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Market Research Report

Global Intelligent Retail Decision Making at NRF Market Insights, Size, and Forecast By Application (Inventory Management, Customer Engagement, Sales Forecasting, Supply Chain Optimization), By Technology (Artificial Intelligence, Machine Learning, Data Analytics, Blockchain), By Deployment (On-Premises, Cloud-Based, Hybrid), By End Use (Retail Stores, E-commerce, Wholesale Distribution), By Region (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa), Key Companies, Competitive Analysis, Trends, and Projections for 2026-2035

Report ID:3575
Published Date:Jan 2026
No. of Pages:248
Base Year for Estimate:2025
Format:
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Key Market Insights

Global Intelligent Retail Decision Making at NRF Market is projected to grow from USD 28.7 Billion in 2025 to USD 145.3 Billion by 2035, reflecting a compound annual growth rate of 16.4% from 2026 through 2035. This market encompasses the application of advanced technologies and data-driven insights to optimize retail operations, enhance customer experiences, and improve profitability, as prominently showcased at the NRF Big Show. It leverages artificial intelligence, machine learning, and sophisticated analytics to empower retailers with actionable intelligence across various facets, including inventory management, pricing strategies, personalized marketing, supply chain optimization, and store operations. Key drivers propelling this growth include the escalating demand for seamless omnichannel experiences, the increasing adoption of digital transformation initiatives by retailers, and the imperative for real-time inventory visibility and demand forecasting to minimize waste and maximize sales. Furthermore, the intensifying competitive landscape necessitates retailers to make faster, more informed decisions to stay ahead. The market is segmented by Technology, Application, End Use, and Deployment, with Data Analytics emerging as the leading segment due to its fundamental role in extracting meaningful insights from vast datasets.

Global Intelligent Retail Decision Making at NRF Market Value (USD Billion) Analysis, 2025-2035

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16.4%
CAGR from
2025 - 2035
Source:
www.makdatainsights.com

Important trends shaping the market include the growing emphasis on predictive analytics for proactive decision making, the integration of IoT devices for granular data collection within physical stores, and the rise of AI-powered personalization engines that cater to individual customer preferences. The push towards sustainable retail practices is also driving the adoption of intelligent systems for waste reduction and efficient resource allocation. However, market growth faces certain restraints, primarily the high initial investment costs associated with implementing these advanced technologies, data security and privacy concerns, and the challenge of integrating disparate legacy systems. The complexity of these solutions often requires specialized talent, posing a further hurdle for many retailers. Despite these challenges, significant opportunities exist in the expansion of intelligent decision making solutions to small and medium sized retailers, who are increasingly recognizing the value of data driven strategies. The continuous evolution of cloud based solutions is also making these technologies more accessible and scalable.

North America stands as the dominant region in the intelligent retail decision making market, driven by a mature retail landscape, early adoption of advanced technologies, significant investment in digital transformation, and the presence of numerous key technology providers and innovative retailers. This region’s strong focus on customer experience and operational efficiency further fuels its market leadership. Asia Pacific, on the other hand, is poised to be the fastest growing region, propelled by rapid urbanization, a burgeoning e-commerce sector, increasing disposable incomes, and the widespread embrace of mobile commerce. This region presents immense untapped potential for technology adoption and market expansion. Key players such as Zebra Technologies, Merchandising Innovations, Microsoft, IBM, Infor, Episerver, Adobe, Salesforce, Cimpress, and Nielsen are actively shaping the market through strategic partnerships, continuous innovation in AI and analytics, and the development of comprehensive retail solutions aimed at addressing specific industry challenges. Their strategies often involve offering integrated platforms that provide end-to-end visibility and decision support across the entire retail value chain.

Quick Stats

  • Market Size (2025):

    USD 28.7 Billion

  • Projected Market Size (2035):

    USD 145.3 Billion

  • Leading Segment:

    Data Analytics (42.8% Share)

  • Dominant Region (2025):

    North America (38.2% Share)

  • CAGR (2026-2035):

    16.4%

What is Intelligent Retail Decision Making at NRF?

Intelligent Retail Decision Making at NRF centers on leveraging advanced analytics and artificial intelligence within retail operations. It defines the strategic application of data driven insights to optimize various aspects of the retail journey, from inventory management and supply chain to personalized customer experiences and pricing strategies. Core concepts include predictive modeling, machine learning, and real time data analysis, enabling retailers to anticipate trends and make proactive choices. Its significance lies in enhancing operational efficiency, improving customer satisfaction, and boosting profitability. Applications span personalized marketing, demand forecasting, loss prevention, and dynamic merchandising, ultimately empowering smarter business decisions for a competitive edge in the evolving retail landscape.

What are the Trends in Global Intelligent Retail Decision Making at NRF Market

  • Hyperpersonalization at Scale AI Driven Choices

  • Predictive Analytics for Proactive Inventory Optimization

  • Edge AI for Real Time Customer Experience Decisions

  • Ethical AI and Transparent Retail Decision Making

Hyperpersonalization at Scale AI Driven Choices

AI tailors every shopping journey, predicting individual needs before customers articulate them. This hyperpersonalization, delivered efficiently across vast customer bases, empowers retailers at NRF. AI analyzes vast data, offering precise product recommendations, customized promotions, and frictionless experiences. It moves beyond segments to deliver unique engagement for each shopper, optimizing decisions.

Predictive Analytics for Proactive Inventory Optimization

Retailers at NRF leverage predictive analytics to anticipate demand fluctuations with greater accuracy. This trend enables proactive inventory adjustments, minimizing stockouts and overstock. By analyzing diverse data sources, businesses optimize stock levels, reduce carrying costs, and enhance product availability, directly improving customer satisfaction and operational efficiency in intelligent retail decision making.

Edge AI for Real Time Customer Experience Decisions

Edge AI processes customer data instantly at the source within stores. This enables retailers to deliver highly personalized, real time recommendations and services directly to shoppers' devices or through staff. It optimizes inventory, streamlines checkout, and enhances engagement by making immediate, intelligent decisions based on current behavior and preferences, ultimately improving satisfaction and sales at NRF Market.

Ethical AI and Transparent Retail Decision Making

Retailers are increasingly leveraging AI for crucial decisions, from inventory to pricing. However, a growing demand for ethical AI practices dictates transparency in these algorithms. Consumers and regulators expect clear explanations of how AI influences retail outcomes. This trend fosters trust, ensures fairness, and aligns AI driven retail with evolving societal values, directly impacting brand perception and customer loyalty.

What are the Key Drivers Shaping the Global Intelligent Retail Decision Making at NRF Market

  • AI-Powered Personalization & Customer Experience

  • Seamless Omnichannel Integration & Data Unification

  • Real-time Inventory Optimization & Supply Chain Agility

  • Predictive Analytics for Demand Forecasting & Merchandising

AI-Powered Personalization & Customer Experience

AI personalizes shopping by understanding individual preferences, predicting needs, and delivering tailored product recommendations and service. This creates seamless, delightful customer journeys, fostering loyalty and driving sales growth in the global intelligent retail market. Experiences become uniquely relevant.

Seamless Omnichannel Integration & Data Unification

Seamless omnichannel integration & data unification drives better decisions by creating a single, complete view of customer interactions across all channels. This empowers retailers to personalize experiences, optimize inventory, and deliver consistent service. Unified data reveals crucial insights into purchasing patterns and preferences, leading to smarter, more effective retail strategies.

Real-time Inventory Optimization & Supply Chain Agility

Retailers need instant visibility into stock levels across all channels to prevent overstocking and stockouts. This driver enables dynamic adjustments to inventory allocation based on real time demand, optimizing product availability and minimizing waste. It empowers agile responses to market shifts, ensuring products are always where customers want them, when they want them.

Predictive Analytics for Demand Forecasting & Merchandising

Leveraging historical data and algorithms, this driver anticipates consumer buying patterns. It optimizes inventory, allocates products efficiently across channels, and personalizes offers. Retailers gain precise insights into future demand, reducing stockouts and overstock, ultimately enhancing customer satisfaction and profitability.

Global Intelligent Retail Decision Making at NRF Market Restraints

Navigating Data Privacy and Ethical AI in Global Retail

Operating internationally, retailers face significant hurdles. Differing data privacy regulations across countries create complex compliance challenges, impacting how customer information can be collected, stored, and used. Ethically deploying AI, particularly for personalized marketing and predictive analytics, requires careful consideration of bias, transparency, and consumer trust, all while balancing innovation with legal and moral obligations in a global context.

Overcoming Integration Complexities for Holistic Retail Intelligence

Integrating disparate retail systems and data sources poses a significant challenge. Retailers struggle to unify customer behavior, inventory, and sales data from online, in-store, and supply chain channels. This fragmentation prevents a singular, comprehensive view of operations and customer journeys, hindering the development of truly holistic AI-driven insights for strategic decision making and personalized experiences.

Global Intelligent Retail Decision Making at NRF Market Opportunities

AI-Powered Retail Growth: Driving Global Profit Through Intelligent Decisions

AI empowers global retailers to optimize operations and personalize customer experiences, driving substantial profit growth. Intelligent decisions, informed by advanced analytics, unlock new revenue streams across diverse markets, particularly within high growth regions like Asia Pacific. NRF Market highlights how AI transforms inventory management, pricing, and marketing strategies, creating a distinct competitive edge. This opportunity leverages real time data to make smarter, faster, and more impactful choices, ensuring sustained expansion and superior financial performance worldwide.

Future-Proofing Retail: Intelligent Decision Systems for Agility & Efficiency

Retailers require intelligent decision systems to navigate rapid market shifts and achieve agility. This global opportunity involves deploying AI driven platforms that enhance operational efficiency, optimize inventory, personalize customer experiences, and streamline supply chains. By leveraging real time data and predictive analytics, retailers can future proof their businesses, make proactive strategic choices, and significantly boost efficiency. This ensures sustained competitive advantage and profitability across dynamic markets worldwide, meeting evolving consumer demands effectively."

Global Intelligent Retail Decision Making at NRF Market Segmentation Analysis

Key Market Segments

By Technology

  • •Artificial Intelligence
  • •Machine Learning
  • •Data Analytics
  • •Blockchain

By Application

  • •Inventory Management
  • •Customer Engagement
  • •Sales Forecasting
  • •Supply Chain Optimization

By End Use

  • •Retail Stores
  • •E-commerce
  • •Wholesale Distribution

By Deployment

  • •On-Premises
  • •Cloud-Based
  • •Hybrid

Segment Share By Technology

Share, By Technology, 2025 (%)

  • Artificial Intelligence
  • Machine Learning
  • Data Analytics
  • Blockchain
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$28.7BGlobal Market Size, 2025
Source:
www.makdatainsights.com

Why is Data Analytics the dominant technology segment in intelligent retail decision making?

Data Analytics holds the largest share because it forms the foundational backbone for nearly all intelligent retail operations. Retailers heavily rely on analyzing vast datasets from sales, customer interactions, and supply chains to derive actionable insights. This fundamental need for understanding performance, identifying trends, and making informed decisions across all departments makes data analytics an indispensable first step before deeper applications of AI or Machine Learning can even be effectively implemented. Its widespread adoption underscores its immediate and tangible value proposition for improving efficiency and profitability.

How do application segments drive the adoption of intelligent retail solutions at NRF?

Application segments such as Inventory Management, Customer Engagement, and Sales Forecasting are primary motivators for retailers seeking intelligent solutions. Retailers are actively addressing critical operational challenges like minimizing stockouts, optimizing product assortment, personalizing customer experiences, and accurately predicting demand. Intelligent tools offer direct solutions to these pain points, promising significant improvements in operational efficiency, customer satisfaction, and revenue generation. The tangible benefits derived from these applications compel retailers to invest in data driven decision making capabilities.

What role do deployment and end use segments play in shaping intelligent retail strategies?

The choice of deployment, whether Cloud Based, On Premises, or Hybrid, significantly influences scalability, security, and cost structures for retailers. Cloud Based solutions are increasingly preferred for their flexibility and accessibility, while On Premises options remain relevant for specific data sovereignty or integration requirements. Furthermore, solutions are often tailored to specific End Use segments like Retail Stores, E-commerce, or Wholesale Distribution, each having unique operational contexts and data needs that intelligent retail technologies must address to deliver optimal value.

What Regulatory and Policy Factors Shape the Global Intelligent Retail Decision Making at NRF Market

Global intelligent retail decision making operates within a multifaceted regulatory environment. Data privacy mandates like GDPR and CCPA are paramount, dictating the ethical collection processing and utilization of consumer data vital for AI powered analytics. Emerging AI ethics frameworks globally emphasize transparency accountability and fairness to mitigate algorithmic bias. Proposed legislation such as the EU AI Act shapes standards for responsible AI deployment in retail operations. Cross border data transfer regulations pose significant challenges for multinational enterprises. Consumer protection laws influence personalized marketing dynamic pricing and targeted advertising requiring non discriminatory practices. Navigating these diverse national and regional mandates demands continuous adaptation robust governance and a proactive compliance strategy for sustained innovation.

What New Technologies are Shaping Global Intelligent Retail Decision Making at NRF Market?

At NRF, intelligent retail decision making thrives on innovation. Advanced AI and machine learning drive hyper personalized customer journeys, predicting trends and optimizing inventory in real time. Computer vision and IoT sensors redefine store operations, enabling frictionless checkout and loss prevention. Edge computing processes data instantly, supporting dynamic pricing and tailored promotions. Blockchain ensures supply chain transparency and ethical sourcing. Generative AI creates dynamic marketing content and virtual shopping assistants. Robotics automate fulfillment and last mile delivery, boosting efficiency and speed. Predictive analytics empower strategic planning across all channels. These technologies collectively foster a more responsive, profitable, and customer centric retail ecosystem globally.

Global Intelligent Retail Decision Making at NRF Market Regional Analysis

Global Intelligent Retail Decision Making at NRF Market

Trends, by Region

Largest Market
Fastest Growing Market
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38.2%

North America Market
Revenue Share, 2025

Source:
www.makdatainsights.com

North America dominates the intelligent retail decision-making market, driven by its advanced technological infrastructure and high adoption of AI/ML solutions. The NRF market serves as a crucial platform, showcasing innovations from major US and Canadian retail tech firms. Retailers here are rapidly integrating predictive analytics, customer behavior insights, and inventory optimization tools. Emphasis is on hyper-personalization, seamless omnichannel experiences, and operational efficiency, leveraging big data and IoT. This region's early embrace of digitalization and substantial investment in R&D ensures its continued leadership in shaping the future of retail decision intelligence.

European retailers at NRF prioritize data-driven decisions amidst diverse national markets. Western European brands focus on AI-powered personalization and omnichannel integration, leveraging mature digital infrastructures. Southern Europe emphasizes loyalty programs and in-store technology to enhance customer experience. Eastern European retailers seek solutions for supply chain optimization and e-commerce expansion, often leapfrogging older technologies. Across all regions, sustainability and ethical sourcing are influencing intelligent retail decisions, with a growing demand for tools that track product lifecycles and transparent supply chains to meet evolving consumer and regulatory expectations.

The Asia Pacific region, characterized by rapid urbanization and a burgeoning middle class, presents a dynamic landscape for Intelligent Retail Decision Making at NRF. With a remarkable CAGR of 24.8%, it's the fastest-growing market globally. Key drivers include increasing digital literacy, widespread smartphone adoption, and a strong appetite for personalized shopping experiences. Countries like China, India, and Southeast Asian nations are at the forefront, leveraging AI, analytics, and IoT to optimize inventory, personalize marketing, and enhance customer journeys, making it a critical region for market penetration and innovation.

Latin America presents a dynamic landscape for Intelligent Retail Decision Making at NRF. Brazil leads with omnichannel adoption and sophisticated fraud detection needs. Mexico follows with growing e-commerce and demand for personalized customer experiences. Argentina, despite economic fluctuations, sees interest in AI-driven inventory optimization. Colombia shows potential in predictive analytics for merchandising, while Chile focuses on supply chain visibility. Across the region, real-time data for pricing, demand forecasting, and inventory management are key drivers, with a strong emphasis on solutions that can integrate with existing, often diverse, retail infrastructures and address localized logistical challenges. Security and data privacy are paramount concerns.

MEA’s Intelligent Retail Decision Making market is rapidly expanding, driven by increasing smartphone penetration and e-commerce adoption. At NRF, retailers from Saudi Arabia, UAE, and South Africa are keen on AI-powered analytics for personalized customer experiences and optimized inventory. The region faces unique challenges like fragmented logistics and diverse consumer behaviors, pushing demand for tailored, localized solutions. Investment in omnichannel strategies and predictive analytics is accelerating, with particular interest in AI for demand forecasting and dynamic pricing to cater to the young, digitally native population and diverse economic landscapes. This reflects a strong regional commitment to advanced retail technologies.

Top Countries Overview

US retailers at NRF drive global intelligent retail decision making. They leverage advanced analytics AI and data to personalize customer experiences optimize supply chains and enhance operational efficiency. This shapes worldwide trends in sophisticated retail technology and strategic business choices influencing international market approaches and investments significantly.

China's intelligent retail sector significantly influences global decision making at NRF. Its rapid adoption of AI analytics, IoT, and personalized customer experiences sets trends. Chinese tech giants drive innovation, forcing international retailers to adapt to data driven strategies and advanced in store technologies to stay competitive in an evolving market.

Indian retailers at NRF explore AI driven solutions for personalized customer experiences supply chain optimization and data analytics. They seek global intelligent retail decision making tools to cater to diverse consumer demands and enhance operational efficiencies embracing technology for future growth in a dynamic market.

Impact of Geopolitical and Macroeconomic Factors

Geopolitical shifts are impacting supply chains and labor markets, compelling retailers to adopt AI for real time inventory and staffing optimization. Trade tensions influence sourcing strategies, driving demand for intelligent systems to manage complex international logistics and reduce operational costs amidst fluctuating tariffs and regulations.

Macroeconomic pressures, including inflation and interest rate hikes, are squeezing consumer spending power, intensifying the need for data driven personalization and dynamic pricing. Retailers are investing in AI to enhance customer experience, predict demand accurately, and optimize promotions, navigating economic uncertainties by improving efficiency and profitability in a competitive landscape.

Recent Developments

  • January 2025

    Zebra Technologies unveiled its 'Store Intelligence Platform' at NRF 2025, an AI-powered suite integrating real-time inventory, associate task management, and customer behavior analytics. This platform leverages edge computing and machine learning to provide hyper-localized decision support for retail operations.

  • November 2024

    Microsoft and Infor announced a strategic partnership aimed at integrating Microsoft Azure's AI capabilities with Infor's industry-specific cloud solutions for retail. This collaboration focuses on delivering advanced predictive analytics for demand forecasting and personalized customer engagement across various retail formats.

  • February 2025

    IBM launched 'Watson Retail Insights', a new consulting service and platform module designed to help retailers leverage their disparate data sources for intelligent decision making. This service utilizes Watson's natural language processing and machine learning to identify actionable insights from customer feedback, sales data, and market trends.

  • October 2024

    Salesforce acquired Merchandising Innovations, a leading provider of AI-driven merchandising optimization software. This acquisition strengthens Salesforce's Commerce Cloud offerings by adding advanced capabilities for assortment planning, pricing strategies, and promotional effectiveness based on real-time market dynamics.

  • December 2024

    Adobe announced a significant update to its 'Adobe Sensei for Retail' suite, introducing new AI features for hyper-personalization of online and in-store experiences. These enhancements include intelligent content recommendation engines and dynamic pricing adjustments based on individual customer profiles and real-time inventory levels.

Key Players Analysis

At NRF, key players in Global Intelligent Retail Decision Making showcase distinct strengths. Zebra Technologies leads in rugged mobile devices and RFID, essential for real time inventory and supply chain visibility, leveraging analytics for operational efficiency. Microsoft and IBM offer robust cloud platforms like Azure and Watson, driving AI and machine learning for predictive analytics and personalized customer experiences. Adobe and Salesforce focus on enhancing customer journeys through their experience platforms and CRM solutions, integrating data for more effective marketing and sales. Infor and Merchandising Innovations provide specialized retail management software and planning tools, optimizing merchandising and store operations. Nielsen contributes vital consumer data and insights, informing strategic decisions, while Episerver and Cimpress address ecommerce and personalized print marketing needs, driving omnichannel engagement. These companies collectively push market growth through AI driven personalization, operational automation, and data driven insights.

List of Key Companies:

  1. Zebra Technologies
  2. Merchandising Innovations
  3. Microsoft
  4. IBM
  5. Infor
  6. Episerver
  7. Adobe
  8. Salesforce
  9. Cimpress
  10. Nielsen
  11. Blue Yonder
  12. SAS Institute
  13. Shopify
  14. SAP
  15. Oracle

Report Scope and Segmentation

Report ComponentDescription
Market Size (2025)USD 28.7 Billion
Forecast Value (2035)USD 145.3 Billion
CAGR (2026-2035)16.4%
Base Year2025
Historical Period2020-2025
Forecast Period2026-2035
Segments Covered
  • By Technology:
    • •Artificial Intelligence
    • •Machine Learning
    • •Data Analytics
    • •Blockchain
  • By Application:
    • •Inventory Management
    • •Customer Engagement
    • •Sales Forecasting
    • •Supply Chain Optimization
  • By End Use:
    • •Retail Stores
    • •E-commerce
    • •Wholesale Distribution
  • By Deployment:
    • •On-Premises
    • •Cloud-Based
    • •Hybrid
Regional Analysis
  • North America
  • • United States
  • • Canada
  • Europe
  • • Germany
  • • France
  • • United Kingdom
  • • Spain
  • • Italy
  • • Russia
  • • Rest of Europe
  • Asia-Pacific
  • • China
  • • India
  • • Japan
  • • South Korea
  • • New Zealand
  • • Singapore
  • • Vietnam
  • • Indonesia
  • • Rest of Asia-Pacific
  • Latin America
  • • Brazil
  • • Mexico
  • • Rest of Latin America
  • Middle East and Africa
  • • South Africa
  • • Saudi Arabia
  • • UAE
  • • Rest of Middle East and Africa

Table of Contents:

1. Introduction
1.1. Objectives of Research
1.2. Market Definition
1.3. Market Scope
1.4. Research Methodology
2. Executive Summary
3. Market Dynamics
3.1. Market Drivers
3.2. Market Restraints
3.3. Market Opportunities
3.4. Market Trends
4. Market Factor Analysis
4.1. Porter's Five Forces Model Analysis
4.1.1. Rivalry among Existing Competitors
4.1.2. Bargaining Power of Buyers
4.1.3. Bargaining Power of Suppliers
4.1.4. Threat of Substitute Products or Services
4.1.5. Threat of New Entrants
4.2. PESTEL Analysis
4.2.1. Political Factors
4.2.2. Economic & Social Factors
4.2.3. Technological Factors
4.2.4. Environmental Factors
4.2.5. Legal Factors
4.3. Supply and Value Chain Assessment
4.4. Regulatory and Policy Environment Review
4.5. Market Investment Attractiveness Index
4.6. Technological Innovation and Advancement Review
4.7. Impact of Geopolitical and Macroeconomic Factors
4.8. Trade Dynamics: Import-Export Assessment (Where Applicable)
5. Global Intelligent Retail Decision Making at NRF Market Analysis, Insights 2020 to 2025 and Forecast 2026-2035
5.1. Market Analysis, Insights and Forecast, 2020-2035, By Technology
5.1.1. Artificial Intelligence
5.1.2. Machine Learning
5.1.3. Data Analytics
5.1.4. Blockchain
5.2. Market Analysis, Insights and Forecast, 2020-2035, By Application
5.2.1. Inventory Management
5.2.2. Customer Engagement
5.2.3. Sales Forecasting
5.2.4. Supply Chain Optimization
5.3. Market Analysis, Insights and Forecast, 2020-2035, By End Use
5.3.1. Retail Stores
5.3.2. E-commerce
5.3.3. Wholesale Distribution
5.4. Market Analysis, Insights and Forecast, 2020-2035, By Deployment
5.4.1. On-Premises
5.4.2. Cloud-Based
5.4.3. Hybrid
5.5. Market Analysis, Insights and Forecast, 2020-2035, By Region
5.5.1. North America
5.5.2. Europe
5.5.3. Asia-Pacific
5.5.4. Latin America
5.5.5. Middle East and Africa
6. North America Intelligent Retail Decision Making at NRF Market Analysis, Insights 2020 to 2025 and Forecast 2026-2035
6.1. Market Analysis, Insights and Forecast, 2020-2035, By Technology
6.1.1. Artificial Intelligence
6.1.2. Machine Learning
6.1.3. Data Analytics
6.1.4. Blockchain
6.2. Market Analysis, Insights and Forecast, 2020-2035, By Application
6.2.1. Inventory Management
6.2.2. Customer Engagement
6.2.3. Sales Forecasting
6.2.4. Supply Chain Optimization
6.3. Market Analysis, Insights and Forecast, 2020-2035, By End Use
6.3.1. Retail Stores
6.3.2. E-commerce
6.3.3. Wholesale Distribution
6.4. Market Analysis, Insights and Forecast, 2020-2035, By Deployment
6.4.1. On-Premises
6.4.2. Cloud-Based
6.4.3. Hybrid
6.5. Market Analysis, Insights and Forecast, 2020-2035, By Country
6.5.1. United States
6.5.2. Canada
7. Europe Intelligent Retail Decision Making at NRF Market Analysis, Insights 2020 to 2025 and Forecast 2026-2035
7.1. Market Analysis, Insights and Forecast, 2020-2035, By Technology
7.1.1. Artificial Intelligence
7.1.2. Machine Learning
7.1.3. Data Analytics
7.1.4. Blockchain
7.2. Market Analysis, Insights and Forecast, 2020-2035, By Application
7.2.1. Inventory Management
7.2.2. Customer Engagement
7.2.3. Sales Forecasting
7.2.4. Supply Chain Optimization
7.3. Market Analysis, Insights and Forecast, 2020-2035, By End Use
7.3.1. Retail Stores
7.3.2. E-commerce
7.3.3. Wholesale Distribution
7.4. Market Analysis, Insights and Forecast, 2020-2035, By Deployment
7.4.1. On-Premises
7.4.2. Cloud-Based
7.4.3. Hybrid
7.5. Market Analysis, Insights and Forecast, 2020-2035, By Country
7.5.1. Germany
7.5.2. France
7.5.3. United Kingdom
7.5.4. Spain
7.5.5. Italy
7.5.6. Russia
7.5.7. Rest of Europe
8. Asia-Pacific Intelligent Retail Decision Making at NRF Market Analysis, Insights 2020 to 2025 and Forecast 2026-2035
8.1. Market Analysis, Insights and Forecast, 2020-2035, By Technology
8.1.1. Artificial Intelligence
8.1.2. Machine Learning
8.1.3. Data Analytics
8.1.4. Blockchain
8.2. Market Analysis, Insights and Forecast, 2020-2035, By Application
8.2.1. Inventory Management
8.2.2. Customer Engagement
8.2.3. Sales Forecasting
8.2.4. Supply Chain Optimization
8.3. Market Analysis, Insights and Forecast, 2020-2035, By End Use
8.3.1. Retail Stores
8.3.2. E-commerce
8.3.3. Wholesale Distribution
8.4. Market Analysis, Insights and Forecast, 2020-2035, By Deployment
8.4.1. On-Premises
8.4.2. Cloud-Based
8.4.3. Hybrid
8.5. Market Analysis, Insights and Forecast, 2020-2035, By Country
8.5.1. China
8.5.2. India
8.5.3. Japan
8.5.4. South Korea
8.5.5. New Zealand
8.5.6. Singapore
8.5.7. Vietnam
8.5.8. Indonesia
8.5.9. Rest of Asia-Pacific
9. Latin America Intelligent Retail Decision Making at NRF Market Analysis, Insights 2020 to 2025 and Forecast 2026-2035
9.1. Market Analysis, Insights and Forecast, 2020-2035, By Technology
9.1.1. Artificial Intelligence
9.1.2. Machine Learning
9.1.3. Data Analytics
9.1.4. Blockchain
9.2. Market Analysis, Insights and Forecast, 2020-2035, By Application
9.2.1. Inventory Management
9.2.2. Customer Engagement
9.2.3. Sales Forecasting
9.2.4. Supply Chain Optimization
9.3. Market Analysis, Insights and Forecast, 2020-2035, By End Use
9.3.1. Retail Stores
9.3.2. E-commerce
9.3.3. Wholesale Distribution
9.4. Market Analysis, Insights and Forecast, 2020-2035, By Deployment
9.4.1. On-Premises
9.4.2. Cloud-Based
9.4.3. Hybrid
9.5. Market Analysis, Insights and Forecast, 2020-2035, By Country
9.5.1. Brazil
9.5.2. Mexico
9.5.3. Rest of Latin America
10. Middle East and Africa Intelligent Retail Decision Making at NRF Market Analysis, Insights 2020 to 2025 and Forecast 2026-2035
10.1. Market Analysis, Insights and Forecast, 2020-2035, By Technology
10.1.1. Artificial Intelligence
10.1.2. Machine Learning
10.1.3. Data Analytics
10.1.4. Blockchain
10.2. Market Analysis, Insights and Forecast, 2020-2035, By Application
10.2.1. Inventory Management
10.2.2. Customer Engagement
10.2.3. Sales Forecasting
10.2.4. Supply Chain Optimization
10.3. Market Analysis, Insights and Forecast, 2020-2035, By End Use
10.3.1. Retail Stores
10.3.2. E-commerce
10.3.3. Wholesale Distribution
10.4. Market Analysis, Insights and Forecast, 2020-2035, By Deployment
10.4.1. On-Premises
10.4.2. Cloud-Based
10.4.3. Hybrid
10.5. Market Analysis, Insights and Forecast, 2020-2035, By Country
10.5.1. South Africa
10.5.2. Saudi Arabia
10.5.3. UAE
10.5.4. Rest of Middle East and Africa
11. Competitive Analysis and Company Profiles
11.1. Market Share of Key Players
11.1.1. Global Company Market Share
11.1.2. Regional/Sub-Regional Company Market Share
11.2. Company Profiles
11.2.1. Zebra Technologies
11.2.1.1. Business Overview
11.2.1.2. Products Offering
11.2.1.3. Financial Insights (Based on Availability)
11.2.1.4. Company Market Share Analysis
11.2.1.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.1.6. Strategy
11.2.1.7. SWOT Analysis
11.2.2. Merchandising Innovations
11.2.2.1. Business Overview
11.2.2.2. Products Offering
11.2.2.3. Financial Insights (Based on Availability)
11.2.2.4. Company Market Share Analysis
11.2.2.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.2.6. Strategy
11.2.2.7. SWOT Analysis
11.2.3. Microsoft
11.2.3.1. Business Overview
11.2.3.2. Products Offering
11.2.3.3. Financial Insights (Based on Availability)
11.2.3.4. Company Market Share Analysis
11.2.3.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.3.6. Strategy
11.2.3.7. SWOT Analysis
11.2.4. IBM
11.2.4.1. Business Overview
11.2.4.2. Products Offering
11.2.4.3. Financial Insights (Based on Availability)
11.2.4.4. Company Market Share Analysis
11.2.4.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.4.6. Strategy
11.2.4.7. SWOT Analysis
11.2.5. Infor
11.2.5.1. Business Overview
11.2.5.2. Products Offering
11.2.5.3. Financial Insights (Based on Availability)
11.2.5.4. Company Market Share Analysis
11.2.5.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.5.6. Strategy
11.2.5.7. SWOT Analysis
11.2.6. Episerver
11.2.6.1. Business Overview
11.2.6.2. Products Offering
11.2.6.3. Financial Insights (Based on Availability)
11.2.6.4. Company Market Share Analysis
11.2.6.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.6.6. Strategy
11.2.6.7. SWOT Analysis
11.2.7. Adobe
11.2.7.1. Business Overview
11.2.7.2. Products Offering
11.2.7.3. Financial Insights (Based on Availability)
11.2.7.4. Company Market Share Analysis
11.2.7.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.7.6. Strategy
11.2.7.7. SWOT Analysis
11.2.8. Salesforce
11.2.8.1. Business Overview
11.2.8.2. Products Offering
11.2.8.3. Financial Insights (Based on Availability)
11.2.8.4. Company Market Share Analysis
11.2.8.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.8.6. Strategy
11.2.8.7. SWOT Analysis
11.2.9. Cimpress
11.2.9.1. Business Overview
11.2.9.2. Products Offering
11.2.9.3. Financial Insights (Based on Availability)
11.2.9.4. Company Market Share Analysis
11.2.9.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.9.6. Strategy
11.2.9.7. SWOT Analysis
11.2.10. Nielsen
11.2.10.1. Business Overview
11.2.10.2. Products Offering
11.2.10.3. Financial Insights (Based on Availability)
11.2.10.4. Company Market Share Analysis
11.2.10.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.10.6. Strategy
11.2.10.7. SWOT Analysis
11.2.11. Blue Yonder
11.2.11.1. Business Overview
11.2.11.2. Products Offering
11.2.11.3. Financial Insights (Based on Availability)
11.2.11.4. Company Market Share Analysis
11.2.11.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.11.6. Strategy
11.2.11.7. SWOT Analysis
11.2.12. SAS Institute
11.2.12.1. Business Overview
11.2.12.2. Products Offering
11.2.12.3. Financial Insights (Based on Availability)
11.2.12.4. Company Market Share Analysis
11.2.12.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.12.6. Strategy
11.2.12.7. SWOT Analysis
11.2.13. Shopify
11.2.13.1. Business Overview
11.2.13.2. Products Offering
11.2.13.3. Financial Insights (Based on Availability)
11.2.13.4. Company Market Share Analysis
11.2.13.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.13.6. Strategy
11.2.13.7. SWOT Analysis
11.2.14. SAP
11.2.14.1. Business Overview
11.2.14.2. Products Offering
11.2.14.3. Financial Insights (Based on Availability)
11.2.14.4. Company Market Share Analysis
11.2.14.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.14.6. Strategy
11.2.14.7. SWOT Analysis
11.2.15. Oracle
11.2.15.1. Business Overview
11.2.15.2. Products Offering
11.2.15.3. Financial Insights (Based on Availability)
11.2.15.4. Company Market Share Analysis
11.2.15.5. Recent Developments (Product Launch, Mergers and Acquisition, etc.)
11.2.15.6. Strategy
11.2.15.7. SWOT Analysis

List of Figures

List of Tables

Table 1: Global Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Technology, 2020-2035

Table 2: Global Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Application, 2020-2035

Table 3: Global Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by End Use, 2020-2035

Table 4: Global Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Deployment, 2020-2035

Table 5: Global Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Region, 2020-2035

Table 6: North America Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Technology, 2020-2035

Table 7: North America Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Application, 2020-2035

Table 8: North America Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by End Use, 2020-2035

Table 9: North America Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Deployment, 2020-2035

Table 10: North America Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Country, 2020-2035

Table 11: Europe Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Technology, 2020-2035

Table 12: Europe Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Application, 2020-2035

Table 13: Europe Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by End Use, 2020-2035

Table 14: Europe Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Deployment, 2020-2035

Table 15: Europe Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Country/ Sub-region, 2020-2035

Table 16: Asia Pacific Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Technology, 2020-2035

Table 17: Asia Pacific Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Application, 2020-2035

Table 18: Asia Pacific Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by End Use, 2020-2035

Table 19: Asia Pacific Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Deployment, 2020-2035

Table 20: Asia Pacific Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Country/ Sub-region, 2020-2035

Table 21: Latin America Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Technology, 2020-2035

Table 22: Latin America Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Application, 2020-2035

Table 23: Latin America Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by End Use, 2020-2035

Table 24: Latin America Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Deployment, 2020-2035

Table 25: Latin America Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Country/ Sub-region, 2020-2035

Table 26: Middle East & Africa Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Technology, 2020-2035

Table 27: Middle East & Africa Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Application, 2020-2035

Table 28: Middle East & Africa Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by End Use, 2020-2035

Table 29: Middle East & Africa Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Deployment, 2020-2035

Table 30: Middle East & Africa Intelligent Retail Decision Making at NRF Market Revenue (USD billion) Forecast, by Country/ Sub-region, 2020-2035

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