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AI (人工知能) & マシンラーニング - 小売り:混乱・分析・機会 2018-2022年

AI (Artificial Intelligence) in Retail: Disruption, Analysis & Opportunities 2018-2022

発行 Juniper Research 商品コード 372628
出版日 ページ情報 英文
納期: 即日から翌営業日
価格
本日の銀行送金レート: 1GBP=152.90円で換算しております。
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AI (人工知能) & マシンラーニング - 小売り:混乱・分析・機会 2018-2022年 AI (Artificial Intelligence) in Retail: Disruption, Analysis & Opportunities 2018-2022
出版日: 2018年02月01日 ページ情報: 英文
概要

当レポートでは、小売りにおけるAI (人工知能) およびマシンラーニングの世界市場について調査分析を行い、市場動向・競合情勢の分析、5ヵ年の市場規模・予測、および主要企業のプロファイルなどを提供しています。

第1章 要点・戦略提言

第2章 人工知能を謎解く

  • イントロダクション
  • AIの定義
  • AIの現況
  • AIの投資状況
  • AIの将来展望

第3章 小売りにおけるAI:市場ダイナミクス・動向・提言

  • 小売りにおけるAIの可能性・展開
    • パーソナライゼーション
    • 需要予測
    • 顧客分析・マーケティング
    • ペイメントプロバイダー分析
    • チャットボット
  • 小売りAIにおける主要動向
  • 小売りにおけるAIの展望

第4章 小売りにおけるAI:ベンダー情勢

  • AI破壊者 & 挑戦者の分析
  • ベンダー分析・Juniper Leaderboardのイントロダクション
  • AIの有力企業
  • ベンダーのプロファイル
    • Adobe
    • Amazon
    • Relex
    • Microsoft
    • Oracle
    • Salesforce
    • Sentient Technologies
    • Slyce
    • ToolsGroup

第5章 小売りにおけるAI:市場予測

  • イントロダクション
  • 調査手法 & 前提条件
  • マシンラーニングサービスを利用する小売業者
  • サプライチェーン需要予測におけるマシンラーニング
  • 顧客サービス & センチメント分析におけるマシンラーニング
  • 自動マーケティングソリューションにおけるマシンラーニング
  • 小売りマシンラーニングの総支出
目次

Overview

Juniper's AI in Retail research provides a detailed overview of how AI & machine learning approaches are being leveraged by retailers to improve the customer experience and their own profitability. The research analyses different use cases such as demand forecasting and personalisation in retail, with assessment of the opportunities available for retailers and technology providers.

The report also discusses the emerging use of chatbots 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 research includes:

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

Key Features:

  • Market Landscape: Investigates key use cases for AI & machine learning in the retail space, assessing AI's potential impact and viability in each area. These use cases are the following:
    • Demand Forecasting;
    • Sentiment Analytics and Customer Service;
    • Automated Marketing;
    • Retail Chatbots.
  • AI Development Landscape: Understand the development stages of AI and what investments have been made by large vendors and what the future outlook for AI deployment is.
  • Benchmark Industry Forecasts for retailer spending on machine learning services, broken down by the following segments:
    • Demand Forecasting;
    • Sentiment Analytics and Customer Service;
    • Automated Marketing.
    • Additional forecast for revenues generated by retail chatbots.
  • Interviews: with leading players across the AI in retail landscape, including:
    • Mode.ai;
    • Sentient Technologies.
  • Juniper Leaderboard: 13 leading AI in Retail vendors compared, scored and positioned on the Juniper Leaderboard matrix.

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, trends and challenges acting on the AI industry?
  • 5. How is the industry expected to develop towards 2022 and beyond?

Companies Referenced:

  • Interviewed: Mode.ai, Sentient Technologies.
  • Profiled: Adobe, Amazon, Baidu, Facebook, IBM, Intel, Microsoft, Oracle, Relex, Salesforce, Sentient Technologies, Slyce, ToolsGroup.
  • Case Studied: Amazon, Cortexica Vision Systems, Google, LevaData, Mall of America, Mode.ai, NVIDIA.
  • Mentioned: 1-800-Flowers, A.S. Adventure, Accenture, AdRoll, Aldo, AO.com, Apple, AppNexus, Ashley Furniture, Aston Martin, Best Buy, Bilka, Blackberry, Charlotte Tilbury, Comb, Comfiz, Conversionista, ConversionXL, CONVRG, Coop Denmark, Costa, Criteo, Deepnify, Dell, Deloitte, Disney, Dixons Carphone, Domino's, eBay, Emarsys, Express, Fashwell, Game, Gannett, Gant, Granify, Graymatter, H&M, Hawkers, HMV, Home Depot, IEEE, IMS Evolve, Infosys, JCPenney, John Lewis, KPMG, L'Oréal, Lacoste, Levi's, Lionsgate, Louis Vuitton, Luxottica, Macy's, MediaMath, MemoMi, Morrisons, Mothercare, Myntra, Nanigans, NEC, Neolane, Netsuite, Nike, North Face, O2, Ocado, Online Dialogue, Pandora, Plantasjen, PwC, Rakuten, River Island, Rossmann, SAP, Satisfi Labs, Sensitel, Sephora, Skechers, SnipSnap, Snapchat, Square, Technion/EE, TellApart, Tesco's, Thread, T-Mobile, Tommy Hilfiger, Toys R Us, Triggit, Tumi, Turn, Twitter, Unilever, UNIQLO, United Colours of Benetton, Very, Videosurf, ViSenze, Waitrose, Walmart, Wayfair, WHSmith, Wipro, WPP, Visa, xPerception, Zalando, Zalora.

Data & Interactive Forecast:

Juniper's AI in Retail forecast suite includes:

  • Country level data splits for:
    • US
    • Canada
    • Denmark
    • Germany
    • Norway
    • Portugal
    • Spain
    • Sweden
    • UK
    • China
    • Japan
    • Plus 8 key global regions
  • Machine Learning spending by retailers, split by segment:
    • Demand Forecasting;
    • Sentiment Analytics and Customer Service;
    • Automated Marketing.
  • Total revenues from retail chatbots.
  • Interactive Scenario tool allowing user the ability to manipulate Juniper's data for xx different metrics.
  • Access to the full set of forecast data of 26 tables and 3,432 data points.

Juniper Research's highly granular interactive excels enable clients to manipulate Juniper's forecast data and charts to test their own assumptions and compare 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. Key Takeaways & Strategic Recommendations

  • 1.1. AI in Retail: Key Takeaways
    • Figure 1.1: Key Forecast Findings, 2022
  • 1.2. Strategic Recommendations

2. Demystifying Artificial Intelligence

  • 2.1. Introduction
    • Figure 2.1: Types of AI
  • 2.2. Defining AI
    • Figure 2.2: Principal Goals of AI Systems
    • 2.2.1. Building Blocks of AI
      • i. Data & Information
      • ii. Algorithms
    • 2.2.2. Definition
  • 2.3. The State of AI Today
    • 2.3.1. AI Milestones
      • Figure 2.3: Significant Milestones in AI Development
      • Figure 2.4: IBM Deep Blue Computer
      • Figure 2.5: IBM Watson at Jeopardy
    • 2.3.2. AI in Use Today
      • Case Study: NVIDIA DGX-1
  • 2.4. AI Investment Landscape
    • i. Avoiding an AI Winter
    • ii. More Haste, Less Speed
      • Case Study: Google's Acuisition of DeepMind
    • 2.4.2. AI Investment Facts & Analysis
      • Figure 2.6: AI Mergers & Acquisitions: Q1 2012-Q2 2017
      • Figure 2.7: Number of AI-related Acquisitions by GAFA, 2016-2017 Year to Date
      • Figure 2.8: Notable AI Acquisitions, 2016-2017
  • 2.5. AI Future Outlook
    • 2.5.1. Near-Term
    • 2.5.2. Long-Term

3. AI in Retail - Market Dynamics, Trends & Recommendations

  • 3.1. AI Potential & Deployment in Retail
    • Figure 3.1: Key Facts, PwC In-Store vs Online Survey
    • 3.1.1. Personalisation
      • Case Study: Cortexica Vision Systems
      • Case Study: Amazon Echo Look
        • Figure 3.2: Amazon's Echo Look
        • Figure 3.3: MemoMi Magic Mirror
      • i. Juniper's Assesment of Use of Case
    • 3.1.2. Demand Forecasting
      • Figure 3.4: Elements of Demand Forecasting
      • i. Juniper's Assesment of Use of Case
        • Case Study: LevaData
    • 3.1.3. Customer Analytics & Marketing
      • i. Juniper's Assesment of Use of Case
    • 3.1.4. Payment Provider Analytics
    • 3.1.5. Chatbots
      • i. Bot Use for Retail
        • Figure 3.5: 1-800-Flowers on Facebook Messenger
      • ii. Benefits for Businesses
        • Case Study: Mode.ai
        • Case Study: Mall Of America Chatbot
      • i. Juniper's Assesment of Use of Case
  • 3.2. Key Trends in Retail AI
  • 3.3. AI Outlook in Retail
    • Figure 3.6: Juniper Phased Evolution: AI in Retail
      • i. Future Developments

4. AI in Retail - Vendor Landscape

  • 4.1. AI in Retail Disruptors & Challengers Analysis
    • Figure.4.1: Juniper AI in Retail Disruptors and Challengers Quadrant
    • 4.1.1. Disruptors
    • 4.1.2. Nascent Players
    • 4.1.3. Catalyst Players
    • 4.1.4. Embryonic Players
  • 4.2. Vendor Analysis & Leaderboard Introduction
    • 4.2.1. Stakeholder Assessment Criteria
      • Table 4.2: AI in Retail Player Capability Criteria
      • Figure 4.3: AI in Retail Stakeholder Leaderboard
      • Table 4.4: AI in Retail Leaderboard Scoring
    • 4.2.2. Vendor Groupings
      • i. Established Leaders
      • ii. Leading Challengers
      • iii. Disruptors & Emulators
    • 4.2.3. Limitations & Interpretation
  • 4.3. AI in Retail Movers & Shakers
  • 4.4. Vendor Profiles
    • 4.4.1. Adobe
      • i. Corporate
        • Table 4.5: Adobe Financial Snapshot ($bn) 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 4.4.2. Amazon
      • i. Corporate
        • Table 4.6: Amazon: Key Financial Data ($bn) 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 4.4.3. Relex
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 4.4.8. Microsoft
      • i. Corporate
        • Table 4.11: Microsoft Financial Snapshot ($bn) 2015-2017
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 4.4.9. Oracle
      • i. Corporate
        • Table 4.12: Oracle Financial Snapshot ($m) 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 4.4.10. Salesforce
      • i. Corporate
        • Table 4.13: Salesforce.com Financial Snapshot ($bn) 2015-2017
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 4.4.11. Sentient Technologies
      • i. Corporate
        • Table 4.14: Sentient Technologies Funding Rounds
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 4.4.12. Slyce
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 4.4.13. ToolsGroup
      • i. Corporate
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. High Level View of Offerings
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities

5. AI in Retail - Market Forecasts

  • 5.1. Introduction
  • 5.2. Methodology & Assumptions
    • Figure 5.1: AI in Retail Forecast Methodology
  • 5.3. Retailers Using Machine Learning Services
    • Figure & Table 5.2: Total Connected Retailers Accessing Machine Learning Services (m), Split by 8 Key Regions, 2017-2022
  • 5.4. Machine Learning in Supply Chain Demand Forecasting
    • Figure & Table 5.3: Total Retailer Spend on Machine Learning for Demand Forecasting ($m), Split by 8 Key Regions 2017-2022
  • 5.5. Machine Learning in Customer Service & Sentiment Analytics 73
    • Figure & Table 5.4: Total Retailer Spend on Machine Learning Assisted Customer Service & Sentiment Analytics ($m), Split by 8 Key Regions 2017-2022
  • 5.6. Machine Learning in Automated Marketing Solutions
    • Figure & Table 5.5: Total Spend by Retailers Using AI-based Automated Marketing Services ($m), Split by 8 Key Regions, 2017-2022
  • 5.7. Total Retail Machine Learning Spend
    • Figure & Table 5.6: Total Retail Machine Learning Spend ($m), Split by 8 Key Regions 2017-2022
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