Brazil Quick Commerce Market Opportunities – Sample Report | Ken Research

Discover market opportunities and use cases driving Brazil’s quick commerce growth, from ultra-fast grocery delivery to hyperlocal essentials and convenience goods.

Link: https://www.kenresearch.com/industry-reports/brazil-quick-commerce-market?utm_source=SEO&utm_medium=Referral&utm_campaign=Vijay

1. Brazil Quick Commerce Market
Outlook To 2027f
Driven by the increasing digital adoption
and internet penetration since the global
covid-19 pandemic
November 2022
Author: Mahika Heda   |   Vertical: Consumer Products and Retail   |   Region: Central and
South America
Shaped by Rising Smartphone & Internet Penetration, Proliferation of Dark Stores, Urbanization & Changing
Consumer Lifestyles.

2. Table of Contents
1. Executive Summary
2. Market Overview
3. Market Segmentation of Brazil Quick Commerce, 2025
4. Industry Analysis
1
1.1 Executive Summary of Brazil Quick Commerce Market
2
2.1. De�nition and Scope
2.2. Market Size Analysis
2.2.1. Historical Market Volume (2017–2021)
2.2.2. Historical Revenue Trends (2017–2021)
2.2.3. 2022–2027 Forecasted Market Volume
2.2.4. 2022–2027 Forecasted Revenue
2.2.5. Five-Year CAGR (2022–2027)
2.3. Key Market Dynamics
2.3.1. Drivers
2.3.2. Restraints
2.3.3. COVID-19 Impact and Recovery
3
3.1 Segmentation by Product Type: (Grocery, Food Service & Ready-to-eat, Personal Care & Health, Electronics)
3.2 Segmentation by Business Model: (Dark Stores, Aggregator Platforms, Hybrid Model)
3.3 Segmentation by Delivery Time: (Under 15 minutes, 15–30 minutes, 30–60 minutes)
3.4 Segmentation by Geography: (Southeast, South, Northeast, North)
4
4.1. Growth Value Drivers
4.1.1. Rising Smartphone & Internet Penetration
4.1.2. Proliferation of Dark Stores
4.1.3. Urbanization & Changing Consumer Lifestyles
4.1.4. Partnerships between Retailers & Tech Players
4.1.5. Payment Ecosystem Innovations
4.2. Industry Challenges
4.2.1. Last-Mile Delivery Costs
4.2.2. Workforce Management & Gig-Worker Regulations
4.2.3. Inventory Management & Shrinkage
4.2.4. Price Sensitivity of End Consumers
4.2.5. Infrastructure Bo�lenecks in Tier-II/III Cities

3. 5. Value Chain and Industry Taxonomy
6. Regulatory Framework
7. Competitive Landscape of the Brazil Quick Commerce
8. Future Market Segmentation of Brazil Quick Commerce, 2030
9. Analyst Recommendations
4.3. Opportunities
4.3.1. Tier-II/III City Expansion
4.3.2. Vertical Integration with FMCG Brands
4.3.3. AI-Driven Demand Forecasting
4.3.4. Subscription & Loyalty Models
4.3.5. Green Logistics & Sustainability Initiatives
4.4. Key Trends
4.4.1. Growth of Cloud Kitchens
4.4.2. Hyperlocal Dark Store Networks
4.4.3. Real-Time Order Tracking
4.4.4. Cross-Border Quick Commerce
4.4.5. Integration with Super Apps
5
5.1. Input Suppliers (Brands, Wholesalers)
5.2. Aggregators (Platforms, Marketplaces)
5.3. Order Management Systems
5.4. Dark Storage & Ful�llment Centers
5.5. Last-Mile Delivery Network
5.6. Customer Interface & Payment Gateways
5.7. Post-Delivery Services
6
6.3. Labor & Gig-Worker Laws
6.1. National E-commerce Regulations
6.2. Food and Drug Administration Guidelines
6.4. Data Protection and Privacy Acts
6.5. Local Municipality Requirements
7
7.3 Financial Parameters: Market Shares, Revenues, and Pro�t Margins
7.1 Major Companies Covered: (iFood, Rappi, Cornershop by Uber, James Delivery, Zé Delivery, and Others)
7.2 Operational Parameters: (Company, Headquarter, Market Share (USD Bn), Annual Revenue (USD Mn), Gross
Merchandise Value (USD Bn), Order Ful�llment Rate (%), and Others)
8
8.1 Segmentation by Product Type: (Grocery, Food Service & Ready-to-eat, Personal Care & Health, Electronics)
8.2 Segmentation by Business Model: (Dark Stores, Aggregator Platforms, Hybrid Model)
8.3 Segmentation by Delivery Time: (Under 15 minutes, 15–30 minutes, 30–60 minutes)
8.4 Segmentation by Geography: (Southeast, South, Northeast, North)
9

4. 10. Research Methodology
11. Disclaimer
12. Contact Us
9.1. Total Addressable Market (TAM) Analysis
9.2. Serviceable Available Market (SAM) Analysis
9.3. Serviceable Obtainable Market (SOM) Analysis
9.4. Go-to-Market Strategies
9.5. Investment & Partnership Recommendations
10
10.1. Data Sources
10.1.1. Primary Research (Expert Interviews, Surveys)
10.1.2. Secondary Research (Reports, Publications, Databases)
10.2. Forecasting Approach
10.3. Assumptions and Limitations
11
12

5. 01
Executive Summary

6. Executive Summary
Exhibit 1.1
Taxonomy & Market Size of Brazil Quick Commerce Mar ket
Brazil Quick Commerce Market
(USD XX Bn)
By Product Type By Business Model
Grocery
XX%
Grocery holds
largest share,
stable growth
expected.
Dairy & Bakery
Fruits &
Vegetables
Meat & Seafood
Food Service &
Ready-to-eat
XX%
Growth in ready-
to-eat meals and
food services.
Fast Food
Local Cuisine
Cafés & Snacks
Personal Care &
Health
XX%
Increasing
demand for
personal care
and health
products.
Toiletries
OTC Medicines
Nutritional
Supplements
Dark Stores
XX%
Dark stores lead
with high growth
rate.
Micro-
ful�llment
Centers
Neighborhood
Mini-
warehouses
Aggregator
Platforms
XX%
Aggregator
platforms
maintain
signi�cant
market presence.
Pure
Marketplace
Branded On-
demand Apps
Hybrid Model
XX%
Hybrid models
show fastest
structural shift.
Retailer-
owned Dark
Stores + In-app
Ordering
Aggregator
Partnerships
with Branded
Outlets
By Delivery Time Under 15 minutes 15–30 minutes 30–60 minutes
By Geography Southeast South Northeast North
Source: Ken Research Analysis
Note 1: Exhibits are illustrative; detailed numbers appear in the full report.
Note 2: XX% denotes CAGR.
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7. 02
Brazil Quick Commerce
Market Outlook

8. Brazil Quick Commerce Market Outlook
Brazil Quick Commerce Market Scenario
Brazil’s quick commerce sector is experiencing evolution, driven by growing urbanization and shifting consumer
preferences for instant, on-demand delivery. Over the next five years, digital adoption and enhanced logistics networks
will serve as primary catalysts, bolstered by strategic partnerships and AI-driven inventory management. Sustainability
initiatives and hyperlocal fulfillment centers are emerging trends that elevate customer satisfaction and operational
efficiency. Key value drivers include technological innovation, flexible last-mile solutions, and product offerings. Tailwinds
such as supportive regulatory frameworks and increased fintech integration will further streamline transactions. By 2027,
the market may see intensified competition, market consolidation, and expansion into underserved regions.
Exhibit 2.1
Illustrative Brazil Quick Commerce Market Overview
XX%
XX%
XX%
XX%
XX%
XX%
XX%
XX%
XX%
XX%
XX%
XX%
2025 2030
By Product Category Grocery Food & Beverage Pharmacy
By Region Southeast South Northeast
Source: Ken Research Analysis
Note 1: The exhibit serves as an illustration, with numbers currently masked. The detailed statistics will be included in the final report.
Note 2: All revenue and financial numbers are for current year and financial year, i.e., 2025 & 2030.
Note 3: XX% in the above exhibit denotes market size.
CAGR: XX%
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9. 03
Market Segmentation of
Brazil Quick Commerce
Market

10. Market Segmentation of Brazil Quick Commerce Market
Brazil Quick Commerce Market Segmentation
By Product Category, Grocery dominates with XX% share, led by Fresh Produce (XX%) and Packaged Foods (XX%) subsegments. By
Platform Type, App-based channels account for XX% driven by mobile penetration. By Service Model, Dark Stores (XX%) lead
ful�llment, while Micro-ful�llment Centers (XX%) show fastest growth. By Delivery-Time Slot, 15–30 minutes windows capture XX%
share. Growth opportunities lie in scaling Micro-ful�llment Centers and app-based integration. Recent developments include
opening new Dark Stores in São Paulo (XX%), integrating AI for inventory, and launching Web-based platforms to penetrate the
Northeast region. These trends underscore robust demand for instant grocery delivery to 2027.
Exhibit 3.1
Illustrative Market Segmentation of Brazil Quick Commerce Market
Home & Personal
Care
XX%
Pharmacy
XX%
Food & Beverage
XX%
Grocery
XX%
Bakery &
Confectionery
(XX%)
Dairy & Eggs
(XX%)
Fresh Produce
(XX%)
Central-West
XX%
Northeast
XX%
South
XX%
Southeast
XX%
Minas Gerais
(XX%)
Rio de Janeiro
(XX%)
São Paulo
(XX%)
Web-based
XX%
App-based
XX%
Micro-ful�llment
Centers
XX%
Dark Stores
XX%
45–60 minutes
XX%
30–45 minutes
XX%
15–30 minutes
XX%
By Product
Category
Grocery By Region Southeast By Platform Type By Service
Model
By Delivery-
Time Slot
Key Takeaways:
Source: Ken Research Analysis
Note 1: This exhibit serves as a visual representation. The �nal chart will include additional segments along with detailed statistics
in the �nal report.
Note 2: All revenue and �nancial numbers are for the current year.
Note 3: "XX%" in the above exhibit denotes market shares.
Grocery dominates the product category with signi�cant market share.
App-based platforms are leading due to high mobile penetration.
Dark Stores are the primary service model, with Micro-ful�llment Centers growing rapidly.
15–30 minute delivery slots are the most popular among consumers.
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11. 04
Overview And Genesis Of
The Brazil Quick
Commerce Market

12. Overview And Genesis Of The Brazil Quick Commerce M arket
Brazil Quick Commerce Market Ecosystem
Brazil’s quick commerce ecosystem comprises large, medium, and small players. Large enterprises (Mercado Livre, Rappi,
iFood) leverage asset-light platforms: key partners include retail chains and couriers, activities focus on tech and
logistics, value proposition is rapid delivery, monetizing via commissions and delivery fees with digital marketing
channels. Medium players (Zaitt, Raia Drogasil) operate micro-fulfillment centers, partnering with local suppliers, offering
curated assortments, revenue from product margins and subscription fees. Small enterprises (dark stores like Kangu,
Emoov) maintain hyperlocal inventory, use direct delivery fleets, target niche neighborhoods, compete on personalization
and speed, sustaining profitability through high order density and lean cost structures.
Exhibit 4.1
Illustrative Ecosystem of Brazil Quick Commerce Market
Entity type Revenue Streams Players
Quick-Commerce Platforms
iFood
Rappi
Cornershop (Uber
Eats)
James Delivery
Loggi
Retailers & Dark-Store
Operators
GPA (Pão de Açúcar/
Extra)
Carrefour Brasil
Assaí Atacadista
Grupo BIG
Dia Brasil
Last-Mile Logistics Providers
Loggi
Delivery Center
Rapiddo
Lalamove
Mandaê
Technology & SaaS Vendors
VTEX
Fast Commerce
Beetrack
Take Blip
Robin
Payment Gateways & Fintechs
Cielo
Rede/Getnet
Stone
PagSeguro
Mercado Pago
Marketing & Advertising
Agencies
iFood Ads
Rappi Ads
Google Ads
Meta for Business
Criteo
Data Analytics & Market
Intelligence
Nielsen
Kantar
Commission on each order▶
Delivery fees (static or distance-based)▶
Monthly/annual subscription for free delivery▶
In-app advertising & promotional placements▶
Wholesale product margins▶
Slot-rental or 'fulfillment-as-a-service' fees charged to
platforms
▶
Commission share on third-party orders▶
Private-label product sales▶
Per-parcel or per-delivery fees (B2B/B2C contracts)▶
Subscription or retainer models for enterprise clients▶
Dynamic pricing (based on distance, weight, time-
windows)
▶
Value-added services (e.g., white-glove, reverse
logistics)
▶
Platform-licensing & setup fees▶
Recurring SaaS/subscription charges (per-store or per-
order)
▶
Custom integration & maintenance contracts▶
Transaction fees and data-analytics services▶
Merchant transaction fees (percentage + fixed fee)▶
Subscription/monthly fees for value-added modules▶
POS hardware rental & installation▶
Interest/FX on digital wallets & BNPL offerings▶
Sponsored product/listing fees in apps▶
Performance-based ad-campaign charges (CPC/CPM)▶
Media-buying & creative retainer contracts▶
Data-monetization & targeting services▶
Subscription/licensing of syndicated data reports▶
Custom research & consulting fees▶
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13. Entity type Revenue Streams Players
Neogrid
eBit|Nielsen
Euromonitor
API-access for real-time dashboards▶
One-off market-entry or competitive-analysis projects▶
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14. 05
Competitive Landscape

15. Competitive Landscape (1/3)
Competitive Landscape Overview
Rappi leverages an asset-light model to provide multi-category on-demand delivery (groceries, pharmacy, meals) via its
mobile app, targeting urban millennials and SMEs; it earns commissions, delivery fees and Rappi Prime subscriptions,
relying on partnerships with local merchants, dark stores and a gig-courier network, underpinned by its tech platform and
logistics hubs. iFood expanded from meal delivery into quick commerce through iFood Shop, serving middle-class
consumers via its app and website, monetizing via delivery charges and marketplace commissions, supported by own
fleet, dark stores and merchant alliances. Cornershop by Uber offers curated grocery delivery to higher-income users,
generating revenue from service fees and retail markups, anchored on its app, fulfillment centers and chain-store
partnerships.
Exhibit 5.1
Illustrative Market Shares of Key Players in Brazil Quick Commerce Market
Note: These are illustrative representations and do not reflect actual data or real-world figures
Source: Ken Research Analysis
Note 1: The XX% in the exhibit represents the market share of each company.
Note 2: The exhibit serves as an illustration, with numbers currently masked.
Note 3: All revenue and �nancial numbers are for the current year.
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16. Competitive Landscape (2/3)
Exhibit 5.2
Illustrative Competition Matrix of Brazil Quick Commerce Market
Major Players in Brazil Quick Commerce Market
Figure represents cumulative market share for only the product segment.:
Note: These are illustrative representations and do not re�ect actual data or real-world �gures
Large Players Medium Players Small Players
Source: Ken Research Analysis
Note 1: The XX% in the exhibit shows the market share of each company, which represents the company's contribution to the total market.
Note 2: The exhibit serves as an illustration, with numbers currently masked.
Note 3: All revenue and �nancial numbers are for the current year.
Note 4: Enterprise size categories based on annual GMV in US$—Large > 1 Bn; Medium 250 Mn–1 Bn; Small < 250 Mn
Brazil Quick Commerce Market: USD
XX Bn
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17. Competitive Landscape (3/3)
Exhibit 5.3
Illustrative Cross Comparison of Key Players in Brazil Quick Commerce Market
Company Headquarter
Gross
Merchandise
Value (USD
Bn)
Revenue
(USD
Mn)
Active
Users
(Mn)
Order
Volume
(Mn)
Avg
Delivery
Time
(Min)
On-time
Delivery
Rate (%)
Fulfillment
Cost Ratio
(% of
revenue)
Rappi
Bogotá,
Colombia
XX XX XX XX XX XX% XX%
iFood Flash
São Paulo,
Brazil
XX XX XX XX XX XX% XX%
Cornershop
Santiago,
Chile
XX XX XX XX XX XX% XX%
James
Delivery
Curitiba,
Brazil
XX XX XX XX XX XX% XX%
Zé Delivery
São Paulo,
Brazil
XX XX XX XX XX XX% XX%
Uber Eats
Flash
San
Francisco,
USA
XX XX XX XX XX XX% XX%
Bringo
São Paulo,
Brazil
XX XX XX XX XX XX% XX%
Loggi
Instant
São Paulo,
Brazil
XX XX XX XX XX XX% XX%
Flink
São Paulo,
Brazil
XX XX XX XX XX XX% XX%
Quickee
São Paulo,
Brazil
XX XX XX XX XX XX% XX%
Source: Ken Research Analysis
Note 1: The XX% and XX values in the exhibit represent placeholder data and will be replaced with actual figures in the final report.
Note 2: All revenue and financial numbers are for the current year.
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18. 06
Analyst Recommendation

19. Analyst Recommendation
Exhibit 6.1
Target Addressable Brazil Quick Commerce Market Outlook To 2027f
Source: Ken Research Analysis
Note 1: The exhibit serves as an illustration, with numbers currently masked. The detailed statistics will be included in the final report.
Note 2: All revenue and financial numbers are for the current year.
TAM/SAM & SOM Analysis
TAM = USD XX Bn (Projected from Brazil’s total
online grocery expenditure forecast,
assuming universal shift to instant delivery.)
SAM = USD XX Bn (Refined to urban
consumables spending where same-day
and sub-two-hour delivery logistics are
feasibly deployed.)
SOM = USD XX Bn (Estimated share
by leading quick-commerce players
in primary São Paulo, Rio, Brasília
urban zones.)
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20. 07
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Ken Research
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24. Research Methodology
1. Desk Research
Sources
Utilized industry reports,
market databases, and
academic journals to gather
data on Brazil’s quick
commerce market trends,
consumer behavior, and
competitive landscape.
2. Expert Interviews
Conducted interviews with
industry experts, including
market analysts and
business leaders, to gain
insights into market
dynamics and emerging
trends.
3. Sanity Check
Models
Applied a proxy model using
metrics such as number of
users, purchase frequency,
and average spend to
estimate the addressable
market, cross-verified with
supply-side data on player
sizes and revenues.
4. Forecasting
Modeling
Developed future market
forecasts using time-series
analysis and scenario
planning, incorporating
economic indicators and
technological
advancements impacting
the quick commerce sector.
SAMPLE SIZE BY STAKEHOLDERS
By Stakeholders Number of Respondents
C Level Executive ~ Respondents
Operational/Regional Managers ~ Respondents
Business Development / Sales Manager ~ Respondents
Others -Key opinion leaders from Associations, Warehouses, and Manufacturing firms.
SAMPLE SIZE BY RESPONDENT CATEGORY IN PERCENTAGE (% )
C Level Executive - XX%
Operational/Regional
Managers - XX%
Business Development /
Sales Manager - XX%
Others - XX%
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MARKET
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The Icon BSD City, Jl. Verdant View IIIA No.15, Tangera Regency, Province Banten-15345, Indonesia
UNITED ARAB EMIRATES (UAE)
105, Al Jaz1, Street no 2 the Greens Dubai United Art Emirates.
QATAR
O&#6684774;ice no. 9, Building no. 171, Zone no. 42, Street no. 230, Al-Rehab Complex, C-Ring road Doha, Qatar -
PO Box no. 30867