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Price discrimination within the online transactions Mnatsakanyan A.
Персонализированные цены внутри Интернет-транзакций
Мнацаканян А. Д.
Мнацаканян Арутюн Давидович /Mnatsakanyan Arutyun Davidovich - студент, факультет бизнеса и менеджмента, Национальный исследовательский университет Высшая школа экономики, г. Москва
Abstract: active dissemination of online stores that started in the developed countries at the beginning of the 21st century, leading to the modernization of the classic "trade" concepts and the displacement of online shopping stores "brick and mortar". To maximize the return on assets use price discrimination mechanisms, Internet companies need to make appropriate decisions, firstly, on the choice of the form of price discrimination, and secondly, on the establishment of the necessary prices and, third, the definition of an optimal level of quality and quantity sales. Only the optimal values of parameters mentioned above, from the viewpoint of scanning theory provide store owners (principals) maximum profit.
Аннотация: активное распространение Интернет-магазинов, начавшееся в развитых странах в начале 21-го века, приводит к модернизации классического понятия «торговля» и вытеснению онлайн-магазинами магазинов «из кирпича и бетона». Для извлечения максимальной прибыли посредством использования механизмов ценовой дискриминации, Интернет-компании должны принимать соответствующие решения, во-первых, о выборе формы ценовой дискриминации, во-вторых, об установлении необходимой цены и, в-третьих, об определении оптимального уровня качества и объема продаж. Только оптимальные значения упомянутых выше показателей, с точки зрения теории сканирования, обеспечат владельцам магазинов (принципалам) максимальную прибыль.
Keywords: Internet, Internet commerce, online transactions, price discrimination. Ключевые слова: Интернет, Интернет-торговля, Интернет-транзакции, ценовая дискриминация.
1. Introduction
As the consumer sector of e-commerce, online retailers are actively implementing custom data collection technology that greatly increases their ability to conduct price discrimination. For example, loyalty cards and air miles accumulation program used to collect data on the trade preferences of each user [3]. Prior to the beginning of the 21st century, the cost of providing discrimination mechanisms was too high, because the handling and analysis of the amount of data required huge man-hour costs. Moreover, the collection, storage and analysis of information in paper form could hardly lead to effective results.
However, with the advent of e-commerce situation in this area has changed totally. Online directories are used by agents in real time, can be individually tailored to fit the current buyer, whereas the principal can determine the type and automatically rebuild the Web site to match the anticipated needs of the user. In particular, this technology can be used in order to offer different prices for different customers. Thus, the cost of online menu modeling is practically zero, and the benefits of price changes in the direction of withdrawal of the buyer of its consumer surplus can be quite substantial. To date, the development of Internet technology allows the use of modern methods of data analysis (data mining), which makes it possible to conduct our massive database of individual consumer preferences.
2. Relevant articles
Article «Evaluating Pricing Strategy Using e-Commerce Data: Evidence and Estimation Challenges», written in 2006 by two professors at New York University has become a kind of a pioneer in the theory of evaluating the effectiveness of price discrimination tools in the shop [1]. Considering as Retailing online software providers, they came to very interesting results. The main aim of the research program was to use empirical data to evaluate the optimal strategy of price discrimination in the software industry. Obviously, this problem is of significant economic importance in terms of choosing between different alternative tools of discrimination. For analysis were used data containing information on the demand and price, from the site of the most expensive in the world of online store Amazon.
Amazon publishes a series of sales for each product he sells, forming a product rank in each category on the basis of the recent demand for it. Further, the "system demand" related to the product (i.e., with a change in demand for connectivity of price dispersion), should be used. Because Amazon does not provide any data on the variable value of the products that it sells, the authors have also had to bring these costs from the available data (we should understand that the seller's income depends not only on price, appointed on the amount of goods sold, but the cost of goods per unit). To estimate the price elasticity of demand is necessary to apply the method of least squares (method of regression analysis). Evaluation of variables they carried costs, bringing the Lerner index for each product (economic indicator of monopoly firms). Finally, check the optimality of pricing, using the first-order conditions of profit maximization.
To search for items included in the general population, they hold a random sample. Their sample contains 330 products made in each of the four main categories of the scope of software offered on Amazon:
• Business products, increasing productivity of workers;
• Programs to ensure access and safety work;
• Products for construction and analysis of the graphs, and other various products for developers;
• Classic operating system software.
This sample is composed of products that have different versions of the products and that are sold together, i.e. in the form of goods, complements or supplements (bundles). For consideration of product groups described above, the authors needed to make a selection by keyword, such as a version of "Prime", "luxury" or "standard". Similarly, for consideration by the groups of products sold together, they select only those products where there is a key word bundle (product package). In addition, the authors also collected data on the secondary sales market, including data on prices used to it (the prices set by sellers who place secondhand copy of the product for sale) and other prices offered by sellers not from Amazon, i.e., sellers, who are not affiliated with Amazon, but are allowed to sell goods on Amazon in exchange for a commission on the price of the product sold [2]. They see only two years of publication, "current" (2005) and "previous" (2004).
3. Model formulation
After filtration products into groups, the authors identify three different tools of price discrimination, the effectiveness of which in the future they will be evaluated:
• Products comprising a two or more versions;
• Products that are sold as part of the whole, ie, They are in addition to anything;
• Products that are released in the successful generation (year).
If the data is collected, it is necessary to move to their analysis, according to the previously mentioned set to the flowchart steps. Firstly, it is necessary to withdraw the demand, based on the available data. To do this, the authors suggest that the data are from a number of Pareto distribution. Then they convert the number of sales in each periodic level of demand, using Pareto's ratio, ie, i™ilog[Q] = a + ft * log [rank], where Q is the demand for goods, rank - rank observed sales and a, p - parameters for specific product categories.
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After analyzing the data, they conclude that their sample covers a variety of products, monitoring who gathered for a long time, and therefore the data set has elements of both the transverse (cross-sectional) and time series (time-series) data.
Secondly, it is necessary to estimate the price elasticity of demand, using the least squares method. They take into account fluctuations in the price for all the products in time and number of steps in each period, which can be traced in the sales ranks, and conclude that consumers are sensitive to price. Accordingly, there is a need to assess the elasticity of demand for the price. Defining the formula the least square regression in the form of general they assess the elasticity for two cases: a high-quality version and low-quality version. It is not difficult to guess that as a result of investigations it turned out that a group of people who prefer low-quality version, is more sensitive to price. These estimates describe the change in the elasticity of demand when the price changes, and form the basis for the analysis of the optimality of the chosen price strategy, since they allow, for example, to assess whether demand will be different, if the company changed its pricing policy by removing version or a set.
4. Conclusion
Current theme becomes even more urgent in the light of scientific and technological progress, given the vast, still fully unrealized possibilities of data mining. In addition, it is clear that in recent years the Internet has become a popular shopping channel, because the Internet offers consumers to shop 24 hours a day and convenient to shop and find the necessary goods among a set of alternatives, from the comfort of home. That is why we see a huge number of works devoted to the subject, whether it's study of the effectiveness of pricing offered by outsourcing companies (shipping companies), or analysis of the effectiveness of different approaches of price discrimination of all three degrees. Anyway, I believe that the work of the study the theoretical bases of the economic theory will be actively published, at least, the next 10 years, more than that, I am sure that this issue will be relevant until any technology boom in this area, because when such opportunities too tempting benefits of carrying out price discrimination.
References
1. Anindya Ghose, Arun Sundararajan. Evaluating Pricing Strategy Using e-Commerce
Data: Evidence and Estimation Challenges / Statical science. Vol. 21. № 2. P. 131-142,
2006.
2. Jakub Mikians, Laszlo Gyarmati, Vijay Erramilli, Nikolaos Laoutaris. Crowd-assisted
Search for Price Discrimination in E-Commerce: First results // CoNEXT'13. December.
№ 9-12, 2013.
3. Carl Shapiro, Hal R. Varian. Information Rules, A strategic Guide to the Network
Economy / Harvard Business School Press, 1999.
Анализ недостатков транспортной логистики в России
Голубев П. В.
Голубев Павел Владимирович / Golubev Pavel Vladimirovich - курсант, командно-инженерный (автомобильно-дорожный) факультет, Военная академия материально-технического обеспечения имени генерала армии А. В. Хрулева,
г. Санкт-Петербург
Аннотация: в статье анализируются проблемы логистики на автомобильном транспорте в России. Данное исследование может быть внедрено в проведение тематических занятий со студентами учебных заведений. Ключевые слова: логистика, проблемы, транспорт.
