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1525307

零售市場規模研究中的全球巨量資料分析，按組件、部署、組織規模、應用程式和區域預測 2022-2032

Global Big Data Analytics in Retail Market Size Study, by Component, by Deployment, by Organization Size, by Application and Regional Forecasts 2022-2032
出版日期: | 出版商: Bizwit Research & Consulting LLP | 英文 285 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2023年，全球零售市場巨量資料分析價值約90.2億美元，預計在2024-2032年預測期內將以22.97%的健康成長率成長。零售業的巨量資料分析使零售商能夠檢測客戶行為，發現購物模式和趨勢，提高客戶服務質量，並實現更好的客戶保留和滿意度。該技術可用於客戶細分、忠誠度分析、定價分析、交叉銷售、供應鏈管理、需求預測、市場籃分析以及財務和固定資產管理。隨著時間的推移，巨量資料分析在零售業的採用激增，增強了組織的決策能力並提供了有價值的業務見解。它提供各種商業機會和獲得新見解的能力提高了其在最終用戶中的受歡迎程度。此外，電子商務的成長、預測分析需求的成長以及物聯網、人工智慧和機器學習等技術在巨量資料分析中的整合正在推動市場成長。

巨量資料分析工具支出的增加大大推動了零售市場對全球巨量資料分析的需求。零售商擴大投資於先進的分析解決方案，以更深入地了解客戶行為、簡化營運並增強決策。隨著線上交易、社群媒體和店內互動等各種來源產生的資料量不斷增加，企業正在尋求複雜的工具來有效地分析和利用這些資訊。這項投資可幫助零售商制定個人化行銷策略、最佳化庫存管理並改善客戶體驗。此外，人工智慧 (AI) 和機器學習 (ML) 的進步正在增強巨量資料分析工具的功能，使其對零售應用更有價值。因此，這些工具支出的增加正在推動全球零售市場巨量資料分析的強勁成長。然而，從不同系統收集資料的問題以及免費開源 VFX 軟體的存在可能會抑制 2024-2032 年預測期內市場的成長。

全球零售市場巨量資料分析的關鍵區域包括北美、歐洲、亞太地區、拉丁美洲以及中東和非洲。 2023年，北美地區在收入方面佔據市場主導地位。該地區先進的技術基礎設施和零售商對數據驅動策略的高採用率。領先的科技公司和巨量資料解決方案供應商的存在推動了複雜分析工具的創新和開發。預計 2024 年至 2032 年預測期內，亞太地區的複合年成長率將達到最高。這是由於其在零售軟體中採用了支援雲端的巨量資料分析，並且取得了顯著的成長。快速的網路連線、智慧型手機的普及、電子商務的興起、客戶購買模式的變化以及零售供應商之間日益激烈的競爭等因素促進了該地區的市場擴張。此外，許多來自北美的零售分析供應商正在擴大其在亞太地區的業務，為該市場創造了利潤豐厚的機會。

目錄

第 1 章：零售市場的全球巨量資料分析執行摘要

  • 全球零售市場規模巨量資料分析及預測（2022-2032）
  • 區域概要
  • 分部摘要
    • 按組件
    • 按部署
    • 按組織規模
    • 按申請
  • 主要趨勢
  • 經濟衰退的影響
  • 分析師推薦與結論

第 2 章：零售市場定義與研究假設中的全球巨量資料分析

  • 研究目的
  • 市場定義
  • 研究假設
    • 包含與排除
    • 限制
    • 供給側分析
      • 可用性
      • 基礎設施
      • 監管環境
      • 市場競爭
      • 經濟可行性（消費者的角度）
    • 需求面分析
      • 監理框架
      • 技術進步
      • 環境考慮
      • 消費者意識和接受度
  • 估算方法
  • 研究涵蓋的年份
  • 貨幣兌換率

第 3 章：零售市場動態中的全球巨量資料分析

  • 市場促進因素
    • 巨量資料分析工具支出增加
    • 電子商務產業的成長
    • 對高品質內容的需求上升
  • 市場挑戰
    • 從不同系統收集和整理資料的問題
    • 免費開源視覺特效軟體的存在
  • 市場機會
    • VR、AI等先進科技融合
    • 新興市場視覺特效支出增加

第 4 章：零售市場產業分析中的全球巨量資料分析

  • 波特的五力模型
    • 供應商的議價能力
    • 買家的議價能力
    • 新進入者的威脅
    • 替代品的威脅
    • 競爭競爭
    • 波特五力模型的未來方法
    • 波特的五力影響分析
  • PESTEL分析
    • 政治的
    • 經濟
    • 社會的
    • 技術性
    • 環境的
    • 合法的
  • 頂級投資機會
  • 最佳制勝策略
  • 顛覆性趨勢
  • 產業專家視角
  • 分析師推薦與結論

第 5 章：零售市場規模和預測的全球巨量資料分析：按組成部分 - 2022-2032

  • 細分儀表板
  • 零售市場的全球巨量資料分析：2022 年和 2032 年組件收入趨勢分析
    • 軟體
    • 服務

第 6 章：零售市場規模和預測中的全球巨量資料分析：按部署分類 - 2022-2032

  • 細分儀表板
  • 零售市場的全球巨量資料分析：2022 年和 2032 年部署收入趨勢分析
    • 本地部署
    • 雲

第 7 章：零售市場規模和預測的全球巨量資料分析：按組織規模 - 2022-2032

  • 細分儀表板
  • 零售市場的全球巨量資料分析：2022 年和 2032 年組織規模收入趨勢分析
    • 大型企業
    • 中小企業

第 8 章：零售市場規模和預測的全球巨量資料分析：按應用分類 - 2022-2032

  • 細分儀表板
  • 零售市場的全球巨量資料分析：2022 年和 2032 年應用收入趨勢分析
    • 銷售和行銷分析
    • 供應鏈營運管理
    • 行銷分析
    • 客戶分析
    • 其他

第 9 章：零售市場規模和預測的全球巨量資料分析：按地區 - 2022-2032

  • 北美洲
    • 美國
    • 加拿大
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 西班牙
    • 義大利
    • 歐洲其他地區
  • 亞太
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 韓國
    • 亞太地區其他地區
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 拉丁美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 南非
    • 中東和非洲其他地區

第 10 章：競爭情報

  • 重點企業SWOT分析
  • 頂級市場策略
  • 公司簡介
    • Oracle Corporation
      • 關鍵訊息
      • 概述
      • 財務（視數據可用性而定）
      • 產品概要
      • 市場策略
    • SAP SE
    • Salesforce.com, Inc.
    • Teradata Corporation
    • Qlik Technologies Inc.
    • TIBCO Software Inc.
    • Adobe
    • IBM Corporation
    • Microsoft Corporation
    • SAS Institute Inc.

第 11 章：研究過程

  • 研究過程
    • 資料探勘
    • 分析
    • 市場預測
    • 驗證
    • 出版
  • 研究屬性
簡介目錄

The global big data analytics in retail market is valued at approximately USD 9.02 billion in 2023 and is anticipated to grow with a healthy growth rate of 22.97% over the forecast period 2024-2032. Big data analytics in retail empowers retailers to detect customer behavior, discover shopping patterns and trends, improve customer service quality, and achieve better customer retention and satisfaction. The technology can be employed for customer segmentation, loyalty analysis, pricing analysis, cross-selling, supply chain management, demand forecasting, market basket analysis, and finance and fixed asset management. The adoption of big data analytics in retail has surged over time, enhancing the decision-making capabilities of organizations and providing valuable business insights. Its ability to offer various business opportunities and gain new insights has increased its popularity among end-users. Additionally, the growth of e-commerce, the rise in demand for predictive analytics, and the integration of technologies such as IoT, AI, and machine learning in big data analytics are driving the market growth.

The increase in spending on big data analytics tools is significantly driving demand for the global big data analytics in retail market. Retailers are increasingly investing in advanced analytics solutions to gain deeper insights into customer behaviour, streamline operations, and enhance decision-making. With the growing volume of data generated from various sources such as online transactions, social media, and in-store interactions, businesses are seeking sophisticated tools to analyse and leverage this information effectively. This investment helps retailers personalize marketing strategies, optimize inventory management, and improve customer experiences. Additionally, advancements in artificial intelligence (AI) and machine learning (ML) are boosting the capabilities of big data analytics tools, making them more valuable for retail applications. Consequently, increased spending on these tools is fuelling robust growth in the global big data analytics in retail market. However, issues in collecting data from disparate systems and presence of free & open-source VFX software can restrain growth of the market during the forecast period 2024-2032.

The key region in the Global Big Data Analytics in Retail Market includes North America, Europe, Asia Pacific, Latin America and Middle East & Africa. In 2023, North America dominates the market in terms of revenue. the region's advanced technological infrastructure and high adoption rates of data-driven strategies among retailers. The presence of leading tech companies and big data solution providers fuels innovation and development of sophisticated analytics tools. Asia-Pacific expected to witness highest CAGR during the forecast period 2024-2032. This is due to its adoption of cloud-enabled big data analytics in retail software witnessing significant growth. Factors such as fast internet connectivity, the proliferation of smartphones, the rise of e-commerce, changing customer purchase patterns, and growing competition among retail vendors contribute to the market expansion in this region. Furthermore, many retail analytics vendors from North America are expanding their presence in Asia-Pacific, creating lucrative opportunities for the market.

Major market players included in this report are:

  • Adobe
  • IBM Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • SAP SE
  • SAS Institute Inc.
  • Salesforce.com, Inc.
  • Teradata Corporation
  • Qlik Technologies Inc.
  • TIBCO Software Inc.

The detailed segments and sub-segment of the market are explained below:

By Component

  • Software
  • Services

By Deployment

  • On-Premise
  • Cloud

By Organization Size

  • Large Enterprise
  • Small & Medium Enterprise

By Application

  • Sales & Marketing Analytics
  • Supply Chain Operations Management
  • Merchandising Analytics
  • Customer Analytics
  • Others

By Region:

  • North America
  • U.S.
  • Canada
  • Europe
  • UK
  • Germany
  • France
  • Spain
  • Italy
  • ROE
  • Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
  • RoAPAC
  • Latin America
  • Brazil
  • Mexico
  • Rest of Latin America
  • Middle East & Africa
  • Saudi Arabia
  • South Africa
  • RoMEA

Years considered for the study are as follows:

  • Historical year - 2022
  • Base year - 2023
  • Forecast period - 2024 to 2032

Key Takeaways:

  • Market Estimates & Forecast for 10 years from 2022 to 2032.
  • Annualized revenues and regional level analysis for each market segment.
  • Detailed analysis of geographical landscape with Country level analysis of major regions.
  • Competitive landscape with information on major players in the market.
  • Analysis of key business strategies and recommendations on future market approach.
  • Analysis of competitive structure of the market.
  • Demand side and supply side analysis of the market.

Table of Contents

Chapter 1. Global Big Data Analytics in Retail Market Executive Summary

  • 1.1. Global Big Data Analytics in Retail Market Size & Forecast (2022-2032)
  • 1.2. Regional Summary
  • 1.3. Segmental Summary
    • 1.3.1. By Component
    • 1.3.2. By Deployment
    • 1.3.3. By Organization Size
    • 1.3.4. By Application
  • 1.4. Key Trends
  • 1.5. Recession Impact
  • 1.6. Analyst Recommendation & Conclusion

Chapter 2. Global Big Data Analytics in Retail Market Definition and Research Assumptions

  • 2.1. Research Objective
  • 2.2. Market Definition
  • 2.3. Research Assumptions
    • 2.3.1. Inclusion & Exclusion
    • 2.3.2. Limitations
    • 2.3.3. Supply Side Analysis
      • 2.3.3.1. Availability
      • 2.3.3.2. Infrastructure
      • 2.3.3.3. Regulatory Environment
      • 2.3.3.4. Market Competition
      • 2.3.3.5. Economic Viability (Consumer's Perspective)
    • 2.3.4. Demand Side Analysis
      • 2.3.4.1. Regulatory frameworks
      • 2.3.4.2. Technological Advancements
      • 2.3.4.3. Environmental Considerations
      • 2.3.4.4. Consumer Awareness & Acceptance
  • 2.4. Estimation Methodology
  • 2.5. Years Considered for the Study
  • 2.6. Currency Conversion Rates

Chapter 3. Global Big Data Analytics in Retail Market Dynamics

  • 3.1. Market Drivers
    • 3.1.1. Increase in spending on big data analytics tools
    • 3.1.2. Growth of e-commerce sector
    • 3.1.3. Rise in demand for high-quality content
  • 3.2. Market Challenges
    • 3.2.1. Issues in collecting and collating data from disparate systems
    • 3.2.2. Presence of free & open-source VFX software
  • 3.3. Market Opportunities
    • 3.3.1. Integration of advanced technologies such as VR & AI
    • 3.3.2. Increased spending on VFX in emerging markets

Chapter 4. Global Big Data Analytics in Retail Market Industry Analysis

  • 4.1. Porter's 5 Force Model
    • 4.1.1. Bargaining Power of Suppliers
    • 4.1.2. Bargaining Power of Buyers
    • 4.1.3. Threat of New Entrants
    • 4.1.4. Threat of Substitutes
    • 4.1.5. Competitive Rivalry
    • 4.1.6. Futuristic Approach to Porter's 5 Force Model
    • 4.1.7. Porter's 5 Force Impact Analysis
  • 4.2. PESTEL Analysis
    • 4.2.1. Political
    • 4.2.2. Economical
    • 4.2.3. Social
    • 4.2.4. Technological
    • 4.2.5. Environmental
    • 4.2.6. Legal
  • 4.3. Top Investment Opportunity
  • 4.4. Top Winning Strategies
  • 4.5. Disruptive Trends
  • 4.6. Industry Expert Perspective
  • 4.7. Analyst Recommendation & Conclusion

Chapter 5. Global Big Data Analytics in Retail Market Size & Forecasts by Component 2022-2032

  • 5.1. Segment Dashboard
  • 5.2. Global Big Data Analytics in Retail Market: Component Revenue Trend Analysis, 2022 & 2032 (USD Billion)
    • 5.2.1. Software
    • 5.2.2. Services

Chapter 6. Global Big Data Analytics in Retail Market Size & Forecasts by Deployment 2022-2032

  • 6.1. Segment Dashboard
  • 6.2. Global Big Data Analytics in Retail Market: Deployment Revenue Trend Analysis, 2022 & 2032 (USD Billion)
    • 6.2.1. On-Premise
    • 6.2.2. Cloud

Chapter 7. Global Big Data Analytics in Retail Market Size & Forecasts by Organization Size 2022-2032

  • 7.1. Segment Dashboard
  • 7.2. Global Big Data Analytics in Retail Market: Organization Size Revenue Trend Analysis, 2022 & 2032 (USD Billion)
    • 7.2.1. Large Enterprise
    • 7.2.2. Small & Medium Enterprise

Chapter 8. Global Big Data Analytics in Retail Market Size & Forecasts by Application 2022-2032

  • 8.1. Segment Dashboard
  • 8.2. Global Big Data Analytics in Retail Market: Application Revenue Trend Analysis, 2022 & 2032 (USD Billion)
    • 8.2.1. Sales & Marketing Analytics
    • 8.2.2. Supply Chain Operations Management
    • 8.2.3. Merchandising Analytics
    • 8.2.4. Customer Analytics
    • 8.2.5. Others

Chapter 9. Global Big Data Analytics in Retail Market Size & Forecasts by Region 2022-2032

  • 9.1. North America Big Data Analytics in Retail Market
    • 9.1.1. U.S. Big Data Analytics in Retail Market
      • 9.1.1.1. Component breakdown size & forecasts, 2022-2032
      • 9.1.1.2. Deployment breakdown size & forecasts, 2022-2032
      • 9.1.1.3. Organization Size breakdown size & forecasts, 2022-2032
      • 9.1.1.4. Application breakdown size & forecasts, 2022-2032
    • 9.1.2. Canada Big Data Analytics in Retail Market
  • 9.2. Europe Big Data Analytics in Retail Market
    • 9.2.1. U.K. Big Data Analytics in Retail Market
    • 9.2.2. Germany Big Data Analytics in Retail Market
    • 9.2.3. France Big Data Analytics in Retail Market
    • 9.2.4. Spain Big Data Analytics in Retail Market
    • 9.2.5. Italy Big Data Analytics in Retail Market
    • 9.2.6. Rest of Europe Big Data Analytics in Retail Market
  • 9.3. Asia-Pacific Big Data Analytics in Retail Market
    • 9.3.1. China Big Data Analytics in Retail Market
    • 9.3.2. India Big Data Analytics in Retail Market
    • 9.3.3. Japan Big Data Analytics in Retail Market
    • 9.3.4. Australia Big Data Analytics in Retail Market
    • 9.3.5. South Korea Big Data Analytics in Retail Market
    • 9.3.6. Rest of Asia Pacific Big Data Analytics in Retail Market
  • 9.4. Latin America Big Data Analytics in Retail Market
    • 9.4.1. Brazil Big Data Analytics in Retail Market
    • 9.4.2. Mexico Big Data Analytics in Retail Market
    • 9.4.3. Rest of Latin America Big Data Analytics in Retail Market
  • 9.5. Middle East & Africa Big Data Analytics in Retail Market
    • 9.5.1. Saudi Arabia Big Data Analytics in Retail Market
    • 9.5.2. South Africa Big Data Analytics in Retail Market
    • 9.5.3. Rest of Middle East & Africa Big Data Analytics in Retail Market

Chapter 10. Competitive Intelligence

  • 10.1. Key Company SWOT Analysis
  • 10.2. Top Market Strategies
  • 10.3. Company Profiles
    • 10.3.1. Oracle Corporation
      • 10.3.1.1. Key Information
      • 10.3.1.2. Overview
      • 10.3.1.3. Financial (Subject to Data Availability)
      • 10.3.1.4. Product Summary
      • 10.3.1.5. Market Strategies
    • 10.3.2. SAP SE
    • 10.3.3. Salesforce.com, Inc.
    • 10.3.4. Teradata Corporation
    • 10.3.5. Qlik Technologies Inc.
    • 10.3.6. TIBCO Software Inc.
    • 10.3.7. Adobe
    • 10.3.8. IBM Corporation
    • 10.3.9. Microsoft Corporation
    • 10.3.10. SAS Institute Inc.

Chapter 11. Research Process

  • 11.1. Research Process
    • 11.1.1. Data Mining
    • 11.1.2. Analysis
    • 11.1.3. Market Estimation
    • 11.1.4. Validation
    • 11.1.5. Publishing
  • 11.2. Research Attributes