Retail Analytics Market Size, Share, Trends, And Global Forecast, 2026-2032 Retailers Llan On AI Forecasting As Retail Analytics Heads For $23.21Bn

(MENAFN- GlobeNewsWire - Nasdaq) New Report values the market at $11.82bn in 2026, with operations and supply chain the leading application and Asia Pacific growing fastest, though stitching together physical and digital data remains a stubborn problem for Oracle, Salesforce, SAP and Adobe

Dublin, Oct. 20, 2026 (GLOBE NEWSWIRE) -- "Retail Analytics Market: Forecast to 2032" has been added to ResearchAndMarkets.com's offering.

Retail Analytics Market to Reach USD 23.21 Billion by 2032 as AI and Omnichannel Investments Accelerate

The global retail analytics market is projected to grow from USD 11.82 billion in 2026 to USD 23.21 billion by 2032, registering a CAGR of 11.9%. Market growth is being driven by rising investment in AI-enabled analytics, unified retail data platforms, cloud-based infrastructure, and intelligent decision-making capabilities.

Retailers are integrating transaction, inventory, pricing, merchandising, loyalty, ecommerce, point-of-sale, fulfillment, and supply chain data to improve visibility across physical and digital channels. Advanced retail analytics supports demand forecasting, assortment optimization, customer segmentation, pricing intelligence, replenishment planning, inventory allocation, and store performance management. Machine learning strengthens forecast accuracy by assessing historical sales, promotions, seasonality, inventory positions, and external market signals.

Real-time analytics enables retail organizations to identify demand changes, inventory imbalances, operational exceptions, and evolving customer behavior. Cloud-based architectures support large-scale data processing and faster access to insights across distributed stores, warehouses, ecommerce operations, and enterprise applications. AI-driven recommendations are also advancing automated pricing, localized merchandising, promotion optimization, and enterprise-wide retail planning.

Autonomous and agentic analytics gains momentum

Autonomous & agentic analytics is expanding rapidly as retailers prioritize intelligent, self-directed decision workflows. Modern platforms can continuously monitor business metrics, detect anomalies, assess alternatives, recommend actions, and initiate predefined operational responses. These capabilities help retailers reduce reliance on static dashboards while extending analytics access to managers and frontline teams.

Vendors are incorporating conversational analytics, generative interfaces, semantic intelligence, autonomous agents, governance, and workflow automation into retail analytics platforms. Salesforce is advancing trusted insights, conversational exploration, proactive recommendations, and autonomous business actions. Oracle applies machine learning to forecasting, assortment, pricing, inventory, and consumer insights, while Microsoft combines real-time analytics, predictive forecasting, assortment optimization, and inventory intelligence across connected retail environments.

Operations & supply chain applications expected to lead in 2026

Operations & supply chain applications are likely to account for a leading market position in 2026 as retailers focus on execution efficiency, product availability, working capital, service levels, and fulfillment performance. Retail organizations increasingly analyze demand, inventory, replenishment, transportation, labor, and store data across complex operating networks.

Analytics platforms support optimized allocations, time-phased inventory plans, supply chain risk management, order management, transportation planning, and fulfillment orchestration. Connecting store inventory, ecommerce demand, warehouses, suppliers, and customer orders also improves omnichannel fulfillment. The increasing impact of supply chain disruptions on margins, availability, and customer satisfaction is reinforcing demand for continuous operational intelligence.

North America maintains market leadership

North America is expected to remain the largest retail analytics market in 2026, supported by mature digital infrastructure, strong ecommerce penetration, widespread cloud adoption, and substantial technology spending. Retailers throughout the region are deploying analytics for merchandising, inventory management, pricing, customer engagement, personalization, anomaly detection, and operational planning.

The presence of leading cloud, artificial intelligence, and analytics providers further supports regional adoption. US retailers are also accelerating the implementation of agentic AI to improve employee productivity, analytical insights, and operational execution across stores and digital commerce channels.

Asia Pacific emerges as the fastest-growing region

Asia Pacific is projected to be the fastest-growing retail analytics market as ecommerce expansion, mobile-first shopping, digital payments, cloud migration, and technology investment generate substantial volumes of retail data. Retailers in China, India, Japan, South Korea, and Southeast Asia are modernizing their analytical capabilities to improve forecasting, personalization, recommendations, assortment optimization, pricing, inventory availability, store productivity, and omnichannel fulfillment.

The Asian Development Bank identifies Asia Pacific as accounting for approximately two-thirds of global ecommerce sales. This large digital economy is increasing demand for scalable analytical infrastructure, effective data governance, and advanced decision intelligence. Regional innovation in retail distribution, profitability analysis, and in-store technology is expected to support continued market expansion throughout the forecast period.

Competitive landscape

Retail analytics vendors are strengthening their market positions through AI-powered solutions, integrated data platforms, cloud interoperability, and workflow automation. The report includes company profiles and competitive analysis covering Oracle Corporation, Salesforce, Microsoft Corporation, SAP SE, Teradata Corporation, Adobe Inc., Zebra Technologies Corporation, Shopify Inc., Lightspeed Commerce Inc., Manhattan Associates, UiPath Inc., NielsenIQ, Databricks, Infor, Strategy Incorporated, Domo, SAS Institute, Sensormatic Solutions, Blue Yonder, Epicor Software Corporation, Circana, o9 Solutions, dunnhumby, Qlik, Alteryx, SymphonyAI, RELEX Solutions, ThoughtSpot, Sisense, RetailNext, Competera, Retalon, Focal Systems, Alloy.ai, Conjura, and other established and emerging providers.

Research coverage

The report categorizes the retail analytics market by offering, analytics type, application, retail type, and region. Analytics categories include traditional & rule-based analytics, AI-embedded & predictive analytics, prescriptive & optimization analytics, and autonomous & agentic analytics. Application coverage includes sales and merchandising management, marketing and customer management, operations & supply chain, workforce & productivity, financial & payments, and enterprise decision intelligence.

Retail segments assessed include grocery food & convenience retail, apparel fashion & luxury retail, consumer electronics retail, pharmacy health & wellness retail, beauty & personal care retail, home living & improvement retail, automotive retail, and other retail types. Regional coverage includes North America, Europe, Asia Pacific, Middle East & Africa, and Latin America.

The research evaluates market drivers, restraints, challenges, opportunities, product innovation, market development, diversification strategies, partnerships, agreements, product launches, mergers and acquisitions, and startup activity. Key growth opportunities include generative AI, conversational analytics, hyperlocal demand forecasting, and predictive replenishment planning. Major challenges include fragmented data across retail systems, integration costs, and the need to convert large data volumes into timely, actionable decisions.

Primary research breakdown

• By Company: Tier 1 - 38%, Tier 2 - 47%, and Tier 3 - 15%
• By Designation: C-level Executives - 31%, Directors - 46%, and Others - 23%
• By Region: North America - 39%, Europe - 22%, Asia Pacific - 28%, Middle East & Africa - 4%, and Latin America - 7%

Primary interviews included chief executive officers, innovation and technology directors, system integrators, and executives from organizations operating in the retail analytics market. Additional participants included sales, marketing, and product managers.

The report provides revenue estimates for the overall market and its subsegments, enabling established companies and new entrants to assess competitive conditions, identify attractive opportunities, strengthen market positioning, and develop informed go-to-market strategies.

Key Attributes:

Report Attribute	Details
No. of Pages	468
Forecast Period	2026 - 2032
Estimated Market Value (USD) in 2026	$11.82 billion
Forecasted Market Value (USD) by 2032	$23.21 billion
Compound Annual Growth Rate	11.9%
Regions Covered	Global

Key Topics Covered:
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.2.1 INCLUSIONS AND EXCLUSIONS
1.3 MARKET SCOPE
1.3.1 MARKET SEGMENTATION
1.3.2 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 STAKEHOLDERS
1.6 SUMMARY OF CHANGES
2 EXECUTIVE SUMMARY
2.1 MARKET HIGHLIGHTS AND KEY INSIGHTS
2.2 KEY MARKET PARTICIPANTS: MAPPING OF STRATEGIC DEVELOPMENTS
2.3 DISRUPTIVE TRENDS IN RETAIL ANALYTICS MARKET
2.4 HIGH-GROWTH SEGMENTS
2.5 REGIONAL SNAPSHOT: MARKET SIZE, GROWTH RATE, AND FORECAST
3 PREMIUM INSIGHTS
3.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN RETAIL ANALYTICS MARKET
3.2 RETAIL ANALYTICS MARKET, BY REGION
3.3 RETAIL ANALYTICS MARKET, BY OFFERING
3.4 NORTH AMERICA: RETAIL ANALYTICS MARKET, BY OFFERING AND SERVICE
4 MARKET OVERVIEW
4.1 INTRODUCTION
4.2 MARKET DYNAMICS
4.2.1 DRIVERS
4.2.1.1 Increasing adoption of AI-driven demand forecasting and inventory optimization
4.2.1.2 Rapid growth of omnichannel retailing and digital commerce data
4.2.1.3 Growing need for real-time customer and shopper behavior insights
4.2.1.4 Growing demand for pricing, merchandising, inventory, and store performance optimization
4.2.2 RESTRAINTS
4.2.2.1 Fragmented retail data across POS, e-commerce, loyalty,and supply chain systems
4.2.2.2 High cost and complexity of integrating enterprise-scale retail analytics platforms
4.2.3 OPPORTUNITIES
4.2.3.1 Expansion of generative AI and conversational analytics across retail decision workflows
4.2.3.2 Increasing use of predictive analytics for hyperlocal demand and replenishment planning
4.2.3.3 Growing adoption of item-level analytics enabled by RFID and 2D barcodes
4.2.4 CHALLENGES
4.2.4.1 Achieving unified analytical view across physical and digital retail channels
4.2.4.2 Converting large volumes of retail data into timely and actionable decisions
4.3 UNMET NEEDS AND WHITE SPACES
4.3.1 WHITE SPACE OPPORTUNITIES
4.4 INTERCONNECTED MARKETS AND CROSS-SECTOR OPPORTUNITIES
4.4.1 INTERCONNECTED MARKETS
4.4.2 CROSS-SECTOR OPPORTUNITIES
4.5 STRATEGIC MOVES BY TIER-1/2/3 PLAYERS
5 INDUSTRY TRENDS
5.1 EVOLUTION OF RETAIL ANALYTICS
5.2 PORTER'S FIVE FORCES ANALYSIS
5.2.1 THREAT OF NEW ENTRANTS
5.2.2 THREAT OF SUBSTITUTES
5.2.3 BARGAINING POWER OF SUPPLIERS
5.2.4 BARGAINING POWER OF BUYERS
5.2.5 INTENSITY OF COMPETITIVE RIVALRY
5.3 MACROECONOMIC INDICATORS
5.3.1 INTRODUCTION
5.3.2 GDP TRENDS AND FORECAST
5.3.3 TRENDS IN AI-POWERED AND AGENTIC RETAIL DECISION INTELLIGENCE
5.3.4 TRENDS IN AI-DRIVEN DEMAND FORECASTING AND DYNAMIC INVENTORY OPTIMIZATION
5.3.5 TRENDS IN REAL-TIME AND UNIFIED RETAIL DATA ANALYTICS
5.3.6 TRENDS IN COMPUTER VISION, EDGE AI, AND SMART-STORE ANALYTICS
5.3.7 TRENDS IN GENERATIVE AI AND CONVERSATIONAL SELF-SERVICE RETAIL ANALYTICS
5.4 SUPPLY CHAIN ANALYSIS
5.4.1 DATA INFRASTRUCTURE & SOURCE SYSTEMS
5.4.2 RETAIL ANALYTICS PLATFORM DEVELOPMENT & DEPLOYMENT
5.4.3 SYSTEM INTEGRATION & IMPLEMENTATION SUPPORT
5.4.4 CONSULTING & CHANNEL PARTNERS AND END USERS
5.5 ECOSYSTEM ANALYSIS
5.6 PRICING ANALYSIS
5.6.1 AVERAGE SELLING PRICE OF RETAIL ANALYTICS SOLUTIONS, BY KEY PLAYER
5.6.2 AVERAGE SELLING PRICE, BY PRICING MODEL
5.7 KEY CONFERENCES AND EVENTS, 2026-2027
5.8 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.9 INVESTMENT AND FUNDING SCENARIO
5.10 CASE STUDY ANALYSIS
5.10.1 CAPE UNION MART SCALES MERCHANDISING AND DEMAND PLANNING WITH ORACLE RETAIL
5.10.2 CARMAX ACCELERATES DATA-DRIVEN CUSTOMER INSIGHTS USING MICROSOFT AZURE OPENAI SERVICE
5.10.3 CHRIST JUWELIERE DRIVES REVENUE AND CUSTOMER RETENTION WITH SAP COMMERCE CLOUD AND SAP EMARSYS
5.10.4 THE WORKS IMPROVES STOCK ACCURACY WITH ZEBRA WORKCLOUD INVENTORY VISIBILITY
5.10.5 WALGREENS DELIVERS ITS 30-MINUTE ORDER PROMISE WITH BLUE YONDER ORDER MANAGEMENT
6 TECHNOLOGICAL ADVANCEMENTS, AI-DRIVEN IMPACT, PATENTS, INNOVATIONS, AND FUTURE APPLICATIONS
6.1 TECHNOLOGY ANALYSIS
6.1.1 KEY EMERGING TECHNOLOGIES
6.1.1.1 Agentic AI & autonomous retail decision intelligence
6.1.1.2 Multimodal generative AI & conversational retail analytics
6.1.1.3 Edge AI & computer vision for real-time store intelligence
6.1.2 COMPLEMENTARY TECHNOLOGIES
6.1.2.1 Real-time retail data fabric, lakehouse & event streaming
6.1.2.2 RFID, 2D barcodes, & product-level digital identity
6.1.2.3 Privacy-enhancing data collaboration & clean rooms
6.1.3 ADJACENT TECHNOLOGIES
6.1.3.1 Unified commerce, POS, & distributed order management
6.1.3.2 Retail media networks & closed-loop measurement
6.1.3.3 Robotics, autonomous supply chains, & intelligent fulfillment
6.2 TECHNOLOGY ROADMAP
6.2.1 SHORT-TERM (2026-2027) | REAL-TIME DATA UNIFICATION, AI ENABLEMENT, & CONNECTED STORE INTELLIGENCE
6.2.2 MID-TERM (2027-2030) | PRESCRIPTIVE OPTIMIZATION, MULTIMODAL ANALYTICS, & ECOSYSTEM EXPANSION
6.2.3 LONG-TERM (2030-2035+) | AUTONOMOUS, TRUSTED, & UBIQUITOUS RETAIL DECISION INTELLIGENCE
6.3 PATENT ANALYSIS
6.3.1 METHODOLOGY
6.3.2 PATENTS FILED, BY DOCUMENT TYPE, 2016-2026
6.3.3 INNOVATION AND PATENT APPLICATIONS
6.4 FUTURE APPLICATIONS
6.4.1 AUTONOMOUS & AI-DRIVEN RETAIL DECISION PLATFORMS
6.4.2 PREDICTIVE DEMAND, INVENTORY & ASSORTMENT ORCHESTRATION
6.4.3 REAL-TIME ADAPTIVE PRICING, PROMOTION, & MARGIN OPTIMIZATION
6.4.4 INTELLIGENT STORE & COMPUTER VISION OPERATIONAL ANALYTICS
6.4.5 AGENTIC COMMERCE & AUTONOMOUS FULFILLMENT INTELLIGENCE
6.5 IMPACT OF AI/GEN AI ON RETAIL ANALYTICS MARKET
6.5.1 BEST PRACTICES IN RETAIL ANALYTICS MARKET
6.5.2 CASE STUDIES OF AI IMPLEMENTATION IN RETAIL ANALYTICS MARKET
6.5.3 CLIENTS' READINESS TO ADOPT GENERATIVE AI IN RETAIL ANALYTICS MARKET
7 REGULATORY LANDSCAPE
7.1 REGIONAL REGULATIONS AND COMPLIANCE
7.1.1 REGULATORY BODIES, GOVERNMENT AGENCIES,AND OTHER ORGANIZATIONS
7.1.2 INDUSTRY STANDARDS
8 CONSUMER LANDSCAPE AND BUYER BEHAVIOR
8.1 DECISION-MAKING PROCESS
8.2 KEY STAKEHOLDERS IN BUYING PROCESS AND THEIR EVALUATION CRITERIA
8.2.1 KEY STAKEHOLDERS IN BUYING PROCESS
8.2.2 BUYING CRITERIA
8.3 ADOPTION BARRIERS AND INTERNAL CHALLENGES
8.4 UNMET NEEDS IN VARIOUS RETAIL TYPES
9 RETAIL ANALYTICS MARKET, BY OFFERING
9.1 INTRODUCTION
9.1.1 OFFERING: RETAIL ANALYTICS MARKET DRIVERS
9.2 SOLUTIONS
9.2.1 ENTERPRISE ANALYTICS SUITES
9.2.1.1 Enabling unified enterprise-wide retail intelligence across business functions
9.2.2 CUSTOMER INTELLIGENCE PLATFORMS
9.2.2.1 Strengthening omnichannel personalization through unified customer and behavioral intelligence
9.2.3 PLANNING & DECISION INTELLIGENCE PLATFORMS
9.2.3.1 Accelerating AI-driven forecasting, inventory, merchandising,and optimization decisions
9.2.4 API-FIRST & HEADLESS ANALYTICS PLATFORMS
9.2.4.1 Embedding composable analytics directly within modern retail applications and workflows
9.2.5 ADVANCED ANALYTICS PLATFORMS
9.2.5.1 Expanding real-time retail intelligence through Gen AI, edge AI,and composable decision technologies
9.3 SERVICES
9.3.1 PROFESSIONAL SERVICES
9.3.1.1 Accelerating retail analytics transformation through integration,data engineering, and implementation expertise
9.3.1.2 Consulting & advisory
9.3.1.3 Implementation & integration
9.3.1.4 Support & training
9.3.2 MANAGED SERVICES
9.3.2.1 Sustaining analytics performance through continuous operations, model monitoring, and specialized expertise
10 RETAIL ANALYTICS MARKET, BY ANALYTICS TYPE
10.1 INTRODUCTION
10.1.1 ANALYTICS TYPE: RETAIL ANALYTICS MARKET DRIVERS
10.2 TRDITIONAL & RULE-BASED ANALYTICS
10.2.1 MAINTAINING CONSISTENT RETAIL PERFORMANCE CONTROL THROUGH STANDARDIZED REPORTING AND PREDEFINED BUSINESS RULES
10.3 AI-EMBEDDED & PREDICTIVE ANALYTICS
10.3.1 ANTICIPATING DEMAND, INVENTORY RISKS, AND CUSTOMER BEHAVIOR THROUGH MACHINE-LEARNING-DRIVEN PREDICTION
10.4 PRESCRIPTIVE & OPTIMIZATION ANALYTICS
10.4.1 CONVERTING PREDICTIVE INSIGHTS INTO OPTIMIZED PRICING, INVENTORY, ASSORTMENT, AND MERCHANDISING DECISIONS
10.5 AUTONOMOUS & AGENTIC ANALYTICS
10.5.1 ADVANCING CONTINUOUS RETAIL DECISION EXECUTION THROUGH GOVERNED AI AGENTS AND AUTONOMOUS WORKFLOWS
11 RETAIL ANALYTICS MARKET, BY APPLICATION
11.1 INTRODUCTION
11.1.1 APPLICATION: RETAIL ANALYTICS MARKET DRIVERS
11.2 SALES & MERCHANDISING MANAGEMENT
11.2.1 IMPROVING SELL-THROUGH AND MARGINS THROUGH AI-DRIVEN ASSORTMENT, PRICING, PROMOTION, AND MERCHANDISE PLANNING
11.2.2 SALES PERFORMANCE MANAGEMENT
11.2.3 MERCHANDISING & ASSORTMENT MANAGEMENT
11.2.4 PRICING & PROMOTION MANAGEMENT
11.2.5 PRODUCT & CATEGORY MANAGEMENT
11.2.6 SPACE & PLANOGRAM MANAGEMENT
11.2.7 OTHERS
11.3 MARKETING & CUSTOMER MANAGEMENT
11.3.1 CONVERTING OMNICHANNEL SHOPPER SIGNALS INTO PERSONALIZED ACQUISITION, RETENTION, AND LOYALTY DECISIONS
11.3.2 CUSTOMER & SHOPPER MANAGEMENT
11.3.3 LOYALTY & RETENTION MANAGEMENT
11.3.4 PERSONALIZATION & RECOMMENDATION MANAGEMENT
11.3.5 CAMPAIGN & MARKETING MANAGEMENT
11.3.6 RETAIL MEDIA MANAGEMENT
11.3.7 OTHERS
11.4 OPERATIONS & SUPPLY CHAIN
11.4.1 STRENGTHENING PRODUCT AVAILABILITY THROUGH PREDICTIVE DEMAND, REPLENISHMENT, ALLOCATION, AND FULFILLMENT INTELLIGENCE
11.4.2 DEMAND PLANNING & FORECASTING
11.4.3 INVENTORY & REPLENISHMENT MANAGEMENT
11.4.4 PROCUREMENT & SUPPLIER MANAGEMENT
11.4.5 SUPPLY CHAIN & LOGISTICS MANAGEMENT
11.4.6 FULFILLMENT & DISTRIBUTION MANAGEMENT
11.4.7 STORE OPERATIONS & EXECUTION MANAGEMENT
11.4.8 OTHERS
11.5 WORKFORCE & PRODUCTIVITY
11.5.1 ALIGNING LABOR CAPACITY WITH STORE DEMAND THROUGH AI-DRIVEN WORKFORCE FORECASTING AND PRODUCTIVITY ANALYTICS
11.5.2 WORKFORCE PLANNING & SCHEDULING
11.5.3 EMPLOYEE PRODUCTIVITY & PERFORMANCE MANAGEMENT
11.5.4 LABOR COST MANAGEMENT & OPTIMIZATION
11.5.5 WORKFORCE ENGAGEMENT & RETENTION MANAGEMENT
11.5.6 TASK & WORKFORCE OPERATIONS MANAGEMENT
11.5.7 OTHERS
11.6 FINANCIAL & PAYMENTS
11.6.1 ENHANCING PROFITABILITY AND TRANSACTION PERFORMANCE THROUGH INTEGRATED FINANCIAL AND PAYMENT ANALYTICS
11.6.2 REVENUE & MARGIN MANAGEMENT
11.6.3 PROFITABILITY & COST MANAGEMENT
11.6.4 PAYMENT & TRANSACTION MANAGEMENT
11.6.5 FINANCIAL PLANNING & FORECASTING
11.6.6 WORKING CAPITAL & CASH FLOW MANAGEMENT
11.6.7 OTHERS
11.7 ENTERPRISE DECISION INTELLIGENCE
11.7.1 UNIFYING CROSS-FUNCTIONAL RETAIL SIGNALS FOR PREDICTIVE, PRESCRIPTIVE, AND EXECUTIVE DECISION-MAKING
11.7.2 BUSINESS PERFORMANCE & EXECUTIVE MANAGEMENT
11.7.3 PREDICTIVE & PRESCRIPTIVE DECISIONING
11.7.4 SCENARIO PLANNING & SIMULATION
11.7.5 AI-DRIVEN INSIGHTS & CONVERSATIONAL INTELLIGENCE
11.7.6 AUTONOMOUS & AGENTIC DECISIONING
11.7.7 OTHERS
12 RETAIL ANALYTICS MARKET, BY RETAIL TYPE
12.1 INTRODUCTION
12.1.1 RETAIL TYPE: RETAIL ANALYTICS MARKET DRIVERS
12.2 GROCERY, FOOD, & CONVENIENCE RETAIL
12.2.1 HIGH TRANSACTION FREQUENCY, PERISHABILITY, AND STORE-LEVEL DEMAND VARIABILITY TO DRIVE ANALYTICS ADOPTION
12.3 APPAREL, FASHION, & LUXURY RETAIL
12.3.1 RAPID ASSORTMENT TURNOVER AND SEASONAL FASHION CYCLES TO DRIVE MERCHANDISING ANALYTICS ADOPTION
12.4 CONSUMER ELECTRONICS RETAIL
12.4.1 SHORT PRODUCT LIFECYCLES AND HIGH PRICE TRANSPARENCY TO DRIVE REAL-TIME PRICING AND INVENTORY ANALYTICS
12.5 PHARMACY, HEALTH, & WELLNESS RETAIL
12.5.1 INTEGRATED PHARMACY AND FRONT-STORE DATA TO DRIVE AVAILABILITY AND PERSONALIZED WELLNESS ANALYTICS
12.6 BEAUTY & PERSONAL CARE RETAIL
12.6.1 HYPER-PERSONALIZATION AND FAST-MOVING BEAUTY TRENDS TO DRIVE CUSTOMER AND ASSORTMENT ANALYTICS ADOPTION
12.7 HOME LIVING & IMPROVEMENT RETAIL
12.7.1 PROJECT-BASED DEMAND AND COMPLEX OMNICHANNEL FULFILLMENT TO DRIVE INVENTORY AND DELIVERY ANALYTICS ADOPTION
12.8 AUTOMOTIVE RETAIL
12.8.1 INVENTORY PRICING COMPLEXITY AND SERVICE-LIFECYCLE VALUE TO DRIVE DEALER ANALYTICS ADOPTION
12.9 OTHER RETAIL TYPES
13 RETAIL ANALYTICS MARKET, BY REGION
13.1 INTRODUCTION
13.2 NORTH AMERICA
13.2.1 NORTH AMERICA: RETAIL ANALYTICS MARKET DRIVERS
13.2.2 US
13.2.2.1 Scaling enterprise retail analytics through AI-led omnichannel decisioning and real-time operations
13.2.3 CANADA
13.2.3.1 Growing retail analytics adoption through omnichannel shopper intelligence and value-focused operational optimization
13.3 EUROPE
13.3.1 EUROPE: RETAIL ANALYTICS MARKET DRIVERS
13.3.2 UK
13.3.2.1 Accelerating unified commerce and AI-enabled decisioning across highly integrated physical and digital retail channels
13.3.3 GERMANY
13.3.3.1 Advancing predictive planning and process optimization through data-intensive, efficiency-focused retail operations
13.3.4 FRANCE
13.3.4.1 Expanding AI-enabled digital commerce and customer intelligence through evolving online shopping journeys
13.3.5 SPAIN
13.3.5.1 Government-backed retail digitalization accelerating omnichannel management and data-driven operational modernization
13.3.6 ITALY
13.3.6.1 Modernizing store-centric retail through accessible AI, multichannel analytics, and data-driven operating efficiency
13.3.7 REST OF EUROPE
13.4 ASIA PACIFIC
13.4.1 ASIA PACIFIC: RETAIL ANALYTICS MARKET DRIVERS
13.4.2 CHINA
13.4.2.1 Accelerating smart retail and digital supply chain modernization across large-scale online and physical commerce
13.4.3 INDIA
13.4.3.1 Open digital commerce and retail formalization to accelerate analytics adoption across fragmented seller ecosystems
13.4.4 JAPAN
13.4.4.1 Maturing e-commerce penetration and complex category mix to drive omnichannel performance and inventory analytics
13.4.5 SOUTH KOREA
13.4.5.1 Mobile-dominant shopping behavior to accelerate real-time customer, product, and fulfillment analytics
13.4.6 ASEAN
13.4.6.1 Cross-border digital integration and payment interoperability to drive scalable multi-market retail analytics
13.4.7 REST OF ASIA PACIFIC
13.4.7.1 Expanding AI adoption and distributed retail networks to drive flexible cloud-based analytics deployment
13.5 MIDDLE EAST & AFRICA
13.5.1 MIDDLE EAST & AFRICA: RETAIL ANALYTICS MARKET DRIVERS
13.5.2 SAUDI ARABIA
13.5.2.1 Digital payments and national e-commerce infrastructure to accelerate real-time retail analytics adoption
13.5.3 UNITED ARAB EMIRATES (UAE)
13.5.3.1 Smart commerce and connected payment infrastructure to drive real-time retail and customer intelligence
13.5.4 TURKEY
13.5.4.1 Rapid e-commerce formalization and marketplace expansion to drive omnichannel retail analytics demand
13.5.5 SOUTH AFRICA
13.5.5.1 Expanding e-commerce and omnichannel fulfillment to drive inventory, customer, and delivery analytics adoption
13.5.6 REST OF MIDDLE EAST & AFRICA
13.6 LATIN AMERICA
13.6.1 LATIN AMERICA: RETAIL ANALYTICS MARKET DRIVERS
13.6.2 BRAZIL
13.6.2.1 Pix-led payment digitalization and e-commerce modernization to drive real-time retail analytics adoption
13.6.3 MEXICO
13.6.3.1 Expanding e-commerce channel diversity and digital payments to drive customer and omnichannel retail intelligence
13.6.4 REST OF LATIN AMERICA
14 COMPETITIVE LANDSCAPE
14.1 OVERVIEW
14.2 KEY PLAYER COMPETITIVE STRATEGIES/RIGHT TO WIN, 2022-2026
14.3 REVENUE ANALYSIS, 2021-2025
14.4 MARKET SHARE ANALYSIS, 2025
14.4.1 MARKET RANKING ANALYSIS, 2025
14.5 PRODUCT COMPARISON
14.5.1 COMPARATIVE ANALYSIS OF ADVANCED RETAIL ANALYTICS PLATFORMS
14.6 COMPANY EVALUATION MATRIX: KEY PLAYERS
14.6.1 STARS
14.6.2 EMERGING LEADERS
14.6.3 PERVASIVE PLAYERS
14.6.4 PARTICIPANTS
14.6.5 COMPANY FOOTPRINT: KEY PLAYERS, 2025
14.6.5.1 Company footprint
14.6.5.2 Regional footprint
14.6.5.3 Solution footprint
14.6.5.4 Application footprint
14.6.5.5 Retail type
14.7 COMPANY EVALUATION MATRIX: STARTUPS/SMES
14.7.1 PROGRESSIVE COMPANIES
14.7.2 RESPONSIVE COMPANIES
14.7.3 DYNAMIC COMPANIES
14.7.4 STARTING BLOCKS
14.7.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2025
14.7.5.1 Detailed list of key startups/SMEs
14.7.5.2 Competitive benchmarking of key startups/SMEs
14.8 COMPANY VALUATION AND FINANCIAL METRICS
14.9 COMPETITIVE SCENARIO
14.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS
15 COMPANY PROFILES
15.1 INTRODUCTION
15.2 KEY PLAYERS
15.2.1 ORACLE CORPORATION
15.2.1.1 Business overview
15.2.1.2 Products/Solutions/Services offered
15.2.1.3 Recent developments
15.2.1.3.1 Product launches & enhancements
15.2.1.3.2 Deals
15.2.1.3.3 Expansions
15.2.1.4 Analyst's view
15.2.1.4.1 Key strengths
15.2.1.4.2 Strategic choices
15.2.1.4.3 Weaknesses and competitive threats
15.2.2 SALESFORCE, INC.
15.2.2.1 Business overview
15.2.2.2 Products/Solutions/Services offered
15.2.2.3 Recent developments
15.2.2.3.1 Product launches & enhancements
15.2.2.3.2 Deals
15.2.2.4 Analyst's view
15.2.2.4.1 Key strengths
15.2.2.4.2 Strategic choices
15.2.2.4.3 Weaknesses and competitive threats
15.2.3 MICROSOFT CORPORATION
15.2.3.1 Business overview
15.2.3.2 Products/Solutions/Services offered
15.2.3.3 Recent developments
15.2.3.3.1 Product launches & enhancements
15.2.3.3.2 Deals
15.2.3.4 Analyst's view
15.2.3.4.1 Key strengths
15.2.3.4.2 Strategic choices
15.2.3.4.3 Weaknesses and competitive threats
15.2.4 ADOBE INC.
15.2.4.1 Business overview
15.2.4.2 Products/Solutions/Services offered
15.2.4.3 Recent developments
15.2.4.3.1 Product launches & enhancements
15.2.4.3.2 Deals
15.2.4.4 Analyst's view
15.2.4.4.1 Key strengths
15.2.4.4.2 Strategic choices
15.2.4.4.3 Weaknesses and competitive threats
15.2.5 SAP SE
15.2.5.1 Business overview
15.2.5.2 Products/Solutions/Services offered
15.2.5.3 Recent developments
15.2.5.3.1 Product launches & enhancements
15.2.5.3.2 Deals
15.2.5.4 Analyst's view
15.2.5.4.1 Key strengths
15.2.5.4.2 Strategic choices
15.2.5.4.3 Weaknesses and competitive threats
15.2.6 TERADATA CORPORATION
15.2.6.1 Business overview
15.2.6.2 Products/Solutions/Services offered
15.2.6.3 Recent developments
15.2.6.3.1 Product launches & enhancements
15.2.6.3.2 Deals
15.2.7 ZEBRA TECHNOLOGIES CORPORATION
15.2.7.1 Business overview
15.2.7.2 Products/Solutions/Services offered
15.2.7.3 Recent developments
15.2.7.3.1 Product launches & enhancements
15.2.7.3.2 Deals
15.2.8 SHOPIFY INC.
15.2.8.1 Business overview
15.2.8.2 Products/Solutions/Services offered
15.2.8.3 Recent developments
15.2.8.3.1 Product launches & enhancements
15.2.8.3.2 Deals
15.2.9 LIGHTSPEED COMMERCE INC.
15.2.9.1 Business overview
15.2.9.2 Products/Solutions/Services offered
15.2.9.3 Recent developments
15.2.9.3.1 Product launches & enhancements
15.2.9.3.2 Deals
15.2.10 MANHATTAN ASSOCIATES, INC.
15.2.10.1 Business overview
15.2.10.2 Products/Solutions/Services offered
15.2.10.3 Recent developments
15.2.10.3.1 Product launches & enhancements
15.2.10.3.2 Deals
15.2.11 DOMO, INC.
15.2.11.1 Business overview
15.2.11.2 Products/Solutions/Services offered
15.2.11.3 Recent developments
15.2.11.3.1 Product launches & enhancements
15.2.11.3.2 Deals
15.2.12 NIELSENIQ
15.2.13 STRATEGY INCORPORATED
15.2.14 SAS INSTITUTE INC.
15.2.15 DATABRICKS, INC.
15.2.16 INFOR, INC.
15.2.17 EPICOR SOFTWARE CORPORATION
15.2.18 SENSORMATIC SOLUTIONS
15.2.19 BLUE YONDER GROUP, INC.
15.2.20 CIRCANA, LLC
15.3 OTHER PLAYERS
15.3.1 ALTERYX, INC.
15.3.2 PEAK AI
15.3.3 SYMPHONYAI HOLDINGS INC.
15.3.4 ALGONOMY
15.3.5 FORM
15.3.6 RETAILNEXT, INC.
15.3.7 PLACER LABS, INC. (PLACER.AI)
15.3.8 COMMERCEIQ, INC.
15.3.9 RELEX SOLUTIONS OY
15.3.10 APTOS, LLC
15.3.11 COMPETERA INC.
15.3.12 DATAWEAVE SOFTWARE PRIVATE LIMITED
15.3.13 RETALON, INC.
15.3.14 DATASEMBLY, INC.
15.3.15 ALLOY.AI.
15.3.16 CONJURA LTD.
15.3.17 O9 SOLUTIONS, INC.
15.3.18 DUNNHUMBY LIMITED
15.3.19 FOCAL SYSTEMS, INC.
15.3.20 QLIK TECHNOLOGIES INC.
15.3.21 THOUGHTSPOT, INC.
15.3.22 SISENSE LTD.
15.3.23 SIGMA COMPUTING
16 RESEARCH METHODOLOGY
16.1 RESEARCH DATA
16.1.1 SECONDARY DATA
16.1.2 PRIMARY DATA
16.1.2.1 Breakup of primary profiles
16.1.2.2 Key industry insights
16.2 MARKET BREAKUP AND DATA TRIANGULATION
16.3 MARKET SIZE ESTIMATION
16.3.1 TOP-DOWN APPROACH
16.3.2 BOTTOM-UP APPROACH
16.4 MARKET FORECAST
16.5 RESEARCH ASSUMPTIONS
16.6 RESEARCH LIMITATIONS
17 ADJACENT AND RELATED MARKETS
17.1 INTRODUCTION
17.2 CUSTOMER EXPERIENCE MANAGEMENT MARKET - GLOBAL FORECAST TO 2028
17.2.1 MARKET DEFINITION
17.2.2 MARKET OVERVIEW
17.2.2.1 Customer experience management market, by offering
17.2.2.2 Customer experience management market, by vertical
17.2.2.3 Customer experience management market, by region
17.3 SUPPLY CHAIN ANALYTICS MARKET - GLOBAL FORECAST TO 2027
17.3.1 MARKET DEFINITION
17.3.2 MARKET OVERVIEW
17.3.2.1 Supply chain analytics market, by component
17.3.2.2 Supply chain analytics market, by service
17.3.2.3 Supply chain analytics market, by vertical
17.3.2.4 Supply chain analytics market, by region
18 APPENDIX
18.1 DISCUSSION GUIDE
18.2 KNOWLEDGESTORE: THE SUBSCRIPTION PORTAL
18.3 CUSTOMIZATION OPTIONS
18.4 RELATED REPORTS
18.5 AUTHOR DETAILS
LIST OF TABLES [239]
LIST OF FIGURES [60]
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