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市场调查报告书
商品编码
1850054

零售分析：市场占有率分析、产业趋势、统计数据和成长预测（2025-2030）

Retail Analytics - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 120 Pages | 商品交期: 2-3个工作天内

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简介目录

零售分析市场规模预计在 2025 年达到 66 亿美元，预计到 2030 年将达到 81.2 亿美元，在此期间的复合年增长率为 4.23%。

零售分析-市场-IMG1

全通路商务的扩张、数据量的激增以及人工智慧主导决策引擎的快速普及，正推动零售商将分析技术融入日常流程。云端交付模式降低了拥有成本并缩短了部署週期，使中阶连锁店能够获得曾经只有全球领先企业才能获得的功能。即时个人化、规范库存工具和高级促销优化正在提高转换率和订单盈利，即便净利率有所下降。竞争优势正转向整合预测性、规范性和生成性功能的平台，这标誌着分析技术正从一项可自由支配的支出转变为基本的零售需求。

全球零售分析市场趋势与洞察

全通路数据爆炸性成长

零售商如今累积了大量交易、行为和社交讯号。数据成长的步伐推动了对整合数据湖的投资，这些数据湖整合了门市、电商和第三方数据，并支援近乎即时的彙报。擅长快速模式识别的营运商可以提高定价灵活性、精准需求预测并提高存货周转，所有这些都能提高毛利率。管理这种激增的数据也刺激了云端需求，因为弹性基础设施可以防止尖峰时段出现效能瓶颈。因此，分析成熟度是衡量商业性韧性的晴雨表。

人工智慧/机器学习的进步实现了即时规范性洞察

利用机器学习进行需求感知可以减少供应链错误和销售机会的流失，而电脑视觉应用则将智慧技术延伸至销售现场。零售商将人工智慧融入建议引擎，可以解锁新的交叉销售收益流，并提升客户终身价值，因为产出结果已从解释性仪錶板转向自动化决策。融合语言、视觉和结构化资料的多模态模型创造了一个统一的智慧层，打破了功能孤岛。这种转变正在重新定义零售商寻求的人才，并重塑供应商的选择标准，现在，预先建立的、可解释的模型变得尤为重要。

资料隐私法规和合规成本

GDPR 和 CCPA 等日益严格的隐私权法正迫使零售商重组其资料管道，以遵守知情同意和最小化规则。现代化预算越来越多地被分配给用于加密和管理敏感资讯的安全工具，从而分散了原本用于分析创新的资金。跨境零售商面临复杂的合规矩阵和更长的全球扩张部署週期。隐私设计框架有助于减轻法规的影响，但它们可能会限制资料粒度，从而可能限制高阶分析在高要求市场中的应用范围。因此，保护义务与分析目标之间的相互作用仍然是一项至关重要的策略平衡。

細項分析

到2024年，云端部署将占到计划的62.00%，到2030年，复合年增长率将达到9.60%。订阅定价可最大限度地减少资本投入，弹性计算则使零售商能够在不降低性能的情况下应对促销高峰。虽然一些连锁店会将敏感资料保存在店内以满足主权和延迟要求，但将本地资料储存与云端处理相结合的混合架构正在成为一种战略中间地带。这些混合设计可以减少诈欺侦测等关键任务工作负载的往返时间，同时利用云端的经济性来训练分析模型。因此，针对特定工作负载的部署选择变得越来越重要，而不是一刀切地采用云端优先或本地部署。

纯电商零售商将继续超越其他业态，到 2024 年，其采用率将达到 60.00%，复合年增长率为 9.20%。其数位原民架构能够快速部署预测模型，摆脱传统整合障碍，提升每次点击的体验。实体店业者正在透过添加感测器、RFID 和电脑视觉技术，将店内行为转化为可操作的数据，从而缩小数据缺口。全通路连锁店正在采用统一的商业分析技术，整合线上和店内洞察，以改善跨通路库存分配和履约路线。这些不同的优先事项表明，经营模式如何决定蓝图，但所有业态都认同需要更快、更细緻的洞察。

零售分析市场按解决方案（软体和服务）、部署（云端、内部部署、混合）、功能（客户管理、供应链管理、行销和商品行销- 定价/产量比率、其他功能 - 订单管理）、零售形式（实体店、纯电子商务、全通路零售）和地区（北美、南美、欧洲、亚太、中东和非洲）细分。

区域分析

由于强大的云端基础设施、深度电商渗透以及与主要分析供应商的接近性，北美将在2024年以38.20%的收入份额引领零售分析市场。美国零售商计划在2025年增加技术预算，其中最大一部分将用于客户资料平台和基于人工智慧的决策引擎。加拿大正在大力投资客户分析，以在成熟市场中脱颖而出，而墨西哥则优先考虑供应链优化，以解决物流不稳定的问题。

受电商爆炸式成长、智慧型手机普及率上升以及雄心勃勃的数位转型计画的推动，到2030年，亚太地区的复合年增长率将达到6.30%，位居全球最快。中国正率先应用社交商务分析，整合支付、配送和社区参与数据，建构端到端的洞察闭环。印度正在采用预测模型，根据多样化的消费趋势，根据都市区地区客製化产品组合。日本和韩国则优先考虑以门市为中心的分析，例如使用电脑视觉进行客流量测量，以振兴实体店。

欧洲市场占有重要地位，其中英国、德国和法国的零售系统最为先进。严格的隐私法规正在推动混合部署，以平衡资料驻留和云端的扩充性。随着零售商使用环境指标来指导采购和包装决策，永续性分析正日益普及。拉丁美洲的成长集中在巴西和阿根廷，分析技术正在帮助这两个国家管理外汇波动并优化区域促销活动。中东和非洲市场规模仍然较小，但在以色列和阿拉伯联合大公国等市场较为活跃，奢侈品零售商和旅游业正加速采用顾客体验分析技术。

其他福利：

  • Excel 格式的市场预测 (ME) 表
  • 3个月的分析师支持

目录

第一章 引言

  • 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 市场状况

  • 市场概况
  • 市场驱动因素
    • 全通路数据爆炸性成长
    • 人工智慧/机器学习的进步实现了即时预测洞察
    • 云端分析降低了拥有成本
    • 对超个人化购物体验的需求
  • 市场限制
    • 资料隐私法规和合规成本
    • 传统 IT 堆迭与分析技能差距
  • 供应链分析
  • 监管格局
  • 技术展望
  • 波特五力分析
    • 新进入者的威胁
    • 买方的议价能力
    • 供应商的议价能力
    • 替代品的威胁
    • 竞争对手之间的竞争
  • 评估宏观经济趋势对市场的影响

第五章市场规模及成长预测

  • 按解决方案
    • 软体
    • 服务
  • 按部署模型
    • 云
    • 本地部署
    • 杂交种
  • 按功能
    • 客户管理
    • 商店营运－库存管理
    • 店铺营运 - 绩效管理
    • 供应链管理
    • 行销与商品行销- 定价/收益
    • 其他功能 - 运输管理
    • 其他功能 - 订单管理
  • 按零售形式
    • 实体店面
    • 纯电商
    • 全通路零售商
  • 按地区
    • 北美洲
      • 美国
      • 加拿大
      • 墨西哥
    • 欧洲
      • 英国
      • 德国
      • 法国
      • 义大利
      • 其他欧洲地区
    • 亚太地区
      • 中国
      • 日本
      • 印度
      • 韩国
      • 其他亚太地区
    • 中东
      • 以色列
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 土耳其
      • 其他中东地区
    • 非洲
      • 南非
      • 埃及
      • 其他非洲国家
    • 南美洲
      • 巴西
      • 阿根廷
      • 南美洲其他地区

第六章 竞争态势

  • 市场集中度
  • 策略趋势
  • 市占率分析
  • 公司简介
    • SAP SE
    • IBM Corporation
    • Oracle Corporation
    • Salesforce Inc.（Tableau）
    • SAS Institute Inc.
    • QlikTech International AB
    • Microsoft Corp.（Power BI, Dynamics 365）
    • Amazon Web Services Inc.（QuickSight）
    • Google LLC（Looker）
    • Blue Yonder Inc.
    • Dunnhumby Ltd.
    • Teradata Corp.
    • RetailNext Inc.
    • Zebra Technologies Corp.
    • Altair Engineering Inc.
    • Alteryx Inc.
    • MicroStrategy Inc.
    • ThoughtSpot Inc.
    • Fujitsu Ltd.
    • Infor Inc.

第七章 市场机会与未来展望

简介目录
Product Code: 52446

The retail analytics market size is estimated at USD 6.60 billion in 2025 and is projected to reach USD 8.12 billion by 2030, registering a 4.23% CAGR across the period.

Retail Analytics - Market - IMG1

Broader omnichannel commerce, surging data volumes, and rapid adoption of AI-driven decision engines are encouraging retailers to embed analytics into day-to-day processes. Cloud delivery models are cutting ownership costs and trimming deployment cycles, which allows mid-tier chains to access capabilities once limited to global leaders. Real-time personalization, prescriptive inventory tools, and advanced promotion optimisation are lifting conversion rates and order profitability even as margins tighten. Competitive advantage is shifting toward platforms that integrate predictive, prescriptive, and generative capabilities, signaling that analytics has moved from a discretionary spend to a fundamental retail requirement.

Global Retail Analytics Market Trends and Insights

Omnichannel data-volume explosion

Retailers now ingest vast pools of transaction, behavioural, and social signals that dwarf prior years and mandate scalable analytics platforms. The pace of data growth is prompting investments in unified data lakes that consolidate store, e-commerce, and third-party feeds to support near real-time reporting. Operators that master rapid pattern recognition improve pricing agility, sharpen demand forecasts, and raise inventory turns, which collectively push gross-margin lift. Managing this surge also accelerates cloud demand because elastic infrastructure prevents performance bottlenecks during peak events. Consequently, analytic maturity becomes a barometer for commercial resilience.

AI/ML advances enabling real-time prescriptive insights

Machine-learning-powered demand sensing is reducing supply-chain errors and missed sales opportunities, while computer vision applications extend intelligence to the sales floor. Retailers that embed AI into recommendation engines capture new cross-sell revenue streams and improve lifetime customer value as output moves from descriptive dashboards to automated decisions. Multimodal models that blend language, vision, and structured data produce unified intelligence layers that break down functional silos. These shifts are redefining the talent profiles retailers seek and are reshaping vendor selection criteria, with emphasis now on pre-built, explainable models.

Data-privacy regulation and compliance costs

Tightening privacy laws such as GDPR and CCPA compel retailers to re-engineer data pipelines to uphold consent and minimisation rules. Modernisation budgets now allocate growing shares to security tooling that encrypts and governs sensitive information, diverting funds from analytics innovation. Cross-border retailers face complex compliance matrices that elongate deployment cycles for global rollouts. Privacy-by-design frameworks help reduce regulatory exposure but also limit data granularity, which may curb the scope of advanced analytics in high-stringency markets. The interplay between protection mandates and analytics ambitions, therefore, remains a key strategic balancing act.

Other drivers and restraints analyzed in the detailed report include:

  1. Uptake of cloud analytics lowering TCO
  2. Demand for hyper-personalised shopping journeys
  3. Legacy IT stacks and analytics skills gap

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Cloud deployments captured 62.00% of projects in 2024 and will grow at a 9.60% CAGR to 2030. Subscription pricing minimises capital commitments, and elastic compute equips retailers to handle promotional peaks without performance dips. Although some chains retain sensitive data on-site to satisfy sovereignty and latency requirements, hybrid architectures that blend local data storage with cloud compute are emerging as a strategic middle ground. These hybrid designs reduce round-trip times for mission-critical workloads such as fraud detection while leveraging cloud economics for analytics model training. As a result, deployment choices increasingly hinge on workload characteristics rather than a blanket cloud-first or on-premises stance.

Pure-play e-commerce retailers captured 60.00% of deployments in 2024 and will continue to outgrow other formats at a 9.20% CAGR. Their digital-native architectures enable rapid adoption of predictive models without legacy integration hurdles, allowing them to refine experiences at every click. Brick-and-mortar operators are bridging the data gap by adding sensors, RFID, and computer vision to convert in-store behaviour into actionable data. Omnichannel chains adopt unified commerce analytics that merge online and store insights to improve cross-channel stock allocation and fulfilment routing. These varied priorities illustrate that business model dictates analytics roadmaps, yet all formats converge on the need for faster, more granular insights.

Retail Analytics Market is Segmented by Solutions (Software and Services), Deployment (Cloud, On-Premises, Hybrid), Function (Customer Management, Supply Chain Management, Marketing and Merchandising - Pricing/Yield, Other Functions - Order Management), Retail Format (Brick-And-Mortar Stores, Pure-Play E-Commerce, Omnichannel Retailers), Geography (North America, South America, Europe, Asia-Pacific, Middle East and Africa).

Geography Analysis

North America led the retail analytics market with 38.20% revenue share in 2024, supported by robust cloud infrastructure, deep e-commerce penetration, and proximity to leading analytics vendors. United States retailers plan to raise technology budgets in 2025, directing the largest allocations to customer-data platforms and AI-based decision engines. Canada trails slightly yet invests heavily in customer analytics to differentiate in a mature market, while Mexico emphasises supply-chain optimisation to address logistics volatility.

Asia-Pacific is set to post the fastest CAGR at 6.30% through 2030, propelled by explosive e-commerce growth, increasing smartphone adoption, and ambitious digital transformation programs. China pioneers social-commerce analytics that integrate payment, delivery, and community engagement data, producing end-to-end insight loops. India's diverse consumer landscape encourages retailers to deploy predictive models that tailor assortments across urban and rural zones. Japan and South Korea emphasise store-centric analytics such as computer-vision footfall measurement to revitalise physical outlets.

Europe retains a meaningful footprint anchored by sophisticated retail systems in the United Kingdom, Germany, and France. Stringent privacy rules drive adoption of hybrid deployments that balance data residency with cloud scalability. Sustainability analytics is gaining traction as retailers use environmental metrics to guide sourcing and packaging decisions. Latin American growth concentrates in Brazil and Argentina, where analytics helps manage currency volatility and optimise localised promotions. Middle East and Africa remain smaller but active in markets such as Israel and the United Arab Emirates, where luxury retail and tourism accelerate the adoption of customer experience analytics.

  1. SAP SE
  2. IBM Corporation
  3. Oracle Corporation
  4. Salesforce Inc. (Tableau)
  5. SAS Institute Inc.
  6. QlikTech International AB
  7. Microsoft Corp. (Power BI, Dynamics 365)
  8. Amazon Web Services Inc. (QuickSight)
  9. Google LLC (Looker)
  10. Blue Yonder Inc.
  11. Dunnhumby Ltd.
  12. Teradata Corp.
  13. RetailNext Inc.
  14. Zebra Technologies Corp.
  15. Altair Engineering Inc.
  16. Alteryx Inc.
  17. MicroStrategy Inc.
  18. ThoughtSpot Inc.
  19. Fujitsu Ltd.
  20. Infor Inc.

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 LANDSCAPE

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Omnichannel data-volume explosion
    • 4.2.2 AI/ML advances for real-time prescriptive insights
    • 4.2.3 Uptake of cloud analytics lowering ownership costs
    • 4.2.4 Demand for hyper-personalised shopping journeys
  • 4.3 Market Restraints
    • 4.3.1 Data-privacy regulation and compliance costs
    • 4.3.2 Legacy IT stacks and analytics skills gap
  • 4.4 Supply Chain Analysis
  • 4.5 Regulatory Landscape
  • 4.6 Technological Outlook
  • 4.7 Porter's Five Forces Analysis
    • 4.7.1 Threat of New Entrants
    • 4.7.2 Bargaining Power of Buyers
    • 4.7.3 Bargaining Power of Suppliers
    • 4.7.4 Threat of Substitute Products
    • 4.7.5 Intensity of Competitive Rivalry
  • 4.8 Assessment of the Impact of Macroeconomic Trends on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

  • 5.1 By Solution
    • 5.1.1 Software
    • 5.1.2 Services
  • 5.2 By Deployment Model
    • 5.2.1 Cloud
    • 5.2.2 On-premise
    • 5.2.3 Hybrid
  • 5.3 By Function
    • 5.3.1 Customer Management
    • 5.3.2 In-store Operation - Inventory Management
    • 5.3.3 In-store Operation - Performance Management
    • 5.3.4 Supply Chain Management
    • 5.3.5 Marketing and Merchandising - Pricing/Yield
    • 5.3.6 Other Functions - Transportation Management
    • 5.3.7 Other Functions - Order Management
  • 5.4 By Retail Format
    • 5.4.1 Brick-and-Mortar Stores
    • 5.4.2 Pure-play E-commerce
    • 5.4.3 Omnichannel Retailers
  • 5.5 By Geography
    • 5.5.1 North America
      • 5.5.1.1 United States
      • 5.5.1.2 Canada
      • 5.5.1.3 Mexico
    • 5.5.2 Europe
      • 5.5.2.1 United Kingdom
      • 5.5.2.2 Germany
      • 5.5.2.3 France
      • 5.5.2.4 Italy
      • 5.5.2.5 Rest of Europe
    • 5.5.3 Asia-Pacific
      • 5.5.3.1 China
      • 5.5.3.2 Japan
      • 5.5.3.3 India
      • 5.5.3.4 South Korea
      • 5.5.3.5 Rest of Asia-Pacific
    • 5.5.4 Middle East
      • 5.5.4.1 Israel
      • 5.5.4.2 Saudi Arabia
      • 5.5.4.3 United Arab Emirates
      • 5.5.4.4 Turkey
      • 5.5.4.5 Rest of Middle East
    • 5.5.5 Africa
      • 5.5.5.1 South Africa
      • 5.5.5.2 Egypt
      • 5.5.5.3 Rest of Africa
    • 5.5.6 South America
      • 5.5.6.1 Brazil
      • 5.5.6.2 Argentina
      • 5.5.6.3 Rest of South America

6 COMPETITIVE LANDSCAPE

  • 6.1 Market Concentration
  • 6.2 Strategic Moves
  • 6.3 Market Share Analysis
  • 6.4 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products & Services, and Recent Developments)
    • 6.4.1 SAP SE
    • 6.4.2 IBM Corporation
    • 6.4.3 Oracle Corporation
    • 6.4.4 Salesforce Inc. (Tableau)
    • 6.4.5 SAS Institute Inc.
    • 6.4.6 QlikTech International AB
    • 6.4.7 Microsoft Corp. (Power BI, Dynamics 365)
    • 6.4.8 Amazon Web Services Inc. (QuickSight)
    • 6.4.9 Google LLC (Looker)
    • 6.4.10 Blue Yonder Inc.
    • 6.4.11 Dunnhumby Ltd.
    • 6.4.12 Teradata Corp.
    • 6.4.13 RetailNext Inc.
    • 6.4.14 Zebra Technologies Corp.
    • 6.4.15 Altair Engineering Inc.
    • 6.4.16 Alteryx Inc.
    • 6.4.17 MicroStrategy Inc.
    • 6.4.18 ThoughtSpot Inc.
    • 6.4.19 Fujitsu Ltd.
    • 6.4.20 Infor Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-space and Unmet-Need Assessment