零售业巨量资料分析：市场占有率分析、产业趋势与统计、成长预测（2024-2029）

零售业巨量资料分析市场规模预计到 2024 年为 63.8 亿美元，预计到 2029 年将达到 166.8 亿美元，在预测期内（2024-2029 年）将成长至 212 亿美元，复合年增长率为 %。

零售业正在经历由先进分析和巨量资料技术驱动的重大变革。随着电子商务、网路购物的发展以及对客户忠诚度的激烈竞争，零售商正在利用巨量资料分析来保持市场竞争力。

主要亮点

• 零售业稳步采用云端、人工智慧和相关技术，被认为是成长最快的产业之一。 NASSCOM 的一项调查显示，70% 的公司表示，他们正专注于利用人工智慧增加支出来增加收益。例如，全球最大的零售商之一沃尔玛正在经历数位转型。该公司正在建立全球最大的私有云端系统，预计每小时可管理Petabyte的资料。
• 预测分析是一种主动方法，允许零售商使用历史资料来预测由于消费行为或市场趋势变化而导致的预期销售成长。这有助于零售商保持领先地位、有效竞争并占领重要的市场占有率。更重视预测分析将有助于您与客户建立永续的关係，包括提高促销效果和促进交叉销售。
• 零售商正在寻找创新的方法，从不断增加的有关消费者行为的结构化和非结构化资讯中获取洞察。透过在零售流程的每个阶段应用巨量资料分析，线下和线上零售商都可以了解客户的购买行为，将其映射到产品，并透过在零售流程的每个阶段应用大数据分析来增加产品销售和利润。资料优先策略来规划我们的行销策略。实施 IPS 系统、具有自助结帐系统功能的商店自动化、机器人和零售自动化等创新方法正在推动零售市场对巨量资料分析的需求。
• 资料整合挑战，例如资料管治、可扩展性以及与从多个来源检索资料以实现资料复製和转换规则相关的问题，可能会限制市场。然而，可以透过设定适当的系统规则来减少这些。
• 由于工厂和製造业关闭、价格上涨、严格封锁以及人们搬回家时供应链中断，COVID-19 大流行扰乱了地区和国家层面的零售市场，产生了巨大影响。然而，鑑于疫情后人类独特的需求，巨量资料使零售商能够透过有针对性的广告、产品推荐、定价等以更个人化的方式回应客户。零售商越来越喜欢这项技术。

零售业巨量资料分析

商品行销和供应链分析部门预计将占据主要份额

• 电子商务正在影响并降低传统实体零售商的重要性，标誌着零售业的一场资料主导的革命。高效率的供应链，或货物从供应商到仓库、商店到客户的最佳运输，对于每个企业都至关重要。因此，巨量资料分析是零售供应链革命的核心，即时追踪产品流和存量基准，利用客户资料来预测采购模式，甚至使用机器人来完成我们庞大的自动化仓库中的订单。精力充沛。
• 随着零售业随着商品行销分析和数位解决方案的整合而不断发展，零售商需要保持积极主动并快速回应客户需求。在英国，继製造业和能源产业之后，零售业的供应链巨量资料分析预计将在预测期内显着成长。此外，预测分析和机器学习人工智慧有望彻底改变零售供应链。
• 事实证明，利用先进的商品行销分析可以帮助零售商克服在全通路零售业取得成功的挑战。根据《麻省理工学院技术评论洞察》以全球消费品和零售业案例进行的一项研究显示，48% 的消费品和零售业受访者认为，实施人工智慧将有助于改善客户服务，其次是品管(47%)、库存管理 (47%)、产品个人化、定价和诈欺侦测。
• 随着全球经济变得更加相互关联和复杂，企业发现很难满足客户的期望。他们需要更快、更果断、更准确地制定供应链决策，并且需要能够快速、透明地执行这些决策。在当今的市场中保持竞争力需要全面的需求规划。此外，为了实现准时到位（OTIF），公司必须可视化其端到端供应链，实时平衡供需，并快速有效地做出正确的决策。必须能够做到。提高客户满意度、优化存量基准和分销网络以及缩短上市时间以最大限度地提高销售额证明了该领域对巨量资料分析的需求。

预计北美将占据最大份额

• 零售业的巨量资料分析可以帮助企业根据客户的购买历史为他们提供建议。其结果是提高了提供客製化购物体验和增强客户服务的能力。这些资料集数量庞大，可以帮助预测趋势并做出资料驱动的策略决策。北美零售市场巨量资料分析的成长是由零售分析工具不断增长的需求以及物联网在零售流程中的使用所推动的，从而提高了零售行业的生产力和效率。
• 该地区的大型零售业销售额正在成长。根据美国零售联合会（NRF）统计，由于消费者信心高涨、失业率低和薪资上涨，去年美国零售额成长了6%至8%，达到4.44兆美元，预计将超过。经济强劲且富有弹性的征兆。
• 此外，北美是采用巨量资料分析的领先创新者和先驱者之一。该地区拥有强大的巨量资料分析供应商立足点，进一步促进了市场成长。其中包括 IBM Corporation、SAS Institute Inc.、Alteryx Inc. 和 Microstrategy Incorporated。由于资料生产和零售消费的增加以及由此带来的销售额的增加，巨量资料分析硬体、软体和服务将需要更多的支出。
• 零售业越来越多地采用工业 4.0 是推动市场成长的关键方面之一。在零售4.0中，零售业的多个业务和流程已经数位化和自动化，包括库存管理、客户服务、客户帐户、供应链管理和商品行销管理活动。预计在预测期内将进一步推动北美零售市场巨量资料分析的成长。

零售业巨量资料分析概述

零售业的巨量资料分析是中度到高度分散的。电子商务、网路购物的成长以及对客户忠诚度的激烈竞争为零售业巨量资料分析创造了巨大的利润机会。整体而言，现有竞争对手之间的竞争非常激烈。未来，大企业不同类型的创新策略将有效拉动市场成长。

其他福利

• Excel 格式的市场预测 (ME) 表
• 3 个月分析师支持

目录

第一章 简介

• 研究假设和市场定义
• 调查范围

第二章调查方法

第三章执行摘要

第四章市场动态

• 市场概况
• 市场驱动因素
  • 更关注预测分析
  • 商品行销和供应链分析部门预计将占据主要份额
• 市场限制因素
  • 从不同系统收集和整理资料的复杂性
• 产业价值链分析
• 产业吸引力－波特五力分析
  • 新进入者的威胁
  • 买方议价能力
  • 供应商的议价能力
  • 替代产品的威胁
  • 竞争公司之间的敌意强度
• COVID-19 对市场的影响

第五章市场区隔

• 按用途
  • 商品行销和供应链分析
  • 社群媒体分析
  • 客户分析
  • 营运情报
  • 其他用途
• 按行业分类
  • 中小企业
  • 大型组织
• 地区
  • 北美洲
  • 欧洲
  • 亚太地区
  • 世界其他地区

第六章 竞争形势

• 公司简介
  • SAP SE
  • Oracle Corporation
  • Qlik Technologies Inc.
  • Zoho Corporation
  • IBM Corporation
  • Retail Next Inc.
  • Alteryx Inc.
  • Salesforce.com Inc.（Tableau Software Inc.）
  • Adobe Systems Incorporated
  • Microstrategy Inc.
  • Hitachi Vantara Corporation
  • Fuzzy Logix LLC

第七章 投资分析

第八章市场机会及未来趋势

The Big Data Analytics in Retail Market size is estimated at USD 6.38 billion in 2024, and is expected to reach USD 16.68 billion by 2029, growing at a CAGR of 21.20% during the forecast period (2024-2029).

The retail industry is witnessing a major transformation through advanced analytics and Big Data technologies. With the growth of e-commerce, online shopping, and high competition for customer loyalty, retailers are utilizing Big Data analytics to stay competitive in the market.

Key Highlights

• The retail industry witnessed a steady adoption of cloud, AI, and related technologies and is considered one of the top sectors in terms of growth. According to a survey by NASSCOM, 70 percent of the companies said they focus on revenue growth by leveraging AI and increasing their spending. For Example, Walmart, one of the largest retailers in the world, is undergoing a digital transformation. It is in the process of building the world's largest private cloud system, which is expected to have the capacity to manage 2.5 petabytes of data every hour.
• Predictive analytics is a proactive approach whereby retailers can use data from the past to predict expected sales growth due to changes in consumer behaviors and market trends. It can help retailers stay ahead of the curve, compete effectively, and gain considerable market share. Increased Emphasis on Predictive Analytics which can help increase promotional effectiveness, drive cross-selling, and much more to build sustainable relationships with the customers.
• Retailers attempt to find innovative ways to draw insights from the ever-increasing amount of structured and unstructured information about consumer behavior. Retailers, both offline and online, are adopting the data-first strategy toward understanding their customers' buying behavior, mapping them to products, and planning marketing strategies to sell their products to increase profits by applying Big Data Analytics at every step of the retail process. Innovative ways such as Implementing IPS systems, Store Automation with self check out, Robots, and Automation in retail, etc., drive the need for Big data analytics in the retail market.
• Data integration challenges could restrain the market, including data governance, scalability, and problems associated with getting data from multiple sources to have data duplication and transformation rules. However, these can be reduced with the proper systematic set of rules.
• The COVID-19 pandemic hugely impacted retail markets at the regional and country level due to the shutdown of factories, and manufacturing plants, increase in prices, strict lockdowns, and supply chain disruptions as people's mobility were confirmed to their homes. However, post-pandemic considering the inherent human needs, Big Data is helping retailers to cater to customers in a more personalized way via targeted advertising, product recommendations, and pricing; the retailers increasingly prefer the technology.

Big Data Analytics in Retail Market Trends

Merchandising and Supply Chain Analytics Segment Expected to Hold Significant Share

• E-commerce has impacted traditional brick-and-mortar retailers, reducing their significance and marking the data-driven revolution in the retail sector. An efficient supply chain, the optimized movement of goods from supplier to warehouse to store to the customer, is critical to every business. Therefore, big data analytics is at the core of revolutionizing the retail supply chain, i.e., tracking and tracing product flow and stock levels in real-time, leveraging customer data to predict buying patterns, and even using robots to fulfill orders in vast automated warehouses tirelessly.
• Retailers must stay proactive and quickly fulfill customer needs as the retail industry continues to evolve with the integration of merchandising analytics and digital solutions. In the United Kingdom, the supply chain Big Data analytics for retail is expected to grow significantly over the forecast period, following the manufacturing and energy sector. It is further expected that predictive analytics and machine learning AI will revolutionize the retail supply chain.
• Leveraging advanced merchandising analytics is proven to help retailers overcome the challenges to thrive in an omnichannel retail world. According to the survey conducted by MIT Technology Review Insights for Big Data Analytics using cases in the consumer goods and retail industry worldwide predicts that 48 percent of respondents from the consumer goods and retail industry state that deployment of artificial intelligence can help improve customer care, followed by Quality control (47%), Inventory Management(47%), personalization of products, pricing, and fraud detection.
• As the global economy becomes interconnected and complex, companies find it challenging to meet customer expectations. They must make supply chain decisions faster, more decisive, and more accurate and can implement those decisions rapidly and transparently. Integrated demand planning is necessary to remain competitive in today's marketplace. Further, to achieve OTIF (On-Time-In-Full), a company must have end-to-end supply chain visibility and be able to balance demand and supply in real-time to make the right decisions quickly and effectively. Improving customer satisfaction, optimizing inventory levels and distribution networks, and achieving a faster time to market for sales maximization prove the need for big data Analytics in this sector.

North America Region Expected to Hold the Largest Share

• Big data analytics in retail helps companies to generate customer recommendations based on their purchase history. It results in an improved ability to offer customized shopping experiences and enhanced customer service. These data sets are available in massive volumes and aid in forecasting trends and making strategic decisions guided by data. The growth of North America's big data analytics in the retail market is driven by the rising demand for retail analytics tools and the usage of the IoT in retail processes, enhancing the productivity and efficiency of the retail industry.
• The region's massive retail industry is experiencing growth in sales. In the United States, according to the National Retail Federation (NRF), retail sales are expected in between 6% to 8% to more than USD 4.44 trillion in the last year, citing high consumer confidence, low unemployment, and rising wages and clear signs of a strong and resilient economy.
• Besides, North America is among the leading innovators and pioneers, in terms of the adoption, of Big Data analytics. The region boasts a strong foothold of Big Data analytics vendors, which further contributes to the market's growth. Some include IBM Corporation, SAS Institute Inc., Alteryx Inc., and Microstrategy Incorporated. Big data analytics hardware, software, and services need more significant expenditures due to the rise in data production and retail consumption with corresponding sales increases.
• The increasing adoption of industry 4.0 across the retail sector is one of the primary aspects encouraging market growth. In retail 4.0, several operations and processes in the retail industry, like inventory management, customer service, customer accounts, supply chain management, and merchandising management activities, became digitized and automated. It is further expected to bolster the growth of North America's big data analytics in the retail market during the forecast period.

Big Data Analytics in Retail Industry Overview

Big data analytics in the retail market is moderately to highly fragmented. The growth of e-commerce, online shopping, and high competition for customer loyalty provides lucrative opportunities in big data analytics in the retail market. Overall, the competitive rivalry among existing competitors is high. Moving forward, different kinds of innovation strategies of large companies boost market growth effectively.

In August 2022, Maxis took a significant stake in Malaysian-based retail analytics startup, ComeBy, to empower innovation and digitalization in the retail industry with greater access to technology and the human network to create more economic multipliers for the country.

Also, in August 2022, DataWeave, an AI-powered Brand Analytics solution company, announced its status as a vetted partner in the Amazon Advertising Partner Network to support brands in optimizing their digital advertising campaigns with actionable data insights. The Amazon Advertising Partner Network, and new Partner Directory, provide brands access to a global community of agencies and tool providers that can help advertisers achieve their business goals using Amazon Ads products.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET DYNAMICS

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Increased Emphasis on Predictive Analytics
  • 4.2.2 Merchandising and Supply Chain Analytics Segment Expected to Hold Significant Share
• 4.3 Market Restraints
  • 4.3.1 Complexities in Collecting and Collating the Data From Disparate Systems
• 4.4 Industry Value Chain Analysis
• 4.5 Industry Attractiveness - Porter Five Forces
  • 4.5.1 Threat of New Entrants
  • 4.5.2 Bargaining Power of Buyers/Consumers
  • 4.5.3 Bargaining Power of Suppliers
  • 4.5.4 Threat of Substitute Products
  • 4.5.5 Intensity of Competitive Rivalry
• 4.6 Impact of COVID-19 on the Market

5 MARKET SEGMENTATION

• 5.1 By Application
  • 5.1.1 Merchandising and Supply Chain Analytics
  • 5.1.2 Social Media Analytics
  • 5.1.3 Customer Analytics
  • 5.1.4 Operational Intelligence
  • 5.1.5 Other Applications
• 5.2 By Business Type
  • 5.2.1 Small and Medium Enterprises
  • 5.2.2 Large-scale Organizations
• 5.3 Geography
  • 5.3.1 North America
  • 5.3.2 Europe
  • 5.3.3 Asia-Pacific
  • 5.3.4 Rest of the World

6 COMPETITIVE LANDSCAPE

• 6.1 Company Profiles
  • 6.1.1 SAP SE
  • 6.1.2 Oracle Corporation
  • 6.1.3 Qlik Technologies Inc.
  • 6.1.4 Zoho Corporation
  • 6.1.5 IBM Corporation
  • 6.1.6 Retail Next Inc.
  • 6.1.7 Alteryx Inc.
  • 6.1.8 Salesforce.com Inc. (Tableau Software Inc.)
  • 6.1.9 Adobe Systems Incorporated
  • 6.1.10 Microstrategy Inc.
  • 6.1.11 Hitachi Vantara Corporation
  • 6.1.12 Fuzzy Logix LLC

7 INVESTMENT ANALYSIS

8 MARKET OPPORTUNITIES AND FUTURE TRENDS