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市場調查報告書

數位語音助手:平台、收益、市場機會

Digital Voice Assistants: Platforms, Revenues & Opportunities 2017-2022

出版商 Juniper Research 商品編碼 369028
出版日期 內容資訊 英文
商品交期: 最快1-2個工作天內
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數位語音助手:平台、收益、市場機會 Digital Voice Assistants: Platforms, Revenues & Opportunities 2017-2022
出版日期: 2017年11月08日 內容資訊: 英文
簡介

本報告提供數位語音助手的市場調查,技術定義和概要,各種功能和消費者導向產品、企業導向產品的案例研究,市場成長的推動因素與課題,價值鏈,主要企業的能力評估、市場上地位分析,裝機量、各種收益的變化與預測,各產品區分、地區的明細等彙整資料。

第1章 語音助手:新的平台

  • 簡介
  • 定義
  • 調查範圍
  • 數位語音助手的要素
  • 指令的理解
    • 案例研究:Mozilla Common Voice
  • 用戶的認識&帳號控制
  • 智慧型設備選擇、網膜化
  • 應用處理
    • 案例研究:Alexa-Cortana Partnership
  • output relay
  • 利用案例
    • 消費者取向
      • 家庭娛樂
      • 家庭經營管理
      • 汽車
    • 企業用
      • 醫療保健
      • 生產率
      • 顧客服務
      • 案例研究:Pepper (SoftBank)
  • 要點
    • 案例研究:Robin AI Voice Assistant

第2章 語音助手市場:趨勢、經營模式

  • 市場區隔
    • OEM
    • 消費者導向應用程式
    • 企業用
  • 市場動態
    • 成長推動因素
    • 阻礙成長要素
    • 市場趨勢
  • 價值鏈
    • 經營模式
    • 主要的差距
  • 要點

第3章 競爭環境

  • 企業環境、分析
  • 企業分析:消費者取向
  • 企業分析:企業用
  • 相關利益者分析:能力評估、市場上地位

第4章 數位語音助手:市場預測

  • 簡介
  • 預測手法
  • 預測摘要
    • 數位語音助手的裝機量的預測
    • 數位助手的收益預測
      • 廣告支出
      • 購買收益
  • 裝機量的預測
    • 行動OEM為基礎的行動助手
    • PC OS為基礎的行動助手
    • 行動應用程式為基礎的行動助手
    • 聯網電視為基礎的行動助手
    • 汽車數位助手
    • 智慧家庭數位助手
    • 穿戴式為基礎的行動助手
  • 收益預測
    • 應用收益
      • 行動應用程式的下載收益
      • App內收費的收益
    • 廣告支出
      • 行動OEM為基礎的數位語音助手
      • 應用程式為基礎的數位語音助手
      • PC為基礎的數位語音助手

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目錄

Overview

The advancement of artificial intelligence capabilities means that Digital Voice Assistants are becoming an integral part of consumer device usage.

Our cutting edge research on Digital Voice Assistants provides a clear guide to these emerging technologies, use cases, trends, and business models. It provides a comprehensive analysis of this emerging market across a range of different verticals:

  • Mobile OEM-based Voice Assistants
  • Mobile App-based Voice Assistants
  • PC OS-based Voice Assistants
  • Automotive Voice Assistants
  • Smart Home Voice Audio Assistants
  • Smart TV-based Voice Assistants
  • Wearable Voice Assistants

This research includes:

  • Market Trends & Opportunities (PDF)
  • 5 Year Market Sizing & Forecast Spreadsheet (Excel).

Key Features

Market Landscape. In-depth assessment and insights into the wider voice assistant market, examining:

  • Current technical elements and uses of digital voice assistants
  • Future technology evolution and outlook
  • Juniper Competitive Web analysis for the following market segments:
    • OEM Voice Assistants
    • Consumer Voice Assistant Apps
    • Enterprise Voice Assistants
  • Strategic case studies
  • Business Models:Discussion of the effectiveness and potential for a range of business models for players in the Digital Voice Assistant value chain, including:
    • Analysis of where the gaps in the current ecosystem lie; and
    • How they can best be filled and monetised.
  • Benchmark industry forecasts: for size, growth and revenues from the various Voice Assistant sectors.
  • Juniper Leaderboards: 15 digital assistant players compared, scored and positioned on the Juniper Leaderboard matrix according to their Consumer and Enterprise voice assistant products.

Key Questions

  • 1. Where will digital voice assistants have their biggest success?
  • 2. What new technologies are voice assistants leveraging, and how?
  • 3. What factors will drive up adoption of Voice Assistants, and where will the early successes be?
  • 4. Where can these technologies be monetised to ensure their longevity?
  • 5. What are the new business models that will emerge from this new technology?

Companies Referenced

  • Profiled in Juniper Leaderboards: Alibaba, Amazon, Apple, Artificial Solutions, Baidu, Dialogflow, Google, IBM, LingLong, Microsoft, Nuance, Robin Labs, Samsung, Sherpa, SoundHound.
  • Case Studied: Amazon, Microsoft, Mozilla Foundation, Robin Labs, SoftBank Robotics.
  • Mentioned: Aldebaran Robotics, Carrefour, Domino's, Facebook, Harman/Kardon, Haven OnDemand, HTC, Huawei, Jabra, JD.com, Lenovo, LifeBEAM, Mozilla Foundation, Nescafé, NVIDIA, Oracle, Orbita, Papa John, Qihoo, Salesforce, Sense Labs, SNCF, Sonos, Tencent, Uber, WeChat, Whirlpool.

Data & Interactive Forecast

The new Digital Voice Assistant forecast suite includes:

  • Market data splits for 8 key regions, and 10 countries including:
    • Brazil
    • Canada
    • China
    • France
    • Germany
    • Japan
    • Mexico
    • South Korea
    • UK
    • US
  • Market data splits for 7 key verticals:
    • Mobile OEM-based Assistants
    • Mobile App-based Assistants
    • PC OS-based Assistants
    • Automotive Assistants
    • Smart Home Audio Assistants
    • Smart TV-based Assistants
    • Wearable Assistants
  • Access to the full set of forecast data of 64 tables and over 7,000 data points.
  • All verticals include:
    • Adoption rates
    • Assistant installed base
    • Numbers in use.
    • Advertising impressions, ad spend and app revenues are also provided where appropriate.

Juniper Research's highly granular interactive Excels enable clients to manipulate Juniper's forecast data and charts to test their own assumptions using the Interactive Scenario Tool; and compare select markets side by side in customised charts and tables. IFxls greatly increase clients' ability to both understand a particular market and to integrate their own views into the model.

Table of Contents

1. Voice Assistants: The New Platform

  • 1.1. Introduction
  • 1.2. Definitions
  • 1.3. Research Scope
  • 1.4. Elements of Digital Voice Assistants
    • Figure 1.1: Elements of Digital Voice Assistant Technology
    • Figure 1.2: Amazon Echo Devices
  • 1.4.1. Command Comprehension
    • Case Study: Mozilla Common Voice
  • 1.4.2. User Identification & Account Control
  • 1.4.3. Intelligent Device Selection & Meshing
  • 1.4.4. Application Processing
    • Figure 1.3: Available Alexa Skills & App Store Apps, Quarters After Launch
      • Case Study: Alexa-Cortana Partnership
  • 1.4.5. Output Relay
  • 1.5. Voice Assistant Use Cases
    • 1.5.1. Consumer Use Cases
      • i. Home Entertainment
      • ii. Home Management
        • Figure 1.4: Whirlpool Smart Washing Machine & Control App
      • iii. Automotive
    • 1.5.2. Enterprise Use Cases
      • i. Healthcare
      • ii. Productivity
      • iii. Customer Service
        • Case Study: Pepper by SoftBank
          • Figure 1.5: Pepper Robot Deployed in Carrefour Supermarket
  • 1.6. Key Takeaways
    • Case Study: Robin AI Voice Assistant

2. Voice Assistant Market: Trends & Business Models

  • 2.1. Voice Assistant Sector Analysis
    • 2.1.1. OEM Voice Assistants
      • Figure 2.1: Juniper Competitive Web for OEM Voice Assistants
    • 2.1.2. Consumer Voice Assistant Apps
      • Figure 2.2: Juniper Competitive Web for Consumer Voice Assistant Apps
    • 2.1.3. Enterprise Voice Assistants
      • Figure 2.3: Juniper Competitive Web for Pureplay Enterprise Voice Assistant Companies
      • Figure 2.4: Juniper Competitive Web for Multi-Business Enterprise Voice Assistant Companies
  • 2.2. Voice Assistant Market Dynamics
    • 2.2.1. Voice Assistant Market Drivers
      • Figure 2.5: Overview of Chatbots, Developer Platforms & Associated Capabilities
    • 2.2.2. Voice Assistant Market Constraints
      • Figure 2.6: Consequences of Increased Flexibility in Spoken Language Dialogue Systems
      • Figure 2.7: Google Answer Box
    • 2.2.3. Voice Assistant Market Trends
  • 2.3. Voice Assistant Value Chain
    • Figure 2.8: Digital Voice Assistant Value Chain
    • 2.3.1. Voice Assistant Business Models
      • i. Platform Companies & Software Integrators
        • Table 2.9: Selection of API Providers & Pricing Schemes
        • Figure 2.10: Orbita Voice Rule-building Tool
      • ii. App Developers
        • Figure 2.11: Google Play Voice Assistant Apps by Monetisation Strategy
      • iii. Device Vendors
      • iv. Retailers & Stores
    • 2.3.2. Key Gaps
      • i. Voice Assistant Integration
      • ii. Voice Assistant Setup Processes
  • 2.4. Key Takeaways

3. Voice Assistant Competitive Landscape

  • 3.1. Player Landscape & Analysis
    • Table 3.1: Digital Voice Assitant Juniper Leaderboard Stakeholder Selection & Categorisation
  • 3.1.1. Player Analysis Criteria: Consumer Digital Voice Assistants
    • Table 3.2: Consumer Voice Assistant Vendor Capability Assessment Factors
  • 3.1.2. Player Analysis Criteria: Enterprise Digital Voice Assistants
    • Table 3.3: Enterprise Voice Assistant Vendor Capability Assessment Factors
  • 3.2. Stakeholder Analysis: Capability Assessment & Market Positioning
    • 3.2.1. Juniper Leaderboard for Consumer Digital Voice Assistants
      • Figure 3.4: Consumer Digital Voice Assistants Leaderboard
      • Table 3.5: Consumer Voice Assistants Leaderboard Scoring
    • 3.2.2. Player Groupings & Conclusion
      • i. Summary
      • ii. Established Leaders
      • iii. Leading Challengers
      • iv. Disruptors & Emulators
    • 3.2.3. Juniper Leaderboard for Enterprise Digital Voice Assistants
      • Figure 3.6: Enterprise Digital Voice Assistants Leaderboard
      • Table 3.7: Enterprise Digital Voice Assistant Scoring
    • 3.2.4. Player Groupings & Conclusion
      • i. Summary
      • ii. Established Leaders
      • iii. Leading Challengers
      • iv. Disruptors & Emulators
    • 3.2.5. Limitations & Interpretation

4. Digital Voice Assistant Forecasts

  • 4.1. Introduction
  • 4.2. Forecast Methodology
    • 4.2.1. Forecast Steps
      • Figure 4.1: Smart Mobile Device OEM-Based Voice Assistant Forecast Methodology
      • Figure 4.2: Smart Mobile Device App-Based Voice Assistant Forecast
  • 3.2. Stakeholder Analysis: Capability Assessment & Market Methodology
    • Figure 4.3: PC OS-based Voice Assistant Forecast Methodology
    • Figure 4.4: Connected TV-based Voice Assistant Forecast Methodology
    • Figure & Table 4.15: Global Automotive Digital Assistants in Use Per Annum (m)
    • Figure 4.5: Automotive Digital Assistants Forecast Methodology
    • Figure 4.6: Smart Home Digital Assistants Forecast Methodology
    • Figure 4.7: Wearables Digital Assistants Forecast Methodology
  • 4.3. Summary Forecasts
    • 4.3.1. Digital Voice Assistant Installed Base Forecasts
      • Figure & Table 4.8: Digital Voice Assistant Installed Base per annum (m), Split by Annum (m) Split by Platform 2017-2022
    • 4.3.2. Digital Assistant Revenue Forecasts
      • i. Advertising Spend
        • Figure & Table 4.9: Global Digital Assistant Advertising Spend per annum (m) Split by Category, 2017-2022
      • ii. Purchase Revenues
        • Table 4.10: Global Digital Assistant Purchase Revenues Per Annum ($m) Split by Category, 2017-
  • 4.4. Installed Base Forecasts
    • 4.4.1. Mobile OEM-based Digital Assistants
      • Figure & Table 4.11: Global Mobile OEM-based Digital Assistants in Use Per
    • 4.4.2. PC OS-based Digital Assistants
      • Figure & Table 4.12: Global PC OS-based Digital Assistants in Use Per Annum (m) Split by 8 Key Regions, 2017-
    • 4.4.3. Mobile App-based Digital Assistants
      • Figure & Table 4.13: Global Mobile App-based Digital Assistants in Use Per Annum (m) Split by 8 Key Regions, 2017-
    • 4.4.4. Connected TV-based Digital Assistants
      • Figure & Table 4.14: Global Connected TV-based Digital Assistants in Use Per Annum (m) Split by 8 Key Regions, 2017-2022
    • 4.4.5. Automotive Digital Assistants
      • Figure & Table 4.15: Global Automotive Digital Assistants in Use Per Annum (m) Split by 8 Key Regions, 2017-
    • 4.4.6. Smart Home Digital Assistants
      • Figure & Table 4.16: Global Smart Home Digital Assistants in Use Per Annum (m) Split by 8 Key Regions, 2017-2022
    • 4.4.7. Wearable-based Digital Assistants
      • Figure & Table 4.17: Global Integrated Wearable Digital Assistants in Use Per
  • 4.5. Revenue Forecasts
    • 4.5.1. App Revenue Forecasts
      • i. Mobile App Download Revenue
        • Figure & Table 4.18: Global Mobile App Download Revenue Per Annum ($m) Split by 8 Key Regions, 2017-2022
      • ii. In-app Purchase Revenue
        • Figure & Table 4.19: Global Voice Assistant In-App Purchase Revenue Per Annum ($m) Split by 8 Key Regions
    • 4.5.2. Advertising Spend
      • i. Mobile OEM-based Digital Voice Assistant Advertising Spend
        • Figure & Table 4.20: Global Mobile OEM-based Voice Assistant Advertising Spend Per Annum ($m) Split by 8 Key Regions, 2017-2022
      • ii. App-based Digital Voice Assistant Advertising Spend
        • Figure & Table 4.21: Global Mobile App-based Voice Assistant Advertising Spend Per Annum ($m) Split by 8 Key Regions, 2017-2022
      • iii. PC OS-based Digital Voice Assistant Advertising Spend
        • Figure & Table 4.22: Global PC OS-based Voice Assistant Advertising Spend Per Annum ($m) Split by 8 Key Regions, 2017-2022
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