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市场调查报告书
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1525307

零售市场规模研究中的全球大数据分析，按组件、部署、组织规模、应用程式和区域预测 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个工作天内

价格
简介目录

2023年，全球零售市场巨量资料分析价值约90.2亿美元，预计在2024-2032年预测期内将以22.97%的健康成长率成长。零售业的巨量资料分析使零售商能够检测客户行为，发现购物模式和趋势，提高客户服务质量，并实现更好的客户保留和满意度。该技术可用于客户细分、忠诚度分析、定价分析、交叉销售、供应链管理、需求预测、市场篮分析以及财务和固定资产管理。随着时间的推移，巨量资料分析在零售业的采用激增，增强了组织的决策能力并提供了有价值的业务见解。它提供各种商业机会和获得新见解的能力提高了其在最终用户中的受欢迎程度。此外，电子商务的成长、预测分析需求的成长以及物联网、人工智慧和机器学习等技术在巨量资料分析中的整合正在推动市场成长。

巨量资料分析工具支出的增加大大推动了零售市场对全球巨量资料分析的需求。零售商越来越多地投资于先进的分析解决方案，以更深入地了解客户行为、简化营运并增强决策。随着线上交易、社群媒体和店内互动等各种来源产生的资料量不断增加，企业正在寻求复杂的工具来有效地分析和利用这些资讯。这项投资可帮助零售商制定个人化行销策略、优化库存管理并改善客户体验。此外，人工智慧 (AI) 和机器学习 (ML) 的进步正在增强巨量资料分析工具的功能，使其对零售应用更有价值。因此，这些工具支出的增加正在推动全球零售市场巨量资料分析的强劲成长。然而，从不同系统收集资料的问题以及免费开源 VFX 软体的存在可能会抑制 2024-2032 年预测期内市场的成长。

全球零售市场大数据分析的关键区域包括北美、欧洲、亚太地区、拉丁美洲以及中东和非洲。 2023年，北美地区在收入方面占据市场主导地位。该地区先进的技术基础设施和零售商对数据驱动策略的高采用率。领先的科技公司和巨量资料解决方案供应商的存在推动了复杂分析工具的创新和开发。预计 2024 年至 2032 年预测期内，亚太地区的复合年增长率将达到最高。这是由于其在零售软体中采用了支援云端的巨量资料分析，并且取得了显着的成长。快速的网路连线、智慧型手机的普及、电子商务的兴起、客户购买模式的变化以及零售供应商之间日益激烈的竞争等因素促进了该地区的市场扩张。此外，许多来自北美的零售分析供应商正在扩大其在亚太地区的业务，为该市场创造了利润丰厚的机会。

目录

第 1 章：零售市场的全球大数据分析执行摘要

  • 全球零售市场规模大数据分析及预测（2022-2032）
  • 区域概要
  • 分部摘要
    • 按组件
    • 按部署
    • 按组织规模
    • 按申请
  • 主要趋势
  • 经济衰退的影响
  • 分析师推荐与结论

第 2 章：零售市场定义与研究假设中的全球大数据分析

  • 研究目的
  • 市场定义
  • 研究假设
    • 包含与排除
    • 限制
    • 供给侧分析
      • 可用性
      • 基础设施
      • 监管环境
      • 市场竞争
      • 经济可行性（消费者的角度）
    • 需求面分析
      • 监理框架
      • 技术进步
      • 环境考虑
      • 消费者意识和接受度
  • 估算方法
  • 研究考虑的年份
  • 货币兑换率

第 3 章：零售市场动态中的全球大数据分析

  • 市场驱动因素
    • 巨量资料分析工具支出增加
    • 电子商务产业的成长
    • 对高品质内容的需求上升
  • 市场挑战
    • 从不同系统收集和整理资料的问题
    • 免费开源视觉特效软体的存在
  • 市场机会
    • VR、AI等先进科技融合
    • 新兴市场视觉特效支出增加

第 4 章：零售市场产业分析中的全球大数据分析

  • 波特的五力模型
    • 供应商的议价能力
    • 买家的议价能力
    • 新进入者的威胁
    • 替代品的威胁
    • 竞争竞争
    • 波特五力模型的未来方法
    • 波特的五力影响分析
  • PESTEL分析
    • 政治的
    • 经济
    • 社会的
    • 技术性
    • 环境的
    • 合法的
  • 顶级投资机会
  • 最佳制胜策略
  • 颠覆性趋势
  • 产业专家视角
  • 分析师推荐与结论

第 5 章：零售市场规模和预测的全球大数据分析：按组成部分 - 2022-2032

  • 细分仪表板
  • 零售市场的全球大数据分析：2022 年和 2032 年组件收入趋势分析
    • 软体
    • 服务

第 6 章：零售市场规模和预测中的全球大数据分析：按部署划分 - 2022-2032

  • 细分仪表板
  • 零售市场的全球大数据分析：2022 年和 2032 年部署收入趋势分析
    • 本地部署
    • 云

第 7 章：零售市场规模和预测的全球大数据分析：按组织规模 - 2022-2032

  • 细分仪表板
  • 零售市场的全球大数据分析：2022 年和 2032 年组织规模收入趋势分析
    • 大型企业
    • 中小企业

第 8 章：零售市场规模和预测的全球大数据分析：按应用分类 - 2022-2032

  • 细分仪表板
  • 零售市场的全球大数据分析：2022 年和 2032 年应用收入趋势分析
    • 销售和行销分析
    • 供应链营运管理
    • 行销分析
    • 客户分析
    • 其他的

第 9 章：零售市场规模和预测的全球大数据分析：按地区 - 2022-2032

  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 西班牙
    • 义大利
    • 欧洲其他地区
  • 亚太
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 韩国
    • 亚太地区其他地区
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 拉丁美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 南非
    • 中东和非洲其他地区

第 10 章：竞争情报

  • 重点企业SWOT分析
  • 顶级市场策略
  • 公司简介
    • Oracle Corporation
      • 关键讯息
      • 概述
      • 财务（视数据可用性而定）
      • 产品概要
      • 市场策略
    • SAP SE
    • Salesforce.com, Inc.
    • Teradata Corporation
    • Qlik Technologies Inc.
    • TIBCO Software Inc.
    • Adobe
    • IBM Corporation
    • Microsoft Corporation
    • SAS Institute Inc.

第 11 章：研究过程

  • 研究过程
    • 资料探勘
    • 分析
    • 市场预测
    • 验证
    • 出版
  • 研究属性
简介目录

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