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市場調查報告書

零售AI (人工智能)的全球市場:各市場區隔分析,供應商定位,市場預測 (2019年∼2023年)

AI (Artificial Intelligence) in Retail: Segment Analysis, Vendor Positioning & Market Forecasts 2019-2023

出版商 Juniper Research 商品編碼 372628
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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零售AI (人工智能)的全球市場:各市場區隔分析,供應商定位,市場預測 (2019年∼2023年) AI (Artificial Intelligence) in Retail: Segment Analysis, Vendor Positioning & Market Forecasts 2019-2023
出版日期: 2019年04月10日內容資訊: 英文
簡介

本報告提供全球零售AI (人工智能) 市場相關調查分析,各部門趨勢 (促進因素,策略性機會,建議) 、各地區分析 (主要8個地區) 、主要企業,產業預測等相關的系統性資訊。

策略、競爭 (PDF)

第1章 零售AI:簡介

第2章 AI:零售的阻礙

  • 零售的顛覆性AI:影響評估
  • 零售AI各市場區隔分析
  • 零售AI預測
  • 零售AI:干擾者、挑戰者

第3章 零售AI:供應商分析

  • 供應商分析、排行榜的簡介
  • 零售AI的有勢力者
  • 業者簡介
    • Adobe
    • Amazon
    • Cortexica
    • Evolv
    • Google
    • IBM
    • Intel
    • Microsoft
    • Oracle
    • Salesforce
    • Relex
    • SAP
    • Slyce
    • ToolsGroup
    • ViSenze

資料、預測 (PDF、Excel)

第1章 零售AI的簡介

第2章 AI零售服務市場預測

  • 簡介
  • 調查手法、前提條件
  • 利用ML服務的零售業者
  • 供應鏈的ML需求預測
  • 客戶服務、感情分析的ML
  • 自動行銷解決方案的ML
  • 零售ML總支出

第3章 零售聊天機器人市場預測

  • 簡介
  • 調查手法、前提條件
  • 聊天機器人的預測

第4章 AI數位電子看板市場預測

  • 簡介
  • 調查手法、前提條件
  • 數位電子看板的預測

第5章 智慧退房市場預測

  • 簡介
  • 調查手法、前提條件
  • 智慧退房的預測

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目錄

Overview

Juniper's latest ‘AI in Retail’ research provides a detailed overview of how AI (Artificial Intelligence) and machine learning strategies are being harnessed by retailers to transform both back office operations and customer-facing efforts. The research analyses the regional outlook for AI in retail adoption, as well as offering analysis of key AI areas, such as demand forecasting and personalisation.

The report also examines the use of chatbots, AI-managed digital signage and smart checkout technologies in the retail environment; assessing their future viability. It also includes insightful player analysis alongside key recommendations for stakeholders in the industry to inform strategic planning.

The analysis covers key industry segments, including:

  • Demand Forecasting
  • Sentiment Analytics and Customer Service
  • Automated Marketing
  • Retail Chatbots

This research suite includes:

  • Deep Dive Strategy & Competition (PDF)
  • 5-Year Deep Dive Data & Forecasting (PDF & Excel)
  • Executive Summary & Core Findings (PDF)
  • 12 months' access to harvest online data platform

Key Features

  • Sector Dynamics: AI drivers, strategic opportunities and recommendations for:
    • Personalisation
    • Demand Forecasting
    • Customer Analytics & Marketing
    • Payment Provider Analytics
    • Retail Chatbots
  • Regional Analysis: Detailed analysis of Juniper's 8 key regions; assessing the current investment landscape, challenges to future investment and a future outlook.
  • Interviews: Leading AI in Retail vendors across the value chain interviewed, including:
    • Cortexica Vision Systems
    • Nosto
    • ViSenze
  • Juniper Leaderboard: Key player capability and capacity assessment for 15 emerging AI in Retail service providers.
  • AI in Retail Disruptors & Challengers Quadrant: Analyses 15 of the emerging and innovative technology companies with the potential to disrupt key retail markets.
  • Benchmark Industry Forecasts: Market segment forecasts for key AI in Retail verticals, including:
    • Demand Forecasting
    • Sentiment Analytics and Customer Service
    • Automated Marketing
    • Retail Chatbots
    • AI Digital Signage
    • Smart Checkouts

Key Questions

  • 1. At what pace are retailers expected to adopt machine learning services?
  • 2. What are the most viable use cases for AI deployment in the retail industry?
  • 3. Who are the key disruptors in this space, and what strategies are vendors employing?
  • 4. What are the key trends, drivers and challenges acting on the AI industry?
  • 5. How will the customer experience change with AI deployment in retail?

Companies Referenced

  • Interviewed: Cortexica Vision Systems, Nosto, ViSenze, ZestFinance.
  • Profiled: Adobe, Amazon, Cortexica Vision Systems, Evolv, Google, IBM, Intel, Microsoft, Oracle, Relex, Salesforce, SAP, Slyce, ToolsGroup, ViSenze.
  • Case Studied: Granify, JP Morgan Chase, Symphony RetailAI.
  • Included in Disruptors & Challengers Quadrant: AntVoice, Cognitive Operational Systems, Daisy Intelligence, Deepomatic, Emarsys, Focal Systems, Granify, Kore.ai, Nosto, Plexure, Satisfi Labs, Seez, Synerise, Syte.ai, Thread.
  • Mentioned: 3PM Solutions, 3Sverige, 44Pixel, A.S. Adventure, AAEON, AB InBev, Accenture, Affirm, AiSensum, Al Tayer, Aldo, Alessi, Amway, Analyteq, AO.com, Apple, Argility, Ashley Furniture, Aston Martin, Avenue Supermarts, Axis, Best Buy, Blispay, BlueStone, Booths, BQ, Bread, Brooks Brothers, California Design Den, Caratlane, Carrefour, Celebrity Cruises, Centrica, Chalhoub Group, Charlotte Tilbury, Charming Charlie, CI&T, Cisco, Clicksco, Cognira, Columbus Consulting, CommerceHub, Conversionista, ConversionXL, COOP, Coop Denmark, Cosabella, Costa, Craveable Brands, CSAV Norasia, DataSine, Dell, Deloitte, Demandtex, Direct Investment, Ditto Labs, Dixons Carphone, Eagle Retailing, eBags, eBay, Ellos Group, Energie Direct, Essent, Euroflorist, Express, Facebook, Farfetch, Fashion Island, Fennobiz, Fit Analytics, Flipkart, Fluid AI, FMCG Retail, Focal Systems, Forecast Solutions, Fullbeauty.com, Future Group, Galleria RTS, Gant, Gap, Glowforge, Goodrich, Goxip, GPA, Graymatter, GreenSky, Grokstyle, GS Shop, GSK, H&M, Hamleys, Hammerson, HipVan, Home Depot, Honeywell, Huawei, Ikea, IMS Evolve, Inbenta, Innogy, Interpark, Irvine Spectrum Centre, ITP Group, JCPenney, John Lewis, Kabbage, Kia, Kingston SCL, Klarna, Kolonial.no, L'Oréal, La Redoute, Landal Greenparks, Lenox, LG, Lululemon Athletica, Lush, Macy's, Maison du Monde, Mall of America, Malong Technologies, Manthan, Marks & Spencer, Mastercard, Maui Jim, MediaCorp, MNC Media, Mobiqa, Morrisons, Myntra, Naver, Neal Analytics, NeoMedia, Neudesic, Nike, Nixor, North Face, O2, Ocado, One Stop, Online Dialogue, Orange, OSP Retail, Pacific Internet, Paytm, Pitney Bowes, Plantasjen, Public, Publicis.Sapient, PWC, Pythian, Quann, Rackspace, Rakuten, Reebonz, Reliance Retail, Renner, River Island, Rossmann, RS, Samsung, Sensitel, Sentient Technologies, Sephora, Singtel, Solteq, Specsavers, Square, Strategix CFT, T. J. Maxx, Target, TelesensKSCL, Tesco, Ticketmaster, Tinyclues, T Mobile, Tommy Hilfiger, Travis Perkins, Trax, Tumi, Under Armour, UNIQLO, United Colours of Benetton, Urban Outfitters, Vente-Exclusive, Verizon, Very, Virgin, Visa, Vivo, Vue.ai, Waitrose, Walmart, Wellio, WHSmith, Wipro, Woolworths, WPP, Yosh.AI, Zabka, Zalando, Zalora.

Data & Interactive Forecast

Juniper's latest ‘AI in Retail’ forecast suite includes:

  • Regional splits for 8 key global regions, as well as country level data splits for:
    • Canada
    • China
    • Denmark
    • Germany
    • Japan
    • Norway
    • Portugal
    • Spain
    • Sweden
    • UK
    • US
  • AI retail services forecast, including users and revenues, across the following segments:
    • Demand Forecasting
    • Sentiment Analytics and Customer Service
    • Automated Marketing
  • Retail chatbot forecast, including the number of successful interactions and revenues from chatbot-based purchases.
  • AI digital signage forecasts, including the number of digital signs managed by AI and the service revenues generated.
  • Smart checkouts forecast, including the number of smart checkouts deployed and the resulting transaction volume and value.
  • Access to the full set of forecast data of 90 tables and over 11,880 datapoints.
  • Interactive Excel Scenario tool allowing users the ability to manipulate Juniper's data for 12 different metrics.

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

Deep Dive Strategy & Competition

1. AI in Retail: Introduction

  • 1.1 Introduction
    • Figure 1.1: AI Skills in Retail
    • Figure 1.2: Types of AI
  • 1.2 Investment Landscape
    • Table 1.3: Selected AI in Retail Investments, 2018-19
  • 1.3 Retail Industry/Start-up Activity by Region
    • 1.3.1 North America
      • i. Current Retail Market
        • Figure 1.4: Total US Retail Sales ($bn), 2010-2018
      • ii. Investment/Development Activity
    • 1.3.2 Latin America
      • i. Current Retail Market
      • ii. Juniper's View: Future Prospects
        • Figure 1.5: Annual GDP Growth (%) Selected Latin American Countries, 2011-2017
      • iii. Investment/Development Activity
    • 1.3.3 West Europe
      • i. Current Retail Market
        • Figure 1.6: UK Retail Sales ($bn), 2011-2018
      • ii. Investment/Development Activity
    • 1.3.4 Central & East Europe
      • i. Current Retail Market
        • Figure 1.7: Annual GDP Growth (%) Selected Central & East European Countries, 2011-2017
      • ii. Investment/Development Activity
      • iii. Juniper's View: Future Prospects
    • 1.3.5 Far East & China
      • i. Current Retail Market
        • Figure 1.8: Annual GDP Growth (%), Selected Countries, 2011-2017
      • ii. Investment/Development Activity
      • iii. Juniper's View: Future Prospects
    • 1.3.6 Indian Subcontinent
      • i. Current Retail Market
        • Figure 1.9: GDP per Capita ($), Selected Countries 2011-2017
      • ii. Investment/Development Activity
      • iii. Juniper's View: Future Prospects
    • 1.3.7 Rest of Asia Pacific
      • i. Current Retail Market
        • Figure 1.10: Total Retail Sales ($m), Singapore, 2010-2017
      • ii. Investment/Development Activity
      • iii. Juniper's View: Future Prospects
    • 1.3.8 Africa & Middle East
      • i. Current Retail Market
        • Figure 1.11: GDP per Capita ($), Selected Countries 2011-2017
      • ii. Investment/Development Activity
      • iii. Juniper's View: Future Prospects

2. AI: Disruption in Retail

  • 2.1 Disruptive AI in Retail - Impact Assessment ..
    • 2.1.1 Summary
      • Table 2.1: AI in Retail Impact Assessment
      • Table 2.2: AI in Retail Impact Assessment Heatmap Key
    • 2.1.2 AI in Retail Impact Assessment Methodology
      • Table 2.3: AI in Retail Impact Assessment Methodology
  • 2.2 AI in Retail Segment Analysis
    • 2.2.1 Personalisation
      • Case Study: Granify
      • i. Visual Search
        • Figure 2.4: ViSenze Visual Search
      • ii. Challenges to Approach
      • iii. Future Outlook
    • 2.2.2 Demand Forecasting
      • Figure 2.5: Elements of Demand Forecasting
      • i. Challenges to Approach
      • ii. Future Outlook
        • Case Study: Symphony RetailAI
    • 2.2.3 Customer Analytics & Marketing
      • i. Challenges to Approach
      • ii. Future Outlook
    • 2.2.4 Payment Provider Analytics
      • i. Challenges to Approach
      • ii. Future Outlook
    • 2.2.5 Chatbots
      • i. Challenges to Approach
      • ii. Future Outlook
    • 2.2.6 The POS Finance Opportunity
      • Case Study: My Chase Plan
      • i. Challenges to Approach
      • ii. Future Outlook
    • 2.2.7 Voice Assistants
      • i. Challenges to Approach
      • ii. Future Outlook
  • 2.3 AI Outlook in Retail
    • Figure 2.6: Juniper Phased Evolution: AI & Connected Retail
    • i. Future Developments
  • 2.4 AI in Retail: Disruptors & Challengers Quadrant
    • 2.4.1 Introduction
      • Figure 2.7: Juniper Disruptors & Challengers Quadrant - AI in Retail
    • 2.4.2 Landscape Analysis
      • i. Overview
      • ii. Disruptors
      • iii. Catalysts
      • iv. Embryonic Stakeholders

3. AI in Retail: Vendor Analysis

  • 3.1 Vendor Analysis & Leaderboard Introduction
    • 3.1.1 Stakeholder Assessment Criteria
      • Table 3.1: AI in Retail Player Capability Criteria
      • Figure 3.2: AI in Retail Stakeholder Leaderboard
      • Table 3.3 AI in Retail Leaderboard Scoring
    • 3.1.2 Vendor Groupings
      • i. Established Leaders
      • ii. Leading Challengers
      • iii. Disruptors & Emulators
    • 3.1.3 Limitations & Interpretation
  • 3.2 AI in Retail Movers & Shakers
  • 3.3 Vendor Profiles
    • 3.3.1 Adobe
      • i. Corporate
        • Table 3.4: Adobe Financial Snapshot ($bn) 2016-2018
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.2 Amazon
      • i. Corporate
        • Table 3.5: Amazon: Key Financial Data ($bn) 2016-2018
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.3 Cortexica
      • i.Corporate
        • Table 3.6: Cortexica Funding Rounds
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.4 Evolv
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.5 Google
      • i. Corporate
        • Table 3.7: Alphabet Financial Snapshot ($bn) 2016-2018
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.6 IBM
      • i. Corporate
        • Table 3.8: IBM Financial Snapshot ($m) 2016-2018
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.7 Intel
      • i. Corporate
        • Table 3.9: Intel Financial Snapshot ($bn) 2016-2018
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.8 Microsoft
      • i. Corporate
        • Table 3.10: Microsoft Financial Snapshot ($bn) 2016-2018
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.9 Oracle
      • i. Corporate
        • Table 3.11: Oracle Financial Snapshot ($m) 2016-2018
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.10 Salesforce
      • i. Corporate
        • Table 3.12: Salesforce.com Financial Snapshot ($bn) 2016-2019
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.11 Relex
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.12 SAP
      • i. Corporate
        • Table 3.13: SAP Financial Snapshot ($bn) 2016-2018
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.13 Slyce
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.14 ToolsGroup
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.3.15 ViSenze
      • i. Corporate
        • Table 3.14: ViSenze Funding Rounds
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities

Deep Dive Data & Forecasting

1. Introduction to AI in Retail

  • 1.1 Introduction
    • Figure 1.1: AI Skills in Retail

2. AI Retail Services Market Forecasts

  • 2.1 Introduction
  • 2.2 Methodology & Assumptions
    • Figure 2.1: AI Retail Services Forecast Methodology
  • 2.3 Retailers Using Machine Learning Services
    • Figure & Table 2.2: Total Connected Retailers Accessing Machine Learning Services (m), Split by 8 Key Regions, 2018-2023
  • 2.4 Machine Learning in Supply Chain Demand Forecasting
    • Figure & Table 2.3: Total Retailer Spend on Machine Learning for Demand Forecasting ($m), Split by 8 Key Regions 2018-2023
  • 2.5 Machine Learning in Customer Service & Sentiment Analytics
    • Figure & Table 2.4: Total Retailer Spend on Machine Learning Assisted Customer Service & Sentiment Analytics ($m), Split by 8 Key Regions 2018-2023
  • 2.6 Machine Learning in Automated Marketing Solutions
    • Figure & Table 2.5: Total Spend by Retailers Using AI-based Automated Marketing Services ($m), Split by 8 Key Regions, 2018-2023
  • 2.7 Total Retail Machine Learning Spend
    • Figure & Table 2.6: Total Retail Machine Learning Spend ($m), Split by 8 Key Regions 2018-2023

3. Retail Chatbots Market Forecasts

  • 3.1 Introduction
  • 3.2 Assumptions & Methodology
    • Figure 3.1: Methodology for Messaging Application Chatbots
    • Figure 3.2: Methodology for Discrete Application Chatbots
    • Figure 3.3: Methodology for Web-based Chatbots
  • 3.3 Chatbot Forecasts
    • 3.3.1 Total Number of Successful Retail Chatbot Interactions
      • Figure & Table 3.4: Total Number of Successful Retail Chatbot Interactions (m) Split by 8 Key Regions 2018-2023
    • 3.3.2 Total Revenues from Retail Chatbots
      • Figure & Table 3.5: Total Revenues from Retail Chatbots per Annum ($m) Split by 8 Key Regions 2018-2023

4. AI Digital Signage Market Forecasts

  • 4.1 Introduction
  • 4.2 Methodology & Assumptions
    • Figure 4.1: Digital Signage Forecast Methodology
  • 4.3 Digital Signage Forecasts
    • 4.3.1 Number of Installed Digital Signs
      • Figure & Table 4.2: Global Number of Installed Digital Signs, ESL (Electronic Shelf Labels) & Large Display (m) Split by 8 Key Regions 2018-2023
    • 4.3.2 Number of Connected Digital Signs Controlled by AI Systems
      • Figure & Table 4.3: Number of Connected Digital Signs Controlled by AI Systems (m) Split by 8 Key Regions 2018-2023

5. Smart Checkouts Market Forecasts

  • 5.1 Introduction
  • 5.2 Methodology & Assumptions
    • Figure 5.1: Smart Checkouts Forecast Methodology
  • 5.3 Smart Checkouts Forecasts
    • 5.3.1 Retail Outlets Adopting Smart Checkout Technologies
      • Figure & Table 5.2: Number of Retail Outlets Adopting Smart Checkout Technologies (,000s) Split by 8 Key Regions 2018-2023
    • 5.3.2 Annual Transaction Value Processed by Smart Checkout Technologies
      • Figure & Table 5.3: Annual Transaction Value Processed by Smart Checkout Technologies ($m) Split by 8 Key Regions 2018-2023
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