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市場調查報告書

巨量資料、機器學習主導的行銷

Big Data and Machine Learning Driven Marketing

出版商 Mobile Market Development Ltd 商品編碼 301227
出版日期 內容資訊 英文 28 Pages
商品交期: 最快1-2個工作天內
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巨量資料、機器學習主導的行銷 Big Data and Machine Learning Driven Marketing
出版日期: 2014年05月07日 內容資訊: 英文 28 Pages
簡介

本報告提供巨量資料服務的行動網路業者的可能性相關調查、內部能適用的技術、提供給客戶的服務的可能性、能取得優點的特徵、對客戶服務要求及商業性永續的經營模式而言的首選價值鏈等相關分析,為您概述為以下內容。

第1章 概要

第2章 簡介

第3章 資訊:業務的新潤滑油

  • 巨量資料的機會
  • 個人資料和客戶的信賴
  • 原始數據來源

第4章 巨量資料價值的開放

  • 巨量資料、MNO的目的
  • MNO的巨量資料的適用
  • 巨量資料分析工具
  • 外部命題的巨量資料

第5章 市場潛在性

  • 機會的規模
  • 情報、分析、CRM趨勢

第6章 MNO巨量資料的市場部門

  • 商務客戶需求
  • 部門的利用案例
  • 客戶隱私、進入權限

第7章 巨量資料提案的包裝

  • MNO的強大UPS
  • 應該怎麼包裝服務?
  • 提供部門特有的服務

第8章 價值鏈

  • 價值鏈的MNO服務
  • 聯盟

第9章 商務必要條件

  • 技術功能
  • 商業功能

第10章 建議

附錄:問題的回饋

目錄

MNOs continue to seek new revenue streams to address increased pressure on margins through competition and regulation. Where such a revenue stream leverages unique knowledge and capabilities within the MNO, it represents an extremely attractive opportunity that should be explored and pursued as a high priority.

Big Data is just such a high priority opportunity, potentially monetising information and capabilities already available in-house. Further, the new revenue stream that could be generated is less susceptible to competition from other players (such as OTT service or content providers). This report examines the potential for Mobile Network Operators to profit from Big Data services, looking at the manner in which such technology can be applied internally, and the potential services that could be offered to customers. It advances a view about the nature of the benefits to be obtained, the customer propositions required and the likely value chain in a commercially sustainable business model.

All MNOs should at least consider addressing this market opportunity. This report provides the information necessary to commence that consideration, and concludes with a series of recommendations to help MNOs maximise their revenues from this area.

Table of Contents

1 Overview

2 Introduction

  • 2.1 Background to the Report
  • 2.2 Report Content
  • 2.3 Currency and Conversions
  • 2.4 Further Questions and Feedback

3 Information - The New Oil of Business

  • 3.1 The Opportunity for Big Data
  • 3.2 Personal Data and Customer Trust
  • 3.3 Types of Data
    • 3.3.1 Socio-Demographic
    • 3.3.2 Behavioural
    • 3.3.3 Psychographic
    • 3.3.4 Social Graph
    • 3.3.5 Individual or Aggregated?
  • 3.4 Raw Data Sources
    • 3.4.1 Call Detail Records
    • 3.4.2 Network
    • 3.4.3 On-Device Software
    • 3.4.4 Customer Relationship Management System
    • 3.4.5 OSS/BSS

4 Unlocking the Value in Big Data

  • 4.1 Big Data and MNO Objectives
  • 4.2 Applying Big Data within the MNO
  • 4.3 Big Data Analytics Tools
  • 4.4 Big Data as an External Proposition

5 Market Potential

  • 5.1 The Scale of the Opportunity
  • 5.2 Trends in Business Intelligence, Analytics and CRM

6 Market Sectors for MNO Big Data

  • 6.1 Business Customer Needs
  • 6.2 Sector Use Cases
    • 6.2.1 Mobile Services
    • 6.2.2 Financial Services
    • 6.2.3 Retail
    • 6.2.4 Transport
    • 6.2.5 Healthcare
    • 6.2.6 Enterprise
    • 6.2.7 Media
    • 6.2.8 Marketing and Advertising
  • 6.3 Customer Privacy and Permissions
    • 6.3.1 Legal Conditions for Data Use
    • 6.3.2 Technical Solutions to Obfuscate Individual Data

7 Packaging a Big Data Proposition

  • 7.1 Strong USPs for the MNO
  • 7.2 How Should Services be Packaged?
    • 7.2.1 Basic Reporting
    • 7.2.2 Tailored to Customer Requirements
    • 7.2.3 Advanced and Integrated Solutions
    • 7.2.4 Service Delivery Options
  • 7.3 Offering Sector Specific Propositions

8 The Value Chain

  • 8.1 MNO Position in the Value Chain
  • 8.2 Partnering

9 Business Requirements

  • 9.1 Technical Capabilities
    • 9.1.1 Privacy and Permissions Management
    • 9.1.2 Data Sources
    • 9.1.3 Real-time Event Processing
    • 9.1.4 Data Storage, Analytics and Machine Learning
    • 9.1.5 Data Warehousing
    • 9.1.6 Supporting Capabilities
  • 9.2 Commercial Capabilities
    • 9.2.1 Sales and Marketing
    • 9.2.2 Product Management

10 Recommendations

Appendix - Feedback Questions

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