According to PayNXT360, the quick commerce market in China is expected to grow by 29.4% annually, reaching US$125,641.7 million by 2025. The quick commerce market in the country has experienced robus...
According to PayNXT360, the quick commerce market in China is expected to grow by 29.4% annually, reaching US$125,641.7 million by 2025. The quick commerce market in the country has experienced robust growth during 2020-2024, achieving a CAGR of 26.8%. This upward trajectory is expected to continue, with the market forecast to grow at a CAGR of 28.0% from 2025 to 2029. By the end of 2029, the quick commerce market is projected to expand from its 2024 value of US$97,095.6 million to approximately US$336,819.7 million. Key Trends & Drivers 1. Instant retail integrates into core e-commerce journeys. • Instant (30–60 minute) delivery is being embedded directly into China’s major e-commerce platforms rather than operating as a standalone niche service. Alibaba has launched a Taobao “Instant Commerce” portal that integrates Ele.me riders, promising delivery within 60 minutes. The portal reportedly handled tens of millions of orders per day within a month of its launch. • JD.com is expanding its instant delivery and takeaway offerings alongside its traditional next-day logistics network, while Meituan has introduced its “Meituan Shangou” (instant retail) brand for 30-minute delivery across multiple categories. Core e-commerce growth in China is maturing, prompting platforms to seek incremental use cases with higher engagement frequency, such as groceries, daily essentials, and food. • Dense urbanisation, high smartphone penetration, and existing food-delivery rider networks make it operationally feasible to extend from restaurant delivery into groceries, convenience items, and consumer electronics. Platforms are also seeking to keep users inside their ecosystem by offering a full spectrum from “instant” to “next-day,” turning speed into a basic service dimension rather than a premium feature. • Instant retail is likely a standard option on the product detail pages of major platforms in Tier 1 and many Tier 2 cities, especially for high-frequency categories. The boundary between “food delivery,” “q-commerce,” and “e-commerce” will continue to blur as consumers experience a single, integrated journey with varying delivery-time promises rather than separate services. • For brands and retailers, integrating with instant delivery platforms is expected to become an essential distribution route in major urban markets, shaping decisions around product assortment, packaging formats, and promotional planning. 2. Subsidy-driven competition reshapes consumer expectations but pressures margins • The competitive landscape is being shaped by heavy investment and subsidies from Alibaba, JD.com, and Meituan as they race to scale instant retail volumes. Recent reporting indicates that Alibaba and JD.com have each earmarked around RMB 10 billion for incentives and discounts around instant delivery propositions, explicitly targeting Meituan’s leadership. • Promotions increasingly bundle discounted food, groceries, and selected general merchandise with fast delivery, conditioning users to expect low prices and rapid fulfillment simultaneously. • Slower macroeconomic demand and intensifying competition in conventional e-commerce are prompting platforms to use subsidies to capture a share in emerging high-frequency use cases. Instant retail is viewed as a means to drive incremental traffic that can later be directed towards higher-margin categories, such as electronics and apparel, justifying near-term investment. • Strong cash positions at the leading platforms provide the capacity to run prolonged campaigns even if unit economics are currently thin. • Consumers will become more price-sensitive and more willing to switch between apps for minor savings, reinforcing the importance of subsidies and targeted promotions in the short term. Profitability will remain uneven: large horizontal platforms may absorb promotion costs, while smaller or vertical players without comparable balance sheets will face pressure to differentiate or exit. • Regulators may pay closer attention if price wars distort competition or put pressure on small merchants and riders, potentially leading to guidance on fair competition and pricing practices. 3. Offline retailers use instant delivery to extend store catchment and utilisation • Supermarkets, hypermarkets, and convenience chains in China are increasingly utilizing q-commerce platforms to transform their physical stores into local fulfillment nodes. Walmart China has integrated all of its stores with Meituan’s platform to support real-time retail, making store inventory available for 30-minute delivery in multiple cities. • Alibaba’s Freshippo (Hema) resumed its expansion in 2024, opening multiple technology-enabled supermarkets while sustaining profitability. Each store is designed to cater to both in-store shoppers and fast delivery orders within a defined local catchment area. JD NOW (formerly JDDJ), operated by Dada Nexus under the JD Group, positions itself as a local on-demand retail platform that links supermarkets, pharmacies, and other offline retailers to instant delivery capabilities across thousands of cities and counties. • Traditional retailers are facing slower offline traffic and are looking to monetise store networks more efficiently, using them as micro-warehouses to meet on-demand orders. Large platforms want broad, near-home inventory coverage without owning all the real estate themselves, making partnerships with chains like Walmart, Freshippo, and regional supermarket groups attractive. • Consumers increasingly treat supermarkets’ assortment as something they can access digitally, and are less willing to trade off speed for travelling to the store for routine purchases. • More national and regional chains are likely to sign exclusive or semi-exclusive partnerships with leading instant retail platforms, making access to certain banners a point of differentiation for the platforms. Store network optimisation (opening smaller high-throughput urban stores and adjusting layout for picking efficiency) will accelerate, with store P&L increasingly linked to online orders fulfilled from the premises. • Smaller offline retailers may choose to either specialise (e.g., in premium fresh or niche categories) or aggregate via local alliances to gain better terms on these platforms, thereby influencing supplier negotiation dynamics. 4. Vertical grocery specialists pursue operational discipline and selective growth • Vertical fresh-grocery e-commerce players are focusing on unit economics, SKU optimisation, and geographic rationalisation after an earlier phase of rapid but unprofitable expansion. Dingdong Maicai has reported full-year profitability for 2024 with double-digit revenue growth, citing improved operations, better category management, and more disciplined city-level expansion. • At the same time, Dada Nexus has reported revenue declines and increased losses in parts of its on-demand operations, highlighting the uneven performance across models and the pressures on partners that sit between retailers and platforms. • The earlier wave of fresh e-commerce in China saw aggressive expansion and subsequent consolidation; recent profitability at players like Dingdong reflects a shift toward efficiency and cash-flow focus rather than pure GMV growth. • Consumer demand for fresh groceries via quick commerce remains strong in major cities. Still, competition from platform-integrated services (Meituan, Ele.me, JD NOW) forces vertical players to differentiate themselves via product quality, private labels, and service consistency, rather than just speed. Capital availability for loss-making vertical models has decreased compared to earlier years, prompting management teams to demonstrate a clear path to sustainable profitability. • Standalone grocery specialists are likely to pursue selective city expansion, focusing on core regions where density supports strong economics, and invest in proprietary supply chains (e.g., fresh produce, meat, prepared foods) to differentiate themselves. • M&A or strategic partnerships with larger platforms and retailers are plausible, as horizontal players may seek access to fresh-supply capabilities and vertical players may benefit from traffic and capital support. For the broader market, this should result in fewer players with more stable operations. At the same time, the role of vertical specialists shifts from competing on speed alone to anchoring category depth and quality in the instant retail ecosystem. Competitive Landscape Over the next two to four years, competition in China’s quick commerce market is projected to stay intense but increasingly consolidated around a few large ecosystem players with nationwide reach. Platform integration will deepen as instant retail becomes a core element of mainstream e-commerce. Retailers and consumer brands are likely to increasingly rely on large platform ecosystems for order fulfillment and last-mile delivery, while independent operators may pivot toward niche categories or partnership-based models. Long-term profitability will depend on improving delivery efficiency, developing private-label portfolios, and strengthening ecosystem collaborations. Current State of the Market • China’s quick commerce market is entering a consolidation phase after years of experimentation and competition across e-commerce, food delivery, and fresh grocery platforms. Integrated ecosystems rather than standalone operators now lead the market. Companies such as Alibaba (via Ele.me and Taobao Instant Delivery), Meituan (Shangou), and JD.com (JD NOW, formerly JDDJ) dominate instant retail logistics and fulfilment in major cities. • China’s quick commerce model has moved beyond its earlier promotion-driven growth phase toward broader ecosystem integration, with delivery times under 60 minutes now standard in Tier 1 and Tier 2 cities. Niche grocery players such as Dingdong Maicai and Missfresh, which previously operated at scale, have reduced their footprint as profitability pressures intensified. Key Players and New Entrants • China’s quick commerce landscape is led by Meituan, Alibaba Group, and JD.com, each capitalizing on extensive logistics infrastructure and integrated merchant–consumer ecosystems. Meituan Shangou holds the leading position, supported by its vast delivery workforce and wide product range. • Alibaba’s Ele.me has been fully connected with Taobao and Freshippo, enhancing its instant delivery capability. JD.com continues to expand its JD NOW service in partnership with Dada Nexus, extending operations to more than 2,000 cities. Among vertical-focused players, Dingdong Maicai stands out as one of the few consistently profitable operators, concentrating on fresh produce and ready-to-cook meals. Regional retailers and convenience store alliances are also entering the segment by collaborating with major platforms to increase reach and access to on-demand logistics networks. Recent Launches, Mergers, and Acquisitions • Strategic collaborations and alliances have emerged as key drivers of competitive differentiation in China’s quick commerce landscape. In 2024, Walmart China expanded its collaboration with Meituan to enable nationwide store integration for 30-minute delivery. Alibaba introduced an “Instant Commerce” section within Taobao to reinforce its position in the on-demand segment, while Dingdong Maicai achieved profitability and restarted its expansion initiatives. • JD.com rebranded its on-demand arm, JDDJ, as JD NOW to create a unified brand identity across its instant logistics operations. Market consolidation has accelerated, with many smaller regional firms either being acquired or exiting the sector amid mounting operational costs and margin pressures. This report provides a detailed data-centric analysis of the quick commerce industry in China 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 China, 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: • China Quick Commerce Market Size and Growth Dynamics -- Gross Merchandise Value -- Gross Merchandise Volume -- Average Order Value -- Order Frequency per Year • China 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 • China Quick Commerce Market Segmentation by Payment Mode -- Instant Bank Transfer -- Wallets and Digital Payments -- Credit and Debit Cards -- Cash on Delivery • China Quick Commerce Market Segmentation by Age Group -- Gen Z (15–25) -- Millennials (26–39) -- Gen X (40–55) -- Baby Boomers (Above 55) • China Quick Commerce Market Segmentation by Location Tier -- Tier 1 Cities -- Tier 2 Cities -- Tier 3 Cities • China Quick Commerce Market Segmentation by Business Model -- Inventory-led Model -- Hyper-local Model -- Multi-vendor Platform Model -- Others • China Quick Commerce Market Segmentation by Delivery Time -- Delivery in 30 Minutes -- Delivery 30–60 Minutes -- Delivery in 3 Hours • China Quick Commerce Consumer Behavior and Demographics -- Average Subscription Uptake by Age Group -- Average Subscription Uptake by Location Tier -- Average Subscription Uptake -- Average Delivery Time • China Quick Commerce Revenue Structure and Composition -- Advertising Revenue -- Delivery Fee Revenue -- Subscription Revenue • China 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 • China 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 • China 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 • China 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 • China 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 • China 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. China Quick Commerce Industry Attractiveness 2.1 China Quick Commerce – Gross Merchandise Value Trend Analysis, 2020-2029 2.2 China Quick Commerce – Gross Merchandise Volume Trend Analysis, 2020-2029 2.3 China Quick Commerce – Average Order Value Trend Analysis, 2020-2029 2.4 China Quick Commerce – Order Frequency Trend Analysis, 2020-2029 2.5 China Quick Commerce – Market Share Analysis by Key Players, 2024 3. China Quick Commerce Operational KPIs 3.1 China Quick Commerce Revenue and Growth Trend, 2020-2029 3.2 China 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. China Quick Commerce Analysis by Product Type 4.1 China Quick Commerce Segment Share by Product Type, 2024 4.2 China 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 China 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 China 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 China 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 China 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 China 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 China 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 China 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 China 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. China Quick Commerce Analysis by Payment Method 5.1 China Quick Commerce Segment Share by Payment Method, 2020-2029 5.2 China 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 China 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 China 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 China 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. China Quick Commerce Analysis by Age Group 6.1 China Quick Commerce Segment Share by Age Group, 2024 6.2 China 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 China 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 China 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 China 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. China Quick Commerce Analysis by Location 7.1 China Quick Commerce Segment Share by Location, 2020-2029 7.2 China 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 China 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 China 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. China Quick Commerce Analysis by Business Model 8.1 China Quick Commerce Segment Share by Business Model, 2024 8.2 China 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 China 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 China 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 China 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. China Quick Commerce Analysis by Delivery Time 9.1 China Quick Commerce Segment Share by Delivery Time, 2020-2029 9.2 China 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 China 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 China 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. China Quick Commerce Consumer Behaviour and Adoption 10.1 China Quick Commerce- Average Subscription Uptake, 2024 10.2 China Quick Commerce- Average Subscription Uptake by Age Group, 2024 10.3 China Quick Commerce- Average Subscription Uptake by Location, 2024 10.4 China Quick Commerce- Average Delivery Time, 2024 11. Further Reading 11.1 About PayNXT360 11.2 Related Research
Table 1: China Quick Commerce – Gross Merchandise Value (US$ Million), 2020–2029 Table 2: China Quick Commerce – Gross Merchandise Volume (Millions), 2020–2029 Table 3: China Quick Commerce – Average Order Value (US$), 2020–2029 Table 4: China Quick Commerce – Order Frequency (Orders per Year), 2020–2029 Table 5: China Quick Commerce Revenue and Growth Trend (US$ 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$), 2020–2029 Table 104: Delivery Time in 3 Hours – Order Frequency (Orders per Year), 2020–2029
Figure 1: PayNXT360’s Methodology Framework Figure 2: China Quick Commerce – Gross Merchandise Value (US$ Million), 2020–2029 Figure 3: China Quick Commerce – Gross Merchandise Volume (Millions), 2020–2029 Figure 4: China Quick Commerce – Average Order Value (US$), 2020–2029 Figure 5: China Quick Commerce – Order Frequency (Orders per Year), 2020–2029 Figure 6: China Quick Commerce – Market Share Analysis by Key Players (%), 2024 Figure 7: China Quick Commerce Revenue and Growth Trend (US$ Million), 2020–2029 Figure 8: China 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: China 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$), 2020–2029 Figure 48: Other Product Category – Order Frequency (Orders per Year), 2020–2029 Figure 49: China Quick Commerce Segment Share by Payment Method, 2024 Figure 50: Instant Bank Transfer – Gross Merchandise Value (US$ 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$), 2020–2029 Figure 62: China Quick Commerce Segment Share by Age Group, 2024 Figure 63: Gen Z (15–25) Age Group – Gross Merchandise Value (US$ 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$), 2020–2029 Figure 75: China Quick Commerce Segment Share by Location, 2024 Figure 76: Tier 1 Cities – Gross Merchandise Value (US$ 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$), 2020–2029 Figure 87: Tier 3 Cities – Order Frequency (Orders per Year), 2020–2029 Figure 88: China Quick Commerce Segment Share by Business Model, 2024 Figure 89: Inventory Model – Gross Merchandise Value (US$ 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$), 2020–2029 Figure 101: China Quick Commerce Segment Share by Delivery Time, 2024 Figure 102: Delivery Time in 30 Minutes – Gross Merchandise Value (US$ 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: China Quick Commerce – Average Subscription Uptake, 2024 Figure 115: China Quick Commerce – Average Subscription Uptake by Age Group, 2024 Figure 116: China Quick Commerce – Average Subscription Uptake by Location, 2024 Figure 117: China Quick Commerce – Average Delivery Time, 2024
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