This paper explores the development of competition in the Russian marketplace sector from 2016 to 2024. To assess market power and differences between major companies, the study uses Data Insight ratings, which include online sales volume, number of orders, and average order value. These indicators enabled the construction of a strength–variety (SV) matrix that integrates the Herfindhal–Hirschmann Index and the Linde Index. Drawing on Russian and international literature, this paper identifies four groups of factors underlying marketplace competitive advantages: pricing strategy, logistics, marketing and technological innovation. It then compares major market players, namely Wildberries, Ozon, Megamarket, AliExpress Russia, Yandex Market and Magnit Market, with respect to each of these factors. In the medium term, the combination of advanced technological solutions and flexible delivery services is expected to be the primary competitive axis, which suggests further research into the impact of digital transformation on market-share dynamics.

competition, e-commerce, online retail, Russian marketplaces, SV (strength-variety) matrix, Wildberries.

In recent years, e-commerce has expanded rapidly in Russia, affecting both cultural and economic domains (Samoylenko, 2024). This trend is particularly evident in the rising popularity of marketplaces. Over the past decade, the number of urban online shoppers increased from 10% to 67%, according to Yandex (2024). The growing preference for online shopping has been accompanied by a shift in consumer values and behaviors. Russia ranked second globally in terms of marketplace traffic growth, with a 12% increase in 2024 (Bryzgalova, 2024).

In 2023, two of the largest domestic marketplaces – Ozon and Wildberries – entered the top ten global leaders in website visits, following major companies from East Asia and the Americas. This rapid growth on the international stage seems even more significant when viewed in the context of the national market. The swift expansion of the Russian key players has already attracted the attention of the Federal Anti-Monopoly Service (Kinyakin & Arylina, 2014), which officially acknowledged the dominance of these marketplaces in Russia. The inherent dynamism and high volatility of the relatively young and rapidly evolving e-commerce sector naturally captures the interest of researchers.

Before proceeding to the literature review, it is essential to define the main concepts and terms used in this study. Digital commerce refers to the process of creating, marketing, distributing, and selling goods and services via electronic channels. This growth has been facilitated by internet technology and the increasing confidence of consumers in online payment systems (Safronova & Kuzheleva, 2024).

The most widespread format for digital commerce is the online store (OS), which is a branded or multibrand platform operated by a single seller who owns the products offered and assumes full responsibility for transactions with the customers (Tkacheva & Kotova, 2021). As a rule, online stores operate alongside physical retail outlets and serve to complement offline sales.

Marketplaces (MPs) differ in their operational models. A marketplace is an online platform where the website owner provides the infrastructure for third-party sellers to list their goods and services, while also ensuring access to customer (Brening & Bagaeva, 2020). The marketplace owner may not own the products listed on the platform, but undertakes intermediary functions, including facilitating transactions, processing financial operations, and organizing logistics.

A distinctive feature of the marketplace business model is the need to attract not only customers but also sellers in order to populate the platform’s assortment. This sets them apart from traditional online stores, which focus exclusively on serving the end customers (Briedis et al., 2020).

This paper seeks to explore the nature of competition between marketplaces as they represent some of the most versatile and at the same time comparable digital retail platforms, which are also highly competitive.

The most dynamic period in the development of marketplaces in Russia was the time between the late 2010s and early 2020s. This development was largely driven by the COVID-19 pandemic (Ovsiychuk et al., 2023). During the lockdowns, the demand for remote online purchases significantly increased, stimulating growth in both online retail volume and the number of market participants. Research into marketplaces and the competition between them began well before that: already in the late 20th century, scholars were actively examining the nature, effectiveness, and management principles of electronic trading platforms (Standing et al., 2010).

In the academic literature, considerable attention has been paid to competition in digital markets. For example, Kollman (2000) identifies two main levels of competition and marketplace leadership: informational and transactional. At the informational level, the author differentiates between two types of leading firms. One type includes those based on choice, i.e. assortment breadth, while the other type prioritizes designation, which means accuracy in meeting customer demands. At the transactional level, leaders are classified according, first, to “content”, which is understood as the quality and reliability of the participants, and, second, to “contacts” referring to the volume of interaction between sellers and buyers with no restrictions. Kollman also proposes a matrix of comparative competition for electronic platforms based on two indicators: the matching rate between sellers and buyers, and the transaction completion rate.

Pressey A. and Ashton J. K. (2009) focus on competition between B2B e-commerce platforms. They point out the clear advantages of marketplaces, which significantly reduce transaction costs between buyers and sellers. At the same time, they identify potential anti-competitive risks, including the possibility of collusion among sellers on a single platform, the risk of participant exclusion resulting from marketplace closure or refusal to collaborate with other platforms, and the potential for monopolization due to network effects, where the first successful platform gains a substantial competitive edge.

A different approach to the theoretical analysis of competition is presented by Chen and Makio (2006). The authors developed a model simulating competition between two marketplaces operating online auction systems. Over multiple rounds, sellers and buyers migrated between platforms depending on price levels, until the market reached a certain equilibrium. The experiment demonstrated that in the marketplaces competing primarily on price, long-term equilibrium can be achieved through price convergence. In our view, these findings are relevant not only to auction-based platforms but also to conventional marketplaces, where numerous sellers offer comparable products and price-based competition generates high price volatility.

Studies addressing the competition among sellers and buyers in a single marketplace abound in European and American academic literature (Redmond, 2013; Lee & Li, 2006), but there are comparatively few works examining the competition between marketplaces. This may be attributed to the near-monopolistic dominance of a single platform – Amazon. A notable exception is the study by Ryan et al. (2012), which investigates competition between an individual online store and a major marketplace, using the case of Amazon. The authors conclude that a marketplace can strengthen its market power by replicating popular products from private sellers and subsequently offering them under its own brand at a lower price.

In China, where at least three marketplaces rank among the world’s top five platforms by sales volume (Ghavami, 2024), the competitive landscape exhibits distinctive characteristics. In a study by Liu (2022) the market was found to be highly concentrated, with 74% of its volume controlled by the giants Alibaba (Tmall) and JD. In these conditions, price competition plays a decisive role. Furthermore, Chinese marketplaces are known to use price obfuscation, which reduces the transparency of price comparisons for consumers. This is done by complicating the differentiation between identical product listings and by creating complex promotional campaigns. As a result, transaction costs for buyers are artificially increased.

Despite the predominance of price as a competitive factor, Chinese marketplaces adopt various pricing strategies to avoid direct price wars. For example, Zhang et al. (2016) examined the pricing policies of JD, Amazon and Dangdang in the Chinese market in the mid 2010’s. They found that while the average prices on these platforms varied significantly, the minimum prices and the price dispersion were largely comparable across the three competitors.

One of the most significant competitive advantages in China’s market is advanced logistics infrastructure, which has been a critical factor for Chinese e-commerce firms. They began to invest heavily in creating their own logistics networks in the 2010s, and this helped them to gain a competitive edge (Lu et al., 2016). Contemporary Russian researchers have observed similar processes emerging on the domestic market. Marketplaces are also building their own logistics systems based on similar principles (Mikhailyuk, 2019).

The specific structure of the Chinese market and the scale of competing players have enabled local marketplaces to secure leadership in technological solutions. According to a study by Wang and Aldave (2024), companies integrating dynamic pricing systems and AI-driven recommendation technologies into their platforms significantly improve their performance metrics. In particular, personalized pricing and assortment optimization have helped increase sales by 15–20%, conversion rates by 25%, customer retention by 18%, and average session duration on the platform by nearly 40%.

A considerable body of both theoretical and empirical research has been devoted to issues of price competition and fulfillment in marketplaces. For instance, in the studies by Wohllebe (2022) and Kaur (2023) identified several characteristics crucial to consumers when selecting marketplaces:

• A wide assortment of products. A broad product selection reduces the transaction costs associated with search and minimizes consumers' need to place orders on multiple marketplaces. This, in turn, requires the platform to attract a substantial number of sellers.
• Website and application interface design. A convenient and intuitive interface facilitates the search for products, their comparison, and the purchasing process. Moreover, an effective UX design promotes the native promotion of complementary and additional products.
• Availability of user-generated content, particularly product and seller reviews. Reviews function as a network good, providing consumers with perceived guarantees of product quality and transaction security – a factor of particular importance in online commerce, where it is impossible to inspect products before purchase.

Beyond factors influencing consumer experience, competition between marketplaces is also shaped by the conditions offered to sellers. According to Sangarsu (2021), in addition to expanding sales channels, sellers are attracted by the availability of integrated marketing, analytics, and accounting tools. Such services simplify the management of sales processes and enable more effective promotion of products to targeted audiences within the platform itself. Yet, there are other, less obvious but equally important factors that remain comparatively underexplored.

Based on the literature review, we have identified several research-based groups of competitive factors specific to the marketplace business model. These are price-related factors (pricing levels and tariffs), logistics-related (delivery and fulfillment), marketing-related (loyalty programs and promotion strategies), and technology-related (innovations and platform development). Each group comprises several sub-criteria affecting both sellers and consumers on marketplaces. These groups and their corresponding sub-criteria will be examined in detail below. A marketplace’s engagement with each factor group may be directly shaped by prevailing market conditions. In this study, we analyze the competition among generalist marketplaces operating in the Russian market between 2016 and 2024, and, based on the existing academic literature, identify the key competitive factors that influence the market positions of major players in the country. Our hypothesis is that companies demonstrating superior performance in the competitive factor framework developed here will command the largest share of the Russian market.

Based on the analysis, a SV matrix and a table were compiled showing the market share of the largest general-purpose marketplace for each year between 2016 and 2024 (Fig. 1).

Fig. 1. 

SV matrix for the Russian marketplace market for 2016-2024. Source - compiled by the author based on data from Data Insight

The SV matrix illustrates the dynamics of the Russian general-purpose marketplace sector over the past nine years. A sharp shift of the market from B4 to G quadrants in 2018-2019 was caused by the growth in Wildberries’ market share and a gradual decline in Ulmart’s. Over the past five years, a reverse trend has been observed: the market has been moving from a more monopolistic state to a more oligopolistic one, owing to the expansion of OZON and the emergence of several relatively large new participants.

The values at each point on the year’s matrix indicate the number of dominant companies during that period. The dominance of two key market leaders has been evident for most years, with the exception of 2019. From 2016 to 2017, Wildberries and Ulmart held the top positions. In the subsequent years, from 2018 to 2023, Wildberries and OZON took the lead. In 2017, the matrix calculation did not identify a distinct group of leaders, as OZON’s share became equal to that of Ulmart. In recent years, the combined share of the two leaders has remained within the 80–90% range of total general-purpose marketplace sales, indicating their central role in attracting both buyers and sellers to marketplace trading.

Table 2 shows the key events that took place in the marketplace market: the entry of new firms, departure of old ones, and also M&A deals. It is evident that as the key players grew, so did the share of all marketplaces in Russian e-commerce. According to analytical reports, marketplaces accounted for up to 60% of all online purchases in Russia in the past five years (Adrianova, 2025). This is confirmed by our calculations. We will also examine some of the characteristics and competitive differences between each marketplace in this article.

Over the recent years, all domestic marketplaces, except Wildberries, have remained unprofitable. Rumyantseva et al., (2022), analysed the financial indicators of the largest Russian marketplaces between 2009 and 2021 and found that, in the 1990s and 2000s, Wildberries and Ozon had the highest gross profit, while Yandex Market and Megamarket had the lowest, sometimes even negative. However, this lack of profitability may be part of the long-term strategy for the companies, as profit is used to repay debts, develop infrastructure, implement marketing strategies, expand into new markets, and maintain competitive pressure.

To examine each of these marketplaces individually, we will focus on their significant achievements and also factors that have contributed to the competitive development of each company, because from the consumer’s perspective, they are very similar.

The marketplaces tiu.ru and ulmart.ru initially operated as online stores: the latter literally grew out of the retail chain Ulmart. In this work, we regard them as marketplaces because, although they primarily operated on the business model of an online store (OS), they provided an opportunity for suppliers to cooperate according to the marketplace model (MP). Ulmart closed in 2020 after completing the bankruptcy procedure that began in 2016. Tiu ceased operations in March 2022 for non-market reasons.

Wildberries and Ozon are two of the oldest and most prominent online marketplaces in Russia, currently leading the country’s online retail market. In 2024, Wildberries merged with the Russian advertising company Russ into a single legal entity (Korochkina, 2024). According to press releases, the goal of the merger was to jointly create a new digital trading platform. Today, Wildberries is the only profitable marketplace in Russia. It offers the lowest commissions for sellers and complete fulfillment with logistics provided from its own warehouses. The average order value is 870 rubles. Here and in the rest of this paper, the average check amount is taken from Data Insight’s data for 2024, unless stated otherwise.

Ozon is the second-largest and oldest Russian marketplace. It belongs to AFK Sistema and Baring Vostok (the largest shareholders). In addition to the marketplace, the company operates several other businesses related to product delivery and travel services. Since 2023, it has been cooperating with Russian Post on delivery and pick-up services. (Savitskaya, 2023). It offers sellers several fulfillment options and a wide range of commission rates. The focus is on technology logistics and advanced marketing tools for sellers. The average order value is 1,790 rubles.

Yandex Market is a service developed by Yandex, which grew from a product aggregator into a marketplace through the acquisition of Beru marketplace, which had been a joint venture of Yandex and Sberbank until 2020. Its main advantage is its integration with other services in the Yandex ecosystem, such as Yandex.Delivery and Yandex.Direct. This integration enhances the capabilities of sellers and provides them with more opportunities. The commission is variable and similar to that of Ozon. The average order value is 4,250 rubles.

MegaMarket - formerly SberMegaMarket - was founded by M.VideoEldorado Group in 2017 under the name Goods.ru. It was bought by Sber and rebranded in 2021, and again in 2022. The latest rebranding involves changing the fulfillment model from using a third-party warehouse to fulfilling orders from our own warehouse. That same year, it experienced “explosive” growth, which the company attributes to the new fulfillment model and the expansion of the seller base (RBC, 2024). Like Yandex Market, this platform is integrated with other services and loyalty/payment systems within the SBER ecosystem. The average order value on the platform is 8,610 rubles.

Magnit Market is a newly active marketplace of the Magnit Company. Founded in 2017 as KazanExpress, between 2021-2022 it was owned by AliExpress Russia, before being fully acquired by Magnit in 2024. In 2017, the original creators intended to combine low prices compared to other marketplaces and fast delivery thanks to the favourable location of their warehouse in Tatarstan (Borodina & Goncharova, 2021). The average order value is 930 rubles.

AliExpress Russia is the Russian division of the global AliExpress, owned by Alibaba Group. In recent years, the company’s sales and market share have decreased significantly, despite overall market growth. The decline is apparently due to structural factors. Experts believe that the main reason for the decline in its market share is the focus on cross-border trade, which decreased by 21% in 2024 (Sityukov, 2025) because of the reduction in the duty-free import threshold for goods from abroad, from €1,000 to €200 in the spring of last year. Another reason is that Russian sellers have learned how to efficiently source best-selling products from China and sell them in the Russian market, leading to a shift of audience from AliExpress towards Wildberries and Ozon. (Mingazov, 2023). The average order value in 2023 was 2,350 rubles.

In the past decade, the competition between these four major players coincided with the peak of the Russian marketplaces’ development. Two of them, Wildberries and Ozon, have been on the market for a long time and still hold a dominant position. The other two, Yandex Market and MegaMarket, are relatively new platforms, but they have been rapidly developing thanks to the support of large ecosystem companies. These four marketplaces are in many respects similar. They all have essential features of a marketplace, actively develop their logistics capabilities for same-day delivery, have similar fulfillment conditions, offer promotions, provide reporting systems for sellers, and have their own payment services.

Recent surveys show that half of buyers do not notice any user differences between Russia’s largest marketplaces but, according to Romanova (2025), 41% identified the price as the main difference between them. Her survey also confirms that the audiences of the largest players mostly do not overlap. Based on this fact and the average order value, it can be assumed that there is segmentation of buyers among marketplaces based on product category and purchasing power.

Another important factor in the competition, apart from pricing, is the struggle to attract sellers. It is clear that commissions and additional marketing services are the main sources of income for marketplaces. To this end, marketplaces aim to create the highest quality and most convenient sales platforms by offering the best terms and loyalty programs in order to attract new sellers and keep the existing ones. (Kholmovsky, 2025).

For the current marketplace market, four main factors with corresponding sub-criteria have been identified for comparison. These factors are significant for both buyers and sellers, and are essential for attracting an audience to the platform:

• Pricing Factor

• Product price level
• Seller commission level
• Payment system (convenience of payment for purchases and frequency of seller payouts)

• Logistics Factor

• Coverage by pick-up points (operational area)
• Availability of FBS, FBO warehouses (convenience for sellers)
• Average delivery speed (from order to pick-up point/threshold)

• Marketing Factor

• Brand recognition and reputation (including service quality)
• Advertising campaigns (seasonal, personalized promotions)
• Loyalty programs (for regular sellers and buyers)

• Technological Factor

• User experience for buyers (UX/UI)
• User experience for sellers (personal account, analytics, promotion)
• Innovations (recommendation algorithms, AI, AR)

Based on recent literature (Fazylova & Turgel, 2024; Murzak, 2021) and publicly available information, each of the existing marketplaces was evaluated on each of the four criteria using a scale ranging from 1 (low) to 5 (very high). Each rating considers the presence and development of the sub-criteria (Table 3). In order to assign a rating to each multi-criterion factor group, a list of potential advantages was made through an external analysis of companies under consideration. After that, each company was evaluated based on whether it actually possessed and used each specific benefit. The more such advantages a company has, the higher its rating will be (for example, a score of 5 is assigned when 85% or more of the listed factors are successfully realized).

A score of 1 or 2 usually indicates that the corresponding factor has a very specific or limited impact, while a score of 4 suggests a strong and competitive influence of that factor on a company’s market position. This means that at least two out of the three sub-criteria for that factor have been met. A score of 5 was assigned to a firm exhibiting a substantial advantage over other market participants. For most factors, ratings were based on an objective aggregation of publicly available information, such as user reviews on aggregator platforms (brand recognition, usability) or comparative analyses of price levels for the most popular and widely sold products. The technological factor proved the hardest to compare, since the technical architecture of many marketplaces is not publicly disclosed; therefore, assessments in this dimension relied on comparisons of known technical advantages and observable user-facing innovations.

Wildberries’s leading position in the Pricing Factor is defined by the lowest prices and the smallest seller commissions, with guaranteed weekly payouts. The price levels on other platforms may differ by a few percentage points due to higher commissions and longer payout periods (more than a week). All platforms except AliExpress and Magnit Market have their own payment systems.

In terms of logistics, Ozon and Wildberries tie for first place, with five points each, thanks to their largest networks of pick-up points and their own warehouses, offering same-day or faster delivery. The other competitors receive four points due to slightly worse fulfillment conditions. AliExpress received the lowest rating because of the longest delivery times.

From a marketing perspective, all platforms receive high marks for their developed loyalty programs with bonus systems, sometimes tied to ecosystems (Yandex, SberBank). They also provide good advertising and marketing tools for sellers. The highest score was awarded primarily due to the size and popularity of Wildberries in Russia. The concept of an online market and shopping is closely linked with this brand, and there is a significant presence of blog content related to it.

Finally, the highest ratings for technological factors go to Ozon and Yandex, which have put a lot of effort into the long-term development of their brands as technological companies with advanced personalization and recommendation systems. The lowest ratings were again given to AliExpress because of its comparatively outdated platform and Magnit Market due to the lack of innovations amid the migration to a new brand. However, publicly available information may not be sufficient to provide an accurate assessment of the current technological innovations of these platforms.

The evaluation results indicate that there is no significant gap in competitive factors between the leaders and the other two players, which is consistent with the current market share distribution. In the future, this evaluation table could be expanded to include weights for each factor, based on their significance for the market and customers. It could also be used to compare not only the Russian marketplaces, but also those in other countries.

One significant limitation of this study is the relative lack of data on the use of AI in market meta-models. At present, most researchers have access only to public information about ongoing projects and development teams. In reality, however, many large companies have probably been using AI tools in their backend systems for a long time, but the effects and specifics of this integration are not directly visible. This lack of visibility applies to all subsequent studies: without granular insights that can only be obtained through in-depth interviews, it is impossible to fully assess the exact impact of AI on competitive positioning. Researchers need to improve the methodology for conducting such inquiries, probably by focusing on the correlation between development budgets, computing capacities, and the comparative benchmark metrics for service performance.

Based on the results of our research, we can conclude that, due to the presence of several major companies, the Russian market is heading towards increased competition and an oligopolistic structure. At present, Wildberries continues to maintain its leading position in the market, although its market share has been decreasing relative to its closest competitors Ozon, Yandex Market, and MegaMarket. Although these three companies are growing, they have not yet achieved financial success. The main strategy of all the marketplaces is active investment in infrastructure and service quality to attract new sellers and buyers, despite temporarily losses in profitability. Given the ongoing active market growth, it is difficult to predict potential future mergers, acquisitions, or bankruptcies. All the major players strive to keep up with each other and outdo the competition in developing the key factors. Besides, they have strong institutional and financial backing from their parent companies, and there has been no significant shift by smaller players into more specialized product categories that could give online shops a competitive advantage over marketplaces.

Russian marketplaces face several significant challenges to their development. One of them is the vastness of the country’s size, which leads to high logistics costs. Another is the relatively small size of the Russian market that limits potential growth opportunities. It is not surprising that marketplaces are already expanding into neighboring markets in the CIS and Customs Union countries. The prospects of direct confrontation with Western and Eastern rivals are difficult to predict at this moment. However, Russian marketplaces are already beginning to develop their competitive advantages in a highly competitive environment.

The present paper examines the main competitive factors of a technologically and economically advanced business model – online marketplaces – in Russia’s economy. Its findings and methodology are therefore readily transferable to the study of marketplace sectors in other economies, such as those of the BRICS countries. Indeed, the BRICS countries commonly exhibit rapidly growing economies and industrial bases, increasing levels of consumption and market demands, as well as developing infrastructure and trading capacity, while facing competition from major established players based in advanced countries.

The Russian marketplaces continue to maintain and develop their main advantage compared to other more traditional forms of online and offline trading - reduced transaction costs for both sellers and buyers. Looking ahead, we predict the continued expansion of online retail at the expense of traditional sales channels. Competitive dynamics are increasingly determined by consumers’ choices of marketplaces, based on their engagement with a platform owner’s ecosystem and / or the platform’s product or price specialization. The future of research into marketplace competition lies in the study of how artificial intelligence affects business models of digital platforms. This includes further development of recommendation systems and algorithms that can accurately predict demand and match products to targeted audiences.