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市场调查报告书
商品编码
1621745

店内分析市场机会、成长动力、产业趋势分析及 2024 年至 2032 年预测

In-store Analytics Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2024 to 2032
出版日期: | 出版商: Global Market Insights Inc. | 英文 180 Pages | 商品交期: 2-3个工作天内

价格
简介目录

2023 年，全球店内分析市场估值为 33 亿美元，预计将大幅成长，预计 2024 年至 2032 年年复合成长率(CAGR) 为 21.3%。设备有助于推动这种扩张。借助 RFID 标籤、信标、智慧货架和视讯分析摄影机等创新，零售商可以即时了解商店营运和客户行为。这些技术产生大量资料，需要复杂的分析才能有效处理和理解。推动店内分析市场的主要因素之一是对高效库存管理的需求不断增长。

零售商面临持续的压力，需要优化库存水平，同时最大限度地减少成本和浪费，同时确保产品的可用性。店内分析提供有关库存水平、产品流动和需求趋势的重要即时信息，从而实现明智的决策。此外，零售分析工具增强了需求预测，帮助检测滞销商品，并使补货流程合理化。随着零售商适应供应链挑战和不断变化的消费者偏好，对库存管理进阶分析工具的投资变得越来越普遍。

从市场组成来看，软体领域在 2023 年占据主导地位，占总市场份额的 70% 以上，预计到 2032 年将超过 120 亿美元。与现有的零售管理系统。零售商正在寻找能够轻鬆连接其销售点 (POS) 系统、库存管理平台和客户关係管理 (CRM) 工具的解决方案。随着企业努力消除资料孤岛并培育统一的分析环境，对这些整合解决方案的需求推动了对相容软体的大量投资。基于云端的部署模型也越来越受到关注，预计到 2032 年这一数字将超过 130 亿美元。

市场范围
开始年份 2023年
预测年份 2024-2032
起始值 33亿美元
预测值 182 亿美元
复合年增长率 21.3%

这些云端服务通常采用按需付费的定价模式，使企业能够增强其分析能力，而无需承担大量的前期成本。这种灵活性对于经历季节性波动或快速成长的零售连锁店尤其有利，使他们能够调整分析能力以满足需求。此外，云端解决方案最大限度地降低了硬体维护成本，并有助于在不同地点快速推出新的分析功能。在资料，到 2023 年，店内分析市场占总收入的 75% 以上。

这种方法提高了利润率并减少了浪费，最终形成了一个更有效率、反应更灵敏的供应链，可以根据预期的需求变化进行调整。

目录

第 1 章：方法与范围

第 2 章：执行摘要

第 3 章：产业洞察

  • 产业生态系统分析
  • 供应商格局
    • 软体供应商
    • 云端服务供应商
    • IT平台提供者
    • 技术整合商
    • 最终用户
  • 利润率分析
  • 技术差异化因素
    • 先进的人工智慧和机器学习演算法
    • 电脑视觉和图像识别
    • IoT 感测器和 RFID 集成
    • 全通路资料整合
    • 其他的
  • 重要新闻和倡议
  • 监管环境
  • 衝击力
    • 成长动力
      • 对增强客户体验的需求不断增长
      • 零售业互联设备的成长
      • 更加重视库存优化
      • 电子商务平台的竞争日益激烈
    • 产业陷阱与挑战
      • 初始实施成本高
      • 与遗留系统的整合复杂性
  • 成长潜力分析
  • 波特的分析
  • PESTEL分析

第 4 章：竞争格局

  • 介绍
  • 公司市占率分析
  • 竞争定位矩阵
  • 战略展望矩阵

第 5 章：市场估计与预测：按组成部分，2021 - 2032 年

  • 主要趋势
  • 软体
    • 数据分析平台
    • 虚拟化工具
    • 其他的
  • 服务
    • 专业服务
    • 託管服务

第 6 章：市场估计与预测：依部署模式，2021 - 2032 年

  • 主要趋势
  • 基于云端
  • 本地

第 7 章：市场估计与预测：依组织规模，2021 - 2032 年

  • 主要趋势
  • 中小企业
  • 大型企业

第 8 章：市场估计与预测：依应用分类，2021 - 2032

  • 主要趋势
  • 行销管理
  • 客户行为分析
  • 商品推销分析
  • 店铺营运
  • 安全和防损
  • 其他的

第 9 章：市场估计与预测：依最终用途，2021 - 2032 年

  • 主要趋势
  • 零售
  • 款待
  • 卫生保健
  • 其他的

第 10 章：市场估计与预测：按地区，2021 - 2032

  • 主要趋势
  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 西班牙
    • 义大利
    • 俄罗斯
    • 北欧人
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 韩国
    • 澳新银行
    • 东南亚
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • MEA
    • 阿联酋
    • 南非
    • 沙乌地阿拉伯

第 11 章：公司简介

  • Capgemini
  • Capillary Technologies
  • Cloud4WI
  • CountBox
  • Happiest Minds
  • Kepler Analytics
  • Mindtree
  • Microsoft
  • Quividi
  • RetailNext
  • Scanalytics
  • sensalytics
  • Sensormatic (Johnson Controls)
  • Sisense
  • SmartConnect
  • Thinkin
  • Trax Technology Solutions
  • V-Count
  • Vispera
  • Walkbase
  • Zebra Technologies
简介目录
Product Code: 11844

The Global In-Store Analytics Market was valued at USD 3.3 billion in 2023 and is expected to grow significantly, with a compound annual growth rate (CAGR) of 21.3% projected from 2024 to 2032. The rapid growth of Internet of Things (IoT) technologies and interconnected devices in retail help in driving this expansion. With innovations like RFID tags, beacons, smart shelves, and video analytics cameras, retailers gain real-time insights into both store operations and customer behavior. These technologies generate vast amounts of data, which require sophisticated analytics for effective processing and understanding. One of the primary factors propelling the in-store analytics market is the increasing demand for efficient inventory management.

Retailers face constant pressure to optimize stock levels while minimizing costs and waste, all while ensuring product availability. In-store analytics provide vital real-time information regarding inventory levels, product movement, and demand trends, enabling informed decision-making. Additionally, retail analytics tools enhance necessity forecasting, help detect slow-moving items, and rationalize restocking processes. As retailers adapt to supply chain challenges and changing consumer preferences, investments in advanced analytics tools for inventory management are becoming more prevalent.

In terms of market components, the software segment dominated in 2023, accounting for over 70% of the total market share, and projected to exceed USD 12 billion by 2032. The increasing need for modern in-store analytics software arises from its seamless integration capabilities with existing retail management systems. Retailers are looking for solutions that can easily connect with their point-of-sale (POS) systems, inventory management platforms, and customer relationship management (CRM) tools. As businesses strive to eliminate data silos and foster unified analytics environments, the demand for these integrated solutions drives significant investments in compatible software. The cloud-based deployment model is also gaining traction, with projections indicating it will exceed USD 13 billion by 2032. Retailers are increasingly adopting cloud solutions for in-store analytics due to their scalability and cost-effectiveness.

Market Scope
Start Year2023
Forecast Year2024-2032
Start Value$3.3 Billion
Forecast Value$18.2 Billion
CAGR21.3%

These cloud services often utilize pay-as-you-go pricing models, allowing businesses to enhance their analytics capabilities without incurring large upfront costs. This flexibility is particularly beneficial for retail chains experiencing seasonal fluctuations or rapid growth, enabling them to adapt their analytics capacity to meet demand. Furthermore, cloud solutions minimize hardware maintenance costs and facilitate the rapid rollout of new analytics features across various locations. In the United States, the in-store analytics market accounted for more than 75% of total revenue in 2023. Retailers in this region are leveraging AI-driven predictive analytics to refine inventory management, utilizing historical sales data and trends to optimize stock levels.

This approach improves profit margins and reduces waste, ultimately leading to a more efficient and responsive supply chain that can adjust to anticipated demand changes.

Table of Contents

Chapter 1 Methodology & Scope

  • 1.1 Research design
    • 1.1.1 Research approach
    • 1.1.2 Data collection methods
  • 1.2 Base estimates and calculations
    • 1.2.1 Base year calculation
    • 1.2.2 Key trends for market estimates
  • 1.3 Forecast model
  • 1.4 Primary research & validation
    • 1.4.1 Primary sources
    • 1.4.2 Data mining sources
  • 1.5 Market definitions

Chapter 2 Executive Summary

  • 2.1 Industry 360° synopsis, 2021 - 2032

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
  • 3.2 Supplier landscape
    • 3.2.1 Software providers
    • 3.2.2 Cloud service providers
    • 3.2.3 IT platform providers
    • 3.2.4 Technology integrators
    • 3.2.5 End users
  • 3.3 Profit margin analysis
  • 3.4 Technology differentiators
    • 3.4.1 Advanced AI & machine learning algorithms
    • 3.4.2 Computer vision and image recognition
    • 3.4.3 IoT sensors and RFID integration
    • 3.4.4 Omnichannel data integration
    • 3.4.5 Others
  • 3.5 Key news & initiatives
  • 3.6 Regulatory landscape
  • 3.7 Impact forces
    • 3.7.1 Growth drivers
      • 3.7.1.1 Rising demand for enhanced customer experience
      • 3.7.1.2 Growth of connected devices in the retail sector
      • 3.7.1.3 Increasing focus on inventory optimization
      • 3.7.1.4 Growing competition from E-commerce platforms
    • 3.7.2 Industry pitfalls & challenges
      • 3.7.2.1 High initial implementation costs
      • 3.7.2.2 Integration complexity with legacy systems
  • 3.8 Growth potential analysis
  • 3.9 Porter's analysis
  • 3.10 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

  • 4.1 Introduction
  • 4.2 Company market share analysis
  • 4.3 Competitive positioning matrix
  • 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2032 ($Bn)

  • 5.1 Key trends
  • 5.2 Software
    • 5.2.1 Data analytics platforms
    • 5.2.2 Virtualization tools
    • 5.2.3 Others
  • 5.3 Services
    • 5.3.1 Professional services
    • 5.3.2 Managed services

Chapter 6 Market Estimates & Forecast, By Deployment Mode, 2021 - 2032 ($Bn)

  • 6.1 Key trends
  • 6.2 Cloud-based
  • 6.3 On-premises

Chapter 7 Market Estimates & Forecast, By Organization Size, 2021 - 2032 ($Bn)

  • 7.1 Key trends
  • 7.2 SME
  • 7.3 Large enterprises

Chapter 8 Market Estimates & Forecast, By Application, 2021 - 2032 ($Bn)

  • 8.1 Key trends
  • 8.2 Marketing management
  • 8.3 Customer behavior analysis
  • 8.4 Merchandising analysis
  • 8.5 Store operations
  • 8.6 Security & loss prevention
  • 8.7 Others

Chapter 9 Market Estimates & Forecast, By End Use, 2021 - 2032 ($Bn)

  • 9.1 Key trends
  • 9.2 Retail
  • 9.3 Hospitality
  • 9.4 Healthcare
  • 9.5 Others

Chapter 10 Market Estimates & Forecast, By Region, 2021 - 2032 ($Bn)

  • 10.1 Key trends
  • 10.2 North America
    • 10.2.1 U.S.
    • 10.2.2 Canada
  • 10.3 Europe
    • 10.3.1 UK
    • 10.3.2 Germany
    • 10.3.3 France
    • 10.3.4 Spain
    • 10.3.5 Italy
    • 10.3.6 Russia
    • 10.3.7 Nordics
  • 10.4 Asia Pacific
    • 10.4.1 China
    • 10.4.2 India
    • 10.4.3 Japan
    • 10.4.4 South Korea
    • 10.4.5 ANZ
    • 10.4.6 Southeast Asia
  • 10.5 Latin America
    • 10.5.1 Brazil
    • 10.5.2 Mexico
    • 10.5.3 Argentina
  • 10.6 MEA
    • 10.6.1 UAE
    • 10.6.2 South Africa
    • 10.6.3 Saudi Arabia

Chapter 11 Company Profiles

  • 11.1 Capgemini
  • 11.2 Capillary Technologies
  • 11.3 Cloud4WI
  • 11.4 CountBox
  • 11.5 Happiest Minds
  • 11.6 Kepler Analytics
  • 11.7 Mindtree
  • 11.8 Microsoft
  • 11.9 Quividi
  • 11.10 RetailNext
  • 11.11 Scanalytics
  • 11.12 sensalytics
  • 11.13 Sensormatic (Johnson Controls)
  • 11.14 Sisense
  • 11.15 SmartConnect
  • 11.16 Thinkin
  • 11.17 Trax Technology Solutions
  • 11.18 V-Count
  • 11.19 Vispera
  • 11.20 Walkbase
  • 11.21 Zebra Technologies