Retail Delivery - Papua New Guinea

Papua New Guinea

Highlights

The Reail Delivery market in Papua New Guinea is projected to reach a revenue of US$94.69m by 2024.
This is expected to show an annual growth rate (CAGR 2024-2029) of -2.74%, resulting in a projected market volume of US$82.41m by 2029.
Additionally, the number of users in the Reail Delivery market is expected to amount to 1.5m users by 2029.
The user penetration in the market is predicted to be 8.6% in 2024 and is projected to increase to 12.7% by 2029.
Furthermore, the average revenue per user (ARPU) is expected to amount to US$105.00.
In global comparison, United States is anticipated to generate the most revenue in the Reail Delivery market, with a projected revenue of US$195.40bn in 2024.
On the other hand, United States is expected to have the highest user penetration rate in the market with a projected rate of 30.4%.
The retail delivery market in Papua New Guinea is experiencing a surge in demand for e-commerce services due to the country's geographical challenges and limited access to physical stores.

Market definitionIn-Scope/Out-of-scopeMarket structureReportsMethodology

Papua New Guinea, a country located in the Pacific region, is experiencing a gradual shift in its retail delivery market.

Customer preferences: Papua New Guinea has a population that is largely dispersed across rural areas, making accessibility a key factor in customer preferences. As a result, traditional brick and mortar stores have been the primary mode of retail for a long time. However, with the rise of e-commerce, there has been a growing preference for online shopping, especially among the younger demographic.

Trends in the market: The retail delivery market in Papua New Guinea is witnessing a shift towards e-commerce, with an increasing number of retailers adopting online platforms to expand their reach. This shift is primarily driven by the growing internet penetration in the country, which has enabled more people to access online shopping platforms. Additionally, the COVID-19 pandemic has accelerated this trend, as more people have been forced to shop online due to social distancing measures.

Local special circumstances: Papua New Guinea's retail delivery market faces several unique challenges, including poor infrastructure and limited access to financial services. These challenges have made it difficult for retailers to expand their reach to remote areas of the country. However, there have been efforts by both the government and private sector to improve infrastructure and increase financial inclusion, which could help to mitigate these challenges in the future.

Underlying macroeconomic factors: Papua New Guinea's economy has been heavily reliant on the mining and agricultural sectors, which have been affected by fluctuations in global commodity prices. This has resulted in a relatively low GDP per capita and high levels of poverty. However, the government has been implementing reforms aimed at diversifying the economy and promoting private sector growth, which could lead to increased consumer spending in the future. Additionally, Papua New Guinea has a young and growing population, which presents opportunities for retailers to tap into a potentially lucrative market.

Data coverage:

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.

Forecasts:

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.

Additional notes:

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.