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市场调查报告书
商品编码
1776784

2032 年零售分析市场预测：按解决方案、部署、零售店类型、现场众包、应用程式和地区进行的全球分析

Retail Analytics Market Forecasts to 2032 - Global Analysis By Solution (Software and Service), Deployment, Retail Store Type, Field Crowdsourcing, Application and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球零售分析市场预计在 2025 年达到 51 亿美元，到 2032 年将达到 204 亿美元，预测期内的复合年增长率为 21.7%。

零售分析涉及运用数据和定量方法来洞察客户行为、销售趋势和零售业务效率。这包括分析销售点数据、存量基准、客户属性、行销宣传活动成效、供应链绩效等。利用商业智慧平台和机器学习等工具，零售商可以优化定价策略、个人化客户体验、预测需求、更有效率地管理库存，并做出数据主导的决策，进而提高盈利和竞争力。

根据Google的零时真相 (ZMOT) 研究，70% 的消费者在店内购物前会先在网路上进行研究。

透过各种管道传播数据

零售分析市场由线上、店内和行动通路产生的数据爆炸性成长所驱动，从而实现数据主导的决策。与电商平台和社群媒体的互动为客户行为分析提供了丰富的资料集。物联网设备在零售环境中的整合可以捕获库存和客流量的即时数据。消费者对个人化购物体验的需求日益增长，推动了分析工具的采用。零售商正在利用这些洞察来优化定价、促销和供应链营运。

与旧有系统整合的挑战

许多零售商在将现代分析平台与过时的旧有系统整合时面临困难，这阻碍了其应用。将大型资料集迁移到云端基础的解决方案的复杂性增加了实施成本。不同旧有系统之间资料格式不一致导致分析流程效率低落。中小企业通常缺乏管理整合的技术专业知识，从而限制了市场成长。对客製化整合解决方案的需求进一步增加了零售商的成本。这些挑战减缓了传统零售业对高阶分析工具的采用。

人工智慧和机器学习（ML）的进步

人工智慧和机器学习在零售分析中的融合，为增强预测模型和客户细分提供了机会。人工智慧主导的工具可以实现即时需求预测并优化库存管理。机器学习演算法改进了建议引擎，提升了客户参与和销售量。云端基础人工智慧平台的日益普及，让即使是小型零售商也能轻鬆掌握高阶分析技术。这些进步有望开闢新的收益来源并提高业务效率。

数据孤岛和品质低下

跨部门资料孤岛阻碍零售商获得统一的客户和业务资料视图。数据品质低（包括不完整或不准确的数据集）导致分析洞察不可靠。缺乏标准化的资料管治实践使资料整合工作变得复杂。零售商面临基于不一致或过时资讯做出错误决策的风险。高昂的资料清理和管理成本给中小企业带来了挑战。这些问题威胁着分析解决方案的有效性和市场成长。

COVID-19的影响：

新冠疫情加速了零售分析的应用，零售商纷纷转向线上和全通路策略。封锁措施增加了对电商的依赖，推动了追踪线上消费行为的分析需求。供应链中断促使零售商转向分析，以优化库存和预测需求。然而，商店客流量的减少最初限制了实体通路的资料收集。疫情过后，对个人化客户体验的关注将持续刺激市场扩张。

预计预测期内软体部分将实现最大幅度成长。

受用于处理全通路资料的高阶分析平台需求不断增长的推动，软体领域预计将在预测期内占据最大的市场占有率。 Tableau 和 Power BI 等工具可让零售商视觉化并有效分析复杂的资料集。可扩展的云端基础平台让各种规模的零售商都能轻鬆取得分析数据。对即时洞察以优化定价和促销活动的需求正在推动软体的采用。持续的更新以及与电商平台的整合进一步巩固了该领域的主导地位。

预计在预测期内，文件和彙报部分将以最高的复合年增长率成长。

预计文件和彙报细分市场将在预测期内实现最高成长率，这得益于人工智慧和机器学习在预测消费者趋势方面的日益普及。预测分析与 CRM 系统的集成为个人化行销策略提供了支援。巨量资料技术投资的不断增长支持了高级预测模型的开发。零售商正在利用这些洞察来优化供应链并提高客户维繫。在竞争激烈的市场中，对竞争差异化的需求进一步推动了该细分市场的成长。

占比最大的地区：

预计亚太地区将在预测期内占据最大的市场占有率，这得益于中国和印度等国家快速数位化和电子商务的扩张。不断壮大的中阶和智慧型手机的广泛普及正在推动线上零售的成长。该地区的零售商正在采用分析技术来改善客户体验并优化业务。该地区精通技术的新兴企业的崛起正在推动对经济高效的分析解决方案的需求。高水准的网路连线和云端运算的采用将进一步推动市场成长。

复合年增长率最高的地区：

预计北美将在预测期内实现最高的复合年增长率，这得益于先进的技术基础设施和分析解决方案的广泛应用。 IBM 和微软等主要参与者的参与正在推动零售分析领域的创新。该地区（尤其是美国）强劲的零售业正在支持分析平台的快速普及。对云端运算和巨量资料技术的投资正在提高解决方案的扩充性。对全通路策略和数据主导决策的关注将推动市场成长。

免费客製化服务：

订阅此报告的客户可享有以下免费自订选项之一：

  • 公司简介
    • 对其他市场公司（最多 3 家公司）进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 地理细分
    • 根据客户兴趣对主要国家市场进行估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 研究范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 次级研究资讯来源
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第五章 全球零售分析市场（按解决方案）

  • 软体
    • 软体分析
    • 部署模式
  • 服务
    • 培训和咨询
    • 整合与部署
    • 託管服务

第六章 全球零售分析市场（依部署）

  • 本地
  • 云

7. 全球零售分析市场（依零售店类型）

  • 大卖场和超级市场
  • 零售连锁

8. 全球零售分析市场（Field Crowdsourcing）

  • 可用性
  • 文件和报告
  • 促销宣传活动管理
  • 客户洞察

第九章全球零售分析市场（按应用）

  • 客户管理
  • 店舖管理
  • 策略与规划
  • 供应链管理
  • 行销和商品行销
  • 其他用途

第十章全球零售分析市场（按地区）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十二章 公司概况

  • 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.
  • Infor Inc.
Product Code: SMRC30115

According to Stratistics MRC, the Global Retail Analytics Market is accounted for $5.1 billion in 2025 and is expected to reach $20.4 billion by 2032 growing at a CAGR of 21.7% during the forecast period. Retail Analytics involves the use of data and quantitative methods to gain insights into customer behavior, sales trends, and operational efficiency within the retail sector. It encompasses analyzing point-of-sale data, inventory levels, customer demographics, marketing campaign effectiveness, and supply chain performance. By leveraging tools like business intelligence platforms and machine learning, retailers can optimize pricing strategies, personalize customer experiences, forecast demand, manage stock more efficiently, and make data-driven decisions to boost profitability and competitiveness.

According to Google's Zero Moment Of Truth (ZMOT) research, 70% of consumers research online before purchasing in-store.

Market Dynamics:

Driver:

Proliferation of data from diverse channels

The retail analytics market is propelled by the explosion of data generated from online, in-store, and mobile channels, enabling data-driven decision-making. E-commerce platforms and social media interactions provide rich datasets for customer behavior analysis. The integration of IoT devices in retail environments captures real-time data on inventory and foot traffic. Growing consumer demand for personalized shopping experiences drives the adoption of analytics tools. Retailers leverage these insights to optimize pricing, promotions, and supply chain operations.

Restraint:

Integration challenges with legacy systems

Many retailers face difficulties integrating modern analytics platforms with outdated legacy systems, hindering adoption. The complexity of migrating large datasets to cloud-based solutions increases implementation costs. Inconsistent data formats across legacy systems lead to inefficiencies in analytics processes. SMEs often lack the technical expertise to manage integration, limiting market growth. The need for customized integration solutions further escalates expenses for retailers. These challenges slow the deployment of advanced analytics tools in traditional retail settings.

Opportunity:

Advancements in AI and machine learning (ML)

The integration of AI and ML in retail analytics offers opportunities to enhance predictive modeling and customer segmentation. AI-driven tools enable real-time demand forecasting, optimizing inventory management. Machine learning algorithms improve recommendation engines, boosting customer engagement and sales. The growing availability of cloud-based AI platforms makes advanced analytics accessible to smaller retailers. These advancements are expected to create new revenue streams and enhance operational efficiency.

Threat:

Data silos and poor data quality

Data silos across departments prevent retailers from achieving a unified view of customer and operational data. Poor data quality, such as incomplete or inaccurate datasets, undermines the reliability of analytics insights. The lack of standardized data governance practices complicates data integration efforts. Retailers risk making flawed decisions based on inconsistent or outdated information. The high cost of data cleansing and management poses challenges for smaller firms. These issues threaten the effectiveness of analytics solutions and market growth.

Covid-19 Impact:

The COVID-19 pandemic accelerated the adoption of retail analytics as retailers pivoted to online and omnichannel strategies. Lockdowns increased reliance on e-commerce, driving demand for analytics to track online consumer behavior. Supply chain disruptions prompted retailers to use analytics for inventory optimization and demand forecasting. However, reduced in-store traffic initially limited data collection from physical channels. Post-pandemic, the focus on personalized customer experiences continues to fuel market expansion.

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period propelled by the growing demand for advanced analytics platforms to process omnichannel data. Tools like Tableau and Power BI enable retailers to visualize and analyze complex datasets effectively. Scalable cloud-based platforms make analytics accessible to retailers of all sizes. The need for real-time insights to optimize pricing and promotions drives software adoption. Continuous updates and integrations with e-commerce platforms further boost this segment's dominance.

The documentation & reporting segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the documentation & reporting segment is predicted to witness the highest growth rate, influenced by the increasing use of AI and ML for forecasting consumer trends. The integration of predictive analytics with CRM systems enhances personalized marketing strategies. Growing investments in big data technologies support the development of advanced predictive models. Retailers are leveraging these insights to optimize supply chains and improve customer retention. The segment's growth is further driven by the need for competitive differentiation in a crowded market.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, fueled by rapid digitalization and the expansion of e-commerce in countries like China and India. The growing middle class and increasing smartphone penetration drive online retail growth. Retailers in the region are adopting analytics to enhance customer experiences and optimize operations. The rise of tech-savvy startups in the region fuels demand for cost-effective analytics solutions. High internet connectivity and cloud adoption further accelerate market growth.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, driven by its advanced technological infrastructure and widespread adoption of analytics solutions. The presence of major players like IBM and Microsoft fosters innovation in retail analytics. The region's strong retail sector, particularly in the U.S., supports rapid adoption of analytics platforms. Investments in cloud computing and big data technologies enhance the scalability of solutions. The focus on omnichannel strategies and data-driven decision-making accelerates market growth.

Key players in the market

Some of the key players in Retail Analytics Market include 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., and Infor Inc.

Key Developments:

In June 2025, SAP SE launched SAP Retail Cloud Insights, a real-time analytics dashboard offering AI-driven demand sensing and dynamic pricing tools for omnichannel retailers.

In May 2025, Salesforce Inc. (Tableau) announced native integration of Einstein AI within Tableau to enhance predictive analytics for inventory and customer engagement.

In April 2025, Microsoft Corp. expanded Power BI retail templates for supply chain visibility and in-store analytics, optimized for Dynamics 365 users.

In March 2025, QlikTech International AB introduced Qlik AutoML for retailers, helping non-technical users build and deploy machine learning models to optimize shelf placement and promotions.

Solutions Covered:

  • Software
  • Service

Deployments Covered:

  • On-Premise
  • Cloud

Retail Store Types Covered:

  • Hypermarkets & Supermarkets
  • Retail Chains

Field Crowdsourcings Covered:

  • On-shelf Availability
  • Documentation & Reporting
  • Promotion Campaign Management
  • Customer Insights

Applications Covered:

  • Customer Management
  • In-store Operation
  • Strategy & Planning
  • Supply Chain Management
  • Marketing & Merchandizing
  • Other Applications

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 Emerging Markets
  • 3.8 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Retail Analytics Market, By Solution

  • 5.1 Introduction
  • 5.2 Software
    • 5.2.1 Software Analytics
    • 5.2.2 Deployment Mode
  • 5.3 Service
    • 5.3.1 Training & Consulting
    • 5.3.2 Integration & deployment
    • 5.3.3 Managed Service

6 Global Retail Analytics Market, By Deployment

  • 6.1 Introduction
  • 6.2 On-Premise
  • 6.3 Cloud

7 Global Retail Analytics Market, By Retail Store Type

  • 7.1 Introduction
  • 7.2 Hypermarkets & Supermarkets
  • 7.3 Retail Chains

8 Global Retail Analytics Market, By Field Crowdsourcing

  • 8.1 Introduction
  • 8.2 On-shelf Availability
  • 8.3 Documentation & Reporting
  • 8.4 Promotion Campaign Management
  • 8.5 Customer Insights

9 Global Retail Analytics Market, By Application

  • 9.1 Introduction
  • 9.2 Customer Management
  • 9.3 In-store Operation
  • 9.4 Strategy & Planning
  • 9.5 Supply Chain Management
  • 9.6 Marketing & Merchandizing
  • 9.7 Other Applications

10 Global Retail Analytics Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 SAP SE
  • 12.2 IBM Corporation
  • 12.3 Oracle Corporation
  • 12.4 Salesforce Inc. (Tableau)
  • 12.5 SAS Institute Inc.
  • 12.6 QlikTech International AB
  • 12.7 Microsoft Corp. (Power BI, Dynamics 365)
  • 12.8 Amazon Web Services Inc. (QuickSight)
  • 12.9 Google LLC (Looker)
  • 12.10 Blue Yonder Inc.
  • 12.11 Dunnhumby Ltd.
  • 12.12 Teradata Corp.
  • 12.13 RetailNext Inc.
  • 12.14 Zebra Technologies Corp.
  • 12.15 Altair Engineering Inc.
  • 12.16 Alteryx Inc.
  • 12.17 MicroStrategy Inc.
  • 12.18 ThoughtSpot Inc.
  • 12.19 Infor Inc.

List of Tables

  • Table 1 Global Retail Analytics Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Retail Analytics Market Outlook, By Solution (2024-2032) ($MN)
  • Table 3 Global Retail Analytics Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global Retail Analytics Market Outlook, By Software Analytics (2024-2032) ($MN)
  • Table 5 Global Retail Analytics Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 6 Global Retail Analytics Market Outlook, By Service (2024-2032) ($MN)
  • Table 7 Global Retail Analytics Market Outlook, By Training & Consulting (2024-2032) ($MN)
  • Table 8 Global Retail Analytics Market Outlook, By Integration & deployment (2024-2032) ($MN)
  • Table 9 Global Retail Analytics Market Outlook, By Managed Service (2024-2032) ($MN)
  • Table 10 Global Retail Analytics Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 11 Global Retail Analytics Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 12 Global Retail Analytics Market Outlook, By Cloud (2024-2032) ($MN)
  • Table 13 Global Retail Analytics Market Outlook, By Retail Store Type (2024-2032) ($MN)
  • Table 14 Global Retail Analytics Market Outlook, By Hypermarkets & Supermarkets (2024-2032) ($MN)
  • Table 15 Global Retail Analytics Market Outlook, By Retail Chains (2024-2032) ($MN)
  • Table 16 Global Retail Analytics Market Outlook, By Field Crowdsourcing (2024-2032) ($MN)
  • Table 17 Global Retail Analytics Market Outlook, By On-shelf Availability (2024-2032) ($MN)
  • Table 18 Global Retail Analytics Market Outlook, By Documentation & Reporting (2024-2032) ($MN)
  • Table 19 Global Retail Analytics Market Outlook, By Promotion Campaign Management (2024-2032) ($MN)
  • Table 20 Global Retail Analytics Market Outlook, By Customer Insights (2024-2032) ($MN)
  • Table 21 Global Retail Analytics Market Outlook, By Application (2024-2032) ($MN)
  • Table 22 Global Retail Analytics Market Outlook, By Customer Management (2024-2032) ($MN)
  • Table 23 Global Retail Analytics Market Outlook, By In-store Operation (2024-2032) ($MN)
  • Table 24 Global Retail Analytics Market Outlook, By Strategy & Planning (2024-2032) ($MN)
  • Table 25 Global Retail Analytics Market Outlook, By Supply Chain Management (2024-2032) ($MN)
  • Table 26 Global Retail Analytics Market Outlook, By Marketing & Merchandizing (2024-2032) ($MN)
  • Table 27 Global Retail Analytics Market Outlook, By Other Applications (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.