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モバイルの解析とビッグデータ 2013-2018年:モバイル事業者、ブランド、OTTの戦略

Mobile Analytics & Big Data

Strategies for MNOs, Brands and OTTs 2013-2018

 

出版社 出版年月価格 ページ数
Juniper Research
ジュニパーリサーチ社
2013年9月お問い合わせください 120

サマリー

この調査レポートは、解析プラットフォームのアプリケーション経由で収集されたビッグデータの収益化を目指すモバイルネットワーク事業者 (MNO)、OTT関連企業、そして主要メーカの戦略を詳細に調査しています。

主な調査内容

  • ビッグデータの課題
  • コスト削減の将来性予測
  • 解析戦略に関する解説

主な予測範囲

  • 解析がもたらすCAPEX削減
  • 顧客ライフサイクル管理の削減
  • 収益アップの可能性

This new report provides an in-depth assessment of MNO (Mobile Network Operator), OTT (Over The Top) player and leading brand strategies as they seek to monetise the growth in Big Data through the application of analytics platforms.
It provides an analysis of where mobile now sits within and across these disparate industries, highlighting and assessing the intra- and inter-segment trends and challenges that have emerged.

Overview

  • Big Data Challenges Assessed  
  • Cost Savings Opportunity Forecast
  • Analytics Strategies Identified

Key Features of the Report:

The use of Big Data and Analytics has the potential to transform organisations through a combination of new customer insights, product information and operational efficiency. This new report provides an in-depth assessment of MNO (Mobile Network Operator), OTT (Over The Top) player and leading brand strategies as they seek to monetise the growth in Big Data through the application of analytics platforms.
It provides an analysis of where mobile now sits within and across these disparate industries, highlighting and assessing the intra- and inter-segment trends and challenges that have emerged.

The Analytics Challenge

The report explores key issues such as the challenges involved in using analytics to deliver personalised data plans in a 4G environment, and highlights the potential problems involved in gathering and utilising customer information.

Monetising Consumer Behaviour Patterns

The report discusses how analytics is being used by the OTT provider and the digital content provider to analyse customer behaviour, provide greater customer loyalty and predict future buyer behaviour.  Key analytics platform providers are profiled, their relative capabilities assessed within the context of a comparative Vendor Matrix.

Expert Analysis and Strategic Recommendations

Learn from expert analysis and strategic recommendations supported by interviews with MNOs, analytics providers and regulators including Actix, Agilis International, Avvasi, Guavus, Telefonica Dynamic Insights and the UK Information Commissioner’s Office.

This ground breaking study also includes an innovative matrix which enables MNOs, OTT providers, brand and platform providers to prioritise their strategic decision-making in the Big Data space.

Market Forecasts

The report contains key forecasts for revenue opportunities and cost savings, split by 8 key regions, including:

  • Capex savings through analytics
  • Customer lifecycle management savings
  • Incremental revenue opportunities

Country-level data is included for customer lifecycle management savings for 15 national markets (Australia, Brazil, Canada, China, France, Germany, Italy, Japan, Malaysia, Mexico, Philippines, Singapore, Spain, UK and US) together with additional per sector data points in the Mobile Analytics & Big Data Interactive Forecast Excel.

Companies Referenced

Interviewed: Actix, Agilis International, Alcatel-Lucent, Amethon Solutions, Avvasi, Guavus, Lavastorm, MACH, Roamware, Syniverse, The Now Factory, Real Impact Analytics, Telefonica, Tellabs, UK Information Commissioner’s Office.

Profiled: Actix, Agilis International, Alcatel-Lucent, Alteryx, Amethon Solutions, Avvasi, Guavus, Intersec, Lavastorm, Syniverse, The Now Factory, Real Impact Analytics, Tekelec, Telefonica, Tellabs.

Case Studied: Amazon, Badoo, Bharti Airtel, C Spire Wireless, Disney Corporation, Dunnhumby, EasyJet, Emirates, Facebook, MTN, Netflix, Nokia, Reliance Communications, SingTel, Telefonica Argentina, Telefonica Dynamic Insights, Tellabs Insight Analytics Services, Turkcell, Twitter, Verizon Precision Market Insights, Vivo, Walmart, Weve, Wind Mobile.

Mentioned: Accenture, Actian, Acxiom, Amdocs, America Movil, Amobee, Apache, Apple, Asda, AT&T, Atlas, Belgacom, Bell Labs, BlackBerry, Bluefin Labs, BlueKai, Bristol Meyers Squibb, Carlyle Group, Cisco, Citus Data, Claro Argentina, Cloudera, Cricket Communications, Datalogix, Dell, Demos, Deutsche Telekom, EE (Everything Everywhere), Epsilon, Ericsson, Ernst & Young , Experian Marketing Services, Four Square, France Telecom, Gates Foundation, GfK, Global Company Communications, Globe, Google, GSMA, H3G, Harvard Business Review, Hitwise Mobile, HP, HTC, Huawei, IBM, Infochimps, JDSU, Juniper Networks, Kabel Deutschland, Kellog, KPMG, Kraft, Kruger, Lucky Sort, Martin Dawes Systems, Merrill Lynch, Microsoft, MTS, NASA, New MACH, Nigerian Communications Commission, O2 UK, Optus, Oracle, Orange, Pandora, Parse, Pfizer, Pipeline, PWC, Roamware, Slingbox, Sonera, Tata Consultancy Services, Telcel, Telia, Telkomsel, Telstra, Teoco, Teradata, Tesco, T-Mobile, Ubalo, Verizon, Vertex Analytics, Vimpelcom, VIPnet, Virgin Mobile, Vodacom, Vodafone, Wall Street & Technology, WEDO Technologies, World Bank, Yahoo, YouTube.

Extra Info

‡ 8 key regions includes:

North America, Latin America, Western Europe, Central & Eastern Europe, Far East & China, Indian Subcontinent, Rest of Asia Pacific and Africa & Middle East

Key Questions

  • What are the implications of Big Data for the mobile ecosystem?
  • What strategies should MNOs, OTTs, enterprises and analytics providers implement to maximise their opportunities from the Big Data explosion?
  • How can analytics enhance customer lifecycle management?
  • How big is the opportunity for cost savings from the implementation of analytics platforms?
  • Which MNOs and OTTs have successfully deployed analytics platforms?
  • What customer data does analytics capture, and how can it be monetised?


目次

Executive Summary

Figure ES3 & Table ES1: Combined Cost Savings & Incremental Revenues Derived from MNO Analytics Deployments ($m) Per Annum, Split by 8 Key Regions 2013-2018

1. Introduction To Big Data And Analytics

1.1 Setting the Scene for the Growth in Big Data and Analytics
Figure 1.1: Total Mobile Data Traffic Generated by Smartphones, Featurephones & Tablets (PB) pa Split by 8 Key Regions 2012?2017
1.2 What is Big Data?
Figure 1.2: The Four Vs of Big Data and Issues
1.2.1 The Definition of Big Data Used in This Report
1.3 Falling ARPU, Rising Costs: the Fundamental Challenge for MNOs
Figure 1.3: Baseline Analysis of Selected National ARPUs 2007-2012
Table 1.1: Selected MNO Churn Rates (per cent)
1.3.1 Data Growth is Outstripping Data Revenue Growth
Figure 1.4: Orange Increase in Year-on-Year Mobile Data Volumes (growth multiple)
1.3.2 Analytics in the LTE Age
1.4 The Mobile Analytics Platform
Figure 1.5: The Key Functions of a MNO Analytics Platform
Figure 1.6: The Four Layers of Analytics
1.4.1 What Does an Analytics Platform Consist of?
1.4.2 Analytics Platform Provision
Figure 1.7: Types of Analytics Platform Provider
1.4.3 Who is the Customer? Customer Analytics
Table 1.2: The Four Stages of Customer Analytics
1.4.4 Maintenance: Operational Analytics
1.4.5 The Provision of New Value Added Services
1.4.6 Benefits Summary
Figure 1.8: The Key Questions for a Data Analytics Platform

2. Big Data: Implications For The Mobile Ecosystem

2.1 Introduction
Table 2.1: Big Data and Analytics uses by the MNO
2.2 The Big Data Challenge
2.2.1 The Challenge for MNOs
i. Capturing Consumer Information
a. The Use of Structured (Internal MNO) Customer Information
b. The Use of Unstructured (External MNO) Customer Information
2.2.2 The Challenge Across the Value Chain
2.3 Capitalising on Big Data and Analytics: a New Skillset
2.3.1 The MNO Approach
2.3.2 The OTT Approach
2.4 Designing a Big Data Strategy
2.4.1 Big Data Strategies for MNOs
Table 2.2: Importance Grades for Big Data and Analytics Strategy Implementation
Table 2.3: MNO Strategies for the Deployment of Big Data and Analytics
2.4.2 Big Data Strategies for OTTs
Table 2.4: OTT Provider Strategies for the Deployment of Big Data and Analytics
2.4.3 Big Data Strategies for Big Brands
Table 2.5: Big Brands Enterprise Strategies for the Deployment of Big Data and Analytics
2.4.4 Big Data Strategies for Analytics Providers
Table 2.6: Analytics Provider Strategies to Enable the Deployment of Big Data and Analytics

3. MNOs And The Use Of Operational Analytics

3. MNOs And The Use Of Operational Analytics
3.1 Operational Analytics and the MNO
Figure 3.1: Traffic Streams on a Mobile Network
Figure 3.2: Operational Analytics and MNO Functions
3.1.1 Operational Analytics and QoE
i. Case Study: Wind Mobile –Using analytics to Monitor Video Quality
3.1.2 The Benefits of Operational Analytics
3.2 Operational Analytics Provision
3.2.1 Operational Analytics and Content Delivery
3.2.2 Operational Analytics and Billing
i. Case Study: Telefonica Argentina – Analytics Platform for New Orders
3.2.3 Operational Analytics and Network Management
i. Case Study: Tellabs Insight Analytics Services
ii. Case Study: Turkcell– Deploying Analytics to Boost Off-Peak Usage
3.2.4 Operational Analytics and Roaming
3.2.5 Operational Analytics and Carrier Management
3.2.6 Operational Analytics and Vendor Settlements
3.2.7 Operational Analytics and Fraud Management
3.3 Implementing the Operational Analytics Platform
3.3.1 Key Trends in Operational Analytics Implementation
3.3.2 Requirements for Operational Analytics Provision
3.4 Using Predictive Analytics to Improve Efficiency

4. MNOs And The Use Of Customer Analytics

4.1 Customer Analytics and the MNO
Figure 4.1: MNO Customer Analytics Functions
4.2 The Challenge of Customer Analytics
4.2.1 Regulatory Concerns
i. ‘The Trusted Keeper of Personal Information’
ii. The Privacy Hierarchy
Figure 4.2: The Privacy Hierarchy Used by the MNO
4.3 Customer Analytics Provision
4.3.1 Customer Analytics and Customer Acquisition Management
4.3.2 Customer Analytics and Customer Churn Management
i. Case Study: MTN– Improving Churn Rates Using Big Data and Analytics
4.3.3 Customer Analytics and Usage Risk Management
Table 4.1: Revenue Assurance Methodology Identified by the TeleManagement Forum
4.3.4 Customer Analytics and User Segmentation
i. Case Study: C Spire Wireless
4.3.5 Customer Analytics and Subscriber Management
i. Case Study: Vivo – Applying Analytics for SME Mobile Customers
4.4 Customer Analytics – New Opportunities
Table 4.2: Amdocs Roadmap for MNO Innovation
4.4.1 The Monetisation of Customer Information
i. Monetisation with Third Parties

5. Emerging Business Models For MNOs

5.1 Introduction
5.2 Challenges to Implementation
5.3 Partnerships for the MNO
5.3.1 Partner Options
i. Case Study: Nokia – Big Data and Analytics Implementation Growing Pains
5.3.2 Business Opportunities
i. Case Study: Verizon Precision Market Insight – Analytics for Third Party Research
ii. Case Study:  Telefonica Dynamic Insights – Monetising Subscriber Data
iii. Case Study: Reliance Communications India – Analytics for Usage and Retention
iv. Case Study: Bharti Airtel India – Using Analytics to Provide New Campaigns
v. Case Study: SingTel – Using Analytics to Provide ‘Everything as a Service’
5.3.3 Emerging Revenue Opportunities
i. Smart Cities and Analytics
Table 5.1: Survey of 100 Smart Cities (per cent)
ii. Mobile Advertising and Analytics
a. Case Study:  Weve –Developing analytics for mobile advertising
Figure 5.1: The Weve analytics platform
iii. New Use Segmentation
5.4 Conclusions: Quantifying Improvements in MNO efficiency

6. OTT Providers And Their Use Of Analytics

6.1 Introduction
6.2 Emerging Analytics Trends in the OTT Space
6.3 Monetisation of Analytics in the OTT Space
6.3.1 Case Study: Amazon
i. Delivering eCommerce Analytics to Third Parties
a. Prospects and Opportunities
6.3.2 Case Study: Facebook
i. Using Analytics to Monetise Users with Advertising
Figure 6.1 & Table 6.1: Active Facebook Users (m) PC only vs Mobile 2005?2013
ii. Facebook and Mobile Advertising
iii. Prospects and Opportunities
6.3.3 Case Study: Badoo
i. Online Dating with Personalised Email Using Analytics
ii. Prospects and Opportunities
6.3.4 Case Study: Netflix
i. Using Analytics to Provide Customer Micro-Segmentation
ii. Prospects and Opportunities
6.3.5 Case Study: Twitter
i. Moving into Real-Time Advertising to Earn Revenue
ii. Prospects and Opportunities
6.4 Conclusion

7. Big Brands Deploying Big Data And Analytics

7.1 Big Data and Analytics – Usage by Enterprise Big Brands
7.2  ‘Early Adopter’ Segments
7.2.1 Retail
i. Case Study: Dunnhumby – Customer Retail Analytics Third Party Specialist
Table 7.1: Dunnhumby Four Stage Loyalty Approach
7.2.2 Financial Services
7.2.3 Airline
i. Case Study: EasyJet – a Low Cost Airline Using Analytics for Personalised Email Campaigns
ii. Case Study: Emirates Airlines – the Issues with Combining Advertising and Big Data
iii. Summary – Airlines and Big Data and Analytics
7.2.4 Other Industries
i. Case Study: Disney Corporation – Making Use of Big Data and Analytics in Theme Parks
ii. Case Study: Walmart - Big Branded Enterprise Offers and the Mobile Channel
7.3 Conclusion

8. The Analytics Opportunity: Summary And Forecasts

8.1 Introduction
8.2 The Challenge of Analytics Deployment
8.3 The Key Role of Analytics
Figure 8.1: The Four Main Goals for Big Data and Analytics
8.4 The Analytics Revenue Opportunity
8.5 Forecasts
Table 8.1: The Three main MNO Categories Set to Benefit from Big Data and Analytics
8.5.1 Capex Savings Through Analytics
Figure 8.2: Methodology for the Potential MNO Capex Cost Reduction Forecast
Figure 8.3: Capex Optimisation Rationale (Percentage Savings Made)
Figure 8.4 & Table 8.2: The Cost Reduction Opportunity for Capex ($m) from Analytics Split by 8 Key Regions 2013-2018
8.5.2 Customer Lifecycle Management
Figure 8.5: The Methodology for MNO Lifecycle Management Potential Opportunity via Analytics – Focused on Churn
Figure 8.6 & Table 8.3: The Potential Revenue Opportunity ($m) for Churn Reduction from Analytics Split by 8 Key Regions 2013-2018
8.5.3 Incremental Revenue Opportunities
Figure 8.7: Methodology for the Increased Revenue Potential Opportunity via Analytics
Figure 8.8 & Table 8.4: The MNO Incremental Revenue Opportunity ($m) from Analytics Split by 8 Key Regions 2013-2018
8.6 Conclusion: The Analytics Advantage
8.6.1 How Will Big Data and Analytics Be Used by the MNO in Practice?
8.6.2 How Likely is the Take Up of Big Data and Analytics by the MNO?
8.6.3 The Changes Required to the MNO Business Model with Big Data and Analytics

9. Analytics Platform Vendor Profiles
 
9.1 Introduction
9.2 Analytics Platform Vendor Positioning
9.3 Vendor Assessment Methodology
Table 9.1: Analytics Platform Vendor Capability
9.4 Limitations and Interpretation
9.5 New Positioning Matrix Results
Figure 9.1: Analytic Platform Vendor Positioning Matrix
9.6 Vendor Groupings
9.6.1 Summary
i. On Track Vendors
ii. Vendors Exceeding Expectations
iii. Vendors with Further Potential
9.6.2 Strategy Conclusions – New Analytics Provider Activity
9.7 Vendor Profiles
9.7.1 Actix
9.7.2 Agilis International
9.7.3 Alcatel-Lucent
9.7.4 Alteryx Inc
9.7.5 Amethon Solutions
9.7.6 Avvasi
9.7.7 Guavus
9.7.8 Intersec
9.7.9 Lavastorm
9.7.10 Syniverse (formerly MACH)
9.7.11 The Now Factory

 

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プレスリリース

[日本語訳]

「ビッグデータ」は2018年までにネットワーク事業者に90億ドルのコスト削減と収益機会を創出するとジュニパーリサーチ社が報告

主要なOTTプロバイダの解析への投資が急騰すると予測

英国ハンプシャー、2013年9月5日
英国調査会社ジュニパーリサーチ社の調査レポート「モバイルの解析とビッグデータ 2013-2018年:モバイル事業者、ブランド、OTTの戦略 - Mobile Analytics & Big Data:Strategies for MNOs, Brands and OTTs 2013-2018」は、解析プラットフォームの採用が継続し、それによってMNO(携帯電話ネットワークオペレータ)にもたらされるコスト削減と収益増加の合計は、90億ドル以上になるだろうと報告している。最も期待されるコスト削減は、解約の減少と、より効果的な投資配分によるもので、MNOはサードパーティへの加入者データの使用許可によっても大きな収益を得ることができる。

この調査レポートは、モバイルバリューチェーンの企業にとって、解析プラットフォームへの参入は、顧客の行動情報を収集し、最もふさわしい顧客層に対して有効に投資するために、非常に重要であると述べている。

“ビッグデータの氾濫”を管理する

多くのネットワークオペレータが、“ビッグデータの氾濫”を収益化し、管理しようとして、サードパーティの解析プラットフォームプロバイダとの提携を模索していたが、究極の戦略が見えてきた。テレフォニカやベライゾンなどのオペレータは、加入者データを収集して企業に提供する社内の部署を創設しているし、英国ではEverything EverywhereやテレフォニカO2、ボーダフォンが、モバイル広告のジョイントベンチャーWeveを立ち上げた。

一方、この調査レポートは、OTTプロバイダの解析プラットフォームへの投資が急増していることに注目し、高度にターゲットを絞った広告を目的とした、フェイスブックのマイクロソフトからのAtlasプラットフォーム獲得を引用している。

関係企業には“強力なプライバシーポリシーが必要”

しかしこの調査レポートは、あらゆる関係者は、顧客とプライバシーに関する法令に留意すべきとも指摘している。「企業やそのパートナーが顧客データの利用に関して厳重なポリシーを持ち、加入者のプライバシーを損なうことなく、データを収集したり、分析したりしなければならない」と調査レポート著者のKeith Breed氏は語る。

[プレスリリース原文]

Press Release: Harvesting ‘Big Data’ Offers Network Operators a $9bn Cost Savings & Revenue Opportunity by 2018: Juniper Report

Harvesting 'Big Data' Offers Network Operators a $9bn Cost Savings & Revenue Opportunity by 2018: Juniper Report

Sharp rise forecast in analytics investment across leading OTT providers

Hampshire, UK – 5th September 2013 – The continued deployment of analytics platforms is expected to deliver combined savings and incremental revenues to MNOs (Mobile Network Operators) totaling more than $9 billion by 2018, a new report from Juniper Research has found. Greatest savings are expected to result from reduced customer churn and more efficient capex allocation, with MNOs also able to derive significant revenues through the licensing of opted-in subscriber data to third parties.

According to the report - Mobile Analytics & Big Data: Strategies for MNOs, Brands and OTTs 2013-2018 – the introduction of an analytics platform is of critical importance to players across the mobile value chain as a means of gaining insight into customer behaviour and matching investment with the most profitable customer segments.

Managing the 'Big Data' deluge

It found that while many network operators were seeking to partner with third party analytics platform providers in a bid to monetise and manage the 'Big Data' deluge, alternative strategies were emerging. Some operators – such as Telefonica and Verizon – have established in-house business units to offer aggregated subscriber data to enterprise customers, while in the UK, Everything Everywhere, Telefonica O2 and Vodafone have founded the advertising joint venture Weve.

Meanwhile, the report highlighted a sharp increase in analytics platform investment amongst OTT providers, citing Facebook’s purchase of the Atlas platform from Microsoft to enable the provision of highly targeted advertising as a key development in this regard.

Players 'Need Robust Privacy Policy'

However, the report cautioned that all users need to be aware of customer and regulatory privacy concerns. According to report author Keith Breed, 'It is imperative for players to ensure that they – and their partners - have a robust policy in place for customer data usage, so that data can be aggregated and analysed without compromising subscriber privacy.'

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