Product Code: TC 9600
The smart shopping cart market is estimated to be USD 326.0 million in 2025 and is expected to reach USD 1,423.1 billion by 2030, growing at a CAGR of 34.3%.
| Scope of the Report |
| Years Considered for the Study | 2025-2030 |
| Base Year | 2025 |
| Forecast Period | 2025-2030 |
| Units Considered | Value (USD Million) |
| Segments | Application Area, By Mode of Sale, By Cart Type |
| Regions covered | North America, Asia Pacific, Europe, Latin America, Middle East & Africa |
Rapid advancements in AI, computer vision, weight sensors, and edge processing are significantly driving the growth of the smart shopping cart market. These technologies have improved item recognition accuracy, reduced hardware costs, and enhanced the reliability of autonomous checkout systems. Modern carts can identify products, including produce without barcodes, detect mis-scans, and process transactions securely in real time. As the underlying technology becomes more affordable and scalable, retailers gain confidence in large-scale deployment and long-term ROI. The maturing technology ecosystem also supports modular upgrades, improved battery efficiency, and better integration with store systems, making smart carts more practical and commercially attractive than earlier generations.
"The direct segment is expected to have the largest market size."
Direct sales involve the vendor working closely with retailers to manage installation, integration, and ongoing service. This model ensures tight alignment with store systems, including POS, inventory, loyalty, and real-time analytics platforms. Retailers benefit from higher customization, faster issue resolution, and prioritized feature enhancements. Vendors gain valuable user feedback that accelerates product improvement. Direct sales are common among large supermarket chains or retailers requiring high levels of accuracy, reliability, and long-term partnership commitments. This model supports advanced deployments where full-stack integration of AI models, edge processing, connectivity, and payment processing is crucial. Recent large-scale deployments by major grocery chains have typically been executed through direct partnerships, ensuring the technology is tailored specifically to the retailer's unique inventory and operational workflows.
"North America is expected to hold the largest market share."
North America's smart shopping cart market is undergoing a rapid transformation, driven by a relentless focus on enhancing customer experiences and reducing operational costs. The region's retail landscape, characterized by fierce competition, drives major players to innovate aggressively with smart cart technology. Innovations in on-cart edge computing and advanced sensor fusion are enhancing accuracy for loss prevention and enabling dynamic pricing at the point of sale. Companies are also exploring innovative financing models, such as "Robot-as-a-Service," to accelerate deployment across diverse retail formats. The high disposable income and established digital payment habits of North American consumers make them particularly receptive to in-cart payment and personalized offers. Additionally, the ongoing evolution of supply chain logistics is influencing cart design, integrating more closely with inventory management systems. This concerted effort to redefine the physical shopping experience through technology cements North America's position as a critical growth hub for smart shopping carts.
Breakdown of primaries
The study offers insights from a range of industry experts, including solution vendors and Tier 1 companies. The break-up of the primaries is as follows:
- By Company Type: Tier 1 - 62%, Tier 2 - 23%, and Tier 3 - 15%
- By Designation: C-level - 38%, D-level - 30%, and Others - 32%
- By Region: North America - 40%, Europe - 15%, Asia Pacific - 35%, Middle East & Africa - 5%, Latin America - 5%
The major players in the smart shopping cart market include Amazon (US), Caper (US), Veeve (US), Shopic (Israel), SuperHii (China), Tracxpoint (Israel), Cust2Mate (Israel), Shekel (Israel), Faytech (US), KBST (Germany), MetroClick (US), Retail AI (Japan), Pentland Firth Software (Germany), VasyERP (India), Smapca (India), SwiftForce (India), Kwikkart (US), ZeroQs (Poland), Shopreme (Austria), and Trollee (Hong Kong). These players have adopted various growth strategies, including partnerships, agreements, collaborations, new product launches, enhancements, and acquisitions, to expand their footprint in the smart shopping cart market.
Research Coverage
The market study covers the smart shopping cart market size and growth potential across different segments, including application areas, modes of sale, cart types, and regions. The application areas studied under the smart shopping cart market include shopping malls, supermarkets, and other application areas. The mode of sale segment includes direct and distributor. The cart type segment comprises fully integrated carts and retrofit carts. The regional analysis of the smart shopping cart market covers North America, Europe, the Asia Pacific, the Middle East & Africa, and Latin America.
Key Benefits of Buying the Report
The report will help market leaders and new entrants with information on the closest approximations of the global smart shopping cart market's revenue numbers and subsegments. It will also help stakeholders understand the competitive landscape, gain valuable insights, and develop effective go-to-market strategies. Moreover, the report will provide stakeholders with insights into the market's pulse, offering them information on key market drivers, restraints, challenges, and opportunities.
The report provides the following insights:
Analysis of key drivers (Growing consumer demand for frictionless, contactless, and personalized shopping, Technological advancements in computer vision, sensors, and edge computing enable reliable, low-latency item recognition), restraints (High upfront hardware and integration costs, Integration complexity with POS, inventory, and loyalty systems), opportunities (Retrofit devices/attachable solutions for existing carts, reducing the deployment cost, In-cart promotions, targeted offers, and ads create recurring revenue streams), and challenges (Robust item recognition across SKUs and packaging changes, Maintaining uptime, battery logistics, and field servicing across thousands of carts complicates scaling) influencing the growth of the smart shopping cart market.
Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and product & service launches in the smart shopping cart market.
Market Development: The report provides comprehensive information about lucrative markets, analyzing the smart shopping cart market across various regions.
Market Diversification: Comprehensive information about new products and services, untapped geographies, recent developments, and investments in the smart shopping cart market.
Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players such as Amazon (US), Caper (US), Veeve (US), Shopic (Israel), SuperHii (China), Tracxpoint (Israel), Cust2Mate (Israel), Shekel (Israel), Faytech (US), KBST (Germany), MetroClick (US), Retail AI (Japan), Pentland Firth Software (Germany), VasyERP (India), Smapca (India), SwiftForce (India), Kwikkart (US), ZeroQs (Poland), Shopreme (Austria), and Trollee (Hong Kong).
TABLE OF CONTENTS
1 INTRODUCTION
- 1.1 STUDY OBJECTIVES
- 1.2 MARKET DEFINITION
- 1.3 MARKET SCOPE
- 1.3.1 MARKET SEGMENTATION & REGIONAL SCOPE
- 1.3.2 INCLUSIONS AND EXCLUSIONS
- 1.4 YEARS CONSIDERED
- 1.5 CURRENCY CONSIDERED
- 1.6 STAKEHOLDERS
2 RESEARCH METHODOLOGY
- 2.1 RESEARCH DATA
- 2.1.1 SECONDARY DATA
- 2.1.1.1 Key data from secondary sources
- 2.1.2 PRIMARY DATA
- 2.1.2.1 Primary interviews with experts
- 2.1.2.2 Breakdown of primary profiles
- 2.1.2.3 Key data from primary sources
- 2.1.2.4 Key industry insights
- 2.2 MARKET BREAKUP AND DATA TRIANGULATION
- 2.3 MARKET SIZE ESTIMATION
- 2.3.1 TOP-DOWN APPROACH
- 2.3.2 BOTTOM-UP APPROACH
- 2.4 MARKET FORECAST
- 2.5 RESEARCH ASSUMPTIONS
- 2.6 LIMITATIONS
3 EXECUTIVE SUMMARY
- 3.1 KEY INSIGHTS AND MARKET HIGHLIGHTS
- 3.2 KEY MARKET PARTICIPANTS: MAPPING OF STRATEGIC DEVELOPMENTS
- 3.3 DISRUPTIONS SHAPING MARKET
- 3.4 HIGH-GROWTH SEGMENTS
- 3.5 SNAPSHOT: GLOBAL MARKET SIZE, GROWTH RATE, AND FORECAST
4 PREMIUM INSIGHTS
- 4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN SMART SHOPPING CART MARKET
- 4.2 SMART SHOPPING CART MARKET, BY MODE OF SALE AND REGION
- 4.3 SMART SHOPPING CART MARKET, BY APPLICATION AREA
- 4.4 SMART SHOPPING CART MARKET, BY MODE OF SALE
5 MARKET OVERVIEW AND INDUSTRY TRENDS
- 5.1 INTRODUCTION
- 5.2 MARKET DYNAMICS
- 5.2.1 DRIVERS
- 5.2.1.1 Growing consumer demand for frictionless, contactless, and personalized shopping
- 5.2.1.2 Technological advancements in computer vision, sensors, and edge computing enable reliable, low-latency item recognition
- 5.2.2 RESTRAINTS
- 5.2.2.1 High upfront hardware and integration costs
- 5.2.2.2 Integration complexity with POS, inventory, and loyalty systems
- 5.2.3 OPPORTUNITIES
- 5.2.3.1 Retrofit devices/attachable solutions for existing carts, reducing deployment cost
- 5.2.3.2 In-cart promotions, targeted offers, and ads create recurring revenue streams
- 5.2.4 CHALLENGES
- 5.2.4.1 Robust item recognition across SKUs and packaging changes
- 5.2.4.2 Maintaining uptime, battery logistics, and field servicing across thousands of carts complicates scaling
- 5.3 INTERCONNECTED MARKETS AND CROSS-SECTOR OPPORTUNITIES
- 5.3.1 INTERCONNECTED MARKETS
- 5.3.2 CROSS-SECTOR OPPORTUNITIES
- 5.4 STRATEGIC MOVES BY TIER-1/2/3 PLAYERS
- 5.4.1 KEY MOVES AND STRATEGIC FOCUS
6 INDUSTRY TRENDS
- 6.1 PORTER'S FIVE FORCES MODEL ANALYSIS
- 6.1.1 THREAT OF NEW ENTRANTS
- 6.1.2 THREAT OF SUBSTITUTES
- 6.1.3 BARGAINING POWER OF SUPPLIERS
- 6.1.4 BARGAINING POWER OF BUYERS
- 6.1.5 INTENSITY OF COMPETITIVE RIVALRY
- 6.2 MACROECONOMIC OUTLOOK
- 6.2.1 INTRODUCTION
- 6.2.2 GDP TRENDS AND FORECAST
- 6.2.3 TRENDS IN GLOBAL SMART SHOPPING CART INDUSTRY
- 6.3 SUPPLY CHAIN ANALYSIS
- 6.4 VALUE CHAIN ANALYSIS
- 6.5 ECOSYSTEM
- 6.6 PRICING ANALYSIS
- 6.6.1 AVERAGE PRICING ANALYSIS
- 6.6.2 INDICATIVE PRICING ANALYSIS, BY CART TYPE
- 6.7 TRADE ANALYSIS
- 6.7.1 EXPORT SCENARIO OF TRAILERS AND SEMI-TRAILERS; OTHER VEHICLES, NOT MECHANICALLY PROPELLED
- 6.7.2 IMPORT SCENARIO OF VEHICLES PUSHED OR DRAWN BY HAND AND OTHER VEHICLES NOT MECHANICALLY PROPELLED BY COUNTRY, 2020-2024 (USD MILLION)
- 6.8 KEY CONFERENCES AND EVENTS
- 6.9 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
- 6.10 INVESTMENT AND FUNDING SCENARIO
- 6.11 CASE STUDY ANALYSIS
- 6.11.1 VEEVE - SMART CART ROLLOUTS & RETAIL MEDIA PIVOT
- 6.11.2 TRACXPOINT - AI CART PLATFORM
- 6.11.3 SHOPREME'S SCAN & GO SDK INTEGRATED INTO REWE'S
- 6.12 IMPACT OF 2025 US TARIFF - SMART SHOPPING CART MARKET
- 6.12.1 INTRODUCTION
- 6.12.2 KEY TARIFF RATES
- 6.12.3 PRICE IMPACT ANALYSIS
- 6.12.4 IMPACT ON COUNTRY/REGION
- 6.12.4.1 US
- 6.12.4.2 Europe
- 6.12.4.3 Asia Pacific
- 6.12.4.4 IMPACT ON IoT END USERS
7 STRATEGIC DISRUPTION: PATENTS, DIGITAL, AND AI ADOPTION
- 7.1 KEY EMERGING TECHNOLOGIES
- 7.1.1 COMPUTER VISION-DRIVEN SKU DETECTION
- 7.1.2 EDGE AI HARDWARE & ON-CART PROCESSING UNITS
- 7.1.3 MULTI-SENSOR FUSION (WEIGHT SENSORS, DEPTH CAMERAS, LIDAR)
- 7.2 COMPLEMENTARY TECHNOLOGIES
- 7.2.1 RFID & NFC-BASED ITEM TRACKING
- 7.2.2 CLOUD ANALYTICS & RETAIL DATA PLATFORMS
- 7.2.3 DIGITAL TWIN & STORE SIMULATION SYSTEMS
- 7.3 TECHNOLOGY/PRODUCT ROADMAP FOR SMART SHOPPING CART MARKET
- 7.3.1 SHORT-TERM ROADMAP (2023-2025)
- 7.3.2 MID-TERM ROADMAP (2026-2028)
- 7.3.3 LONG-TERM ROADMAP (2029-2030)
- 7.3.4 SMART SHOPPING CART ECOSYSTEM
- 7.3.4.1 Web management platform
- 7.3.4.2 Cloud infrastructure
- 7.3.4.3 Products & hardware
- 7.3.4.4 Middleware
- 7.3.4.5 ERP & POS system integration
- 7.4 PATENT ANALYSIS
- 7.4.1 LIST OF MAJOR PATENTS
- 7.5 IMPACT OF AI/GENERATIVE AI ON SMART SHOPPING CART MARKET
- 7.5.1 TOP USE CASES AND MARKET POTENTIAL OF GENERATIVE AI IN SMART SHOPPING CARTS
- 7.5.2 BEST PRACTICES OF SMART SHOPPING CART MARKET
- 7.5.3 CASE STUDIES OF AI IMPLEMENTATION IN SMART SHOPPING CART MARKET
- 7.5.3.1 Case study 1: Caper AI-powered Smart Cart Deployment
- 7.5.3.2 Case study 2: Shopic Clip-On Device Rollout
- 7.5.3.3 Case study 3: Cust2Mate Intelligent Cart Program
- 7.5.3.4 Case study 4: Shekel Scales & Vision System Integration
- 7.5.4 INTERCONNECTED ADJACENT ECOSYSTEM AND IMPACT ON MARKET PLAYERS
- 7.5.5 CLIENTS' READINESS TO ADOPT GENERATIVE AI IN SMART SHOPPING CARTS
- 7.6 TECHNOLOGIES ADOPTED BY COMPETITORS
- 7.7 BUSINESS MODELS
- 7.8 RETAILERS CURRENTLY TESTING OR ADOPTING SMART CARTS
8 REGULATORY LANDSCAPE AND COMPLIANCE
- 8.1 REGULATORY LANDSCAPE
- 8.1.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
- 8.1.2 INDUSTRY STANDARDS
- 8.1.2.1 North America
- 8.1.2.1.1 US
- 8.1.2.1.2 Canada
- 8.1.2.2 Europe
- 8.1.2.3 Asia Pacific
- 8.1.2.3.1 China
- 8.1.2.3.2 Japan
- 8.1.2.3.3 India
- 8.1.2.4 Middle East & Africa
- 8.1.2.4.1 UAE
- 8.1.2.4.2 Saudi Arabia
- 8.1.2.4.3 South Africa
- 8.1.2.5 Latin America
9 CUSTOMER LANDSCAPE & BUYER BEHAVIOR
- 9.1 DECISION-MAKING PROCESS
- 9.2 KEY STAKEHOLDERS AND BUYING CRITERIA
- 9.2.1 KEY STAKEHOLDERS IN BUYING PROCESS
- 9.2.2 BUYING CRITERIA
- 9.3 ADOPTION BARRIERS & INTERNAL CHALLENGES
- 9.4 UNMET NEEDS IN VARIOUS END-USE VERTICALS
10 SMART SHOPPING CART MARKET, BY TECHNOLOGY
- 10.1 INTRODUCTION
- 10.1.1 TECHNOLOGY: SMART SHOPPING CART MARKET DRIVERS
- 10.2 COMPUTER VISION
- 10.2.1 VISUALIZING CART'S CONTENTS FOR SEAMLESS TRACKING
- 10.3 AI MODULES
- 10.3.1 POWER REAL-TIME DECISIONS AND PERSONALIZED INTERACTIONS
- 10.4 SENSORS
- 10.4.1 CAPTURING GRANULAR DATA FOR MULTI-MODAL VERIFICATION
- 10.5 EDGE COMPUTING
- 10.5.1 ENABLE LOW-LATENCY PROCESSING FOR REAL-TIME CART FUNCTIONS
- 10.6 CONNECTIVITY
- 10.6.1 MAINTAIN SEAMLESS COMMUNICATION BETWEEN CART AND STORE SYSTEMS
- 10.7 DISPLAY
- 10.7.1 ENHANCE USER INTERACTION AND IMPROVE SHOPPING EFFICIENCY
- 10.8 PAYMENT PROCESSING
- 10.8.1 ENABLE SECURE, FRICTIONLESS DIGITAL TRANSACTIONS
11 SMART SHOPPING CART MARKET, BY CART TYPE
- 11.1 INTRODUCTION
- 11.1.1 CART TYPE: SMART SHOPPING CART MARKET DRIVERS
- 11.2 FULLY INTEGRATED CARTS
- 11.2.1 INCREASING DEMAND FOR HIGH-PRECISION, END-TO-END IN-STORE AUTOMATION
- 11.3 RETROFIT KITS
- 11.3.1 LOW UPFRONT COST AND RAPID DEPLOYMENT CAPABILITIES
12 SMART SHOPPING CART MARKET, BY APPLICATION AREA
- 12.1 INTRODUCTION
- 12.1.1 APPLICATION AREA: SMART SHOPPING CART MARKET DRIVERS
- 12.2 SHOPPING MALLS
- 12.2.1 ENHANCE MULTI-STORE EXPERIENCE AND SHOPPER ENGAGEMENT
- 12.3 SUPERMARKETS
- 12.3.1 OPTIMIZE HIGH-FREQUENCY, HIGH-SKU SHOPPING JOURNEYS
- 12.4 OTHER APPLICATION AREAS
13 SMART SHOPPING CART MARKET, BY MODE OF SALE
- 13.1 INTRODUCTION
- 13.1.1 MODE OF SALE: SMART SHOPPING CART MARKET DRIVERS
- 13.2 DIRECT
- 13.2.1 DEEP INTEGRATION AND CONTROL OVER RETAILER EXPERIENCE
- 13.3 DISTRIBUTOR
- 13.3.1 EXPAND MARKET REACH AND ENABLE LOCALIZED SUPPORT
14 SMART SHOPPING CART MARKET, BY REGION
- 14.1 INTRODUCTION
- 14.2 NORTH AMERICA
- 14.2.1 US
- 14.2.1.1 Accelerated Retail Digitalization Driving Smart Cart Uptake
- 14.2.2 CANADA
- 14.2.2.1 Growing Retail Modernization Supporting Smart Cart Pilots
- 14.3 EUROPE
- 14.3.1 UK
- 14.3.1.1 AI-led Store Innovation Fueling Smart Trolley Deployments
- 14.3.2 GERMANY
- 14.3.2.1 Expansion of Seamless Checkout Technologies Enabling Smart Cart Adoption
- 14.3.3 FRANCE
- 14.3.3.1 Retail Automation Investments Catalyzing Smart Cart Trials
- 14.3.4 ITALY
- 14.3.4.1 Increasing Omnichannel Retail Focus Encouraging Smart Cart Use Cases
- 14.3.5 REST OF EUROPE
- 14.4 ASIA PACIFIC
- 14.4.1 CHINA
- 14.4.1.1 Focus on Alternative Retail Tech
- 14.4.2 INDIA
- 14.4.2.1 Conglomerate Unveils Smart Cart Demo
- 14.4.3 JAPAN
- 14.4.3.1 National Chain Trials Scanning Carts
- 14.4.4 AUSTRALIA & NEW ZEALAND
- 14.4.4.1 Government Pilots Driving Trusted RAG Use Cases
- 14.4.5 REST OF ASIA PACIFIC
- 14.5 MIDDLE EAST & AFRICA
- 14.5.1 ISRAEL
- 14.5.1.1 Tech Exporter of Smart Carts
- 14.5.2 UAE
- 14.5.2.1 Vision 2030 Investments Scaling Knowledge-centric AI
- 14.5.3 SOUTH AFRICA
- 14.5.3.1 First Smart Trolley Trials
- 14.5.4 REST OF MIDDLE EAST & AFRICA
- 14.6 LATIN AMERICA
- 14.6.1 CHILE
- 14.6.1.1 Driving AI-enabled Checkout Transformation Across Leading Supermarket Chains
- 14.6.2 MEXICO
- 14.6.2.1 Accelerating Retail Modernization Through Large-scale Smart Cart Pilots
- 14.6.3 REST OF LATIN AMERICA
15 COMPETITIVE LANDSCAPE
- 15.1 INTRODUCTION
- 15.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2023-2025
- 15.3 MARKET SHARE ANALYSIS, 2025
- 15.4 BRAND/PRODUCT COMPARISON
- 15.5 COMPANY VALUATION AND FINANCIAL METRICS
- 15.6 COMPANY EVALUATION MATRIX: MAJOR PLAYERS, 2025
- 15.6.1 STARS
- 15.6.2 EMERGING LEADERS
- 15.6.3 PERVASIVE PLAYERS
- 15.6.4 PARTICIPANTS
- 15.6.5 COMPANY FOOTPRINT: MAJOR PLAYERS, 2025
- 15.6.5.1 Company footprint
- 15.6.5.2 Region footprint
- 15.6.5.3 Application area footprint
- 15.6.5.4 Mode of sale footprint
- 15.7 COMPETITIVE SCENARIO
- 15.7.1 PRODUCT LAUNCHES AND ENHANCEMENTS
- 15.7.2 DEALS
16 COMPANY PROFILES
- 16.1 KEY PLAYERS
- 16.1.1 AMAZON
- 16.1.1.1 Business overview
- 16.1.1.2 Products/Solutions/Services offered
- 16.1.1.3 Recent developments
- 16.1.1.3.1 Product launches
- 16.1.1.3.2 Deals
- 16.1.1.4 MnM view
- 16.1.1.4.1 Key strengths/Right to win
- 16.1.1.4.2 Strategic choices
- 16.1.1.4.3 Weaknesses and competitive threats
- 16.1.2 CAPER
- 16.1.2.1 Business overview
- 16.1.2.2 Products/Solutions/Services offered
- 16.1.2.3 Recent developments
- 16.1.2.4 MnM view
- 16.1.2.4.1 Key strengths/Right to win
- 16.1.2.4.2 Strategic choices
- 16.1.2.4.3 Weaknesses and competitive threats
- 16.1.3 VEEVE
- 16.1.3.1 Business overview
- 16.1.3.2 Products/Solutions/Services offered
- 16.1.3.3 Recent developments
- 16.1.3.3.1 Product launches
- 16.1.3.3.2 Deals
- 16.1.3.4 MnM view
- 16.1.3.4.1 Key strengths/Right to win
- 16.1.3.4.2 Strategic choices
- 16.1.3.4.3 Weaknesses and competitive threats
- 16.1.4 SHOPIC
- 16.1.4.1 Business overview
- 16.1.4.2 Products/Solutions/Services offered
- 16.1.4.3 Recent developments
- 16.1.4.4 MnM view
- 16.1.4.4.1 Key strengths/Right to win
- 16.1.4.4.2 Strategic choices
- 16.1.4.4.3 Weaknesses and competitive threats
- 16.1.5 SUPERHII
- 16.1.5.1 Business overview
- 16.1.5.2 Products/Solutions/Services offered
- 16.1.5.3 Recent developments
- 16.1.5.3.1 Product launches
- 16.1.5.4 MnM view
- 16.1.5.4.1 Key strengths/Right to win
- 16.1.5.4.2 Strategic choices
- 16.1.5.4.3 Weaknesses and competitive threats
- 16.1.6 TRACXPOINT
- 16.1.6.1 Business overview
- 16.1.6.2 Products/Solutions/Services offered
- 16.1.6.3 Recent developments
- 16.1.7 CUST2MATE
- 16.1.7.1 Business overview
- 16.1.7.2 Products/Solutions/Services offered
- 16.1.7.3 Recent developments
- 16.1.8 SHEKEL
- 16.1.8.1 Business overview
- 16.1.8.2 Products/Solutions/Services offered
- 16.1.9 FAYTECH
- 16.1.9.1 Business overview
- 16.1.9.2 Products/Solutions/Services offered
- 16.1.10 KBST
- 16.1.10.1 Business overview
- 16.1.10.2 Products/Solutions/Services offered
- 16.2 OTHER PLAYERS
- 16.2.1 METROCLICK
- 16.2.2 RETAIL AI
- 16.2.3 PENTLAND FIRTH SOFTWARE
- 16.2.4 VASY ERP
- 16.2.5 SMAPCA
- 16.2.6 SWIFTFORCE
- 16.2.7 KWIKKART
- 16.2.8 ZEROQS
- 16.2.9 SHOPREME
- 16.2.10 TROLLEE
17 APPENDIX
- 17.1 DISCUSSION GUIDE
- 17.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
- 17.3 CUSTOMIZATION OPTIONS
- 17.4 RELATED REPORTS
- 17.5 AUTHOR DETAILS