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市場調查報告書
商品編碼
1621745

店內分析市場機會、成長動力、產業趨勢分析及 2024 年至 2032 年預測

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個工作天內

價格
簡介目錄

2023 年，全球店內分析市場估值為 33 億美元，預計將大幅成長，預計 2024 年至 2032 年年複合成長率(CAGR) 為 21.3%。設備有助於推動這種擴張。借助 RFID 標籤、信標、智慧貨架和視訊分析攝影機等創新，零售商可以即時了解商店營運和客戶行為。這些技術產生大量資料，需要複雜的分析才能有效處理和理解。推動店內分析市場的主要因素之一是對高效庫存管理的需求不斷成長。

零售商面臨持續的壓力，需要最佳化庫存水平，同時最大限度地減少成本和浪費，同時確保產品的可用性。店內分析提供有關庫存水平、產品流動和需求趨勢的重要即時資訊，從而實現明智的決策。此外，零售分析工具增強了需求預測，幫助檢測滯銷商品，並使補貨流程合理化。隨著零售商適應供應鏈挑戰和不斷變化的消費者偏好，對庫存管理進階分析工具的投資變得越來越普遍。

從市場組成來看，軟體領域在 2023 年佔據主導地位，佔總市場佔有率的 70% 以上，預計到 2032 年將超過 120 億美元。與現有的零售管理系統。零售商正在尋找能夠輕鬆連接其銷售點 (POS) 系統、庫存管理平台和客戶關係管理 (CRM) 工具的解決方案。隨著企業努力消除資料孤島並培育統一的分析環境，對這些整合解決方案的需求推動了對相容軟體的大量投資。基於雲端的部署模型也越來越受到關注，預計到 2032 年這一數字將超過 130 億美元。

市場範圍
開始年份 2023年
預測年份 2024-2032
起始值 33億美元
預測值 182 億美元
複合年成長率 21.3%

這些雲端服務通常採用按需付費的定價模式，使企業能夠增強其分析能力，而無需承擔大量的前期成本。這種靈活性對於經歷季節性波動或快速成長的零售連鎖店尤其有利，使他們能夠調整分析能力以滿足需求。此外，雲端解決方案最大限度地降低了硬體維護成本，並有助於在不同地點快速推出新的分析功能。在資料，到 2023 年，店內分析市場佔總收入的 75% 以上。

這種方法提高了利潤率並減少了浪費，最終形成了一個更有效率、反應更靈敏的供應鏈，可以根據預期的需求變化進行調整。

目錄

第 1 章：方法與範圍

第 2 章：執行摘要

第 3 章：產業洞察

  • 產業生態系統分析
  • 供應商格局
    • 軟體供應商
    • 雲端服務供應商
    • IT平台提供者
    • 技術整合商
    • 最終用戶
  • 利潤率分析
  • 技術差異化因素
    • 先進的人工智慧和機器學習演算法
    • 電腦視覺和圖像識別
    • IoT 感測器和 RFID 整合
    • 全通路資料整合
    • 其他
  • 重要新聞和舉措
  • 監管環境
  • 衝擊力
    • 成長動力
      • 對增強客戶體驗的需求不斷成長
      • 零售業互聯設備的成長
      • 更加重視庫存最佳化
      • 電子商務平台的競爭日益激烈
    • 產業陷阱與挑戰
      • 初始實施成本高
      • 與遺留系統的整合複雜性
  • 成長潛力分析
  • 波特的分析
  • PESTEL分析

第 4 章：競爭格局

  • 介紹
  • 公司市佔率分析
  • 競爭定位矩陣
  • 戰略展望矩陣

第 5 章：市場估計與預測：按組成部分，2021 - 2032 年

  • 主要趨勢
  • 軟體
    • 數據分析平台
    • 虛擬化工具
    • 其他
  • 服務
    • 專業服務
    • 託管服務

第 6 章：市場估計與預測：依部署模式，2021 - 2032 年

  • 主要趨勢
  • 基於雲端
  • 本地

第 7 章：市場估計與預測：依組織規模，2021 - 2032 年

  • 主要趨勢
  • 中小企業
  • 大型企業

第 8 章：市場估計與預測：依應用分類，2021 - 2032

  • 主要趨勢
  • 行銷管理
  • 客戶行為分析
  • 商品推銷分析
  • 店鋪營運
  • 安全和防損
  • 其他

第 9 章：市場估計與預測：依最終用途，2021 - 2032 年

  • 主要趨勢
  • 零售
  • 款待
  • 衛生保健
  • 其他

第 10 章：市場估計與預測：按地區，2021 - 2032

  • 主要趨勢
  • 北美洲
    • 美國
    • 加拿大
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 西班牙
    • 義大利
    • 俄羅斯
    • 北歐人
  • 亞太地區
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳新銀行
    • 東南亞
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • MEA
    • 阿拉伯聯合大公國
    • 南非
    • 沙烏地阿拉伯

第 11 章：公司簡介

  • 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
簡介目錄
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