Amazon Marketplace Research Articles

Price competition online: Platforms versus branded websites

AbstractThe focus of this theoretical study is price competition when some firms operate their own branded website while others sell their products through an online platform, such as Amazon Marketplace. On one hand, selling through Amazon expands a firm's reach to more customers, but on the other hand, starting a website can help the firm to increase the perceived value of its product, that is, to build brand equity. In the short run the composition of firms is fixed, whereas in the long run each firm chooses between Amazon and its own website. I derive the equilibrium prices and profits, analyze the firms' behavior in the long run, and compare the equilibrium outcome with the social optimum. Comparative statics analysis reveals some interesting results. For example, I find that the number of firms that choose Amazon may go down in response to an increase in the total number of firms. A pure‐strategy Nash equilibrium may not exist; I show that price dispersion among firms of the same type is more likely in less concentrated markets and/or when the increase in the perceived value of the product is relatively small.

Identifying Arbitrage Opportunities in Retail Markets using Predictive Analytics

While the body of literature on arbitrage opportunity identification is well established in the financial, energy, and real-estate markets, research on arbitrage in the retail marketplace is relatively limited. As arbitrage opportunities in this context differ inherently from other markets, we propose a data analytics framework to identify such an arbitrage by leveraging a machine learning model to predict the optimal purchasing point from the price movement. Our predictive approach is enhanced by incorporating user-generated content which demonstrates its informative power. Overall, the enhanced model attains the precision rate of more than 90 percent while the recall rate is higher than 80 percent in a cross-validation test using the data collected from Amazon Marketplace. In addition, we conduct a field experiment to verify the external validity of the model in a real-life setting. The result shows that our model is capable of generating as much as a 113.31% profit margin.

Marketing functions and B2C e-marketplaces: An exploratory analysis

Business-to-consumer e-marketplaces are an important and fast-growing distribution channel. Nevertheless, there is a lack of literature that attempts to describe and classify business-to-consumer e-marketplaces and to analyze their role in the distribution channel. This article seeks to address this deficiency and to add clarity to our understanding of these two-sided platforms. A new innovative marketing functions concept is developed: the PILT framework. It consists of four main functions—product, information, logistics, and transaction—and ten subfunctions. Applying this framework reveals that business-to-consumer e-marketplaces are infomediaries, completely fulfilling only information functions. All other functions must be managed by the sellers themselves or outsourced to other intermediaries such as logistics companies or banks. A case study of Amazon and Walmart Marketplace is presented to further illustrate the PILT framework. It reveals that Amazon Marketplace fulfills more marketing functions than Walmart Marketplace, which may be an important success factor for B2C e-marketplaces.

Cross-Border E-Commerce Data Set: Choosing the Right Fulfillment Option

We provide a set of data on cross-border e-commerce sales from a fast-fashion e-retailer, Babyonlinedress, which stocks wedding gowns and full dresses from China but sells to markets overseas through multiple cross-border platforms, including Amazon, AliExpress, DHgate, etc. The data set contains normalized weekly orders on Amazon Marketplace over a three-year period, from January 1, 2015 to December 31, 2017. In total, we have collected M = 13,719 weekly orders for N = 1,166 different stock keeping units (SKUs) in the two fulfillment channels facilitated by Amazon: (i) Fulfillment by Amazon (FBA) and (ii) Fulfillment by Seller (FBS, also known as Fulfillment by Merchant). The orders for some SKUs were fulfilled entirely by seller during their life cycles on Amazon, whereas the rest were first fulfilled by seller for several months (testing period) before being transferred to FBA. This data set serves to illuminate the impact of cross-border fulfillment options on sales and bottom line of e-retailers.

Potential Trend for Online Shopping Data Based on the Linear Regression and Sentiment Analysis

How to reduce the cost of competition in the industry, identify effective customers, and understand the emotional needs and consumer preferences of customers, so as to carry out fast and accurate commercial marketing, is an important research topic. In this paper, we discussed the method for the analysis of three product data which represent the customer-supplied ratings and reviews for microwave ovens, baby pacifiers, and hair dryers sold in the Amazon marketplace over the time period. The sentiment analysis, linear regression analysis, and descriptive statistics were implemented to analyze the three datasets. Based on the sentiment analysis given by the naive Bayesian classification algorithm, we found that the star rating is positively correlated with the reviews, while the helpfulness ratings have no specific relationship with the star rating and reviews. We use multiple regression analysis and clustering algorithm analysis to get the relationship between the 4 indexes such as time, star rating, reviews, and helpfulness rating. We find that there is a positive correlation between the 4 indexes, and the reputation of the product online market is improving as time grows. Based on the analysis of the positive reviews and star ratings, we suggested indicating a potentially successful or failing product by the positive reviews. We also discussed the relations between the star ratings and number of reviews. Finally, we selected the words from the Amazon sentiment dictionary as candidate words. By counting the candidate words’ appearance in the review, the keywords that can reflect the star rating were found.

Gene Teller: an extensible Alexa Skill for gene-relevant databases.

SummaryVoice assistants have become increasingly embedded in consumer electronics, as the quality of their interaction improves and the cost of hardware continues to drop. Despite their ubiquity, these assistants remain underutilized as a means of accessing biological research data. Gene Teller is a voice assistant service based on the Alexa Skills Kit and Amazon Lambda functions that enables scientists to query for gene-centric information in an intuitive manner. It includes several features, such as synonym disambiguation and short-term memory, that enable a natural conversational interaction, and is extensible to include new resources. The underlying architecture, based on Simple Storage Service and Amazon Web Services Lambda, is cost efficient and scalable.Availability and implementationA publicly accessible version of Gene Teller is available as an Alexa Skill from the Amazon Marketplace at https://www.amazon.com/dp/B08BRD8SS8. The source code is freely available on GitHub at https://github.com/solinvicta/geneTeller.

Price Competition and Platform Design in Two-Sided Networks

The owners of large online platforms, like Airbnb or Amazon Marketplace, are able to affect the probability that a given buyer observes a given seller on their platform. Buyers and sellers therefore interact on a bipartite graph with links between them weighted by the probability of observation. Platform owners are then able to design the network by choosing the observation probabilities. A seller's price is decreasing in their Bonacich centrality. In order to maximize profits, the platform owner connects an entrant to buyers according to a measure of their own centrality in the graph. While a network where buyers observe and trade with all sellers with probability one maximizes consumer surplus, such a network does not necessarily maximize the platform's profit. The platform owner faces a trade-off between increasing observability and reducing competition, which explains why buyers tend not to be able to observe all sellers on online platforms. If sellers are identical, increasing seller prominence for any consumer segment increases competition, and profit is maximized when it is randomized across all sellers.

Regtech as an antitrust enforcement tool

Abstract Financial technology (Fintech) has been applied to business models in the financial services sector. Associated with this has been the rise of regulatory technology (Regtech) in that sector. However, the major application of Regtech in financial services is as a tool for regulatory compliance, rather than for regulatory enforcement. This article explores an approach to applying Regtech techniques to antitrust enforcement. It does this by applying those techniques to the detection of resale price maintenance (vertical price fixing). The exploration is limited to pricing on e-commerce platforms such as AliExpress, eBay, and Amazon Marketplace. The Regtech application is effected by presenting the literature and cases on resale price maintenance and the application of the law in Australia, the US, and the EU. The article examines the application of machine learning in the Regtech environment and the ways in which application programming interfaces could be used. The article proposes approaches to machine learning solutions for the detection of potentially infringing resale price maintenance conduct. It also presents the basis of an algorithm for detecting that conduct.

Price Dynamics on Amazon Marketplace: A Multivariate Random Forest Variable Selection Approach

Amazon Marketplace is an ecommerce platform where Amazon is a seller itself and also the platform host to third-party (3P) sellers. Business press has reported that Amazon uses complex pricing strategies for items it sells directly. However, there is limited academic and business press understanding of price dynamics in the marketplace. The modeling challenge is the high dimensionality of the covariate space and sparsity of price changes for some sellers. We develop a novel multivariate random forest (MVRF) based variable selection method to identify the key predictors of price change. This variable selection method has potentially wider application to similar high dimensional modeling situations, e.g., in selecting variables for the design of field and laboratory experiments, and alternative statistical and structural models. We test the robustness of the proposed variable selection method against extant benchmarks such as univariate random forest (RF) and LASSO. We provide descriptive insights on the price change mechanism among the sellers. Our results reveal several unexpected patterns including that price and feature changes of even the smallest and new entrant 3P sellers are significant predictors of Amazon’s price change. These findings have academic, managerial and regulatory relevance. We demonstrate additional managerial relevance via a stylized price forecasting example.

Italian court orders Amazon not to sell luxury cosmetics subject to a selective distribution network

Abstract Case 50977/2018 Sisley Italia et al v Amazon Core Europe et al (Sisley v Amazon) (2019) District Court of Milan, Commercial Law Chamber, Decision of 28 March 2019 In upholding an application for an interlocutory injunction, the District Court of Milan ordered Amazon to refrain from marketing, selling, promoting or advertising on its Amazon Marketplace Sisley high - quality cosmetics. The court considered that offering luxury products alongside low-quality goods on the same Internet site: (i) is incompatible with the qualitative standards, which the members of the selective distribution system covering the said cosmetics have to respect; (ii) constitutes trade mark infringement as the existence of a lawful selective distribution network is a legitimate reason for the non-application of the exhaustion principle.

Selective Distribution Networks for the Marketing of Luxury Branded Products Versus the Amazon Marketplace: The Sisley v Amazon Case before the Court of Milan

By an interlocutory order the District Court of Milan found that putting on sale on an electronic marketplace branded luxury products according to selling modalities that differ from the qualitative standards that the supplier has imposed on the resellers admitted to the selective distribution network created for the marketing of those products amounts to a trademark infringement. The Court of Milan confirms that the existence of a lawful competition-compliant selective distribution system constitutes a legitimate reason for not applying the principle of exhaustion. And more importantly, for the first time in Italy, the Court of Milan expressly found that the online sale of branded luxury products in defiance of the selling conditions imposed by the head of a selective distribution network infringes the trademark rights of the latter on such products. This paper reviews the decision of the Court of Milan and gives an insight into its practical implications.

The EU’s Competition Investigation into Amazon’s Marketplace

In September 2018, the European Commission sent out formal requests for information to investigate allegations of an anticompetitive conduct by Amazon. The investigation relates to the interdependencies between Amazon’s third-party sales platform for retailers (“Amazon Marketplace”) and Amazon’s own online retail operations. Operating both on an upstream intermediation market for businesses (“merchants”) and downstream retail markets vis-a-vis its end customers (“shoppers”) has created a strong conflict of interest for Amazon. This article outlines the background to the EC’s investigation and the current focus and likely theories of harm regarding Amazon’s conduct involved in the investigation.

Fusion of technology management and financing management - Amazon's transformative endeavor by orchestrating techno-financing systems

Amazon became the world R&D leader in 2017 by rapidly increasing R&D investment. Its R&D investment in 2017 was double that of 2015, 5 times that of 2012, and 10 times that of 2011. This rapid increase continued in 2018, and Amazon accomplished a skyrocketing increase in its market capitalization, closing to being the world's biggest company.Such a rapid increase in R&D and subsequent market value has raised questions about how to conduct R&D and secure a large amount of funds needed for high-risk investments. Amazon has provided hypothetical answers to both of these questions.Amazon has been conducting innovative R&D to transform routine or periodic alterations into significant improvements during the R&D process and claiming huge expenses for such transformation activities as expenses for R&D.The company's ample free cash flow generated by sophisticated cash conversion cycle (CCC) management endorsed a large amount of investment for such a high-risk strategy.Increased R&D induced business advancement and lean cost structure construction leading to a further increase in cash flow has stimulated interactions between vendors, customers, and Amazon via the Amazon marketplace. Activated interaction-accelerated CCC advancement, a subsequent free cash flow increase, and user-driven innovation have thus simultaneously accelerated R&D transformation.All of these components - R&D transformation and technopreneurial strategy consisting of technology management, as well as marketplace development and CCC-driven cash flow generation consisting of financing management - function together as a consolidated sophisticated machine. Thus, Amazon has succeeded in fusing technology management and financing management by orchestrating all techno-financing systems.In light of increasing concerns regarding R&D expansion without the dilemma of a productivity decline that most digital economies are now confronting, this paper demonstrates the above hypothetical answers.Following the authors' preceding analysis of Amazon's unique technology management toward a new concept of R&D in the digital economy, an intensive empirical analysis focusing on the development trajectory of Amazon's techno-financing system over the last two decades was conducted.An insightful suggestion as to neo-open innovation that fuses technology management and financing management is thus provided.

Analysis of book sales prediction at Amazon marketplace in India: a machine learning approach

Prediction of customer demand is an important part of Supply Chain Management, as it helps to avoid over or under production and reduces delivery time. In the context of e-commerce, accurate prediction of customer demand, typically captured by sales volume, requires careful analysis of multiple factors, namely, type of product, country of purchase, price, discount rate, free delivery option, online review sentiment etc., and their interactions. For e-tailers such as, Amazon, this kind of prediction capability is also extremely important in order to manage the supply chain efficiently as well as ensure customer satisfaction. This study investigates the efficacy of various modeling techniques, namely, regression analysis, decision-tree analysis and artificial neural network, for predicting the sales of books at amazon.in, using various relevant factors and their interactions as predictor variables. Sentiment analysis is carried out to measure the polarity of online reviews, which are included as predictors in these models. The importance of each independent predictor variable, such as discount rate, review sentiment etc., is analyzed based on the outcome of each model to determine top significant predictors which can be controlled by the marketer to influence sales. In terms of accuracy of prediction, the artificial neural network model is found to perform better than the decision-tree based model. In addition, the regression analysis, with and without sentiment and interaction factors, generates comparable results. The comparative analysis of these models reveals several significant findings. Firstly, all three models confirm that review volume is the most important and significant predictor of sales of books at amazon.in. Secondly, discount rate, discount amount and average ratings have minimal or insignificant effect on sales prediction. Thirdly, both negative sentiment and positive sentiment of the reviews are individually significant predictors as per regression and decision-tree model, but they are not significant at all as per neural network model. This observation from the neural network model is contrary to the extant research which claims that both negative and positive sentiment are significant with the former having more influence in predicting sales. Finally, the interaction effects of review volume with negative and positive sentiment are also found to be significant predictors as per all three models. Hence, overall, out of various factors used for sales prediction of books, review volume, negative sentiment, positive sentiment and their interactions are found to be the most significant ones across all models. The results of this study can be utilized by online sellers to accurately predict the sales volume by adjusting these significant factors, thereby managing the supply chain effectively.

Strategic contracting and hybrid use of agency and wholesale contracts in e-commerce platforms

This paper examines strategic contracting between a monopoly platform and suppliers that sell their goods through the platform. I consider two competing suppliers: a high-volume supplier with the larger potential demand and a low-volume supplier with the smaller one. Each supplier chooses one of two contracts: wholesale or agency. The platform has to strategically determine the royalty rate for the agency contract by taking into account which contracts the suppliers will choose. I show that the platform offers a low (high) royalty rate to induce the suppliers to adopt the agency (wholesale) contract when product substitutability is low (high) enough. More interestingly, when the degree of substitution is at an intermediate level, asymmetric contracting, in which only the low-volume supplier adopts the agency contract, can arise in equilibrium. This result is related to the fact that many long-tail and niche products with lower potential market sizes are traded on platform-based marketplaces, such as Amazon Marketplace and Walmart Marketplace.

Strategic Contracting and Hybrid Use of Agency and Wholesale Contracts in E-Commerce Platforms

This paper examines strategic contracting between a monopoly platform and suppliers that sell their goods through the platform. I consider two competing suppliers: a high-volume supplier with the larger potential demand and a low-volume supplier with the smaller one. Each supplier chooses one of two contracts: wholesale or agency. The platform has to strategically determine the royalty rate for the agency contract by taking into account which contracts the suppliers will choose. I show that the platform offers a low (high) royalty rate to induce the suppliers to adopt the agency (wholesale) contract when product substitutability is low (high) enough. More interestingly, when the degree of substitution is at an intermediate level, asymmetric contracting, in which only the low-volume supplier adopts the agency contract, can arise in equilibrium. This result is related to the fact that many long-tail and niche products with lower potential market sizes are traded on platform-based marketplaces, such as Amazon Marketplace and Walmart Marketplace.

An Analysis of Popular Online Erectile Dysfunction Supplements

IntroductionErectile dysfunction supplements (ED-Ss) are featured on online marketplaces like Amazon.com, with dedicated pages and claims that they naturally treat ED. However, their efficacy and safety are largely unknown, limiting the ability to counsel patients regarding their use. AimTo evaluate the highest rated and most frequently reviewed ED-Ss on Amazon.com to facilitate patient counseling regarding marketing myths, ingredient profiles, and evidence for product efficacy and safety. MethodsThe Amazon marketplace was queried using the key term “erectile dysfunction” with default search settings and ranking items based on relevance. The top 6 ED-S products identified on September 29, 2018, were reviewed based on price, ratings, reviews, manufacturer, and ingredients. Consumer reviews were categorized using subtopics within the International Index of Erectile Function (IIEF) questionnaire to better understand ED-S efficacy and then reanalyzed following filtration of untrustworthy comments using ReviewMeta.com, a proprietary Amazon review analysis software. OutcomesQuantitative and qualitative evaluation of ED-S products sold on Amazon.com. ResultsThe top 6 ED-Ss had an average of 2,121 ± 1,282 reviews and a mean rating of 3.92 ± 0.42 stars. A total of 21 ingredients were identified in these ED-Ss. Ginseng, horny goat weed, L-arginine, and tongkat ali were the most popular ingredients included in the analyzed products. Our literature review identified 413 studies involving the 21 identified ingredients, of which 59 (16%) involved human subjects. Among these 69 human studies, only 12 (17%) investigated supplement ingredients individually and reported improvement in ED. Analysis of top-ranked customer reviews from the first 2 pages of reviews for each supplement revealed differences in IIEF scores before and after ReviewMeta.com filtration. After filtration, we observed a 77% decrease in reviews reporting improved erection strength, an 83% decrease in reviews reporting improved ability to maintain erection, a 90% decrease in reviews reporting increased sexual satisfaction, an 88% decrease in reviews reporting increased enjoyment with intercourse, and an 89% decrease in reviews reporting increased erection confidence. Strengths & LimitationsStudy strengths include a novel approach to ascertaining consumers’ perceptions and satisfaction with ED-Ss and practical summary information that clinicians can provide to patients. Limitations include selection bias, the small number of supplements analyzed, and the proprietary nature of the Amazon review analysis software. ConclusionsOur investigation revealed that human studies evaluating the efficacy of ED-S ingredients are limited and have yielded no definitive findings of the effects on ED. Patients considering ED-S use should receive appropriate counseling, given the prevalence of disingenuous reviews and the ready availability of Food and Drug Administration–approved drug therapies.Balasubramanian A, Thirumavalavan N, Srivatsav A, et al. An Analysis of Popular Online Erectile Dysfunction Supplements. J Sex Med 2019;16:843–852.

Testosterone Imposters: An Analysis of Popular Online Testosterone Boosting Supplements

IntroductionTestosterone-boosting supplements (T-Boosters) are prominently featured on Amazon.com, with numerous dedicated pages and claims that they “naturally” increase testosterone levels. AimTo evaluate the highest rated and frequently reviewed T-Boosters on Amazon.com to facilitate patient counseling regarding marketing myths, T-Booster formulations, and evidence for efficacy and safety. MethodsThe Amazon marketplace was queried using the key words “testosterone” + “booster,” with default search settings and ranking items based on relevance. The top 5 T-Boosters identified on July 22, 2018, were reviewed based on price, ratings, reviews, manufacturer details, and ingredients. Consumer reviews were categorized using core themes in the Androgen Deficiency in the Aging Male (ADAM) questionnaire as a proxy to understand T-Booster efficacy and reanalyzed after filtration of untrustworthy comments using ReviewMeta.com, a proprietary Amazon customer review analysis software. Main Outcome MeasuresQuantitative and qualitative evaluation of T-Boosters on Amazon.com was performed. ResultsThe top 5 T-Boosters had an average ± SD of 2,761 ± 5,112 reviews and a rating of 4.56 ± 0.25 stars. 19 unique ingredients were identified across these T-Boosters, and literature review revealed 191 studies involving the 10 most common ingredients, of which 19% involved human subjects, 53% animal models, 15% in vitro studies, and 12% case reports or review articles. Among 37 human studies, 30% observed an increase in T levels, 3% a decrease, 46% no effect, and 22% were indeterminate. Analysis of top customer reviews from the first 2 pages of reviews for each supplement revealed differences in the ADAM score before and after ReviewMeta.com filtration. After filtration, there was a 91% decrease in users reporting increased libido, a 59% decrease in reports of increased energy, a 93% decrease in reports of improved strength/endurance, a 60% decrease in reports of improved erections, an elimination of reports of improved work performance, a 67% decrease in reports of improved sleep, and an 89% decrease in reports of improved sports ability. Clinical ImplicationsOur study can serve as a guide for providers to counsel patients about the efficacy of popular online T-Boosters as well as the prevalence of disingenuous reviews associated with these products on online marketplaces like Amazon.com. Strengths & LimitationsStrengths include the novel approach to assess consumers’ perceptions and satisfaction of T-Boosters, as well as summary information that clinicians can provide patients. Limitations include selection bias, a small number of supplements analyzed, and the proprietary nature of the Amazon review analysis software. ConclusionT-Boosters are easily available online. Our investigation revealed that limited human studies have evaluated T-Boosters, resulting in no definitive findings of efficacy. In the absence of additional human studies, patients should be cautioned before considering T-Boosters, given the availability of highly effective therapies approved by the Food and Drug Administration.Balasubramanian A, Thirumavalavan N, Srivatsav A, et al. Testosterone Imposters: An Analysis of Popular Online Testosterone Boosting Supplements. J Sex Med 2019;16:203–212.

Should platforms be allowed to charge ad valorem fees?

Many platforms that facilitate transactions between buyers and sellers charge ad valorem fees in which fees depend on the transaction price set by sellers. Given these platforms do not incur significant costs that vary with transaction prices, their use of ad valorem fees has been questioned. In this paper, using a model that connects ad valorem fees to third‐degree price discrimination, we evaluate the welfare consequences of banning such fees. We find the use of ad valorem fees generally increases welfare, including for calibrated versions of the model based on data from Amazon's marketplace and Visa's signature debit cards.

Impact of Bilateral Rating System on Ride-Sharing Platforms

Traditional online platforms (e.g., Amazon Marketplace) use Unilateral Rating System (URS), where customers rate sellers. However, sharing economy platforms (e.g., Uber, Airbnb) have adopted Bilateral Rating System (BRS) that also allows service providers to rate customers, and even selects customers based on their ratings. BRS is often purported to be better than URS, as BRS unlocks more hidden information than URS by revealing ratings of customers to service providers before they make acceptance/rejection decisions. We compare URS and BRS in the context of the ride-sharing service to study its impact on all stakeholders in the system. Conventional wisdom suggests that BRS would harm riders, as riders can be rejected by drivers while platforms should benefit from BRS, since it unlocks the value in the riders’ rating that can be potentially extracted. We find that contrary to this intuition, BRS may increase riders’ welfare under some conditions, as drivers rejecting low-rating riders can reduce the excess demand and the associated inconvenience cost born by all riders in the system. BRS can both improve or reduce the platform’s revenue, as there is a tug-of-war between the platform and drivers on the revenue share. Under the right conditions, BRS can result in a win-win-win situation for the drivers, riders and the platform.