Analyzing the Profitability of Large E-commerce Companies through Revenue and Earnings from 2020-2025

Methodology

Data collection

Data in this study were obtained from a reputable source. Financial performance, such as revenue and earnings, country was retrieved from CompaniesMarketCap.com (https://companiesmarketcap.com/e-commerce/largest-e-commerce-companies-by-market-cap/), a publicly accessible source that compiles financial information for large e-commerce companies around the world. Revenue and earnings data are collected from the year 2020 to 2025, consisting of 17 companies used in this study. Net profit margin is calculated as an indicator to determine whether the companies are profitable or not.

Statistical analysis

This study used JAMOVI software version 2.6.17 to analyze the data. The mean and standard deviation are used to determine the mean revenue and earnings for each company. A line graph was also used to determine trends for mean revenue and earnings across the 5 years. Before employing linear regression, researchers assess the assumptions of normality (Shapiro-Wilk) and the collinearity test to determine whether to use linear regression or not. In case the normality did not pass, an alternative analysis was conducted to assess the effect or relationship, which is a non-parametric Spearman’s rho correlation test. In addition, bar graphs are used to analyze the number of companies by country. To assess the effect of revenue and earnings across the years, a linear regression is used, and to measure profitability, the indicator of [5] is utilized for each company, allowing a comprehensive analysis of profitability.

Results and discussions

This study aims to identify which among those large e-commerce companies from the Companies Market Cap website are best generating revenues and earnings from 2020-2025. As a result, annual data for revenue and earnings were used in this study. This will involve mean, standard deviation, Trend Analysis, and Linear Regression Analysis.

Trend analysis of which companies are generating revenue and earnings over the 5 years

Table 1 shows the generated revenue and earnings of the large e-commerce companies in the world using Mean and Standard Deviations. Based on the table, the top 3 e-commerce companies with the highest mean revenue are Amazon by ( $\overline{x}$ = $542,000,000,000), which can vary by $107,000,000,000 (SD) followed by Jingdong Mall with a mean revenue of ( $\overline{x}$ = $150,000,000,000), varied by $22,300,000,000 (SD) and Alibaba who have a mean of ( $\overline{x}$ = $125,000,000,000) which vary by $16,000,000,000 (SD), the company with the lowest mean annual revenue is Big Commerce ( $\overline{x}$ = $265,000,000) varied by $72,000,000 (SD).

On the other hand, in terms of earnings, the e-commerce company with the highest mean earnings is still Amazon ( $\overline{x}$ = $43,100,000,000), which can vary $31,800,000,000 (SD), followed by Alibaba with mean earnings of ( $\overline{x}$ = 19,600,000,000) varied by $4,930,000,000 (SD). Lowest mean earnings are Big Commerce with a decline in earnings of ( $\overline{x}$ = $-0.0000000102) varied by $153,000,000 (SD).

The graph in Figure 2 above shows the trend of each e-commerce company’s average revenue from 2020-2025. According to the data gathered and the graph above, the Amazon company is on a rising trend. This means that over time, the revenue of Amazon is increasing and is forecasted to increase again in the year 2026. Other companies such as Alibaba, Jingdong Mall, and PDD Holdings (Pinduoduo). In addition, other companies show a nearly straight line from 2020-2025, meaning that their revenues in the 5 years are nearly identical.

Figure 2: Line graph showing the Mean Revenue of the E-commerce. Companies from 2020-2025.

Furthermore, Figure 3 shows the trend of the e-commerce company’s average earnings from 2020-2025. Based on the graph shows that Amazon is still among the leading companies with the highest mean earnings during the 5 years. This means that employees, especially in the finance team, play a crucial role in maintaining efficient cost management and strategic investment decisions to sustain this profitability. It also means that the company is able to respond to market fluctuations, optimize revenue streams, and maintain its competitive advantage around the world. Other companies with nearly straight lines during the 5 years are identical.

Figure 3: Line graph showing the Mean Revenue of the E-commerce. Companies from 2020-2025.

Number of companies by country

Figure 4 above shows the number of companies by country category. Based on the data acquired, 35.29% of the companies are from the U.S.A., highlighting the country’s strong representation in the global e-commerce market. This is due to their very well-established infrastructure as one of the developed countries. The electronic commerce industry in the U.S has grown rapidly over the past 10 years as consumers are more aware of online sales and are tempted by the convenience of comparing, buying products and services online [14]. Companies from the U.S.A. are Amazon, Coupang, eBay, Wayfair, Etsy, Groupon, BigCommerce, and Revolve. Following this, China is the second most represented country, accounting for a 23.53% share of e-commerce companies in the study. This also highlights China’s fast-growing global digital economy supported by its high technology and innovative platforms. Chinese companies are PDD Holdings (Pinduoduo), Alibaba, Jingdong Mall, and Vipshop. In addition, Canada is the third country accounting for 11.76% share in this study. For instance, Shopify has established itself by contributing to small and medium-sized enterprises worldwide, as well as its share in the industry. Other companies for Canada in this study are Lightspeed POS. Remaining companies from Argentina (Mercado Libre), Singapore (Sea Limited), Poland (Allegro.eu), Taiwan (Momo.com Inc.), and Germany (Westwing Group) contribute a 5.88% share in this study.

Figure 4: E-comMercer companies by country.

Effect of revenue and earnings on years for the top 3 leading e-commerce companies

The top 3 leading e-commerce companies based on the results in the previous chapter are Amazon with a mean revenue of ( $\overline{x}$ = $542,000,000,000), Jingdong Mall ( $\overline{x}$ = $150,000,000,000), and Alibaba with a mean revenue of ( $\overline{x}$ = $125,000,000,000). The earnings for each company are also considered regardless of the amount attained. To better validate whether this study uses this analysis in determining the effect between years and financial indicators such as revenue and earnings, the researcher assesses the normality assumptions (Shapiro-Wilk) as well as collinearity to decide whether to use linear regression or not. Otherwise, Spearman correlation, a non-parametric test, was used if the normality did not pass as an alternative analysis for linear regression.

Table 2 above shows the linear regression results between years as the independent variable and revenue and earnings as the dependent variable for the Amazon company. To assess whether to utilize linear regression or not, the assumptions were checked. Results show for revenue that Shapiro-Wilk (p = 0.282) tests yield greater p - values at 5% threshold. Thus, conclude that the assumption of normality can be treated as normally distributed. Also, collinearity tests (VIF = 1.00, Tolerance = 1.00) mean that 1 indicates no multicollinearity at all, so use linear regression. For earnings, the normality test did not satisfy at yielding a p -value of 0.029, which is less than 0.05; therefore, use Spearman.

Based on the data gathered, the model above explains 98.9% of the revenues and 53.2% of the earnings across the years. At 1% level of significance, it shows that the year has a significant effect on the company’s revenue (p < .001). Results from the previous chapter also state that the company’s revenue has increased over the past 5 years. On the other side, the Years indicates a positive coefficient (β = 56,700,000,000), meaning that for every additional year, Amazon’s revenue increases by approximately $56,700,000,000. Therefore, there is enough evidence to reject the null hypothesis and indicate that, as years progress, the company’s revenue grows significantly.

Furthermore, since the normality assumption did not pass for Amazon’s earnings. An alternative analysis was used (Spearman’s rho). Results show that there is a high positive relationship between year and earnings. However, the p-value of 0.058 is greater than or nearly at the 5% level of significance, indicating that the relationship between the increase in years and the increase in earnings is not real.

Table 3 above shows the regression results between revenue, earnings, and years for the Jingdong Mall company. To assess whether to utilize linear regression or not, the assumptions were checked. Results show for revenue that Shapiro-Wilk (p = 0.779) tests yield greater p-values at 5% threshold. Thus, conclude that the assumption of normality can be treated as normally distributed. Also, collinearity tests (VIF = 1.00, Tolerance = 1.00) mean that 1 indicates no multicollinearity at all, so use linear regression. For earnings, the normality test is also normally distributed (p = 0.996) at 5% level of significance. Collinearity tests (VIF = 1.00, Tolerance = 1.00) mean that there is no multicollinearity associated. Therefore, use linear regression for both revenue and earnings.

Based on the data gathered, the model above explains 79.1% revenue and only 13.5% earnings across the years. The p-value for revenue (p = 0.018) is less than at 5% threshold. As a result, it is significant and there is sufficient evidence to reject the null hypothesis, indicating that revenue affects years. Additionally, the coefficient is (β = 510,600,000,000) positive, meaning that every year, the revenue for Jingdong Mall company increases by $510,600,000,000, further validated by the significant value.

Nevertheless, the p-value for earnings (p = 0.473) is higher than the 5% level of significance. Thus, it is insignificant and fails to reject the null hypothesis, demonstrating no meaningful relationship between earnings and years. The coefficient is (β = 636,000,000) positive, meaning that every additional year, the earnings for Jingdong Mall increase by $636,000,000. However, this relationship is rejected as indicated by its higher p-value.

Table 4 above shows the linear regression results between revenue, earnings, and years for Alibaba Company. To assess whether to utilize linear regression or not, the assumptions were checked. Results show for revenue that Shapiro-Wilk (p = 0.987) tests yield greater p - values at 5% threshold. Thus, conclude that the assumption of normality can be treated as normally distributed. Also, collinearity tests (VIF = 1.00, Tolerance = 1.00) mean that 1 indicates no multicollinearity at all, so use linear regression. For earnings, the normality test is also normally distributed (p = 0.417) at 5% level of significance, and the null hypothesis is rejected. Collinearity tests (VIF = 1.00, Tolerance = 1.00) mean that there is no multicollinearity associated. Therefore, use linear regression for both revenue and earnings.

Based on the data collected, the model explains 64.6 of % revenue and 1.38 of % very low earnings across the years. At 5% level of significance, the p-value for revenue (p = 0.054) did not reach the conventional threshold and was nearly at 0.050. Consequently, it is not significant and fails to reject the null hypothesis, meaning that revenue has no significant effect on years. Although the positive coefficient of (b = 6,870,000,000) indicates an increase of $6,870,000,0000, it is not statistically significant. Similarly, the earnings also had a greater p-value of (p = 0.825), showing no relationship between earnings and years. A positive coefficient of (b = 309,000,000) demonstrates that for every additional year, there is an increase of $309,000,000, but not statistically meaningful as indicated by its higher p - value. Further studies are recommended for this study involving other financial or non-financial indicators, as well as other types of research, to dig deeper and acquire more reliable and exact results.

Measure the Profitability of Large E-commerce Companies through Fundamental Analysis using Net Profit Margin from Mean Revenue and Earnings

To measure the profitability of the e-commerce companies. The mean annual revenue and earnings are utilized to determine the Net Profit Margin using the Formula. Results for Net profit margin are used as an indicator to measure if the company is generating profit. The formula for NPM is the following:

$Net Profit Margin \left(\%\right)=\frac{Net Income/Earnings}{Revenue}\times 100$

Where Net Income/Earnings is the mean earnings, and the Revenue is the mean revenue from the previous chapter. According to [15], the term earnings refers to a company’s profit of its bottom line. They highlight its profitability compared to analyst estimates, along with the company’s historical performance, which can be done relative to its competitors and industry peers. This metric is the profit a company earned for a period, usually a quarter or fiscal year. You can find it listed as net income on the income statement. When investors refer to a company’s profit, they typically refer to net income or the profit for the period. Similarly, income is considered synonymous with net income or profit.

Table 5 above shows the net profit margin of the companies as a basis to determine whether or not it is profitable based on Chu’s indicator. According to the table above, PDD Holding (Pinduoduo) stands out with the highest net profit margin of 25.02% indicating that it is one of the most profitable companies. According to [16], PDD Holdings reported higher income even as revenue growth wasn't as strong as expected. U.S.-listed shares of PDD Holdings (PDD) rose Thursday after the parent of the Temu shopping app posted a higher quarterly profit even though it missed sales estimates as Chinese consumers pulled back spending and it faced higher costs. This reflects the well-strategic management of the company, contributing to its economic growth. Furthermore, Alibaba, Allegro.EU, Shopify, and eBay also demonstrate a healthy net profit margin. Meanwhile, Amazon, Mercado Libre, Revolve, and Vipshop show moderate profitability, maintaining stable returns. Otherwise, the remaining companies exhibit relatively low profit generation, highlighting challenges in retaining at least a good profit [17,18].

Conclusion and recommendation

The study investigates the effect of revenue and earnings on the years and measures the profitability for each selected large e-commerce company. Results show that the top 3 companies with the highest mean revenue are Amazon, Jingdong Mall, and Alibaba, while for earnings are only Amazon and Alibaba are listed. These results reflect the competitive advantage for each company in the market, maintaining sustainability while leaving their brand in the minds of consumers. This platform allows millions of people around the world, including investors, small and medium business enterprises, personal brands and etc. by creating opportunities for to venture in the market as well. Aside from this, most large-scale e-commerce companies are mostly seen in the U.S.A., China, and Canada.

Linear regression results show the effect of revenue and earnings for the top 3 leading e-commerce companies, namely Amazon, Jingdong Mall, and Alibaba. Results show that revenue has a significant effect across the years, while earnings have no significant relationship by years, as indicated using non-parametric Spearman rho’s alternative analysis for linear regression due to normality violation. Consequently, for Jingdong Mall’s revenue has a significant effect across the years was found, similar to Amazon, no significant effect was found for earnings across the years, and for the Alibaba company, both variables did not significantly influence by years. Measures of profitability show that PDD Holdings (Pinduoduo) dominates among all e-commerce companies listed with the highest net profit margin (25.02%), followed by eBay (19.70%), Alibaba (15.68%), Allegro.eu (13.80%), and Shopify (12.14%), where they are interpreted as Healthy profit. Following this, Mercado Libre (9.56%), Amazon (7.95%), Vipshop (7.58%), and Revolve (6.72%) maintain a good profit in the competitive market, while other companies make low profits. Researchers in this study recommend further conducting studies by incorporating other non-financial indicators (user growth, repurchase rate), segmented industries (cross-border, e-commerce, fresh e-commerce), or adopting qualitative research such as interviewing corporate financial leaders, which allows more deeper analysis in terms of investigating the profitability of e-commerce companies.

Data availability statement

The data that support the findings in this study can be accessed in this Google Drive file: https://drive.google.com/drive/folders/12mUad5p5N3pWN7YZe5b-k4-mf8di5UwO?usp=drive_link