According to PayNXT360, the quick commerce market in Singapore is expected to grow by 5.0% annually, reaching US$91.2 million by 2025. The quick commerce market in the country has experienced robust...
According to PayNXT360, the quick commerce market in Singapore is expected to grow by 5.0% annually, reaching US$91.2 million by 2025. The quick commerce market in the country has experienced robust growth during 2020-2024, achieving a CAGR of 4.5%. This upward trajectory is expected to continue, with the market forecast to grow at a CAGR of 4.8% from 2025 to 2029. By the end of 2029, the quick commerce market is projected to expand from its 2024 value of US$86.9 million to approximately US$110.0 million. Key Trends & Drivers 1. Quick-commerce expanding beyond food to everyday essentials and unplanned purchases • In Singapore, the quick-commerce (Q-commerce) model, originally focused on ultra-fast meals or restaurant delivery, is now increasingly serving groceries, personal care items, pet supplies, last-minute gifts, and other "immediate need" purchases. For example, Deliveroo Singapore reports that its platform now offers use cases such as flowers, health/medicine items, and last-minute gifting alongside food. • Dense urban living and busy schedules: Singapore's high population density and time-pressured dual-income households create a demand for fast replenishment of essentials. • Platform capability evolution: Delivery platforms that already have infrastructure for food are leveraging that to add non-food SKUs (groceries, pharmacy, gift items) with relatively modest incremental investment. • Consumer behaviour shift: The pandemic accelerated the habit of using apps for "urgent" small baskets and on-demand needs, which now supports Q-commerce into adjacent categories beyond food. • The category breadth will further widen: Q-commerce players will increasingly support non-food essentials and even last-minute gifting/occasional-use items. • The value proposition will shift slightly: While speed remains important, convenience (broader assortment, flexibility of delivery window) becomes equally key. Indeed, consumers here are reportedly comfortable with 30-45 minute windows rather than sub-10-minute ultra-fast. • Margins and cost structure will evolve: With broader assortments and less extreme speed, platforms can improve cost efficiency (e.g., batching, longer windows) while still meeting demand. • Retailers will view Q-commerce as complementary to traditional e-commerce and store operations, where Q-commerce will become a "top-up" or immediate-need channel rather than a primary channel for all purchases. 2. Delivery economics and logistics optimisation becoming a critical competitive lever • Operators in Singapore are increasingly optimizing their fulfillment infrastructure, delivery routing, micro-fulfillment, and dark-store networks to make the Q-commerce model viable in a high-cost urban environment. For example, market commentary on Singapore highlights the "emergence of micro-fulfilment hubs" and the need for delivery routing efficiency, given the high urban density and rising rental costs. • Cost pressures: Singapore's urban environment means that rental, labour, and delivery costs are elevated; to maintain margins in low-basket Q-commerce, delivery efficiency becomes essential. • Urban density enables efficiency: With a geographically compact city-state, dense apartment clusters allow for multiple drops per route, making hyperlocal fulfillment feasible. • Consumer expectation of rapid delivery: The expectation of short delivery windows (30–45 minutes) places pressure on logistics infrastructure to respond. • Leveraging data and routing technology: Operators are deploying algorithms, batching, localized hubs, and dark-store models to optimize last-mile costs. • The fulfilment footprint will become more distributed and closer to the consumer, with more micro-hubs, dark stores, and shared-footprint models appearing, especially in dense residential zones. • Delivery windows may standardize around a "good enough" speed (e.g., 30-45 minutes) rather than the extreme sub-10 minutes, allowing for cost-optimized logistics models. • Consolidation pressure: Players that cannot achieve delivery-cost efficiencies may struggle to scale profitably, leading to fewer dominant players or stronger partnerships/collaborations. • Logistics innovation will intensify, with increased use of routing optimization, predictive demand forecasting, and shared rider fleets, among other measures, to reduce per-drop costs. 3. Digital payment & checkout experience integration supporting seamless, rapid-commerce • The integration of rapid checkout, mobile payments, QR codes, app UX improvements, and seamless transaction flows is enabling Q-commerce to scale in Singapore. For instance, retail-tech commentary suggests that Singapore's retail trends in 2025 will emphasize AI-powered checkout, increased mobile payment adoption, and easier returns. • High digital penetration: Singapore has a high smartphone penetration rate, an advanced mobile wallet and QR payment infrastructure, and consumers are accustomed to ordering through apps. • Expectation of frictionless transactions: In Q-commerce, checkout friction (such as slow loading and payment delays) erodes the value of "instant" delivery, so platforms invest in streamlined user experiences. • Backend integration enables rapid fulfillment: Payment and checkout systems are tied into inventory, routing, and delivery workflows, making the end-to-end rapid model operationally feasible. • Checkout will become "invisible": Users will expect the entire order-to-delivery flow to feel seamless; platforms may leverage "one-tap", saved-cards, auto-reordering/subscriptions for repeat essentials. • Greater data-driven personalisation: With integrated digital payment and order systems, players will use insights (e.g., frequent small-basket orders) to personalise offers, recommend items, and drive frequency. • New business models/monetisation: Faster checkout and app loyalty may enable subscription tiers, membership models, premium delivery windows, and bundling of services (e.g., Q-commerce and ride-hailing), especially in Singapore's ecosystem, where players like foodpanda already operate broadly. • Platform ecosystem convergence: Payment, fulfillment, logistics, and ordering will converge meaning Q-commerce players may expand into adjacent services (subscriptions, marketplaces, even offline fulfillment) within Singapore's digital-payments-enabled environment. 4. Regulatory and sustainability pressures shaping the Q-commerce operating model • In Singapore, the Q-commerce ecosystem is increasingly influenced by regulatory requirements (e.g., gig-worker protections), high real estate and rental costs, and emerging sustainability expectations (including packaging, waste, and delivery emissions). These non-consumer-facing factors are becoming central to operations. Commentators observe that Q-commerce players in Singapore face cost headwinds from labour regulations and rental pressures. • Labour regulation: Singapore's regulatory regime is evolving in respect to platform workers/gig-economy protections, which drives cost and operating-model adjustments in rapid-delivery segments. • Real estate and rental dynamics: High rental rates for fulfillment hubs or ground-floor units in densely populated areas impose cost pressures that necessitate creative fulfillment models. • Environmental / consumer expectations: As Singaporean consumers and regulators pay more attention to packaging waste, delivery emissions, and sustainability, Q-commerce operators must adapt their logistics and packaging solutions. • Cost-profit viability: Because Q-commerce often involves smaller baskets and rapid fulfillment, the cost base is thin; regulatory and sustainability pressures therefore have material implications for profitability and business model choices. • Operating models will evolve: Shared fulfilment hubs, multi-brand micro-fulfilment, and rider-pool sharing may become standard to absorb cost pressures. • Sustainability will become a competitive differentiator: Players that can reduce packaging, optimise routing for lower emissions, and communicate responsible delivery practices may gain consumer preference and regulatory goodwill. • Consolidation or scale advantage will intensify, as cost pressures rise. Smaller or undercapitalized players may struggle, leading to further consolidation in the Singapore market. • Business model innovation: Subscription models, value-added services, and "bundled delivery" may emerge, allowing operators to improve unit economics and offset regulatory/sustainability cost burdens. Competitive Landscape Over the next two to four years, Singapore's quick-commerce sector is expected to undergo notable consolidation, with a smaller number of large, well-capitalised players likely to dominate the market. Rising logistics and real estate costs, coupled with the need for efficient fulfillment networks, will make scale a critical determinant of competitiveness. Differentiation among leading platforms will increasingly shift away from speed alone toward ecosystem strength encompassing subscription programs, seamless checkout experiences, loyalty integration, and data-driven personalization. Fulfillment strategies are also expected to become more localized and cost-efficient, driven by micro-hubs and clustered delivery models that optimize last-mile economics. This operational sophistication, however, raises barriers for smaller or new entrants attempting to gain traction. In parallel, the next phase of competition will likely favour ecosystem-integrated operators that can link quick-commerce with mobility, digital payments, and broader marketplace offerings, leveraging Singapore's advanced fintech and super-app environment. Despite this evolution, pricing pressure and margin constraints will persist, with the sustainability of unit economics remaining a central challenge for the industry. Current State of the Market • The quick-commerce (Q-commerce) segment in Singapore is marked by high competitive intensity and is increasingly integrated with food delivery, grocery, and on-demand essential services. Platforms originally built for meal delivery are now rapidly expanding their offerings to include everyday household items and groceries. While order volumes per transaction remain smaller than conventional e-commerce, the frequency and immediacy of deliveries are driving operator focus. Growing urban density and consumer expectations for rapid fulfilment create a dynamic environment. • However, margins remain under pressure due to higher fulfilment costs, dense urban routing, and the need for rapid delivery infrastructure. Market saturation among major players means that differentiation increasingly relies on logistics, fulfillment footprint, and app ecosystem rather than speed alone. Key Players and New Entrants • Major incumbent players in the Singapore Q-commerce and delivery space include foodpanda, GrabMart (under Grab Holdings), and Deliveroo. For example, foodpanda has publicly stated that quick-commerce is one of its three pillars for growth in Singapore. Newer entrants or expansions by large e-commerce/tech players are also relevant (e.g., e-commerce marketplaces building fulfilment networks). The competitive environment for new entrants is challenging, as scale effects, logistics costs, and consumer expectations set high barriers to entry. Analysts have noted that smaller players may have to consolidate or exit. Recent Launches, Mergers, and Acquisitions • Several noteworthy operational developments have emerged. For instance, foodpanda has launched an automated "dark store" / micro-fulfilment centre network in Singapore to improve delivery speed and inventory management. In addition, the broader food-delivery and quick‐commerce space is seeing strategic consolidation pressure, with commentary pointing to potential mergers or acquisitions among smaller operators in Singapore. Furthermore, foodpanda's movement into house-brand grocery products and investing in its quick-commerce proposition underscores competitive escalation. This report provides a detailed data-centric analysis of the quick commerce industry in Singapore offering comprehensive coverage of both overall and quick commerce markets. It includes more than 100+ KPIs, covering gross merchandise value, gross merchandise volume, average order value, and order frequency. The report offers an in-depth analysis of quick commerce, including product type, payment mode, age group, location tier, business model, and delivery time. It further categorizes the market by revenue streams (advertising, delivery fee, and subscription-based models). In addition, the analysis captures consumer demographics by age and location alongside behavioral indicators such as subscription uptake and average delivery time. Collectively, these datasets provide a comprehensive view of market size, consumer behavior, and operational efficiency within the quick commerce ecosystem. PayNXT360’s research methodology is based on industry best practices. It's unbiased analysis leverages a proprietary analytics platform to offer a detailed view of emerging business and investment market opportunities.
This report provides a detailed data-driven analysis of the quick commerce market in Singapore, focusing on the rapid delivery ecosystem and its growth trajectory. It examines key market segments, operational models, and consumer behavior shaping the evolution of instant delivery services: • Singapore Quick Commerce Market Size and Growth Dynamics -- Gross Merchandise Value -- Gross Merchandise Volume -- Average Order Value -- Order Frequency per Year • Singapore Quick Commerce Market Segmentation by Product Type -- Groceries and Staples -- Fruits and Vegetables -- Snacks and Beverages -- Personal Care and Hygiene -- Pharmaceuticals and Health Products -- Home Décor -- Clothing and Accessories -- Electronics -- Others • Singapore Quick Commerce Market Segmentation by Payment Mode -- Instant Bank Transfer -- Wallets and Digital Payments -- Credit and Debit Cards -- Cash on Delivery • Singapore Quick Commerce Market Segmentation by Age Group -- Gen Z (15–25) -- Millennials (26–39) -- Gen X (40–55) -- Baby Boomers (Above 55) • Singapore Quick Commerce Market Segmentation by Location Tier -- Tier 1 Cities -- Tier 2 Cities -- Tier 3 Cities • Singapore Quick Commerce Market Segmentation by Business Model -- Inventory-led Model -- Hyper-local Model -- Multi-vendor Platform Model -- Others • Singapore Quick Commerce Market Segmentation by Delivery Time -- Delivery in 30 Minutes -- Delivery 30–60 Minutes -- Delivery in 3 Hours • Singapore Quick Commerce Consumer Behavior and Demographics -- Average Subscription Uptake by Age Group -- Average Subscription Uptake by Location Tier -- Average Subscription Uptake -- Average Delivery Time • Singapore Quick Commerce Revenue Structure and Composition -- Advertising Revenue -- Delivery Fee Revenue -- Subscription Revenue • Singapore Quick Commerce Operational Metrics by Product Type -- Gross Merchandise Value by Product Type -- Gross Merchandise Volume by Product Type -- Average Order Value by Product Type -- Order Frequency by Product Type • Singapore Quick Commerce Operational Metrics by Payment Mode -- Gross Merchandise Value by Payment Mode -- Gross Merchandise Volume by Payment Mode -- Average Order Value by Payment Mode • Singapore Quick Commerce Operational Metrics by Age Group -- Gross Merchandise Value by Age Group -- Gross Merchandise Volume by Age Group -- Average Order Value by Age Group • Singapore Quick Commerce Operational Metrics by Location Tier -- Gross Merchandise Value by Location Tier -- Gross Merchandise Volume by Location Tier -- Average Order Value by Location Tier -- Order Frequency by Location Tier • Singapore Quick Commerce Operational Metrics by Business Model -- Gross Merchandise Value by Business Model -- Gross Merchandise Volume by Business Model -- Average Order Value by Business Model • Singapore Quick Commerce Operational Metrics by Delivery Time -- Gross Merchandise Value by Delivery Time -- Gross Merchandise Volume by Delivery Time -- Average Order Value by Delivery Time -- Order Frequency by Delivery Time
• Comprehensive Market Intelligence: Gain a holistic understanding of the overall quick commerce with detailed operational metrics such as gross merchandise value, gross merchandise volume, average order value, and order frequency across key product categories. • Granular Segmentation and Cross-Analysis: Explore the fast-growing quick commerce ecosystem through detailed segmentation by product type, payment mode, age group, location tier, business model, and delivery time, providing data into evolving consumer behavior and purchasing dynamics. • Consumer Behavior and Ecosystem Readiness: Understand how demographics and payment method adoption are shaping consumer preferences and driving the expansion of instant delivery services in both urban and semi-urban markets. • Data-Driven Forecasts and KPI Tracking: Access a comprehensive dataset of 100+ key performance indicators (KPIs) with historical and forecast data through 2029, offering visibility into growth drivers, market trends, and investment opportunities across the quick commerce sector. • Decision-Ready Databook Format: Presented in a structured, data-centric format compatible with analytical and financial modeling, the Databook enables quick commerce companies, retailers, investors, and logistics partners to make informed, evidence-based strategic decisions.
1. About this Report 1.1 Summary 1.2 Methodology 1.3 Definitions 1.4 Disclaimer 2. Singapore Quick Commerce Industry Attractiveness 2.1 Singapore Quick Commerce – Gross Merchandise Value Trend Analysis, 2020-2029 2.2 Singapore Quick Commerce – Gross Merchandise Volume Trend Analysis, 2020-2029 2.3 Singapore Quick Commerce – Average Order Value Trend Analysis, 2020-2029 2.4 Singapore Quick Commerce – Order Frequency Trend Analysis, 2020-2029 2.5 Singapore Quick Commerce – Market Share Analysis by Key Players, 2024 3. Singapore Quick Commerce Operational KPIs 3.1 Singapore Quick Commerce Revenue and Growth Trend, 2020-2029 3.2 Singapore Quick Commerce Revenue Structure, Composition, and Growth Analysis by Segment, 2024 3.2.1 Advertising Revenue, 2020-2029 3.2.2 Delivery Fee Revenue, 2020-2029 3.2.3 Subscription Revenue, 2020-2029 4. Singapore Quick Commerce Analysis by Product Type 4.1 Singapore Quick Commerce Segment Share by Product Type, 2024 4.2 Singapore Quick Commerce Analysis by Groceries & Staples: Market Size and Forecast, 2020-2029 4.2.1 Groceries & Staples- Gross Merchandise Value Trend Analysis, 2020-2029 4.2.2 Groceries & Staples- Gross Merchandise Volume Trend Analysis, 2020-2029 4.2.3 Groceries & Staples- Average Order Value Trend Analysis, 2020-2029 4.2.4 Groceries & Staples- Order Frequency Trend Analysis, 2020-2029 4.3 Singapore Quick Commerce Analysis by Fruits & Vegetables: Market Size and Forecast, 2020-2029 4.3.1 Fruits & Vegetables- Gross Merchandise Value Trend Analysis, 2020-2029 4.3.2 Fruits & Vegetables- Gross Merchandise Volume Trend Analysis, 2020-2029 4.3.3 Fruits & Vegetables- Average Order Value Trend Analysis, 2020-2029 4.3.4 Fruits & Vegetables- Order Frequency Trend Analysis, 2020-2029 4.4 Singapore Quick Commerce Analysis by Snacks & Beverages: Market Size and Forecast, 2020-2029 4.4.1 Snacks & Beverages- Gross Merchandise Value Trend Analysis, 2020-2029 4.4.2 Snacks & Beverages- Gross Merchandise Volume Trend Analysis, 2020-2029 4.4.3 Snacks & Beverages- Average Order Value Trend Analysis, 2020-2029 4.4.4 Snacks & Beverages- Order Frequency Trend Analysis, 2020-2029 4.5 Singapore Quick Commerce Analysis by Personal Care & Hygiene: Market Size and Forecast, 2020-2029 4.5.1 Personal Care & Hygiene- Gross Merchandise Value Trend Analysis, 2020-2029 4.5.2 Personal Care & Hygiene- Gross Merchandise Volume Trend Analysis, 2020-2029 4.5.3 Personal Care & Hygiene- Average Order Value Trend Analysis, 2020-2029 4.5.4 Personal Care & Hygiene- Order Frequency Trend Analysis, 2020-2029 4.6 Singapore Quick Commerce Analysis by Pharmaceuticals & Health Products: Market Size and Forecast, 2020-2029 4.6.1 Pharmaceuticals & Health Products- Gross Merchandise Value Trend Analysis, 2020-2029 4.6.2 Pharmaceuticals & Health Products- Gross Merchandise Volume Trend Analysis, 2020-2029 4.6.3 Pharmaceuticals & Health Products- Average Order Value Trend Analysis, 2020-2029 4.6.4 Pharmaceuticals & Health Products- Order Frequency Trend Analysis, 2020-2029 4.7 Singapore Quick Commerce Analysis by Home Décor: Market Size and Forecast, 2020-2029 4.7.1 Home Décor- Gross Merchandise Value Trend Analysis, 2020-2029 4.7.2 Home Décor- Gross Merchandise Volume Trend Analysis, 2020-2029 4.7.3 Home Décor- Average Order Value Trend Analysis, 2020-2029 4.7.4 Home Décor- Order Frequency Trend Analysis, 2020-2029 4.8 Singapore Quick Commerce Analysis by Clothing & Accessories: Market Size and Forecast, 2020-2029 4.8.1 Clothing & Accessories- Gross Merchandise Value Trend Analysis, 2020-2029 4.8.2 Clothing & Accessories- Gross Merchandise Volume Trend Analysis, 2020-2029 4.8.3 Clothing & Accessories- Average Order Value Trend Analysis, 2020-2029 4.8.4 Clothing & Accessories- Order Frequency Trend Analysis, 2020-2029 4.9 Singapore Quick Commerce Analysis by Electronics: Market Size and Forecast, 2020-2029 4.9.1 Electronics- Gross Merchandise Value Trend Analysis, 2020-2029 4.9.2 Electronics- Gross Merchandise Volume Trend Analysis, 2020-2029 4.9.3 Electronics- Average Order Value Trend Analysis, 2020-2029 4.9.4 Electronics- Order Frequency Trend Analysis, 2020-2029 4.10 Singapore Quick Commerce Analysis by Other Product Category: Market Size and Forecast, 2020-2029 4.10.1 Other Product Category- Gross Merchandise Value Trend Analysis, 2020-2029 4.10.2 Other Product Category- Gross Merchandise Volume Trend Analysis, 2020-2029 4.10.3 Other Product Category- Average Order Value Trend Analysis, 2020-2029 4.10.4 Other Product Category- Order Frequency Trend Analysis, 2020-2029 5. Singapore Quick Commerce Analysis by Payment Method 5.1 Singapore Quick Commerce Segment Share by Payment Method, 2020-2029 5.2 Singapore Quick Commerce Analysis by Instant Bank Transfer: Market Size and Forecast, 2020-2029 5.2.1 Instant Bank Transfer- Gross Merchandise Value Trend Analysis, 2020-2029 5.2.2 Instant Bank Transfer- Gross Merchandise Volume Trend Analysis, 2020-2029 5.2.3 Instant Bank Transfer- Average Order Value Trend Analysis, 2020-2029 5.3 Singapore Quick Commerce Analysis by Wallets & Digital Payments: Market Size and Forecast, 2020-2029 5.3.1 Wallets & Digital Payments- Gross Merchandise Value Trend Analysis, 2020-2029 5.3.2 Wallets & Digital Payments- Gross Merchandise Volume Trend Analysis, 2020-2029 5.3.3 Wallets & Digital Payments- Average Order Value Trend Analysis, 2020-2029 5.4 Singapore Quick Commerce Analysis by Credit & Debit Cards: Market Size and Forecast, 2020-2029 5.4.1 Credit & Debit Cards- Gross Merchandise Value Trend Analysis, 2020-2029 5.4.2 Credit & Debit Cards- Gross Merchandise Volume Trend Analysis, 2020-2029 5.4.3 Credit & Debit Cards- Average Order Value Trend Analysis, 2020-2029 5.5 Singapore Quick Commerce Analysis by Cash on Delivery: Market Size and Forecast, 2020-2029 5.5.1 Cash on Delivery- Gross Merchandise Value Trend Analysis, 2020-2029 5.5.2 Cash on Delivery- Gross Merchandise Volume Trend Analysis, 2020-2029 5.5.3 Cash on Delivery- Average Order Value Trend Analysis, 2020-2029 6. Singapore Quick Commerce Analysis by Age Group 6.1 Singapore Quick Commerce Segment Share by Age Group, 2024 6.2 Singapore Quick Commerce Analysis by Gen Z (15–25) Age Group: Market Size and Forecast, 2020-2029 6.2.1 Gen Z (15–25) Age Group- Gross Merchandise Value Trend Analysis, 2020-2029 6.2.2 Gen Z (15–25) Age Group- Gross Merchandise Volume Trend Analysis, 2020-2029 6.2.3 Gen Z (15–25) Age Group- Average Order Value Trend Analysis, 2020-2029 6.3 Singapore Quick Commerce Analysis by Millennials (26–39) Age Group: Market Size and Forecast, 2020-2029 6.3.1 Millennials (26–39) Age Group- Gross Merchandise Value Trend Analysis, 2020-2029 6.3.2 Millennials (26–39) Age Group- Gross Merchandise Volume Trend Analysis, 2020-2029 6.3.3 Millennials (26–39) Age Group- Average Order Value Trend Analysis, 2020-2029 6.4 Singapore Quick Commerce Analysis by Gen X (40–55) Age Group: Market Size and Forecast, 2020-2029 6.4.1 Gen X (40–55) Age Group- Gross Merchandise Value Trend Analysis, 2020-2029 6.4.2 Gen X (40–55) Age Group- Gross Merchandise Volume Trend Analysis, 2020-2029 6.4.3 Gen X (40–55) Age Group- Average Order Value Trend Analysis, 2020-2029 6.5 Singapore Quick Commerce Analysis by Baby Boomers (Above 55+) Age Group: Market Size and Forecast, 2020-2029 6.5.1 Baby Boomers (Above 55+) Age Group- Gross Merchandise Value Trend Analysis, 2020-2029 6.5.2 Baby Boomers (Above 55+) Age Group- Gross Merchandise Volume Trend Analysis, 2020-2029 6.5.3 Baby Boomers (Above 55+) Age Group- Average Order Value Trend Analysis, 2020-2029 7. Singapore Quick Commerce Analysis by Location 7.1 Singapore Quick Commerce Segment Share by Location, 2020-2029 7.2 Singapore Quick Commerce Analysis by Tier 1 Cities: Market Size and Forecast, 2020-2029 7.2.1 Tier 1 Cities- Gross Merchandise Value Trend Analysis, 2020-2029 7.2.2 Tier 1 Cities- Gross Merchandise Volume Trend Analysis, 2020-2029 7.2.3 Tier 1 Cities- Average Order Value Trend Analysis, 2020-2029 7.2.4 Tier 1 Cities- Order Frequency Trend Analysis, 2020-2029 7.3 Singapore Quick Commerce Analysis by Tier 2 Cities: Market Size and Forecast, 2020-2029 7.3.1 Tier 2 Cities- Gross Merchandise Value Trend Analysis, 2020-2029 7.3.2 Tier 2 Cities- Gross Merchandise Volume Trend Analysis, 2020-2029 7.3.3 Tier 2 Cities- Average Order Value Trend Analysis, 2020-2029 7.3.4 Tier 2 Cities- Order Frequency Trend Analysis, 2020-2029 7.4 Singapore Quick Commerce Analysis by Tier 3 Cities: Market Size and Forecast, 2020-2029 7.4.1 Tier 3 Cities- Gross Merchandise Value Trend Analysis, 2020-2029 7.4.2 Tier 3 Cities- Gross Merchandise Volume Trend Analysis, 2020-2029 7.4.3 Tier 3 Cities- Average Order Value Trend Analysis, 2020-2029 7.4.4 Tier 3 Cities- Order Frequency Trend Analysis, 2020-2029 8. Singapore Quick Commerce Analysis by Business Model 8.1 Singapore Quick Commerce Segment Share by Business Model, 2024 8.2 Singapore Quick Commerce Analysis by Inventory Model: Market Size and Forecast, 2020-2029 8.2.1 Inventory Model- Gross Merchandise Value Trend Analysis, 2020-2029 8.2.2 Inventory Model- Gross Merchandise Volume Trend Analysis, 2020-2029 8.2.3 Inventory Model- Average Order Value Trend Analysis, 2020-2029 8.3 Singapore Quick Commerce Analysis by Hyperlocal Model: Market Size and Forecast, 2020-2029 8.3.1 Hyperlocal Model- Gross Merchandise Value Trend Analysis, 2020-2029 8.3.2 Hyperlocal Model- Gross Merchandise Volume Trend Analysis, 2020-2029 8.3.3 Hyperlocal Model- Average Order Value Trend Analysis, 2020-2029 8.4 Singapore Quick Commerce Analysis by Multi-vendor Platform Model: Market Size and Forecast, 2020-2029 8.4.1 Multi-vendor Platform Model- Gross Merchandise Value Trend Analysis, 2020-2029 8.4.2 Multi-vendor Platform Model- Gross Merchandise Volume Trend Analysis, 2020-2029 8.4.3 Multi-vendor Platform Model- Average Order Value Trend Analysis, 2020-2029 8.5 Singapore Quick Commerce Analysis by Other Business Models: Market Size and Forecast, 2020-2029 8.5.1 Other Business Models- Gross Merchandise Value Trend Analysis, 2020-2029 8.5.2 Other Business Models- Gross Merchandise Volume Trend Analysis, 2020-2029 8.5.3 Other Business Models- Average Order Value Trend Analysis, 2020-2029 9. Singapore Quick Commerce Analysis by Delivery Time 9.1 Singapore Quick Commerce Segment Share by Delivery Time, 2020-2029 9.2 Singapore Quick Commerce Analysis by Delivery Time In 30 Minutes: Market Size and Forecast, 2020-2029 9.2.1 Delivery Time In 30 Minutes- Gross Merchandise Value Trend Analysis, 2020-2029 9.2.2 Delivery Time In 30 Minutes- Gross Merchandise Volume Trend Analysis, 2020-2029 9.2.3 Delivery Time In 30 Minutes- Average Order Value Trend Analysis, 2020-2029 9.2.4 Delivery Time In 30 Minutes- Order Frequency Trend Analysis, 2020-2029 9.3 Singapore Quick Commerce Analysis by Delivery Time 30–60 Minutes: Market Size and Forecast, 2020-2029 9.3.1 Delivery Time 30–60 Minutes- Gross Merchandise Value Trend Analysis, 2020-2029 9.3.2 Delivery Time 30–60 Minutes- Gross Merchandise Volume Trend Analysis, 2020-2029 9.3.3 Delivery Time 30–60 Minutes- Average Order Value Trend Analysis, 2020-2029 9.3.4 Delivery Time 30–60 Minutes- Order Frequency Trend Analysis, 2020-2029 9.4 Singapore Quick Commerce Analysis by Delivery Time In 3 Hours: Market Size and Forecast, 2020-2029 9.4.1 Delivery Time In 3 Hours- Gross Merchandise Value Trend Analysis, 2020-2029 9.4.2 Delivery Time In 3 Hours- Gross Merchandise Volume Trend Analysis, 2020-2029 9.4.3 Delivery Time In 3 Hours- Average Order Value Trend Analysis, 2020-2029 9.4.4 Delivery Time In 3 Hours- Order Frequency Trend Analysis, 2020-2029 10. Singapore Quick Commerce Consumer Behaviour and Adoption 10.1 Singapore Quick Commerce- Average Subscription Uptake, 2024 10.2 Singapore Quick Commerce- Average Subscription Uptake by Age Group, 2024 10.3 Singapore Quick Commerce- Average Subscription Uptake by Location, 2024 10.4 Singapore Quick Commerce- Average Delivery Time, 2024 11. Further Reading 11.1 About PayNXT360 11.2 Related Research
Table 1: Singapore Quick Commerce – Gross Merchandise Value (US$ Million), 2020–2029 Table 2: Singapore Quick Commerce – Gross Merchandise Volume (Millions), 2020–2029 Table 3: Singapore Quick Commerce – Average Order Value (US$), 2020–2029 Table 4: Singapore Quick Commerce – Order Frequency (Orders per Year), 2020–2029 Table 5: Singapore Quick Commerce Revenue and Growth Trend (US$ Million), 2020–2029 Table 6: Advertising Revenue (US$ Million), 2020–2029 Table 7: Delivery Fee Revenue (US$ Million), 2020–2029 Table 8: Subscription Revenue (US$ Million), 2020–2029 Table 9: Groceries & Staples – Gross Merchandise Value (US$ Million), 2020–2029 Table 10: Groceries & Staples – Gross Merchandise Volume (Millions), 2020–2029 Table 11: Groceries & Staples – Average Order Value (US$), 2020–2029 Table 12: Groceries & Staples – Order Frequency (Orders per Year), 2020–2029 Table 13: Fruits & Vegetables – Gross Merchandise Value (US$ Million), 2020–2029 Table 14: Fruits & Vegetables – Gross Merchandise Volume (Millions), 2020–2029 Table 15: Fruits & Vegetables – Average Order Value (US$), 2020–2029 Table 16: Fruits & Vegetables – Order Frequency (Orders per Year), 2020–2029 Table 17: Snacks & Beverages – Gross Merchandise Value (US$ Million), 2020–2029 Table 18: Snacks & Beverages – Gross Merchandise Volume (Millions), 2020–2029 Table 19: Snacks & Beverages – Average Order Value (US$), 2020–2029 Table 20: Snacks & Beverages – Order Frequency (Orders per Year), 2020–2029 Table 21: Personal Care & Hygiene – Gross Merchandise Value (US$ Million), 2020–2029 Table 22: Personal Care & Hygiene – Gross Merchandise Volume (Millions), 2020–2029 Table 23: Personal Care & Hygiene – Average Order Value (US$), 2020–2029 Table 24: Personal Care & Hygiene – Order Frequency (Orders per Year), 2020–2029 Table 25: Pharmaceuticals & Health Products – Gross Merchandise Value (US$ Million), 2020–2029 Table 26: Pharmaceuticals & Health Products – Gross Merchandise Volume (Millions), 2020–2029 Table 27: Pharmaceuticals & Health Products – Average Order Value (US$), 2020–2029 Table 28: Pharmaceuticals & Health Products – Order Frequency (Orders per Year), 2020–2029 Table 29: Home Décor – Gross Merchandise Value (US$ Million), 2020–2029 Table 30: Home Décor – Gross Merchandise Volume (Millions), 2020–2029 Table 31: Home Décor – Average Order Value (US$), 2020–2029 Table 32: Home Décor – Order Frequency (Orders per Year), 2020–2029 Table 33: Clothing & Accessories – Gross Merchandise Value (US$ Million), 2020–2029 Table 34: Clothing & Accessories – Gross Merchandise Volume (Millions), 2020–2029 Table 35: Clothing & Accessories – Average Order Value (US$), 2020–2029 Table 36: Clothing & Accessories – Order Frequency (Orders per Year), 2020–2029 Table 37: Electronics – Gross Merchandise Value (US$ Million), 2020–2029 Table 38: Electronics – Gross Merchandise Volume (Millions), 2020–2029 Table 39: Electronics – Average Order Value (US$), 2020–2029 Table 40: Electronics – Order Frequency (Orders per Year), 2020–2029 Table 41: Others – Gross Merchandise Value (US$ Million), 2020–2029 Table 42: Others – Gross Merchandise Volume (Millions), 2020–2029 Table 43: Others – Average Order Value (US$), 2020–2029 Table 44: Others – Order Frequency (Orders per Year), 2020–2029 Table 45: Instant Bank Transfer – Gross Merchandise Value (US$ Million), 2020–2029 Table 46: Instant Bank Transfer – Gross Merchandise Volume (Millions), 2020–2029 Table 47: Instant Bank Transfer – Average Order Value (US$), 2020–2029 Table 48: Wallets & Digital Payments – Gross Merchandise Value (US$ Million), 2020–2029 Table 49: Wallets & Digital Payments – Gross Merchandise Volume (Millions), 2020–2029 Table 50: Wallets & Digital Payments – Average Order Value (US$), 2020–2029 Table 51: Credit & Debit Card – Gross Merchandise Value (US$ Million), 2020–2029 Table 52: Credit & Debit Card – Gross Merchandise Volume (Millions), 2020–2029 Table 53: Credit & Debit Card – Average Order Value (US$), 2020–2029 Table 54: Cash on Delivery – Gross Merchandise Value (US$ Million), 2020–2029 Table 55: Cash on Delivery – Gross Merchandise Volume (Millions), 2020–2029 Table 56: Cash on Delivery – Average Order Value (US$), 2020–2029 Table 57: Gen Z (15–25) Age Group – Gross Merchandise Value (US$ Million), 2020–2029 Table 58: Gen Z (15–25) Age Group – Gross Merchandise Volume (Millions), 2020–2029 Table 59: Gen Z (15–25) Age Group – Average Order Value (US$), 2020–2029 Table 60: Millennials (26–39) Age Group – Gross Merchandise Value (US$ Million), 2020–2029 Table 61: Millennials (26–39) Age Group – Gross Merchandise Volume (Millions), 2020–2029 Table 62: Millennials (26–39) Age Group – Average Order Value (US$), 2020–2029 Table 63: Gen X (40–55) Age Group – Gross Merchandise Value (US$ Million), 2020–2029 Table 64: Gen X (40–55) Age Group – Gross Merchandise Volume (Millions), 2020–2029 Table 65: Gen X (40–55) Age Group – Average Order Value (US$), 2020–2029 Table 66: Baby Boomers (Above 55+) Age Group – Gross Merchandise Value (US$ Million), 2020–2029 Table 67: Baby Boomers (Above 55+) Age Group – Gross Merchandise Volume (Millions), 2020–2029 Table 68: Baby Boomers (Above 55+) Age Group – Average Order Value (US$), 2020–2029 Table 69: Tier 1 Cities – Gross Merchandise Value (US$ Million), 2020–2029 Table 70: Tier 1 Cities – Gross Merchandise Volume (Millions), 2020–2029 Table 71: Tier 1 Cities – Average Order Value (US$), 2020–2029 Table 72: Tier 1 Cities – Order Frequency (Orders per Year), 2020–2029 Table 73: Tier 2 Cities – Gross Merchandise Value (US$ Million), 2020–2029 Table 74: Tier 2 Cities – Gross Merchandise Volume (Millions), 2020–2029 Table 75: Tier 2 Cities – Average Order Value (US$), 2020–2029 Table 76: Tier 2 Cities – Order Frequency (Orders per Year), 2020–2029 Table 77: Tier 3 Cities – Gross Merchandise Value (US$ Million), 2020–2029 Table 78: Tier 3 Cities – Gross Merchandise Volume (Millions), 2020–2029 Table 79: Tier 3 Cities – Average Order Value (US$), 2020–2029 Table 80: Tier 3 Cities – Order Frequency (Orders per Year), 2020–2029 Table 81: Inventory Model – Gross Merchandise Value (US$ Million), 2020–2029 Table 82: Inventory Model – Gross Merchandise Volume (Millions), 2020–2029 Table 83: Inventory Model – Average Order Value (US$), 2020–2029 Table 84: Hyperlocal Model – Gross Merchandise Value (US$ Million), 2020–2029 Table 85: Hyperlocal Model – Gross Merchandise Volume (Millions), 2020–2029 Table 86: Hyperlocal Model – Average Order Value (US$), 2020–2029 Table 87: Multi-vendor Platform Model – Gross Merchandise Value (US$ Million), 2020–2029 Table 88: Multi-vendor Platform Model – Gross Merchandise Volume (Millions), 2020–2029 Table 89: Multi-vendor Platform Model – Average Order Value (US$), 2020–2029 Table 90: Others – Gross Merchandise Value (US$ Million), 2020–2029 Table 91: Others – Gross Merchandise Volume (Millions), 2020–2029 Table 92: Others – Average Order Value (US$), 2020–2029 Table 93: Delivery Time in 30 Minutes – Gross Merchandise Value (US$ Million), 2020–2029 Table 94: Delivery Time in 30 Minutes – Gross Merchandise Volume (Millions), 2020–2029 Table 95: Delivery Time in 30 Minutes – Average Order Value (US$), 2020–2029 Table 96: Delivery Time in 30 Minutes – Order Frequency (Orders per Year), 2020–2029 Table 97: Delivery Time 30–60 Minutes – Gross Merchandise Value (US$ Million), 2020–2029 Table 98: Delivery Time 30–60 Minutes – Gross Merchandise Volume (Millions), 2020–2029 Table 99: Delivery Time 30–60 Minutes – Average Order Value (US$), 2020–2029 Table 100: Delivery Time 30–60 Minutes – Order Frequency (Orders per Year), 2020–2029 Table 101: Delivery Time in 3 Hours – Gross Merchandise Value (US$ Million), 2020–2029 Table 102: Delivery Time in 3 Hours – Gross Merchandise Volume (Millions), 2020–2029 Table 103: Delivery Time in 3 Hours – Average Order Value (US$), 2020–2029 Table 104: Delivery Time in 3 Hours – Order Frequency (Orders per Year), 2020–2029
Figure 1: PayNXT360’s Methodology Framework Figure 2: Singapore Quick Commerce – Gross Merchandise Value (US$ Million), 2020–2029 Figure 3: Singapore Quick Commerce – Gross Merchandise Volume (Millions), 2020–2029 Figure 4: Singapore Quick Commerce – Average Order Value (US$), 2020–2029 Figure 5: Singapore Quick Commerce – Order Frequency (Orders per Year), 2020–2029 Figure 6: Singapore Quick Commerce – Market Share Analysis by Key Players (%), 2024 Figure 7: Singapore Quick Commerce Revenue and Growth Trend (US$ Million), 2020–2029 Figure 8: Singapore Quick Commerce Revenue Structure, Composition, and Growth Analysis by Segment (US$ Million), 2024 Figure 9: Advertising Revenue (US$ Million), 2020–2029 Figure 10: Delivery Fee Revenue (US$ Million), 2020–2029 Figure 11: Subscription Revenue (US$ Million), 2020–2029 Figure 12: Singapore Quick Commerce Segment Share by Product Type, 2024 Figure 13: Groceries & Staples – Gross Merchandise Value (US$ Million), 2020–2029 Figure 14: Groceries & Staples – Gross Merchandise Volume (Millions), 2020–2029 Figure 15: Groceries & Staples – Average Order Value (US$), 2020–2029 Figure 16: Groceries & Staples – Order Frequency (Orders per Year), 2020–2029 Figure 17: Fruits & Vegetables – Gross Merchandise Value (US$ Million), 2020–2029 Figure 18: Fruits & Vegetables – Gross Merchandise Volume (Millions), 2020–2029 Figure 19: Fruits & Vegetables – Average Order Value (US$), 2020–2029 Figure 20: Fruits & Vegetables – Order Frequency (Orders per Year), 2020–2029 Figure 21: Snacks & Beverages – Gross Merchandise Value (US$ Million), 2020–2029 Figure 22: Snacks & Beverages – Gross Merchandise Volume (Millions), 2020–2029 Figure 23: Snacks & Beverages – Average Order Value (US$), 2020–2029 Figure 24: Snacks & Beverages – Order Frequency (Orders per Year), 2020–2029 Figure 25: Personal Care & Hygiene – Gross Merchandise Value (US$ Million), 2020–2029 Figure 26: Personal Care & Hygiene – Gross Merchandise Volume (Millions), 2020–2029 Figure 27: Personal Care & Hygiene – Average Order Value (US$), 2020–2029 Figure 28: Personal Care & Hygiene – Order Frequency (Orders per Year), 2020–2029 Figure 29: Pharmaceuticals & Health Products – Gross Merchandise Value (US$ Million), 2020–2029 Figure 30: Pharmaceuticals & Health Products – Gross Merchandise Volume (Millions), 2020–2029 Figure 31: Pharmaceuticals & Health Products – Average Order Value (US$), 2020–2029 Figure 32: Pharmaceuticals & Health Products – Order Frequency (Orders per Year), 2020–2029 Figure 33: Home Décor – Gross Merchandise Value (US$ Million), 2020–2029 Figure 34: Home Décor – Gross Merchandise Volume (Millions), 2020–2029 Figure 35: Home Décor – Average Order Value (US$), 2020–2029 Figure 36: Home Décor – Order Frequency (Orders per Year), 2020–2029 Figure 37: Clothing & Accessories – Gross Merchandise Value (US$ Million), 2020–2029 Figure 38: Clothing & Accessories – Gross Merchandise Volume (Millions), 2020–2029 Figure 39: Clothing & Accessories – Average Order Value (US$), 2020–2029 Figure 40: Clothing & Accessories – Order Frequency (Orders per Year), 2020–2029 Figure 41: Electronics – Gross Merchandise Value (US$ Million), 2020–2029 Figure 42: Electronics – Gross Merchandise Volume (Millions), 2020–2029 Figure 43: Electronics – Average Order Value (US$), 2020–2029 Figure 44: Electronics – Order Frequency (Orders per Year), 2020–2029 Figure 45: Other Product Category – Gross Merchandise Value (US$ Million), 2020–2029 Figure 46: Other Product Category – Gross Merchandise Volume (Millions), 2020–2029 Figure 47: Other Product Category – Average Order Value (US$), 2020–2029 Figure 48: Other Product Category – Order Frequency (Orders per Year), 2020–2029 Figure 49: Singapore Quick Commerce Segment Share by Payment Method, 2024 Figure 50: Instant Bank Transfer – Gross Merchandise Value (US$ Million), 2020–2029 Figure 51: Instant Bank Transfer – Gross Merchandise Volume (Millions), 2020–2029 Figure 52: Instant Bank Transfer – Average Order Value (US$), 2020–2029 Figure 53: Wallets & Digital Payments – Gross Merchandise Value (US$ Million), 2020–2029 Figure 54: Wallets & Digital Payments – Gross Merchandise Volume (Millions), 2020–2029 Figure 55: Wallets & Digital Payments – Average Order Value (US$), 2020–2029 Figure 56: Credit & Debit Cards – Gross Merchandise Value (US$ Million), 2020–2029 Figure 57: Credit & Debit Cards – Gross Merchandise Volume (Millions), 2020–2029 Figure 58: Credit & Debit Cards – Average Order Value (US$), 2020–2029 Figure 59: Cash on Delivery – Gross Merchandise Value (US$ Million), 2020–2029 Figure 60: Cash on Delivery – Gross Merchandise Volume (Millions), 2020–2029 Figure 61: Cash on Delivery – Average Order Value (US$), 2020–2029 Figure 62: Singapore Quick Commerce Segment Share by Age Group, 2024 Figure 63: Gen Z (15–25) Age Group – Gross Merchandise Value (US$ Million), 2020–2029 Figure 64: Gen Z (15–25) Age Group – Gross Merchandise Volume (Millions), 2020–2029 Figure 65: Gen Z (15–25) Age Group – Average Order Value (US$), 2020–2029 Figure 66: Millennials (26–39) Age Group – Gross Merchandise Value (US$ Million), 2020–2029 Figure 67: Millennials (26–39) Age Group – Gross Merchandise Volume (Millions), 2020–2029 Figure 68: Millennials (26–39) Age Group – Average Order Value (US$), 2020–2029 Figure 69: Gen X (40–55) Age Group – Gross Merchandise Value (US$ Million), 2020–2029 Figure 70: Gen X (40–55) Age Group – Gross Merchandise Volume (Millions), 2020–2029 Figure 71: Gen X (40–55) Age Group – Average Order Value (US$), 2020–2029 Figure 72: Baby Boomers (Above 55+) Age Group – Gross Merchandise Value (US$ Million), 2020–2029 Figure 73: Baby Boomers (Above 55+) Age Group – Gross Merchandise Volume (Millions), 2020–2029 Figure 74: Baby Boomers (Above 55+) Age Group – Average Order Value (US$), 2020–2029 Figure 75: Singapore Quick Commerce Segment Share by Location, 2024 Figure 76: Tier 1 Cities – Gross Merchandise Value (US$ Million), 2020–2029 Figure 77: Tier 1 Cities – Gross Merchandise Volume (Millions), 2020–2029 Figure 78: Tier 1 Cities – Average Order Value (US$), 2020–2029 Figure 79: Tier 1 Cities – Order Frequency (Orders per Year), 2020–2029 Figure 80: Tier 2 Cities – Gross Merchandise Value (US$ Million), 2020–2029 Figure 81: Tier 2 Cities – Gross Merchandise Volume (Millions), 2020–2029 Figure 82: Tier 2 Cities – Average Order Value (US$), 2020–2029 Figure 83: Tier 2 Cities – Order Frequency (Orders per Year), 2020–2029 Figure 84: Tier 3 Cities – Gross Merchandise Value (US$ Million), 2020–2029 Figure 85: Tier 3 Cities – Gross Merchandise Volume (Millions), 2020–2029 Figure 86: Tier 3 Cities – Average Order Value (US$), 2020–2029 Figure 87: Tier 3 Cities – Order Frequency (Orders per Year), 2020–2029 Figure 88: Singapore Quick Commerce Segment Share by Business Model, 2024 Figure 89: Inventory Model – Gross Merchandise Value (US$ Million), 2020–2029 Figure 90: Inventory Model – Gross Merchandise Volume (Millions), 2020–2029 Figure 91: Inventory Model – Average Order Value (US$), 2020–2029 Figure 92: Hyperlocal Model – Gross Merchandise Value (US$ Million), 2020–2029 Figure 93: Hyperlocal Model – Gross Merchandise Volume (Millions), 2020–2029 Figure 94: Hyperlocal Model – Average Order Value (US$), 2020–2029 Figure 95: Multi-vendor Platform Model – Gross Merchandise Value (US$ Million), 2020–2029 Figure 96: Multi-vendor Platform Model – Gross Merchandise Volume (Millions), 2020–2029 Figure 97: Multi-vendor Platform Model – Average Order Value (US$), 2020–2029 Figure 98: Other Business Models – Gross Merchandise Value (US$ Million), 2020–2029 Figure 99: Other Business Models – Gross Merchandise Volume (Millions), 2020–2029 Figure 100: Other Business Models – Average Order Value (US$), 2020–2029 Figure 101: Singapore Quick Commerce Segment Share by Delivery Time, 2024 Figure 102: Delivery Time in 30 Minutes – Gross Merchandise Value (US$ Million), 2020–2029 Figure 103: Delivery Time in 30 Minutes – Gross Merchandise Volume (Millions), 2020–2029 Figure 104: Delivery Time in 30 Minutes – Average Order Value (US$), 2020–2029 Figure 105: Delivery Time in 30 Minutes – Order Frequency (Orders per Year), 2020–2029 Figure 106: Delivery Time 30–60 Minutes – Gross Merchandise Value (US$ Million), 2020–2029 Figure 107: Delivery Time 30–60 Minutes – Gross Merchandise Volume (Millions), 2020–2029 Figure 108: Delivery Time 30–60 Minutes – Average Order Value (US$), 2020–2029 Figure 109: Delivery Time 30–60 Minutes – Order Frequency (Orders per Year), 2020–2029 Figure 110: Delivery Time in 3 Hours – Gross Merchandise Value (US$ Million), 2020–2029 Figure 111: Delivery Time in 3 Hours – Gross Merchandise Volume (Millions), 2020–2029 Figure 112: Delivery Time in 3 Hours – Average Order Value (US$), 2020–2029 Figure 113: Delivery Time in 3 Hours – Order Frequency (Orders per Year), 2020–2029 Figure 114: Singapore Quick Commerce – Average Subscription Uptake, 2024 Figure 115: Singapore Quick Commerce – Average Subscription Uptake by Age Group, 2024 Figure 116: Singapore Quick Commerce – Average Subscription Uptake by Location, 2024 Figure 117: Singapore Quick Commerce – Average Delivery Time, 2024
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