零售業巨量資料分析：市場佔有率分析、產業趨勢與統計、成長預測（2024-2029）

零售業巨量資料分析市場規模預計到 2024 年為 63.8 億美元，預計到 2029 年將達到 166.8 億美元，在預測期內（2024-2029 年）將成長至 212 億美元，年複合成長率為 21.20%。

零售業正在經歷由先進分析和巨量資料技術驅動的重大變革。隨著電子商務、網路購物的發展以及對客戶忠誠度的激烈競爭，零售商正在利用巨量資料分析來保持市場競爭力。

主要亮點

• 零售業穩步採用雲端、人工智慧和相關技術，被認為是成長最快的產業之一。 NASSCOM 的一項調查顯示，70% 的公司表示，他們正專注於利用人工智慧增加支出來增加收益。例如，全球最大的零售商之一沃爾瑪正在經歷數位轉型。該公司正在建立全球最大的私有雲端系統，預計每小時可管理Petabyte的資料。
• 預測分析是一種主動方法，允許零售商使用歷史資料來預測由於消費行為或市場趨勢變化而導致的預期銷售成長。這有助於零售商保持領先地位、有效競爭並佔領重要的市場佔有率。更重視預測分析將有助於您與客戶建立永續的關係，包括提高促銷效果和促進交叉銷售。
• 零售商正在尋找創新的方法，從不斷增加的有關消費者行為的結構化和非結構化資訊中獲取洞察。透過在零售流程的每個階段應用巨量資料分析，線下和線上零售商都可以了解客戶的購買行為，將其映射到產品，並透過在零售流程的每個階段應用巨量資料分析來增加產品銷售和利潤。資料優先策略來規劃我們的行銷策略。實施 IPS 系統、具有自助結帳系統功能的商店自動化、機器人和零售自動化等創新方法正在推動零售市場對巨量資料分析的需求。
• 資料整合挑戰，例如資料管治、可擴展性以及與從多個來源檢索資料以實現資料複製和轉換規則相關的問題，可能會限制市場。然而，可以透過設定適當的系統規則來減少這些。
• 由於工廠和製造業關閉、價格上漲、嚴格封鎖以及人們搬回家時供應鏈中斷，COVID-19 大流行擾亂了地區和國家層面的零售市場，產生了巨大影響。然而，鑑於疫情後人類獨特的需求，巨量資料使零售商能夠透過有針對性的廣告、產品推薦、定價等以更個人化的方式回應客戶。零售商越來越喜歡這項技術。

零售業巨量資料分析

商品行銷和供應鏈分析部門預計將佔據主要佔有率

• 電子商務正在影響並降低傳統實體零售商的重要性，標誌著零售業的一場資料主導的革命。高效率的供應鏈，或貨物從供應商到倉庫、商店到客戶的最佳運輸，對於每個企業都至關重要。因此，巨量資料分析是零售供應鏈革命的核心，即時追蹤產品流和存量基準，利用客戶資料來預測採購模式，甚至使用機器人來完成我們龐大的自動化倉庫中的訂單。精力充沛。
• 隨著零售業隨著商品行銷分析和數位解決方案的整合而不斷發展，零售商需要保持積極主動並快速回應客戶需求。在英國，繼製造業和能源產業之後，零售業的供應鏈巨量資料分析預計將在預測期內顯著成長。此外，預測分析和機器學習人工智慧有望徹底改變零售供應鏈。
• 事實證明，利用先進的商品行銷分析可以幫助零售商克服在全通路零售業取得成功的挑戰。根據《麻省理工學院技術評論洞察》以全球消費品和零售業案例進行的一項研究顯示，48% 的消費品和零售業受訪者認為，實施人工智慧將有助於改善客戶服務，其次是品管(47%)、庫存管理 (47%)、產品個人化、定價和詐欺偵測。
• 隨著全球經濟變得更加相互關聯和複雜，企業發現很難滿足客戶的期望。他們需要更快、更果斷、更準確地制定供應鏈決策，並且需要能夠快速、透明地執行這些決策。在當今的市場中保持競爭力需要全面的需求規劃。此外，為了實現準時到位（OTIF），公司必須可視化其端到端供應鏈，即時平衡供需，并快速有效地做出正確的決策。必須能夠做到。提高客戶滿意度、最佳化存量基準和分銷網路以及縮短上市時間以最大限度地提高銷售額證明了該領域對巨量資料分析的需求。

預計北美將佔據最大佔有率

• 零售業的巨量資料分析可以幫助企業根據客戶的購買歷史為他們提供建議。其結果是提高了提供客製化購物體驗和增強客戶服務的能力。這些資料集數量龐大，可以幫助預測趨勢並做出資料驅動的策略決策。北美零售市場巨量資料分析的成長是由零售分析工具不斷成長的需求以及物聯網在零售流程中的使用所推動的，從而提高了零售行業的生產力和效率。
• 該地區的大型零售業銷售額正在成長。根據美國零售聯合會（NRF）統計，由於消費者信心高漲、失業率低和薪資上漲，去年美國零售額成長了6%至8%，達到4.44兆美元，預計將超過。經濟強勁且富有彈性的徵兆。
• 此外，北美是採用巨量資料分析的領先創新者和先驅者之一。該地區擁有強大的巨量資料分析供應商立足點，進一步促進了市場成長。其中包括 IBM Corporation、SAS Institute Inc.、Alteryx Inc. 和 Microstrategy Incorporated。由於資料生產和零售消費的增加以及由此帶來的銷售額的增加，巨量資料分析硬體、軟體和服務將需要更多的支出。
• 零售業擴大採用工業 4.0 是推動市場成長的關鍵方面之一。在零售4.0中，零售業的多個業務和流程已經數位化和自動化，包括庫存管理、客戶服務、客戶帳戶、供應鏈管理和商品行銷管理活動。預計在預測期內將進一步推動北美零售市場巨量資料分析的成長。

零售業巨量資料分析概述

零售業的巨量資料分析是中度到高度分散的。電子商務、網路購物的成長以及對客戶忠誠度的激烈競爭為零售業巨量資料分析創造了巨大的利潤機會。整體而言，現有競爭對手之間的競爭非常激烈。未來，大企業不同類型的創新策略將有效拉動市場成長。

其他福利

• Excel 格式的市場預測 (ME) 表
• 3 個月分析師支持

目錄

第1章 簡介

• 研究假設和市場定義
• 調查範圍

第2章調查方法

第3章執行摘要

第4章市場動態

• 市場概況
• 市場促進因素
  • 更關注預測分析
  • 商品行銷和供應鏈分析部門預計將佔據主要佔有率
• 市場限制因素
  • 從不同系統收集和整理資料的複雜性
• 產業價值鏈分析
• 產業吸引力－波特五力分析
  • 新進入者的威脅
  • 買方議價能力
  • 供應商的議價能力
  • 替代產品的威脅
  • 競爭公司之間的敵意強度
• COVID-19 對市場的影響

第5章市場區隔

• 按用途
  • 商品行銷和供應鏈分析
  • 社群媒體分析
  • 客戶分析
  • 營運情報
  • 其他用途
• 按行業分類
  • 中小企業
  • 大型組織
• 地區
  • 北美洲
  • 歐洲
  • 亞太地區
  • 世界其他地區

第6章 競爭形勢

• 公司簡介
  • 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7章 投資分析

第8章市場機會及未來趨勢

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