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디지털 소매 기술

Digital Retail Technologies

리서치사 Juniper Research Ltd
발행일 2020년 09월 상품 코드 959508
페이지 정보 영문
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£ 2,990 ₩ 4,584,000 Web Access - Full Research Suite (Enterprise Wide License)


디지털 소매 기술 Digital Retail Technologies
발행일 : 2020년 09월 페이지 정보 : 영문

세계의 소매 업계에서 디지털 기술의 보급·활용 상황과 이에 따른 소매업의 구조 변화 동향에 대해 분석했으며, 주요 기술(소매업용 AI, 점포내용 IT 기술 등)의 개요·기능 및 컴포넌트, 각 기술의 보급률·시장 규모의 동향 전망(향후 5년간), 지역별·주요 국가의 상세 동향(총 8 지역·19개국), 대형 소매업체의 성공 사례, 주요 벤더의 개요와 시장 포지셔닝 등의 정보를 정리하여 전해드립니다.

목차

제1장 디지털 소매 기술 : 요점과 전략 제안

  • 주요 포인트
  • 전략적 권장사항

제2장 소매 기술 시장

  • 서론
  • 향후 점포내 소매 기술
  • 점포내 소매의 현황
    • 범용 기술
      • 스마트 체크아웃
      • 비콘
      • RFID
      • 로봇
      • 스마트 미러
  • 소매업체 기술 혁신 지수
    • 소매업체 포지셔닝 지수 : 해설
      • Walmart
      • Carrefour
      • Amazon
      • Costco
      • JD.com
      • Target Corporation
      • Waitrose
      • Aldi Group
      • Lidl
      • Tesco
      • Best Buy
      • IKEA
      • Home Depot
      • Aeon
      • Auchan

제3장 소매업의 인공지능(AI) : 시장 혼란

  • 서론
    • 정의 : 소매업계에서 AI란 무엇인가?
  • 사용되고 있는 각종 기술
    • 컴퓨터 비전(CV)
      • 확장/가상현실(AR & VR)
    • 로봇
    • 자연언어처리(NLP)
      • 점내 로봇
      • 점내 가상비서
      • 챗봇
    • 센서
  • 소매업에서 AI의 상황
    • 퍼스널라이제이션(개별화)과 마케팅
      • 퍼스널라이즈드 웹사이트 컨텐츠
      • 퍼스널라이즈드 제품 추천 기능
      • 비주얼 검색
      • 증강현실(AR)
    • 고객 서비스
    • 수요 예측
  • 소매업체 포지셔닝 지수에서의 AI
    • '소매업체 포지셔닝 지수에서의 AI'에 대한 견해
      • Adobe
      • Amazon
      • Cortexica Vision Systems
      • Evolv
      • Google
      • IBM
      • Intel
      • Microsoft
      • Oracle
      • Relex
      • Salesforce
      • SAP
      • Slyce
      • ToolsGroup
      • ViSenze

제4장 미래의 소매 기술 : 시장 예측

  • 서론
    • 분석 방법과 전제조건
  • 예측 : 소매업용 AI
    • 기계학습 서비스를 사용하는 소매업체
    • 공급망의 수요 예측에서 기계학습
    • 고객 서비스와 감정 분석에서 기계학습
    • 자동 마케팅 솔루션에서 기계학습
    • 소매업용 기계학습 : 총지출액
  • 점포내 소매 기술
    • 블루투스(Bluetooth) 비콘 설치 기반
    • RFID 설치 기반
    • 소매 로봇 설치 기반
    • 디지털 사이니지 설치 기반
    • 스마트 체크아웃 설치 기반
    • 회계용 앱 결제 금액
KSA 20.09.16

Juniper Research's new ‘Digital Retail Technologies ’ research report provides a detailed examination on how the retail market is being disrupted by the introduction of new digital strategies. The report focuses on the technologies being used to disrupt the established business models in brick-and-mortar retail, including the use of smart checkouts, RFID, beacons and others. The report also includes an extensive analysis of how AI is being leveraged in the retail market, to enable improved customer experiences and greater retailer efficiency.

The research report also positions AI vendors via a Juniper Research Positioning Index; providing a key resource when considering the AI in retail market. This is complemented by a Juniper Research Positioning Index for retailers; showing the extent to which the world's largest retailers are embracing new technology. This is accompanied by an extensive forecast suite, which analyses in depth the adoption of technologies across a high number of countries and segments.

This research suite comprises:

  • Strategy & Forecasts (PDF)
  • 5-Year Deep Dive Data & Forecasting (PDF/IFxl)
  • 12 months' access to harvest online data platform

Key Features

  • Future Retail Technologies Market Dynamics: Detailed analysis of the current state of technological adoption in the retail market and future outlook; examining technologies including:
    • Beacons
    • RFID
    • Robotics
    • Smart Checkouts
    • Digital Signage
  • AI Use in Retail Analysis: Extensive analysis of the increasing adoption and prospects for the use of AI in the retail market, including three main areas:
    • Demand Forecasting
    • Customer Service
    • Personalisation & Marketing
  • Juniper Research AI in Retail Vendor Positioning Index: Key player capability and capacity assessment for 15 AI in retail vendors:
    • Adobe
    • Amazon
    • Cortexica Vision Systems
    • Evolv
    • Google
    • IBM
    • Intel
    • Microsoft
    • Oracle
    • Relex
    • Salesforce
    • SAP
    • Slyce
    • ToolsGroup
    • ViSenze
  • Juniper Research Retailer Technological Innovation Index: 15 leading retailers positioned on their use of technology.
    • Aeon
    • Aldi
    • Amazon
    • Auchan
    • Best Buy
    • Carrefour
    • Costco
    • Home Depot
    • IKEA
    • JD.com
    • Lidl
    • Target
    • Tesco
    • Waitrose
    • Walmart
  • Benchmark Industry Forecasts: Forecasts for adoption and revenue provided across the following segments:
    • Beacons
    • RFID
    • Robotics
    • Smart Checkouts
    • Digital Signage
    • AI Demand Forecasting
    • AI Marketing
    • AI Customer Service

Key Questions

  • 1. What are the cutting-edge retail technologies of today, and how should they be used?
  • 2. Who are the leading retailers when it comes to in-store technology?
  • 3. At what pace are retailers expected to adopt machine learning services?
  • 4. What are the most viable use cases for AI deployment in the retail industry?
  • 5. Who are the key disruptors in this space, and what strategies are vendors employing?

Companies Referenced

  • Included in Juniper Research Retailer Technological Innovation Index: Aeon, Aldi, Amazon, Auchan, Best Buy, Carrefour, Costco, Home Depot, IKEA, JD.com, Lidl, Target, Tesco, Waitrose, Walmart.
  • Included in Juniper Research AI in Retail Vendor Positioning Index: Adobe, Amazon, Cortexica Vision Systems, Evolv, Google, IBM, Intel, Microsoft, Oracle, Relex, Salesforce, SAP, Slyce, ToolsGroup, ViSenze.
  • Mentioned: Alibaba, Alphabet, Amazon Echo, Amazon Web Services (AWS), Amplifon, Apple, Aston Martin, Azure, Bed Bath & Beyond, Body Labs, Bossa Nova Robotics, Cash Converters, Computing-Tabulating-Recording Company, CPG, CRX (Collaborative Retail Exchange), eBay, Fabletics, Gap, Headspace, Imperial College London, Information Resources, Inc. (IRI), Intelligence Retail, Jet, Kroger, Lennox, M&S, Macy's, Natuzzi Italia, O2, On The Spot, One Door, Pentium, Pottery Barn, Pricer, Publix, Sainsbury's, SilverCloud, SoftBank, Symphony Retail AI, Telefónica, Tencent, Tommy Hilfiger.

Data & Interactive Forecast

Juniper Research's ‘Digital Retail Technologies ’ forecast suite includes:

  • Forecast splits for 8 key regions, as well as 19 country-level data splits for:
    • Australia
    • Brazil
    • Canada
    • China
    • Denmark
    • France
    • Germany
    • India
    • Japan
    • Mexico
    • Netherlands
    • Norway
    • Portugal
    • Saudi Arabia
    • South Korea
    • Spain
    • Sweden
    • UK
    • US
  • Segment forecasts for, including adoption and revenue:
    • Beacons
    • RFID
    • Robotics
    • Smart Checkouts
    • Digital Signage
    • AI Demand Forecasting
    • AI Marketing
    • AI Customer Service
  • Interactive Scenario Tool allowing users to manipulate Juniper Research's data for 10 different metrics.
  • Access to the full set of forecast data of 74 tables and more than 15,000 datapoints.

Juniper Research's highly granular IFxls (interactive Excels) enable clients to manipulate our forecast data and charts to test their own assumptions, by 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. Digital Retail Technologies: Key Takeaways & Strategic Recommendations

  • 1.1. Key Takeaways
  • 1.2. Strategic Recommendations

2. The Retail Technology Marketplace

  • 2.1. Introduction
  • 2.2. Future In-tore Retail Technologies
  • 2.3. Current Status of In-store Retail
    • 2.3.1. Common Technologies
      • Figure 1.1: In-store Retail Technologies
      • i. Smart Checkouts
      • ii. Beacons
      • iii. RFID
      • iv. Robotics
        • Figure 1.2: Pepper from SoftBank Robotics
      • v. Smart Mirrors
  • 2.4. Retailer Technological Innovation Index
    • Table 2.4: Retailer Technology Innovation Index Scoring Criteria Definitions
    • Table 2.5: Retailer Technology Innovation Index Scoring
    • Figure & Table 2.6: Retailer Technology Innovation Index Model Phased Evolution
    • Figure 1.6: Juniper Research Retailer Technology Innovation Index
    • 2.4.1. Retail Positioning Index Commentary
      • i. Walmart
      • ii. Carrefour
      • iii. Amazon
      • iv. Costco
      • v. JD.com
      • vi. Target Corporation
      • vii. Waitrose
      • viii. Aldi Group
      • ix. Lidl
      • x. Tesco
      • xi. Best Buy
      • xii.IKEA
      • xiii. Home Depot
      • xiv. Aeon
      • xv. Auchan

3. AI in Retail: Market Disruption

  • 3.1. Introduction
    • 3.1.1. Definition: What Is AI in Retail?
      • Figure 2.1: AI Skills in Retail
      • Figure 2.2: Types of AI
  • 3.2. Different Technologies Used
    • 3.2.1. CV
      • i. AR & VR
    • 3.2.2. Robotics
    • 3.2.3. NLP
      • i. In-store Robots
      • ii. In-store Virtual Assistants
      • iii. Chatbots
    • 3.2.4. Sensors
  • 3.3. Status of AI in Retail
    • 3.3.1. Personalisation and Marketing
      • i. Personalised Website Content
      • ii. Personalised Product Recommendations
      • iii. Visual Search
      • iv. AR
    • 3.3.2. Customer Service
      • Figure 2.3: Number of Retail Messenger App Chatbots Accessed per Annum (m), Split by 8 Key Regions, 2020-2025
    • 3.3.3. Demand Forecasting
  • 3.4. AI in Retail Vendor Positioning Index
    • Table 2.4: AI in Retail Vendor Positioning Index Score Criteria Definitions
    • Table 2.5: AI in Retail Vendor Positioning Index Scores
    • Figure & Table 2.6: AI in Retail Vendor Positioning Index - Phased Evolution Model
    • Figure 2.4: Juniper Research AI in Retail Vendor Positioning Index
    • 3.4.1. AI in Retail Vendor Positioning Index Commentary
      • i. Adobe
      • ii. Amazon
      • iii. Cortexica Vision Systems
      • iv. Evolv
      • v. Google
      • vi. IBM
      • vii. Intel
      • viii. Microsoft
      • ix. Oracle
      • x. Relex
      • xi. Salesforce
      • xii. SAP
      • xiii. Slyce
      • xiv. ToolsGroup
      • xv. ViSenze

4. Future Retail Technologies: Market Forecasts

  • 4.1. Introduction
    • 4.1.1. Future In-store Retail Technologies Methodology & Assumptions
    • 4.1.2. AI in Retail Methodology & Assumptions
      • Figure 3.1: AI Retail Services Forecast Methodology
      • Figure 3.2: Retail RFID/Beacons Forecast Methodology
      • Figure 3.3: Retail Digital Signage Forecast Methodology
      • Figure 3.4: Retail Smart Checkouts Forecast Methodology
      • Figure 3.5: Retail Robots Forecast Methodology
      • Figure 3.6: Retail Checkout Apps Forecast Methodology
  • 4.2. AI in Retail Forecasts
    • 4.2.1. Retailers Using Machine Learning Services
      • Figure & Table 3.7: Total Connected Retailers Accessing Machine Learning Services (m), Split by 8 Key Regions, 2020-2025
    • 4.2.2. Machine Learning in Supply Chain Demand Forecasting
      • Figure & Table 3.8: Total Retailer Spend on Machine Learning for Demand Forecasting ($m), Split by 8 Key Regions 2020-2025
    • 4.2.3. Machine Learning in Customer Service & Sentiment Analytics
      • Figure & Table 3.9: Total Retailer Spend on Machine Learning Assisted Customer Service & Sentiment Analytics ($m), Split by 8 Key Regions 2020-2025
    • 4.2.4. Machine Learning in Automated Marketing Solutions
      • Figure & Table 3.10: Total Spend by Retailers Using AI-based Automated Marketing Services ($m), Split by 8 Key Regions, 2020-2025
    • 4.2.5. Total Retail Machine Learning Spend
      • Figure & Table 3.11: Total Retail Machine Learning Spend ($m), Split by 8 Key Regions 2020-2025
  • 4.3. In-tore Retail Technology
    • 4.3.1. Bluetooth Beacons Installed Base
      • Figure & Table 3.12: Total Retail Bluetooth Beacons in Service (m),Split by 8 Key Regions 2020-2025
    • 4.3.2. RFID Installed Base
      • Figure & Table 3.13: Total RFID Tags in Service in Retail (m), Split by 8 Key Regions 2020-2025
    • 4.3.3. Retail Robots Installed Base
      • Figure & Table 3.14: Number of Robots in Retail Outlets, Installed Base per annum (,000s), Split by 8 Key Regions 2020-2025
    • 4.3.4. Digital Signage Installed Base
      • Figure & Table 3.15: Global Number of Installed Digital Signs, ESL & Large Display (m), Split by 8 Key Regions 2020-2025
    • 4.3.5. Smart Checkouts Installed Base
      • Figure & Table 3.16: Total Retail Machine Learning Spend ($m), Split by 8 Key Regions 2020-2025
    • 4.3.6. Checkout Apps Transaction Value
      • Figure & Table 3.17: Total Retail Machine Learning Spend ($m), Split by 8 Key Regions 2020-2025
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