小売業におけるAI：セグメント分析、ベンダの位置づけ＆市場予測 2019-2023年
AI in Retail
このレポートは小売業向け人工知能（AI）を調査し、小売業の顧客体験とバックオフィス業務にAIと機械学習がどのように活用されているかを分析しています。
調査対象国
カ... もっと見る
サマリー
このレポートは小売業向け人工知能（AI）を調査し、小売業の顧客体験とバックオフィス業務にAIと機械学習がどのように活用されているかを分析しています。
調査対象国
-
カナダ
-
中国
-
デンマーク
-
ドイツ
-
日本
-
ノルウェー
-
ポルトガル
-
スペイン
-
スウェーデン
-
英国
-
米国
主な掲載内容
セグメント別予測
-
需要予測
-
感情分析と顧客サービス
-
自動マーケティング
-
小売業向けチャットボット
-
デジタルサイネージ
-
スマートチェックアウト
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).
-
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
-
At what pace are retailers expected to adopt machine learning services?
-
What are the most viable use cases for AI deployment in the retail industry?
-
Who are the key disruptors in this space, and what strategies are vendors employing?
-
What are the key trends, trends and challenges acting on the AI industry?
-
How is the industry expected to develop towards 2022 and beyond?
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 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.
Regions:
8 Key Regions - includes North America, Latin America, West Europe, Central & East Europe, Far East & China, Indian Subcontinent, Rest of Asia Pacific and Africa & Middle East
Countries:
Canada, China, Denmark, Germany, Japan, Norway, Portugal, Spain, Sweden, UK, USA
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目次
Table of Contents
1. Deep Dive Strategy & Competition
1. AI in Retail: Introduction
1.1 Introduction . 7
Figure 1.1: AI Skills in Retail . 8
Figure 1.2: Types of AI . 8
1.2 Investment Landscape . 9
Table 1.3: Selected AI in Retail Investments, 2018-19 . 9
1.3 Retail Industry/Start-up Activity by Region . 10
1.3.1 North America . 10
i. Current Retail Market . 10
Figure 1.4: Total US Retail Sales ($bn), 2010-2018 . 10
ii. Investment/Development Activity . 11
iii. Juniper’s View: Future Prospects . 11
1.3.2 Latin America . 11
i. Current Retail Market . 11
Figure 1.5: Annual GDP Growth (%) Selected Latin American Countries, 2011-2017 . 12
ii. Investment/Development Activity . 12
iii. Juniper’s View: Future Prospects . 12
1.3.3 West Europe . 13
i. Current Retail Market . 13
Figure 1.6: UK Retail Sales ($bn), 2011-2018 . 13
ii. Investment/Development Activity . 14
iii. Juniper’s View: Future Prospects . 14
1.3.4 Central & East Europe . 14
i. Current Retail Market . 14
Figure 1.7: Annual GDP Growth (%) Selected Central & East European Countries, 2011-2017 . 15
ii. Investment/Development Activity . 15
iii. Juniper’s View: Future Prospects . 15
1.3.5 Far East & China . 16
i. Current Retail Market . 16
Figure 1.8: Annual GDP Growth (%), Selected Countries, 2011-2017 . 16
ii. Investment/Development Activity . 16
iii. Juniper’s View: Future Prospects . 17
1.3.6 Indian Subcontinent . 17
i. Current Retail Market . 17
Figure 1.9: GDP per Capita ($), Selected Countries 2011-2017 . 18
ii. Investment/Development Activity . 18
iii. Juniper’s View: Future Prospects . 18
1.3.7 Rest of Asia Pacific . 18
i. Current Retail Market . 18
Figure 1.10: Total Retail Sales ($m), Singapore, 2010-2017 . 19
ii. Investment/Development Activity . 19
iii. Juniper’s View: Future Prospects . 19
1.3.8 Africa & Middle East . 19
i. Current Retail Market . 19
Figure 1.11: GDP per Capita ($), Selected Countries 2011-2017 . 20
ii. Investment/Development Activity . 20
iii. Juniper’s View: Future Prospects . 20
2. AI: Disruption in Retail
2.1 Disruptive AI in Retail ? Impact Assessment . 22
2.1.1 Summary. 22
Table 2.1: AI in Retail Impact Assessment . 22
Table 2.2: AI in Retail Impact Assessment Heatmap Key . 22
2.1.2 AI in Retail Impact Assessment Methodology . 23
Table 2.3: AI in Retail Impact Assessment Methodology . 23
2.2 AI in Retail Segment Analysis . 24
2.2.1 Personalisation . 24
Case Study: Granify . 25
i. Visual Search . 26
Figure 2.4: ViSenze Visual Search . 26
ii. Challenges to Approach . 27
iii. Future Outlook . 27
2.2.2 Demand Forecasting . 28
Figure 2.5: Elements of Demand Forecasting . 28
i. Challenges to Approach . 30
ii. Future Outlook . 30
Case Study: Symphony RetailAI . 31
2.2.3 Customer Analytics & Marketing. 31
i. Challenges to Approach . 32
ii. Future Outlook . 32
2.2.4 Payment Provider Analytics . 32
i. Challenges to Approach . 33
ii. Future Outlook . 33
2.2.5 Chatbots . 33
i. Challenges to Approach . 34
ii. Future Outlook . 34
2.2.6 The POS Finance Opportunity . 34
Case Study: My Chase Plan . 36
i. Challenges to Approach . 36
ii. Future Outlook . 37
2.2.7 Voice Assistants . 37
i. Challenges to Approach . 37
ii. Future Outlook . 37
2.3 AI Outlook in Retail . 38
Figure 2.6: Juniper Phased Evolution: AI & Connected Retail . 38
i. Future Developments . 39
2.4 AI in Retail: Disruptors & Challengers Quadrant . 40
2.4.1 Introduction . 40
Figure 2.7: Juniper Disruptors & Challengers Quadrant ? AI in Retail . 40
2.4.2 Landscape Analysis . 41
i. Overview . 41
ii. Disruptors . 41
iii. Catalysts . 43
iv. Embryonic Stakeholders . 46
3. AI in Retail: Vendor Analysis
3.1 Vendor Analysis & Leaderboard Introduction . 48
3.1.1 Stakeholder Assessment Criteria . 48
Table 3.1: AI in Retail Player Capability Criteria . 49
Figure 3.2: AI in Retail Stakeholder Leaderboard . 50
Table 3.3 AI in Retail Leaderboard Scoring . 51
3.1.2 Vendor Groupings . 52
i. Established Leaders . 52
ii. Leading Challengers . 52
iii. Disruptors & Emulators . 54
3.1.3 Limitations & Interpretation . 55
3.2 AI in Retail Movers & Shakers . 56
3.3 Vendor Profiles . 59
3.3.1 Adobe . 59
i. Corporate . 59
Table 3.4: Adobe Financial Snapshot ($bn) 2016-2018 . 59
ii. Geographic Spread . 59
iii. Key Clients & Strategic Partnerships . 59
iv. High Level View of Offerings . 59
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 60
3.3.2 Amazon . 60
i. Corporate . 60
Table 3.5: Amazon: Key Financial Data ($bn) 2016-2018 . 61
ii. Geographic Spread . 61
iii. Key Clients & Strategic Partnerships . 61
iv. High Level View of Offerings . 61
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 62
3.3.3 Cortexica. 62
i. Corporate . 62
Table 3.6: Cortexica Funding Rounds . 62
ii. Geographic Spread . 62
iii. Key Clients & Strategic Partnerships . 63
iv. High Level View of Offerings . 63
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 63
3.3.4 Evolv . 64
i. Corporate . 64
ii. Geographic Spread . 64
iii. Key Clients & Strategic Partnerships . 64
iv. High Level View of Offerings . 64
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 64
3.3.5 Google . 65
i. Corporate . 65
Table 3.7: Alphabet Financial Snapshot ($bn) 2016-2018 . 65
ii. Geographic Spread . 65
iii. Key Clients & Strategic Partnerships . 65
iv. High Level View of Offerings . 66
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 66
3.3.6 IBM . 67
i. Corporate . 67
Table 3.8: IBM Financial Snapshot ($m) 2016-2018 . 67
ii. Geographic Spread . 67
iii. Key Clients & Strategic Partnerships . 67
iv. High Level View of Offerings . 68
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 68
3.3.7 Intel. 68
i. Corporate . 68
Table 3.9: Intel Financial Snapshot ($bn) 2016-2018 . 69
ii. Geographic Spread . 69
iii. Key Clients & Strategic Partnerships . 69
iv. High Level View of Offerings . 69
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 69
3.3.8 Microsoft . 70
i. Corporate . 70
Table 3.10: Microsoft Financial Snapshot ($bn) 2016-2018 . 70
ii. Geographic Spread . 70
iii. Key Clients & Strategic Partnerships . 70
iv. High Level View of Offerings . 71
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 71
3.3.9 Oracle . 71
i. Corporate . 71
Table 3.11: Oracle Financial Snapshot ($m) 2016-2018 . 72
ii. Geographic Spread . 72
iii. Key Clients & Strategic Partnerships . 72
iv. High Level View of Offerings . 72
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 72
3.3.10 Salesforce . 73
i. Corporate . 73
Table 3.12: Salesforce.com Financial Snapshot ($bn) 2016-2019 . 73
ii. Geographic Spread . 73
iii. Key Clients & Strategic Partnerships . 73
iv. High Level View of Offerings . 74
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 74
3.3.11 Relex . 74
i. Corporate . 74
ii. Geographic Spread . 75
iii. Key Clients & Strategic Partnerships . 75
iv. High Level View of Offerings . 75
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 75
3.3.12 SAP . 76
i. Corporate . 76
Table 3.13: SAP Financial Snapshot ($bn) 2016-2018 . 76
ii. Geographic Spread . 76
iii. Key Clients & Strategic Partnerships . 76
iv. High Level View of Offerings . 77
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 77
3.3.13 Slyce . 77
i. Corporate . 77
ii. Geographic Spread . 78
iii. Key Clients & Strategic Partnerships . 78
iv. High Level View of Offerings . 78
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 78
3.3.14 ToolsGroup . 79
i. Corporate . 79
ii. Geographic Spread . 79
iii. Key Clients & Strategic Partnerships . 79
iv. High Level View of Offerings . 79
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 80
3.3.15 ViSenze . 80
i. Corporate . 80
Table 3.14: ViSenze Funding Rounds . 80
ii. Geographic Spread . 81
iii. Key Clients & Strategic Partnerships . 81
iv. High Level View of Offerings . 81
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 81
Deep Dive Data & Forecasting
1. Introduction to AI in Retail
1.1 Introduction . 4
Figure 1.1: AI Skills in Retail . 4
2. AI Retail Services Market Forecasts
2.1 Introduction . 7
2.2 Methodology & Assumptions . 7
Figure 2.1: AI Retail Services Forecast Methodology . 8
2.3 Retailers Using Machine Learning Services . 9
Figure & Table 2.2: Total Connected Retailers Accessing Machine Learning
Services (m), Split by 8 Key Regions, 2018-2023 . 9
2.4 Machine Learning in Supply Chain Demand Forecasting. 10
Figure & Table 2.3: Total Retailer Spend on Machine Learning for Demand
Forecasting ($m), Split by 8 Key Regions 2018-2023 . 10
2.5 Machine Learning in Customer Service & Sentiment Analytics 11
Figure & Table 2.4: Total Retailer Spend on Machine Learning Assisted Customer
Service & Sentiment Analytics ($m), Split by 8 Key Regions 2018-2023 . 11
2.6 Machine Learning in Automated Marketing Solutions . 12
Figure & Table 2.5: Total Spend by Retailers Using AI-based Automated
Marketing Services ($m), Split by 8 Key Regions, 2018-2023 . 12
2.7 Total Retail Machine Learning Spend . 13
Figure & Table 2.6: Total Retail Machine Learning Spend ($m), Split by 8 Key
Regions 2018-2023 . 13
3. Retail Chatbots Market Forecasts
3.1 Introduction . 15
3.2 Assumptions & Methodology . 15
Figure 3.1: Methodology for Messaging Application Chatbots . 16
Figure 3.2: Methodology for Discrete Application Chatbots . 17
Figure 3.3: Methodology for Web-based Chatbots . 18
3.3 Chatbot Forecasts . 19
3.3.1 Total Number of Successful Retail Chatbot Interactions . 19
Figure & Table 3.4: Total Number of Successful Retail Chatbot Interactions (m)
Split by 8 Key Regions 2018-2023 . 19
3.3.2 Total Revenues from Retail Chatbots . 20
Figure & Table 3.5: Total Revenues from Retail Chatbots per Annum ($m) Split by
8 Key Regions 2018-2023 . 20
4. AI Digital Signage Market Forecasts
4.1 Introduction . 22
4.2 Methodology & Assumptions . 22
Figure 4.1: Digital Signage Forecast Methodology . 23
4.3 Digital Signage Forecasts . 24
4.3.1 Number of Installed Digital Signs . 24
Figure & Table 4.2: Global Number of Installed Digital Signs, ESL (Electronic
Shelf Labels) & Large Display (m) Split by 8 Key Regions 2018-2023 . 24
4.3.2 Number of Connected Digital Signs Controlled by AI
Systems . 25
Figure & Table 4.3: Number of Connected Digital Signs Controlled by AI Systems
(m) Split by 8 Key Regions 2018-2023. 25
5. Smart Checkouts Market Forecasts
5.1 Introduction . 27
5.2 Methodology & Assumptions . 27
Figure 5.1: Smart Checkouts Forecast Methodology . 28
5.3 Smart Checkouts Forecasts . 29
5.3.1 Retail Outlets Adopting Smart Checkout Technologies . 29
Figure & Table 5.2: Number of Retail Outlets Adopting Smart Checkout
Technologies (,000s) Split by 8 Key Regions 2018-2023 . 29
5.3.2 Annual Transaction Value Processed by Smart Checkout
Technologies . 30
Figure & Table 5.3: Annual Transaction Value Processed by Smart Checkout
Technologies ($m) Split by 8 Key Regions 2018-2023 . 30
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Summary
このレポートは小売業向け人工知能（AI）を調査し、小売業の顧客体験とバックオフィス業務にAIと機械学習がどのように活用されているかを分析しています。
調査対象国
-
カナダ
-
中国
-
デンマーク
-
ドイツ
-
日本
-
ノルウェー
-
ポルトガル
-
スペイン
-
スウェーデン
-
英国
-
米国
主な掲載内容
セグメント別予測
-
需要予測
-
感情分析と顧客サービス
-
自動マーケティング
-
小売業向けチャットボット
-
デジタルサイネージ
-
スマートチェックアウト
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).
-
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
-
At what pace are retailers expected to adopt machine learning services?
-
What are the most viable use cases for AI deployment in the retail industry?
-
Who are the key disruptors in this space, and what strategies are vendors employing?
-
What are the key trends, trends and challenges acting on the AI industry?
-
How is the industry expected to develop towards 2022 and beyond?
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 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.
Regions:
8 Key Regions - includes North America, Latin America, West Europe, Central & East Europe, Far East & China, Indian Subcontinent, Rest of Asia Pacific and Africa & Middle East
Countries:
Canada, China, Denmark, Germany, Japan, Norway, Portugal, Spain, Sweden, UK, USA
ページTOPに戻る
Table of Contents
Table of Contents
1. Deep Dive Strategy & Competition
1. AI in Retail: Introduction
1.1 Introduction . 7
Figure 1.1: AI Skills in Retail . 8
Figure 1.2: Types of AI . 8
1.2 Investment Landscape . 9
Table 1.3: Selected AI in Retail Investments, 2018-19 . 9
1.3 Retail Industry/Start-up Activity by Region . 10
1.3.1 North America . 10
i. Current Retail Market . 10
Figure 1.4: Total US Retail Sales ($bn), 2010-2018 . 10
ii. Investment/Development Activity . 11
iii. Juniper’s View: Future Prospects . 11
1.3.2 Latin America . 11
i. Current Retail Market . 11
Figure 1.5: Annual GDP Growth (%) Selected Latin American Countries, 2011-2017 . 12
ii. Investment/Development Activity . 12
iii. Juniper’s View: Future Prospects . 12
1.3.3 West Europe . 13
i. Current Retail Market . 13
Figure 1.6: UK Retail Sales ($bn), 2011-2018 . 13
ii. Investment/Development Activity . 14
iii. Juniper’s View: Future Prospects . 14
1.3.4 Central & East Europe . 14
i. Current Retail Market . 14
Figure 1.7: Annual GDP Growth (%) Selected Central & East European Countries, 2011-2017 . 15
ii. Investment/Development Activity . 15
iii. Juniper’s View: Future Prospects . 15
1.3.5 Far East & China . 16
i. Current Retail Market . 16
Figure 1.8: Annual GDP Growth (%), Selected Countries, 2011-2017 . 16
ii. Investment/Development Activity . 16
iii. Juniper’s View: Future Prospects . 17
1.3.6 Indian Subcontinent . 17
i. Current Retail Market . 17
Figure 1.9: GDP per Capita ($), Selected Countries 2011-2017 . 18
ii. Investment/Development Activity . 18
iii. Juniper’s View: Future Prospects . 18
1.3.7 Rest of Asia Pacific . 18
i. Current Retail Market . 18
Figure 1.10: Total Retail Sales ($m), Singapore, 2010-2017 . 19
ii. Investment/Development Activity . 19
iii. Juniper’s View: Future Prospects . 19
1.3.8 Africa & Middle East . 19
i. Current Retail Market . 19
Figure 1.11: GDP per Capita ($), Selected Countries 2011-2017 . 20
ii. Investment/Development Activity . 20
iii. Juniper’s View: Future Prospects . 20
2. AI: Disruption in Retail
2.1 Disruptive AI in Retail ? Impact Assessment . 22
2.1.1 Summary. 22
Table 2.1: AI in Retail Impact Assessment . 22
Table 2.2: AI in Retail Impact Assessment Heatmap Key . 22
2.1.2 AI in Retail Impact Assessment Methodology . 23
Table 2.3: AI in Retail Impact Assessment Methodology . 23
2.2 AI in Retail Segment Analysis . 24
2.2.1 Personalisation . 24
Case Study: Granify . 25
i. Visual Search . 26
Figure 2.4: ViSenze Visual Search . 26
ii. Challenges to Approach . 27
iii. Future Outlook . 27
2.2.2 Demand Forecasting . 28
Figure 2.5: Elements of Demand Forecasting . 28
i. Challenges to Approach . 30
ii. Future Outlook . 30
Case Study: Symphony RetailAI . 31
2.2.3 Customer Analytics & Marketing. 31
i. Challenges to Approach . 32
ii. Future Outlook . 32
2.2.4 Payment Provider Analytics . 32
i. Challenges to Approach . 33
ii. Future Outlook . 33
2.2.5 Chatbots . 33
i. Challenges to Approach . 34
ii. Future Outlook . 34
2.2.6 The POS Finance Opportunity . 34
Case Study: My Chase Plan . 36
i. Challenges to Approach . 36
ii. Future Outlook . 37
2.2.7 Voice Assistants . 37
i. Challenges to Approach . 37
ii. Future Outlook . 37
2.3 AI Outlook in Retail . 38
Figure 2.6: Juniper Phased Evolution: AI & Connected Retail . 38
i. Future Developments . 39
2.4 AI in Retail: Disruptors & Challengers Quadrant . 40
2.4.1 Introduction . 40
Figure 2.7: Juniper Disruptors & Challengers Quadrant ? AI in Retail . 40
2.4.2 Landscape Analysis . 41
i. Overview . 41
ii. Disruptors . 41
iii. Catalysts . 43
iv. Embryonic Stakeholders . 46
3. AI in Retail: Vendor Analysis
3.1 Vendor Analysis & Leaderboard Introduction . 48
3.1.1 Stakeholder Assessment Criteria . 48
Table 3.1: AI in Retail Player Capability Criteria . 49
Figure 3.2: AI in Retail Stakeholder Leaderboard . 50
Table 3.3 AI in Retail Leaderboard Scoring . 51
3.1.2 Vendor Groupings . 52
i. Established Leaders . 52
ii. Leading Challengers . 52
iii. Disruptors & Emulators . 54
3.1.3 Limitations & Interpretation . 55
3.2 AI in Retail Movers & Shakers . 56
3.3 Vendor Profiles . 59
3.3.1 Adobe . 59
i. Corporate . 59
Table 3.4: Adobe Financial Snapshot ($bn) 2016-2018 . 59
ii. Geographic Spread . 59
iii. Key Clients & Strategic Partnerships . 59
iv. High Level View of Offerings . 59
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 60
3.3.2 Amazon . 60
i. Corporate . 60
Table 3.5: Amazon: Key Financial Data ($bn) 2016-2018 . 61
ii. Geographic Spread . 61
iii. Key Clients & Strategic Partnerships . 61
iv. High Level View of Offerings . 61
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 62
3.3.3 Cortexica. 62
i. Corporate . 62
Table 3.6: Cortexica Funding Rounds . 62
ii. Geographic Spread . 62
iii. Key Clients & Strategic Partnerships . 63
iv. High Level View of Offerings . 63
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 63
3.3.4 Evolv . 64
i. Corporate . 64
ii. Geographic Spread . 64
iii. Key Clients & Strategic Partnerships . 64
iv. High Level View of Offerings . 64
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 64
3.3.5 Google . 65
i. Corporate . 65
Table 3.7: Alphabet Financial Snapshot ($bn) 2016-2018 . 65
ii. Geographic Spread . 65
iii. Key Clients & Strategic Partnerships . 65
iv. High Level View of Offerings . 66
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 66
3.3.6 IBM . 67
i. Corporate . 67
Table 3.8: IBM Financial Snapshot ($m) 2016-2018 . 67
ii. Geographic Spread . 67
iii. Key Clients & Strategic Partnerships . 67
iv. High Level View of Offerings . 68
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 68
3.3.7 Intel. 68
i. Corporate . 68
Table 3.9: Intel Financial Snapshot ($bn) 2016-2018 . 69
ii. Geographic Spread . 69
iii. Key Clients & Strategic Partnerships . 69
iv. High Level View of Offerings . 69
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 69
3.3.8 Microsoft . 70
i. Corporate . 70
Table 3.10: Microsoft Financial Snapshot ($bn) 2016-2018 . 70
ii. Geographic Spread . 70
iii. Key Clients & Strategic Partnerships . 70
iv. High Level View of Offerings . 71
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 71
3.3.9 Oracle . 71
i. Corporate . 71
Table 3.11: Oracle Financial Snapshot ($m) 2016-2018 . 72
ii. Geographic Spread . 72
iii. Key Clients & Strategic Partnerships . 72
iv. High Level View of Offerings . 72
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 72
3.3.10 Salesforce . 73
i. Corporate . 73
Table 3.12: Salesforce.com Financial Snapshot ($bn) 2016-2019 . 73
ii. Geographic Spread . 73
iii. Key Clients & Strategic Partnerships . 73
iv. High Level View of Offerings . 74
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 74
3.3.11 Relex . 74
i. Corporate . 74
ii. Geographic Spread . 75
iii. Key Clients & Strategic Partnerships . 75
iv. High Level View of Offerings . 75
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 75
3.3.12 SAP . 76
i. Corporate . 76
Table 3.13: SAP Financial Snapshot ($bn) 2016-2018 . 76
ii. Geographic Spread . 76
iii. Key Clients & Strategic Partnerships . 76
iv. High Level View of Offerings . 77
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 77
3.3.13 Slyce . 77
i. Corporate . 77
ii. Geographic Spread . 78
iii. Key Clients & Strategic Partnerships . 78
iv. High Level View of Offerings . 78
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 78
3.3.14 ToolsGroup . 79
i. Corporate . 79
ii. Geographic Spread . 79
iii. Key Clients & Strategic Partnerships . 79
iv. High Level View of Offerings . 79
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 80
3.3.15 ViSenze . 80
i. Corporate . 80
Table 3.14: ViSenze Funding Rounds . 80
ii. Geographic Spread . 81
iii. Key Clients & Strategic Partnerships . 81
iv. High Level View of Offerings . 81
v. Juniper’s View: Key Strengths & Strategic Development Opportunities . 81
Deep Dive Data & Forecasting
1. Introduction to AI in Retail
1.1 Introduction . 4
Figure 1.1: AI Skills in Retail . 4
2. AI Retail Services Market Forecasts
2.1 Introduction . 7
2.2 Methodology & Assumptions . 7
Figure 2.1: AI Retail Services Forecast Methodology . 8
2.3 Retailers Using Machine Learning Services . 9
Figure & Table 2.2: Total Connected Retailers Accessing Machine Learning
Services (m), Split by 8 Key Regions, 2018-2023 . 9
2.4 Machine Learning in Supply Chain Demand Forecasting. 10
Figure & Table 2.3: Total Retailer Spend on Machine Learning for Demand
Forecasting ($m), Split by 8 Key Regions 2018-2023 . 10
2.5 Machine Learning in Customer Service & Sentiment Analytics 11
Figure & Table 2.4: Total Retailer Spend on Machine Learning Assisted Customer
Service & Sentiment Analytics ($m), Split by 8 Key Regions 2018-2023 . 11
2.6 Machine Learning in Automated Marketing Solutions . 12
Figure & Table 2.5: Total Spend by Retailers Using AI-based Automated
Marketing Services ($m), Split by 8 Key Regions, 2018-2023 . 12
2.7 Total Retail Machine Learning Spend . 13
Figure & Table 2.6: Total Retail Machine Learning Spend ($m), Split by 8 Key
Regions 2018-2023 . 13
3. Retail Chatbots Market Forecasts
3.1 Introduction . 15
3.2 Assumptions & Methodology . 15
Figure 3.1: Methodology for Messaging Application Chatbots . 16
Figure 3.2: Methodology for Discrete Application Chatbots . 17
Figure 3.3: Methodology for Web-based Chatbots . 18
3.3 Chatbot Forecasts . 19
3.3.1 Total Number of Successful Retail Chatbot Interactions . 19
Figure & Table 3.4: Total Number of Successful Retail Chatbot Interactions (m)
Split by 8 Key Regions 2018-2023 . 19
3.3.2 Total Revenues from Retail Chatbots . 20
Figure & Table 3.5: Total Revenues from Retail Chatbots per Annum ($m) Split by
8 Key Regions 2018-2023 . 20
4. AI Digital Signage Market Forecasts
4.1 Introduction . 22
4.2 Methodology & Assumptions . 22
Figure 4.1: Digital Signage Forecast Methodology . 23
4.3 Digital Signage Forecasts . 24
4.3.1 Number of Installed Digital Signs . 24
Figure & Table 4.2: Global Number of Installed Digital Signs, ESL (Electronic
Shelf Labels) & Large Display (m) Split by 8 Key Regions 2018-2023 . 24
4.3.2 Number of Connected Digital Signs Controlled by AI
Systems . 25
Figure & Table 4.3: Number of Connected Digital Signs Controlled by AI Systems
(m) Split by 8 Key Regions 2018-2023. 25
5. Smart Checkouts Market Forecasts
5.1 Introduction . 27
5.2 Methodology & Assumptions . 27
Figure 5.1: Smart Checkouts Forecast Methodology . 28
5.3 Smart Checkouts Forecasts . 29
5.3.1 Retail Outlets Adopting Smart Checkout Technologies . 29
Figure & Table 5.2: Number of Retail Outlets Adopting Smart Checkout
Technologies (,000s) Split by 8 Key Regions 2018-2023 . 29
5.3.2 Annual Transaction Value Processed by Smart Checkout
Technologies . 30
Figure & Table 5.3: Annual Transaction Value Processed by Smart Checkout
Technologies ($m) Split by 8 Key Regions 2018-2023 . 30
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