The Restaurant Delivery market in the Philippines is projected to reach a revenue of ₱€0.81bn by 2024.
This represents a significant growth opportunity for the industry.
Furthermore, it is expected that the market will continue to expand with an annual growth rate (CAGR 2024-2028) of 4.07%, resulting in a projected market volume of ₱€0.95bn by 2028.
This indicates a growing demand for food delivery services in the country.
User penetration, which measures the percentage of the population using these services, is expected to increase from 11.0% in 2024 to 12.0% by 2028.
The average revenue per user (ARPU) is projected to amount to ₱€63.86.
This figure reflects the amount of money spent by each user on restaurant delivery services.
When comparing the global market, it is worth noting that in the United States is expected to generate the most revenue in the Restaurant Delivery market, with an estimated amount of ₱€33,780.00m in 2024.
This highlights the dominance of the American market in this industry.
Additionally, it is interesting to observe that in South Korea is projected to have the highest user penetration rate in the Restaurant Delivery market, with a projected rate of 53.5%.
This suggests a strong adoption of food delivery services in South Korea.
Overall, the Restaurant Delivery market in the Philippines presents significant growth potential, with increasing revenue, user base, and user penetration.
These trends are in line with the global market and highlight the growing importance of food delivery services in the country.
The restaurant delivery market in the Philippines is experiencing a surge in demand due to the country's growing urban population and increasing preference for convenience.
The Restaurant-to-Consumer Delivery market includes the delivery of meals carried out directly by the restaurants. The order may be made via platforms (e.g. Delivery Hero, Just Eat) or directly through a restaurant website (e.g. Domino's). The aggregation services collect the menus of independent restaurants and specialized delivery services. In other words, they merely lay the technical foundation for the searchability of restaurants and the processing of transactions. The restaurant itself takes care of the delivery process.
Revenue includes the gross merchandise value (GMV), defined as the total sales dollar value for merchandise/food sold through the Online Food Delivery marketplace. User and revenue figures represent B2C services.
Meals ordered online which are directly delivered by the restaurant, no matter if ordered via a platform (e.g. Delivery Hero) or a restaurant website (e.g. Domino's)
Online orders that are picked up in the restaurant
Online meal order and delivery both carried out by a platform (e.g. Deliveroo)
The Restaurant Delivery model gained track fast, as it provided an easy solution for restaurants to switch from phone to online orders. Players like Just Eat, Delivery Hero or Takeaway.com strongly pushed the global markets in the recent decade, and global fast food chains like Domino's have adopted their digital strategies, too. The fierce competition resulted in thinner margins as companies poach customers from each other, so currently the market is in its consolidation phase with major M&A deals happening in all regions. Since adoption rates for this market are already high, it is likely towill likely grow slower in the next years and probably convergedelivery models.
The data encompasses B2C enterprises. Figures are based on Gross Merchandise Value (GMV) and represent what consumers pay for these products and services. The user metrics show the number of customers who have made at least one online purchase within the past 12 months.
Modeling approach / Market size:
Market sizes are determined through a bottom-up approach, building on predefined factors for each market. As a basis for evaluating markets, we use annual financial reports of the market-leading companies, third-party studies and reports, as well as survey results from our primary research (e.g., the Statista Global Consumer Survey). In addition, we use relevant key market indicators and data from country-specific associations, such as GDP, GDP per capita, and internet connection speed. This data helps us estimate the market size for each country individually.
In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, the S-curve function and exponential trend smoothing. The main drivers are internet users, urban population, usage of key players, and attitudes toward online services.
The market is updated twice a year in case market dynamics change. The impact of the COVID-19 pandemic and the Russia-Ukraine war are considered at a country-specific level. GCS data is reweighted for representativeness.