店内分析市场－全球产业规模、份额、趋势、机会和预测，按类型、按技术、按最终用户产业、按地区和竞争进行细分，2020-2030 年预测

2024 年全球店内分析市场价值为 51.6 亿美元，预计到 2030 年将达到 132.8 亿美元，预测期内复合年增长率为 16.89%。

市场概况
预测期	2026-2030
2024年市场规模	51.6亿美元
2030年市场规模	132.8亿美元
2025-2030年复合年增长率	16.89%
成长最快的领域	物联网
最大的市场	北美洲

店内分析市场是指由技术、工具和服务组成的生态系统，使零售商和其他实体店业者能够收集、分析和解读店内顾客互动和行为资料。这些解决方案利用人工智慧、机器学习、电脑视觉和物联网设备等先进技术，提供有关顾客流量模式、停留时间、产品互动度、排队管理、货架性能和整体购物旅程的可行洞察。透过撷取和分析这些资料，企业可以优化门市布局、人员配置、产品展示、促销和行销活动，从而提升顾客体验、提高营运效率并推动销售。

店内分析解决方案广泛应用于零售和电商、超市和大卖场、时尚服饰店、消费性电子产品商店以及餐饮场所等。由于零售商日益增长的需求，采用数据驱动型策略以应对日益激烈的竞争、不断变化的消费者期望以及向全通路零售的转变，市场正在蓬勃发展。智慧摄影机、感测器、信标和其他硬体设备的普及，加上基于云端的分析平台，使得即时监控和可操作的洞察成为可能，并且只需极少的人工干预。

关键市场驱动因素

人工智慧和机器学习的技术进步推动情感分析市场

主要市场挑战

资料隐私和监管合规挑战

主要市场趋势

人工智慧和机器学习在店内分析的整合

目录

第 1 章：产品概述

第二章：研究方法

第三章：执行摘要

第四章：顾客之声

第五章：全球店内分析市场展望

• 市场规模和预测
  • 按价值
• 市场占有率和预测
  • 按类型（影片分析、客流量统计和热图、货架分析、队列管理分析、客户旅程分析）
  • 按技术（人工智慧、机器学习、物联网、云端运算、电脑视觉）
  • 按最终用户产业（零售和电子商务、超市和大卖场、消费性电子产品、时尚和服饰、食品和饮料、其他）
  • 按地区（北美、欧洲、南美、中东和非洲、亚太地区）
• 按公司分类（2024 年）
• 市场地图

第六章：北美店内分析市场展望

• 市场规模和预测
• 市场占有率和预测
• 北美：国家分析
  • 美国
  • 加拿大
  • 墨西哥

第七章：欧洲店内分析市场展望

• 市场规模和预测
• 市场占有率和预测
• 欧洲：国家分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

第八章：亚太店内分析市场展望

• 市场规模和预测
• 市场占有率和预测
• 亚太地区：国家分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

第九章：中东和非洲店内分析市场展望

• 市场规模和预测
• 市场占有率和预测
• 中东和非洲：国家分析
  • 沙乌地阿拉伯
  • 阿联酋
  • 南非

第 10 章：南美洲店内分析市场展望

• 市场规模和预测
• 市场占有率和预测
• 南美洲：国家分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第 11 章：市场动态

• 驱动程式
• 挑战

第 12 章：市场趋势与发展

• 合併与收购（如有）
• 产品发布（如有）
• 最新动态

第十三章：公司简介

• Trax Retail
• RetailNext
• ShopperTrak (Sensormatic Solutions
• Nomi (by Intel)
• V-Count
• Dor Technologies
• Falkonry
• FootFallCam
• Amsive Analytics
• Cenium Analytics

第 14 章：策略建议

第15章调查会社について・免责事项

Global In-store Analytics Market was valued at USD 5.16 billion in 2024 and is expected to reach USD 13.28 billion by 2030 with a CAGR of 16.89% during the forecast period.

Market Overview
Forecast Period	2026-2030
Market Size 2024	USD 5.16 Billion
Market Size 2030	USD 13.28 Billion
CAGR 2025-2030	16.89%
Fastest Growing Segment	Internet of Things
Largest Market	North America

The In-store Analytics Market refers to the ecosystem of technologies, tools, and services that enable retailers and other physical store operators to collect, analyze, and interpret data from in-store customer interactions and behaviors. These solutions leverage advanced technologies such as artificial intelligence, machine learning, computer vision, and Internet of Things-enabled devices to provide actionable insights into customer traffic patterns, dwell times, product engagement, queue management, shelf performance, and overall shopping journeys. By capturing and analyzing this data, businesses can optimize store layouts, staffing, product placements, promotions, and marketing campaigns to enhance customer experience, increase operational efficiency, and drive sales.

In-store analytics solutions are widely deployed across retail and e-commerce, supermarkets and hypermarkets, fashion and apparel outlets, consumer electronics stores, and food and beverage establishments, among others. The market is rising due to the growing need for retailers to adopt data-driven strategies in response to increasing competition, evolving consumer expectations, and the shift toward omnichannel retailing. The proliferation of smart cameras, sensors, beacons, and other hardware devices, coupled with cloud-based analytics platforms, enables real-time monitoring and actionable insights with minimal manual intervention.

Key Market Drivers

Technological Advancements in Artificial Intelligence and Machine Learning Driving the Emotion Analytics Market

In the rapidly evolving landscape of the Emotion Analytics Market, technological advancements in artificial intelligence and machine learning stand as pivotal forces propelling growth and innovation, enabling organizations to harness sophisticated algorithms that decode human emotions from diverse data sources such as facial expressions, voice tones, text sentiments, and physiological signals, thereby transforming customer interactions, employee engagements, and market research methodologies into more intuitive and responsive frameworks that drive competitive advantage and operational efficiency.

These advancements facilitate the development of real-time emotion detection systems that integrate seamlessly with existing business infrastructures, allowing companies in sectors like retail, healthcare, and finance to personalize experiences, mitigate risks, and optimize strategies based on granular emotional insights, which in turn enhances customer loyalty, reduces churn rates, and boosts revenue streams through targeted interventions that resonate on a deeper psychological level. Machine learning models, particularly deep learning architectures like convolutional neural networks and recurrent neural networks, have revolutionized the accuracy and scalability of emotion analytics by processing vast datasets with unprecedented speed and precision, adapting dynamically to cultural nuances and contextual variations that traditional methods could not address, thus opening new avenues for global market expansion and cross-cultural applications.

The convergence of artificial intelligence with Internet of Things devices and big data analytics further amplifies this driver's impact, as it empowers businesses to collect multimodal data from wearable technologies, smart cameras, and social media platforms, feeding into predictive models that forecast emotional trends and behavioral patterns, enabling proactive decision-making that anticipates consumer needs before they are explicitly voiced. Moreover, the integration of natural language processing within these systems allows for sentiment analysis of unstructured data from customer reviews, call center interactions, and social media feeds, providing actionable intelligence that informs product development, marketing campaigns, and crisis management protocols, all while ensuring compliance with data privacy regulations through advanced anonymization techniques.

As organizations increasingly prioritize empathetic branding and human-centered design, these technological strides in artificial intelligence and machine learning not only streamline internal processes but also foster innovation in emerging fields like affective computing, where virtual assistants and chatbots evolve to respond empathetically, enhancing user satisfaction and fostering long-term relationships that translate into sustained market share gains.

The democratization of these technologies through cloud-based platforms and open-source frameworks has lowered barriers to entry, allowing even small and medium-sized enterprises to leverage emotion analytics for strategic gains, such as refining user interfaces in e-commerce or improving patient outcomes in telemedicine by detecting distress signals early. Furthermore, the continuous refinement of algorithms through transfer learning and federated learning approaches ensures that models remain robust against biases and adaptable to diverse populations, addressing ethical concerns and promoting inclusive growth within the Emotion Analytics Market.

Investments in research and development by leading tech firms are accelerating this momentum, with breakthroughs in edge computing enabling on-device emotion processing that reduces latency and enhances privacy, critical for applications in autonomous vehicles where driver emotional states influence safety protocols, or in virtual reality environments where immersive experiences are tailored to user moods for maximum engagement. The synergy between artificial intelligence and blockchain technology also promises secure, transparent data handling in emotion analytics, building trust among stakeholders and facilitating collaborative ecosystems where shared insights drive industry-wide advancements.

As regulatory landscapes evolve to accommodate these innovations, businesses that adopt cutting-edge artificial intelligence and machine learning solutions in emotion analytics are positioned to lead in customer-centric paradigms, where emotional intelligence becomes a core competency rather than an afterthought, ultimately reshaping competitive dynamics and unlocking new revenue potentials through hyper-personalized offerings that align with evolving consumer expectations. The proliferation of 5G networks complements these advancements by enabling high-fidelity data transmission for real-time analytics, crucial for live events or customer service scenarios where immediate emotional feedback loops can turn potential dissatisfaction into delight, thereby fortifying brand reputation and market positioning.

Collaborative efforts between academia and industry are yielding hybrid models that combine supervised and unsupervised learning, improving the interpretability of emotion predictions and allowing for more nuanced business applications, such as sentiment-driven stock trading algorithms or employee wellness programs that preempt burnout through proactive interventions. The ethical deployment of these technologies, guided by principles of fairness and transparency, ensures sustainable growth in the Emotion Analytics Market, mitigating risks associated with misinterpretation of emotions and fostering a ecosystem where innovation serves societal good.

As quantum computing looms on the horizon, its potential to process complex emotional datasets at speeds unattainable today promises to further elevate the capabilities of artificial intelligence and machine learning, positioning the Emotion Analytics Market at the forefront of the fourth industrial revolution, where emotional data becomes as valuable as financial metrics in strategic planning and execution, driving holistic business transformations that prioritize human elements in digital strategies.

Recent academic studies report that transfer learning approaches in facial emotion recognition achieve an average accuracy of 96%, demonstrating the high effectiveness of advanced AI models in human-computer interaction.

Recent studies highlight impressive advancements in AI-driven emotion recognition, with convolutional neural network models achieving a test accuracy of 95% across seven basic emotions including anger, disgust, fear, happiness, sadness, surprise, and neutral. Transfer learning techniques have proven highly effective, yielding an average accuracy of 96% in facial emotion recognition for human-computer interaction applications. These accuracies underscore the robustness of machine learning and deep learning methods in analyzing facial expressions, eye movements, and biosignals, enhancing real-time emotion detection in educational and interactive environments while addressing challenges in accuracy, privacy, and cross-cultural validity.

Key Market Challenges

Data Privacy and Regulatory Compliance Challenges

One of the most pressing challenges facing the In-store Analytics Market is the growing concern around data privacy and regulatory compliance. In-store analytics solutions often rely on the collection of sensitive customer data, including video footage, behavioral patterns, and biometric information such as facial recognition or gait analysis. While these insights are crucial for optimizing store layouts, enhancing customer experience, and driving personalized marketing strategies, improper handling of such data can lead to legal repercussions, reputational damage, and loss of consumer trust. Regulatory frameworks such as the General Data Protection Regulation in Europe and similar data protection laws in other regions impose strict guidelines on data collection, storage, processing, and consent management.

Businesses must ensure that all analytics systems adhere to these regulations, including implementing encryption, anonymization, and secure data storage measures. Failure to comply can result in substantial fines and restrictions, discouraging smaller and medium-sized retailers from investing in advanced in-store analytics solutions. Additionally, customers are increasingly aware of how their personal data is used, demanding transparency and control over their information.

Meeting these expectations requires businesses to invest in comprehensive privacy policies, staff training, and compliance monitoring, which can increase operational costs. The need to balance the benefits of actionable in-store insights with stringent regulatory requirements continues to be a significant barrier to widespread adoption, especially in regions with evolving or complex data privacy laws. Retailers must navigate these challenges carefully to maintain trust while leveraging in-store analytics to drive business growth.

Key Market Trends

Integration of Artificial Intelligence and Machine Learning in In-store Analytics

A key trend in the In-store Analytics Market is the increasing integration of artificial intelligence and machine learning technologies to enhance data processing, predictive capabilities, and actionable insights. Retailers are adopting advanced algorithms that can analyze customer behavior patterns, identify preferences, and forecast trends with high accuracy. Machine learning models enable systems to continuously learn from historical and real-time data, improving the precision of traffic counting, heat mapping, shelf performance analysis, and queue management. Artificial intelligence enhances the interpretation of multimodal data sources, such as video feeds, sensor inputs, and point-of-sale information, facilitating deeper understanding of shopper behavior and engagement levels.

Retailers can utilize these insights to optimize store layouts, allocate staff efficiently, and develop targeted marketing strategies, thereby increasing customer satisfaction and operational efficiency. Furthermore, artificial intelligence-driven emotion recognition and sentiment analysis tools are being integrated into in-store analytics to capture subtle customer reactions to products, displays, and promotions. This development allows brands to tailor their offerings dynamically, offering personalized experiences that resonate with shoppers on an emotional level.

The convergence of artificial intelligence, machine learning, and predictive analytics also enables retailers to anticipate customer needs, adjust inventory in real time, and create adaptive in-store experiences. As artificial intelligence and machine learning technologies continue to evolve and become more accessible, their adoption in the In-store Analytics Market is expected to grow, shaping the future of intelligent, data-driven retail operations globally. This trend reflects the broader digital transformation in retail, where technology-driven insights are central to competitive advantage.

Key Market Players

• Trax Retail
• RetailNext
• ShopperTrak (Sensormatic Solutions
• Nomi (by Intel)
• V-Count
• Dor Technologies
• Falkonry
• FootFallCam
• Amsive Analytics
• Cenium Analytics

Report Scope:

In this report, the Global In-store Analytics Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

In-store Analytics Market, By Type:

• Video Analytics
• Traffic Counting and Heat Mapping
• Shelf Analytics
• Queue Management Analytics
• Customer Journey Analytics

In-store Analytics Market, By Technology:

• Artificial Intelligence
• Machine Learning
• Internet of Things
• Cloud Computing
• Computer Vision

In-store Analytics Market, By End-User Industry:

• Retail and E-commerce
• Supermarkets and Hypermarkets
• Consumer Electronics
• Fashion and Apparel
• Food and Beverage
• Others

In-store Analytics Market, By Region:

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • Germany
  • France
  • United Kingdom
  • Italy
  • Spain
• South America
  • Brazil
  • Argentina
  • Colombia
• Asia-Pacific
  • China
  • India
  • Japan
  • South Korea
  • Australia
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • South Africa

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global In-store Analytics Market.

Available Customizations:

Global In-store Analytics Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Key Industry Partners
• 2.4. Major Association and Secondary Sources
• 2.5. Forecasting Methodology
• 2.6. Data Triangulation & Validation
• 2.7. Assumptions and Limitations

3. Executive Summary

• 3.1. Overview of the Market
• 3.2. Overview of Key Market Segmentations
• 3.3. Overview of Key Market Players
• 3.4. Overview of Key Regions/Countries
• 3.5. Overview of Market Drivers, Challenges, and Trends

4. Voice of Customer

5. Global In-store Analytics Market Outlook

• 5.1. Market Size & Forecast
  • 5.1.1. By Value
• 5.2. Market Share & Forecast
  • 5.2.1. By Type (Video Analytics, Traffic Counting and Heat Mapping, Shelf Analytics, Queue Management Analytics, Customer Journey Analytics)
  • 5.2.2. By Technology (Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, Computer Vision)
  • 5.2.3. By End-User Industry (Retail and E-commerce, Supermarkets and Hypermarkets, Consumer Electronics, Fashion and Apparel, Food and Beverage, Others)
  • 5.2.4. By Region (North America, Europe, South America, Middle East & Africa, Asia Pacific)
• 5.3. By Company (2024)
• 5.4. Market Map

6. North America In-store Analytics Market Outlook

• 6.1. Market Size & Forecast
  • 6.1.1. By Value
• 6.2. Market Share & Forecast
  • 6.2.1. By Type
  • 6.2.2. By Technology
  • 6.2.3. By End-User Industry
  • 6.2.4. By Country
• 6.3. North America: Country Analysis
  • 6.3.1. United States In-store Analytics Market Outlook
    • 6.3.1.1. Market Size & Forecast
      • 6.3.1.1.1. By Value
    • 6.3.1.2. Market Share & Forecast
      • 6.3.1.2.1. By Type
      • 6.3.1.2.2. By Technology
      • 6.3.1.2.3. By End-User Industry
  • 6.3.2. Canada In-store Analytics Market Outlook
    • 6.3.2.1. Market Size & Forecast
      • 6.3.2.1.1. By Value
    • 6.3.2.2. Market Share & Forecast
      • 6.3.2.2.1. By Type
      • 6.3.2.2.2. By Technology
      • 6.3.2.2.3. By End-User Industry
  • 6.3.3. Mexico In-store Analytics Market Outlook
    • 6.3.3.1. Market Size & Forecast
      • 6.3.3.1.1. By Value
    • 6.3.3.2. Market Share & Forecast
      • 6.3.3.2.1. By Type
      • 6.3.3.2.2. By Technology
      • 6.3.3.2.3. By End-User Industry

7. Europe In-store Analytics Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Type
  • 7.2.2. By Technology
  • 7.2.3. By End-User Industry
  • 7.2.4. By Country
• 7.3. Europe: Country Analysis
  • 7.3.1. Germany In-store Analytics Market Outlook
    • 7.3.1.1. Market Size & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share & Forecast
      • 7.3.1.2.1. By Type
      • 7.3.1.2.2. By Technology
      • 7.3.1.2.3. By End-User Industry
  • 7.3.2. France In-store Analytics Market Outlook
    • 7.3.2.1. Market Size & Forecast
      • 7.3.2.1.1. By Value
    • 7.3.2.2. Market Share & Forecast
      • 7.3.2.2.1. By Type
      • 7.3.2.2.2. By Technology
      • 7.3.2.2.3. By End-User Industry
  • 7.3.3. United Kingdom In-store Analytics Market Outlook
    • 7.3.3.1. Market Size & Forecast
      • 7.3.3.1.1. By Value
    • 7.3.3.2. Market Share & Forecast
      • 7.3.3.2.1. By Type
      • 7.3.3.2.2. By Technology
      • 7.3.3.2.3. By End-User Industry
  • 7.3.4. Italy In-store Analytics Market Outlook
    • 7.3.4.1. Market Size & Forecast
      • 7.3.4.1.1. By Value
    • 7.3.4.2. Market Share & Forecast
      • 7.3.4.2.1. By Type
      • 7.3.4.2.2. By Technology
      • 7.3.4.2.3. By End-User Industry
  • 7.3.5. Spain In-store Analytics Market Outlook
    • 7.3.5.1. Market Size & Forecast
      • 7.3.5.1.1. By Value
    • 7.3.5.2. Market Share & Forecast
      • 7.3.5.2.1. By Type
      • 7.3.5.2.2. By Technology
      • 7.3.5.2.3. By End-User Industry

8. Asia Pacific In-store Analytics Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Type
  • 8.2.2. By Technology
  • 8.2.3. By End-User Industry
  • 8.2.4. By Country
• 8.3. Asia Pacific: Country Analysis
  • 8.3.1. China In-store Analytics Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Type
      • 8.3.1.2.2. By Technology
      • 8.3.1.2.3. By End-User Industry
  • 8.3.2. India In-store Analytics Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Type
      • 8.3.2.2.2. By Technology
      • 8.3.2.2.3. By End-User Industry
  • 8.3.3. Japan In-store Analytics Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Type
      • 8.3.3.2.2. By Technology
      • 8.3.3.2.3. By End-User Industry
  • 8.3.4. South Korea In-store Analytics Market Outlook
    • 8.3.4.1. Market Size & Forecast
      • 8.3.4.1.1. By Value
    • 8.3.4.2. Market Share & Forecast
      • 8.3.4.2.1. By Type
      • 8.3.4.2.2. By Technology
      • 8.3.4.2.3. By End-User Industry
  • 8.3.5. Australia In-store Analytics Market Outlook
    • 8.3.5.1. Market Size & Forecast
      • 8.3.5.1.1. By Value
    • 8.3.5.2. Market Share & Forecast
      • 8.3.5.2.1. By Type
      • 8.3.5.2.2. By Technology
      • 8.3.5.2.3. By End-User Industry

9. Middle East & Africa In-store Analytics Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Type
  • 9.2.2. By Technology
  • 9.2.3. By End-User Industry
  • 9.2.4. By Country
• 9.3. Middle East & Africa: Country Analysis
  • 9.3.1. Saudi Arabia In-store Analytics Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Type
      • 9.3.1.2.2. By Technology
      • 9.3.1.2.3. By End-User Industry
  • 9.3.2. UAE In-store Analytics Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Type
      • 9.3.2.2.2. By Technology
      • 9.3.2.2.3. By End-User Industry
  • 9.3.3. South Africa In-store Analytics Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Type
      • 9.3.3.2.2. By Technology
      • 9.3.3.2.3. By End-User Industry

10. South America In-store Analytics Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Type
  • 10.2.2. By Technology
  • 10.2.3. By End-User Industry
  • 10.2.4. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil In-store Analytics Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Type
      • 10.3.1.2.2. By Technology
      • 10.3.1.2.3. By End-User Industry
  • 10.3.2. Colombia In-store Analytics Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Type
      • 10.3.2.2.2. By Technology
      • 10.3.2.2.3. By End-User Industry
  • 10.3.3. Argentina In-store Analytics Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Type
      • 10.3.3.2.2. By Technology
      • 10.3.3.2.3. By End-User Industry

11. Market Dynamics

• 11.1. Drivers
• 11.2. Challenges

12. Market Trends and Developments

• 12.1. Merger & Acquisition (If Any)
• 12.2. Product Launches (If Any)
• 12.3. Recent Developments

13. Company Profiles

• 13.1. Trax Retail
  • 13.1.1. Business Overview
  • 13.1.2. Key Revenue and Financials
  • 13.1.3. Recent Developments
  • 13.1.4. Key Personnel
  • 13.1.5. Key Product/Services Offered
• 13.2. RetailNext
• 13.3. ShopperTrak (Sensormatic Solutions
• 13.4. Nomi (by Intel)
• 13.5. V-Count
• 13.6. Dor Technologies
• 13.7. Falkonry
• 13.8. FootFallCam
• 13.9. Amsive Analytics
• 13.10. Cenium Analytics

14. Strategic Recommendations

15. About Us & Disclaimer