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市場調査レポート

データ収益化 - OTTを超える機会:金融、小売、通信、コネクテッドオブジェクト

Data Monetisation - Opportunities Beyond OTT: Finance, Retail, Telecom and Connected Objects

発行 IDATE DigiWorld 商品コード 322279
出版日 ページ情報 英文 62 Pages
納期: 即日から翌営業日
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データ収益化 - OTTを超える機会:金融、小売、通信、コネクテッドオブジェクト Data Monetisation - Opportunities Beyond OTT: Finance, Retail, Telecom and Connected Objects
出版日: 2014年12月31日 ページ情報: 英文 62 Pages
概要

当レポートでは、主要産業別のデータ収益化オプションについて調査分析し、ビッグデータ市場の概要、データ収益化の機会、産業別の現状(高額商品、抱き合わせ販売、直接広告/マーケティング、付加価値サービス、仲介、データ再販)、産業別の現実的な機会などについて、体系的な情報を提供しています。

第1章 エグゼクティブサマリー

第2章 調査手法・定義

第3章 産業別のデータ収益化オプション

  • ビッグデータの概要
    • 市場内容
    • 市場構造
    • 市場規模
  • 産業別の機会
    • 産業別のビッグデータの主要機会
    • データ収益化と産業
    • 産業間の違い
  • アグリゲーターとの戦い

第4章 データ収益化と金融

  • 収集データの種類
  • プライバシーポリシー
  • 主要機会

第5章 データ収益化と小売

  • 小売における収集データの種類
  • プライバシーポリシー
  • 主要機会

第6章 データ収益化と通信会社

  • 収集データの種類
  • プライバシーポリシー
  • 主要機会

第7章 データ収益化とスマート製品

  • イントロダクション
  • 収集データの種類
  • プライバシーポリシー
  • 機会

図表

目次
Product Code: M14155IN2

This report presents data monetisation options for selected verticals, beyond traditional development in pure online markets.

The first part of the report provides an overview of the big data market and its impacts in terms of data monetisation opportunities open to all verticals.

The second part presents current initiatives for data monetisation around upsell and cross-selling, direct advertising/marketing, value-added services (servicisation), intermediation and data resale for four key verticals: finance, retail, telecom and connected objects.

The report provides an assessment of the major real opportunities in the selected verticals and their articulation with aggregators and platforms coming from the online world.

Table of Contents

1. Executive Summary

2. Methodology & definitions

3. Data monetisation options for verticals

  • 3.1. Introduction to big data
    • 3.1.1. Market description
    • 3.1.2. Market structure
    • 3.1.3. Market size
  • 3.2. Opportunities for verticals
    • 3.2.1. Major opportunities of big data for verticals
    • 3.2.2. Data monetisation and verticals
    • 3.2.3. Differences between verticals
  • 3.3. The battle with aggregators

4. Data monetisation and finance

  • 4.1. Type of data gathered
  • 4.2. Privacy policies
  • 4.3. Main opportunities

5. Data monetisation and retail

  • 5.1. Type of data gathered in retail
  • 5.2. Privacy policies
  • 5.3. Main opportunities
    • 5.3.1. Geo-fencing
    • 5.3.2. Loyalty card programmes and customer insights
    • 5.3.3. In-store tracking
    • 5.3.4. Tracking buying emotions

6. Data monetisation and telcos

  • 6.1. Type of data gathered
  • 6.2. Privacy policies
  • 6.3. Main opportunities

7. Data monetisation and smart products

  • 7.1. Introduction
  • 7.2. Types of data collected
  • 7.3. Privacy policies
  • 7.4. Opportunities
    • 7.4.1. Development of new services associated with products (servicisation)
    • 7.4.2. Insights and Aggregated data sales

Tables

  • Table 1: Type of data used by vertical
  • Table 2: Main potential uses of big data by vertical players, by type of activity
  • Table 3: Key options for data monetisation
  • Table 4: Summary view of major opportunities for the four verticals
  • Table 5: Data characteristics per vertical
  • Table 6: Key options for data monetisation in finance
  • Table 7: Type of data gathered in the retail segment
  • Table 8: Retailer policies for data collection
  • Table 9: Key options for data monetisation
  • Table 10: Key options for data monetisation for telcos
  • Table 11: Price comparison between non-connected and connected smart home systems
  • Table 12: Key options for data monetisation

Figures

  • Figure 1: Respective positioning of verticals regarding data monetisation
  • Figure 2: Variety of data sources
  • Figure 3: Technologies used to derive value from big data
  • Figure 4: Big data value chain
  • Figure 5: Big data landscape
  • Figure 6: Main use of the RTD platform provided by Oracle to its customers
  • Figure 7: Worldwide big data revenue forecasts, 2012-2015
  • Figure 8: State of big data investments
  • Figure 9: Big data and analytics software market, in 2016
  • Figure 10: Online advertising revenues, worldwide and regional, 2010-2018
  • Figure 11: SundaySky overview of bill explanation in video, for AT&T
  • Figure 12: Adoption of big data per vertical
  • Figure 13: Respective positioning of verticals regarding data monetisation
  • Figure 14: Retention rate of Apple is extremely high
  • Figure 15: 71% of financial companies realise competitive edge through big data
  • Figure 16: The types of personal information collected by MasterCard
  • Figure 17: Public trust levels in public services, banks, e-commerce and social networks with personal data, 2009, 2011 and 2013
  • Figure 18: How the personal data collected is used by MasterCard
  • Figure 19: The opt-out option from data anonymisation and analysis in MasterCard's privacy policy
  • Figure 20: Intuit privacy policy explaining that information is shared but not identifiable
  • Figure 21: The higher-end tariffs include more features, some of which rely on big data analysis
  • Figure 22: Bank of America using Cardlytics platform to provide ads within online statements
  • Figure 23: Mint's all-in-one interface for users to follow their finances
  • Figure 24: Mint personalised offers on finding savings
  • Figure 25: The Open Bank Project
  • Figure 26: Plaid Connect API
  • Figure 27: Barclays leaflet to inform changes to allow Barclays to aggregate and share data
  • Figure 28: The Citi Wallet
  • Figure 29: Credit card comparison with cashback rewards
  • Figure 30: Data collected by retailers in-store
  • Figure 31: iBeacon trialled by Tesco
  • Figure 32: shopkick application
  • Figure 33: Evolution in data use by Tesco
  • Figure 34: Nordstrom dashboard
  • Figure 35: Analysing emotional state before buying energy drinks
  • Figure 36: Types of data gathered by telcos
  • Figure 37: "How we use your information" - privacy policy of O2
  • Figure 38: O2 'Bolt Ons' allow for additional sales on top of standard tariffs
  • Figure 39: Charge to Mobile API by BlueVia
  • Figure 40: Direct-to-bill on Facebook: some operators offer easy two-click process
  • Figure 41: The business cycle of Weve, integrating mobile wallet and financing
  • Figure 42: How PrecisionID works
  • Figure 43: i-concier service by NTT DOCOMO
  • Figure 44: Screenshot of a Smart Steps insight result
  • Figure 45: Different sensors on the human body
  • Figure 46: Type of data collected at Withings
  • Figure 47: Use of personal data at Withings
  • Figure 48: Multiple data measurement monitoring
  • Figure 49: Version comparison
  • Figure 50: Coaching service description
  • Figure 51: Partnership solutions for vertical companies
  • Figure 52: Home by SFR solution
  • Figure 53: Data resale business model description
  • Figure 54: Benefits & rewards description
  • Figure 55: Withings Pulse description

List of players reviewed

  • Amazon
  • Apple
  • Axa
  • Barclays
  • Cardlytics
  • Citibank
  • Discovery
  • emozia
  • Facebook
  • Google
  • Intersec
  • Intuit
  • JP Morgan Chase
  • Macy's
  • MasterCard
  • Mint
  • Nordstrom
  • NTT DOCOMO
  • Open Bank Project
  • Oracle
  • Orange
  • parks & honey
  • Plaid
  • RunKeeper
  • SFR
  • shopkick
  • SingTel
  • SundaySky
  • Telefónica/O2
  • Tesco
  • Verizon
  • Weve
  • Withings

Slideshow contents

Data monetisation options for verticals

  • Big data - a disruptive concept for data monetisation
  • Big data technologies and market structure
  • Big data market size

Opportunities for verticals

  • Opportunities for verticals
  • Differences between verticals
  • Retail
  • Finance
  • Telecom
  • Connected objects

Outlook

  • Major opportunities for the four verticals
  • Battle with aggregators
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