Cover Image
市场调查报告书

商店内零售技术的未来预测 (2017-2022年):引进、实行、策略

Future In-Store Retail Technologies: Adoption, Implementation & Strategy 2017-2022

出版商 Juniper Research 商品编码 564399
出版日期 内容信息 英文
商品交期: 最快1-2个工作天内
价格
Back to Top
商店内零售技术的未来预测 (2017-2022年):引进、实行、策略 Future In-Store Retail Technologies: Adoption, Implementation & Strategy 2017-2022
出版日期: 2017年10月10日 内容信息: 英文
简介

调查本报告提供全球商店内零售技术的市场,主要的商店内零售技术类型和概要,各技术区分现状,主要经营者的引进案例,用户数、硬件设备收益的预测等汇整数据。

第1章 零售技术市场

  • 简介
    • 调查范围
  • 商店内零售状况
    • 目前技术
    • 谘询或补充的电子商务
  • 主要的消费者取向商店内零售趋势
    • 商店内电子商务
      • 案例研究:Warhammer的商店内购买
      • 案例研究:Tuft & Needle
    • 各种全通路
      • 个性化
    • 行动&线上的发现途径
      • 案例研究:NearSt
    • 线上-离线零售业者

第2章 入店&追踪技术

  • 简介
  • 入店&追踪手法
    • Wi-Fi追踪
    • 蓝芽信标
      • 装机量预测
  • 入店&追踪:利用案例
    • 商店设计
      • 案例研究:Ipsos Retail Performance的Wi-Fi Analytics
    • 参与度&存取
      • 离线互动
      • 线上第一次互动
      • 机器人&助手技术
      • 案例研究:Pepper (SoftBank)
  • 要点

第3章 选择&服务技术

  • 简介
  • AR (扩增实境)
    • 广告、行销
      • 案例研究:Shazam的AR平台
    • 智能镜
    • 产品信息
  • 语音助手
  • RFID
    • 智能镜子
    • 产品追踪
    • 市场规模、预测
      • 装机量
      • 收益
  • 智能人体模型
    • 案例研究:Amazon
  • 机器人
    • 零售机器人:市场规模、预测
      • 装机量
      • 硬件设备收益
      • 案例研究:LoweBot
  • 动态定价&数位电子看板
    • 个性化定价
    • 数位电子看板的预测
      • 装机量
      • 硬件设备收益
  • Wi-Fi
  • 要点

第4章 付款技术

  • 简介
  • 付款技术
    • NFC付款
    • 自我扫描应用程序
      • 案例研究:Moby Mart Autonomous Store
      • 收益预测
    • mPOS
    • 智能退房
      • 案例研究:Amazon Go
      • 预测
  • 要点
目录

Overview

Juniper Research's incisive strategic guide to Future In-store Retail Technologies provides a range of insights for a sector that is increasingly relevant for all forms of retail players.

This research takes an in-depth look at technologies affecting each step of the in-store consumer journey; highlighting where and how they can best be leveraged by retailers and technologies firms in this must-have guide.

This research includes:

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

Key Features:

  • Trends Assessment: Examination of the macro trends impacting the whole retail landscape, and what they mean for the future of retail.
  • Targeted Technology Analysis: Step-by-step discussion of each stage of the in-store consumer journey, and an evaluation of best practice use of retail technologies at each point.
  • Market Innovators: Case studies of some best-in-class examples of retail technology usage, including:
    • Amazon
    • Ipsos Mori
    • Lowes
    • Moby Mart
    • NearSt
    • SoftBank
    • Shazam
    • Tuft & Needle
  • Benchmark Industry Forecasts: 5-year forecasts for in-store retail technologies in terms of adoption, revenues and, monetary impact of these new technologies.

Key Questions:

  • What are the cutting-edge retail technologies of today, and how should they be used?
  • What kinds of retailers are using these technologies?
  • What impact will these technologies have on the in-store experience?
  • How many places will be deploying these technologies in the coming years?
  • Where are the biggest markets for these technologies?

Companies Referenced:

Aldebaran Robotics, Amazon, Apple, Bonobos, Carrefour, Games Workshop, Google, IBM, Ipsos, Lowe's, Macy's, Microsoft, Moby Mart, NCR, NearSt, Nescafé, Qualcomm, Sears, Shazam, SNCF, SoftBank Robotics, Suntory, Tuft & Needle, Whole Foods, Zappar.

Data & Interactive Forecast

Juniper's Future In-store Retail Technologies forecast suite includes:

  • Regional data splits for 8 key regions as well as country level splits for:
    • Canada
    • China
    • Denmark
    • Germany
    • Japan
    • Norway
    • Portugal
    • Spain
    • Sweden
    • UK
    • US
  • Adoption rates and Installed base for:
    • Retail RFID
    • Retail Bluetooth beacons
    • Retail digital signage
    • Retail robots
    • Smart checkouts
    • Checkout apps
  • Shipments and hardware revenue for:
    • Retail RFID
    • Retail Bluetooth beacons
    • Retail digital signage
    • Retail robots
  • Service and/or transaction revenues for:
    • Retail RFID
    • Retail Bluetooth beacons
    • Checkout apps

Juniper Research's highly granular IFxls (interactive forecast 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. The Retail Technology Marketplace

  • 1.1. Introduction
    • 1.1.1. Research Scope
      • Figure 1.1: Consumer & In-store Journeys Compared
  • 1.2. Status of In-Store Retail
    • 1.2.1. Current Technologies
      • Figure 1.2: Share of NCR Checkout Shipments that are Self-Service, 2015-2016
    • 1.2.2. eCommerce as Adversary, or Supplementary?
      • Figure 1.3: Online Gross Merchandising Value Sales ($bn), Selected Leading Online Storefronts, 2014-2016
  • 1.3. Key Consumer In-Store Retail Trends
    • 1.3.1. In-Store eCommerce
      • i. Case Study: Warhammer Store In-Store Purchasing
      • ii. Case Study: Tuft & Needle Stores
    • 1.3.2. Omnivorous Omnichannel
      • i. Personalisation
    • 1.3.3. The Discovery Channel of Mobile & Online
      • i. Case Study: NearSt
    • 1.3.4. Online-to-Offline Retailers

2. Store Entry & Tracking Technologies

  • 2.1. Introduction
  • 2.2. Store Entry & Tracking Methods
    • Figure 2.1: Store Entry & Tracking Methods
    • 2.2.1. Wi-Fi Tracking
    • 2.2.2. Bluetooth Beacons
      • i. Bluetooth Beacons Installed Base Forecast
    • Figure 2.2: Total Retail Bluetooth Beacons in Service Split by 8 Key Regions (m), 2017-2022
  • 2.3. Store Entry & Tracking Use Cases
    • 2.3.1. Store Design
      • i. Case Study: Ipsos Retail Performance Wi-Fi Analytics
    • 2.3.2. Engagement & Access
      • i. Offline Interactions
        • Figure 2.3: Proportion of Smartphones Launched with NFC, 2010-2017 YTD
      • ii. Online-First Interactions
      • iii. Robots & Assistant Technologies
      • iv. Case Study: Pepper by SoftBank
        • Figure 2.4: Pepper Robot Deployed in Carrefour Supermarket
  • 2.4. Key Takeaways

3. Selection & Service Technologies

  • 3.1. Introduction
    • Figure 3.1: Juniper Strategy Quadrant for Selection & Service Technologies
  • 3.2. AR (Augmented Reality)
    • 3.2.1. Advertising & Marketing
      • Figure 3.2: Shazam AR App Demo
      • i. Case Study: Shazam AR Platform
    • 3.2.2. Smart Mirrors
      • Figure 3.3: SenseFit SenseMi Virtual Fitting Room
    • 3.2.3. Product Information
  • 3.3. Voice Assistants
  • 3.4. RFID
    • 3.4.1. Smart Mirrors
    • 3.4.2. Product Tracking
    • 3.4.3. RFID Market Sizing & Forecasts
      • i. Installed Base
        • Figure & Table 3.4: Retail RFID Tags Deployed per annum (m) Split by 8 Key Regions 2017-2022
      • ii. RFID Revenues
        • Figure 3.5: Retail RFID Tags & Systems Spend ($m) Split by 8 Key Regions 2017-2022
  • 3.5. Smart Mannequins
    • Figure 3.6: Amazon Adjustable Mannequin Patent
      • i. Case Study: Amazon Adjustable Mannequin Patent
  • 3.6. Robots
    • 3.6.1. Retail Robots Market Sizing & Forecasts
      • i. Retail Robots Installed Base Forecast
        • Figure & Table 3.7: Number of Retail Robots, Installed Base per annum (000s), Split by 8 Key Regions 2017-2022
      • ii. Retail Robots Hardware Revenues
        • Figure & Table 3.8: Retail Robots Hardware Revenues per annum, split by 8 Key Regions ($m) 2017-2022
      • iii. Case Study: LoweBot
  • 3.7. Dynamic Pricing & Digital Signage
    • 3.7.1. Personalised Pricing
    • 3.7.2. Digital Signage Forecasts
      • i. Digital Signing Installed Base
        • Figure & Table 3.9: Number of Digital Signs, Installed Base per annum (m), 2017-2022
      • ii. Digital Signage Hardware Revenues
        • Figure & Table 3.10: Number of Digital Signs, Installed Base per annum (m), 2017-2022
  • 3.8. Wi-Fi
  • 3.9 Key Takeaways

4. Payment Technologies

  • 4.1. Introduction
    • Figure 4.1: Juniper Strategy Quadrant for Payment Technologies
  • 4.2. Payment Technologies
    • 4.2.1. NFC Payments
    • 4.2.2. Self-scanning Apps
      • Figure 4.2: Moby Mart Autonomous Store
        • i. Case Study: Moby Mart Autonomous Store
        • ii. Self-Scanning Apps Revenue Forecast
          • Figure & Table 4.3: Annual Retail Revenue Increase Attributable to Checkout Apps ($m), Split by 8 Key Regions, 2017-2022
    • 4.2.3. mPOS
    • 4.2.4. Smart Checkouts
      • i. Case Study: Amazon Go
      • ii. Smart Checkouts Forecast
        • Figure & Table 4.4: Number of Retail Outlets Adopting Smart Checkout Technologies (000s), Split by 8 Key Regions, 2017-2022
  • 4.3. Key Takeaways
Back to Top