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デジタル小売技術

Digital Retail Technologies

出版日: | 発行: Juniper Research Ltd | ページ情報: 英文 | 納期: 即日から翌営業日

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本日の銀行送金レート: 1GBP=140.11円
デジタル小売技術
出版日: 2020年09月07日
発行: Juniper Research Ltd
ページ情報: 英文
納期: 即日から翌営業日
  • 全表示
  • 概要
  • 目次
概要

当レポートでは、世界の小売業界におけるデジタル技術の普及・活用状況と、それに伴う小売業の構造変化の動きについて分析し、主要な技術 (小売業向けAI、店舗内用IT技術など) の概要・機能やコンポーネント、各技術の普及率・市場規模の動向見通し (今後5年間分)、地域別・主要国の詳細動向 (全8地域・19ヶ国)、大手小売業者における成功事例、主要ベンダーのプロファイルと市場ポジショニング、といった情報を取りまとめてお届けいたします。

目次

第1章 デジタル小売技術:要点と戦略提言

  • 重要なポイント
  • 戦略的推奨事項

第2章 小売技術市場

  • イントロダクション
  • 将来の店舗内小売技術
  • 店舗内小売の現状
    • 汎用技術
      • スマートチェックアウト
      • ビーコン
      • RFID
      • ロボット
      • スマートミラー
  • 小売業者の技術革新指数
    • 小売業者のポジショニング指数:解説
      • Walmart
      • Carrefour
      • Amazon
      • Costco
      • JD.com
      • Target Corporation
      • Waitrose
      • Aldi Group
      • Lidl
      • Tesco
      • Best Buy
      • IKEA
      • Home Depot
      • イオン
      • Auchan

第3章 小売業における人工知能 (AI):市場の混乱

  • イントロダクション
    • 定義:小売業界におけるAIとは何か?
  • 使用されている各種技術
    • コンピュータービジョン (CV)
      • 拡張/仮想現実 (AR&VR)
    • ロボット
    • 自然言語処理 (NLP)
      • 店内ロボット
      • 店内仮想アシスタント
      • チャットボット
    • センサー
  • 小売業におけるAIの状況
    • パーソナライゼーション (個別化) とマーケティング
      • パーソナライズされたWebサイト・コンテンツ
      • パーソナライズされた製品推奨機能
      • ビジュアル検索
      • 拡張現実 (AR)
    • 顧客サービス
    • 需要予測
  • 小売業者のポジショニング指数におけるAI
    • 「小売業者のポジショニング指数におけるAI」への見解
      • Adobe
      • Amazon
      • Cortexica Vision Systems
      • Evolv
      • Google
      • IBM
      • Intel
      • Microsoft
      • Oracle
      • Relex
      • Salesforce
      • SAP
      • Slyce
      • ToolsGroup
      • ViSenze

第4章 未来の小売技術:市場予測

  • イントロダクション
    • 分析手法と前提条件
  • 将来予測:小売業向けAI
    • 機械学習サービスを使用する小売業者
    • サプライチェーンの需要予測における機械学習
    • カスタマーサービスと感情分析における機械学習
    • 自動マーケティングソリューションにおける機械学習
    • 小売業向け機械学習:総支出額
  • 店舗内小売技術
    • Bluetoothビーコンの設置ベース
    • RFIDの設置ベース
    • 小売ロボットの設置ベース
    • デジタルサイネージの設置ベース
    • スマートレジの設置ベース
    • 会計用アプリの決済金額
目次

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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