    • 7.9.4. Lessons learned
    • 7.9.5. Analysis and commentary
  • 7.10. Xtime
    • 7.10.1. Business model
    • 7.10.2. Lessons learned
    • 7.10.3. Analysis and commentary

8. Highlighted use case examples

  • 8.1. Telematics for managing postage costs
    • 8.1.1. Millions wasted in postage
    • 8.1.2. Economic value from telematics data
  • 8.2. Internal data utilization
    • 8.2.1. Improving future designs
    • 8.2.2. Warranty costs
  • 8.3. Vehicle valuation and pricing
  • 8.4. Parts management
    • 8.4.1. Logistical challenges
    • 8.4.2. Cost savings with improved efficiencies
  • 8.5. Data aggregation and brokering
    • 8.5.1. Numerous monetization models

9. Compelling B2B opportunities

10. Lessons learned from other industries

  • 10.1. E-commerce
  • 10.2. Mobile social media

11. Final analysis and commentary

12. Methodology

13. Abbreviations

14. References

Back to Top