MODERN PRICING STRATEGIES IN THE INTERNET MARKET

The 11 th Internatonal Days of Statstcs and Economcs, Prague, September 14-16, 2017 MODERN PRICING STRATEGIES IN THE INTERNET MARKET Maksm Patshn Sergey Kulpn Abstract The artcle provdes an overvew of current scentfc research n the feld of prcng n the Internet market. Prce choosng s one of the most mportant aspects for onlne busnesses. Prcng requres both a scentfc approach and ntuton that helps to understand how customers perceve a brand and goods. The Internet has changed the ways of consumer prce nformaton analyss whch prevously have had a dssemnated character. A user can comple nformaton about a good prce of varous retalers n just a few clcks. Increased prcng nformaton transparency causes strong competton among retalers and requres real-tme montorng and rapd response. Many onlne sellers use the compettve strategy of behavour. They constantly montor compettors' prces and on the bass of the obtaned data set ther own prces. The growth of the Internet commerce has prompted the development of applcatons for algorthmc prcng when sellers set the prce usng calculaton algorthms. In addton, the authors offer general prcng model for the Internet market. The artcle s conceptual n nature, and wll be useful to researchers n the feld of onlne prcng and can be used n further studes. Key words: prcng strateges, the Internet market, prcng problems JEL Code: JEL D40, JEL M31 Introducton Prcng s one of the most mportant aspects for Internet companes. Prcng requres both academc approach and ntuton whch helps to understand whch brand and product wll be attractve for customers. As experts say at the very begnnng retaler must desde what wll help hm reach necessary proft level: hgh prces and low level of sales or lower prces and respectvely hgher level of sales. 1146

The 11 th Internatonal Days of Statstcs and Economcs, Prague, September 14-16, 2017 Internet has changed the ways customers analyze prcng nformaton whch had dssemnated nature before. In several clcks customer can summarze the nformaton about prces from dfferent retalers for ths or that product. Hgher prcng nformaton transparency causes tough competton among retalers and requres montorng n real-tme and quck response. Many onlne sellers use compettve behavor strategy constantly montorng compettors prces and defnng ther own prces on the bass of obtaned data. The growth of Internet commerce has trggered the growth of applcatons for algorythm-based prcng: that s when sellers establsh prces usng calculaton algorythms (Chen, 2016). Stes of travel agences and large well-known Internet retalers have adapted prcng strateges algorythms. Dynamc prcng nstruments and technologes are also accessble for smaller companes. (Fsher, 2017). The artcal has two man parts. The frst part presents the study background. In the second part readers can fnd dscrptonf of prcng model for the Internet market. 1 Study background In the context of low nformaton assymmetry created as a result of product Internet aggregators researchers analyse many prcng problems n the Internet. Prcng depends on many factors. In the research (Lu, 2012) authors dentfy 3 factors nfluencng Internet company prcng strategy: reputaton of onlne seller, customer awareness level and competton. Researchers have found that due to the presence of less and more nformed customers at the market compabes wth hgher reputaton declare lower prces than companes wth lower reputaton. Such a research demonstrated that the stronger the competton at the Internet market s the hgher the negatve effect of prce s: companes wth hgher reputaton often gve lower prces as compared to the companes wth low reputaton. In the research (Wu, 2014) authors state that onlne sellers use randomsed prcng strategy or n other words, random prce strategy. Ths strategy allows for 30% ncrease of retaler s ncome. Researchers from Xdan Unversty n Chna study customer behavor and respectvely optmal prcng strategy of an Internet company whch uses 2 dstrubuton channels: offcal manufacturer s ste and onlne retalers stes. Customers usng offcal ste tend to compare prces for manufacturer s goods wth onlne retalers prces. The hgher the reputaton of manufacturer s, the hgher the number of customers purchasng va offcal ste s despte the fact that prces at the offcal ste are hgher (Le, 2015). 1147

The 11 th Internatonal Days of Statstcs and Economcs, Prague, September 14-16, 2017 In the artcle (L, 2013) researchers analysed a model affectng prcng polcy, returns polcy and nternet company qualty polcy. Wthn the framework of drect onlne sales optmal sales prce, qualty polcy and return polcy complement each other. When the qualty of the product mproves, drect dstrbutor must provde comfortable return polcy and offer hgher prces. When qualty level s low, drect dstrbutor should take a place at cheap prducts market and offer goods wth low qualty low prce. Russan researchers also look nto prcng stratege n the Internet. Researcher Sgarev A.A. sngles out the followng prcng strateges applcable to Internat market: dscounts at secondary market strategy; perodcal dscounts strategy; random dscounts strategy (varable prces sales); segmented market strategy; market penetraton strategy; market masterng curve strategy; sgnalng strategy; electve prces strategy (prcng for a set of benefts); set prce strategy (prcng for a product and consumables); above the nomnal strategy; mage strategy (Sgarev, 2014). Many researches are devoted to prcng dscrmnaton. The work by M. Fassnacht and S. Unterhuber analyses prcng dscrmnaton nfluence on key consumer reactons at onlne and offlne markets. In partcular they menton that the prce dfference sze at onlne and offlne markets depends on the type of offered product: for goods requrng consumer experence (clothes for example) the dfference s more pronounced than for other product types (Fassnacht, 2016). The researchers pay attenton to other prcng strateges at the Internet market that have overlaps wth tradtonal market: prcng strateges for dscounts (Ma, 2016), pay as much as you want prcng strategy (Dorn, 2016), prcng strateges for Internet servces wth network effect (Pang, 2012), prcng strateges at peak sales seasons (Zhang, 2015), and etc. 2 Prcng model for Internet market As mentoned by Ltvn any customer rrespectve of whch channel (tradtonal or electronc) he uses to make a purchase asks hmself how valuable ths product s for hm and how ths value concdes wth the prce (Ltvn, 2003). formula: Hs artcle contans basc decsonmakng model for makng purchases n the form of a P V, (1) where Р a product prce, consumer ndex, V value of the product for the consumer. 1148

The 11 th Internatonal Days of Statstcs and Economcs, Prague, September 14-16, 2017 Ths model s far away from real lfe. There are many postve and negatve factors nfluencng purchase decsons at the Internet market. In the artcle (Kulpn, 2013) proves that purchasng ths or that product n the Internet shop mples certan transacton costs for the customer, such as: search costs, deal costs, delvery costs. These costs can be descrbed n a set of formulas. 2.1 Search costs Such costs can nclude costs for searchng for nformaton, comparng products, detaled analyss of the product, product tral, etc. In general these costs are manly related to tme costs and can be preented n a formula: SC sc tsc, (2) where SC search costs, sc value of tme unt for an ndvdual, tsc an amount of tme spent. 2.2 Deal costs These costs nclude costs related to negotatng the purchase (e.g. watng for manager s reply after the order s placed), payment procedure (onlne or offlne), etc. These costs can be presented n the followng form: DC dc tdc CF, (3) where DC deal costs, dc value of tme unt for an ndvdual, tdc amount of tme spent, CF comssons related to bank transfers, for example. 2.3 Recevng costs These costs nclude watng perod untl the product s delvered. These costs can be demonstrated by the formula: RC DLC rc trc, (4) where RC recevng costs, DLC delvery costs, rc value of tme unt for an ndvdual, trc amount of tme spent. Accordng to the prevous research coverng product polcy at the Internet market (Kulpn, 2015) Internet goods can be subdvded nto the followng groups: deal (nformaton), demand stmulatng and non-stmulatng ones. Accordng to that one must menton that for deal (nformaton) Internet product DLC > 0, for other products DLC = 0. 1149

The 11 th Internatonal Days of Statstcs and Economcs, Prague, September 14-16, 2017 2.4 Overall prcng model for the Internet market Besdes costs Internet consumer receves certan benefts from makng purchases va the Internet. Frst of all t s expected value of the product for the customer whch s a functon of the real product value. We shall mark t as E(V). When purchasong at the Internet market consumer always takes a rsk of obtanng the product real value of whch does not concde wth ts expected value. Secondly, when an ndvdual purchases nformaton beneft n the Internet the does not need to leave the house, travel by metro, waste tme n a traffc jam or money for the tcket. Each of these factors can be sngled out but for the sake of model optmzaton we suggest to unte ther jont nfluence and mark t as G gan for ndvdual. Therefore prelmnary prcng model for the Internet market looks as follows: E V G P SC DC RC. (5) One must also consder other negatve NPF and postve OPF factors, whch can not be dentfed durng the ntal anlyss as well as the atttude of the consumer to the rsk and other consumer psychologcal factors nfluencng expected value of the product for the consumer (varable r). Complete prcng model at the Internet market looks as follows: P E V r G OPF SC DC RC NPF. (6) 1 Therefore the consumer s lkely to purchase a product s the sum of ts expected value (consderng the rsk and other postve factors) expressed n prce unts s hgher (or equals) the sum of negatve factors and costs charactrerstc of Internet market. Concluson The artcle gves a bref revew of modern research n the feld of Internet market prcng strateges. In the second part of the artcle the authors present overall prcng model at the Internet market consderng postve and negatve factors nfluencng decson makng process of a consumer wllng to make a purchase va Internet. As a concluson one must say that nowadays the problem of prcng at the Internet market s relevant and wdely dscussed whch s proven by multple research n ths area. The Internet has created new condtons for conductng economc exchange between companes and customers generatng new area for researchers all over the world. 1150

The 11 th Internatonal Days of Statstcs and Economcs, Prague, September 14-16, 2017 Acknowledgment Research has been performed wth the support of the Russan Foundaton for Basc Research, grant No 16-36-00146 «Research of marketng nsttutes n vrtual market space». References Dorn, T., & Suessmar, A. (2016). Is t really worth t? A test of pay-what-you-want prcng strateges n a German consumer behavour context. Global Busness and Economcs Revew, 18(1), 82-100. do:10.1504/gber.2016.073321 Fassnacht, M., & Unterhuber, S. (2016). Consumer response to onlne/offlne prce dfferentaton. Journal of Retalng and Consumer Servces, 28, 137-148. do:10.1016/j.jretconser.2015.09.005 Fsher, M., Gallno, S., & L, J. (2017). Competton-Based Dynamc Prcng n Onlne Retalng: A Methodology Valdated wth Feld Experments. SSRN Electronc Journal, 43. do:10.2139/ssrn.2547793 Chen, L., Mslove, A., & Wlson, C. (2016). An Emprcal Analyss of Algorthmc Prcng on Amazon Marketplace. In Proceedngs of the 25th Internatonal Conference on World Wde Web (pp. 1339-1349). Montréal, Québec, Canada. Kulpn, S. & Popov, E. (2013). Typology of transacton costs n the Internet. In Proceedngs of the 11th Eurasa Busness and Economcs Socety Conference (pp. 454-465). Ekaternburg, Russa. Le, J., Ja, J., & Wu, T. (2015). Prcng Strateges n Dual-onlne Channels Based on Consumers Shoppng Choce. Proceda Computer Scence, 60, 1377-1385. do:10.1016/j.procs.2015.08.212 L, Y., Xu, L., & L, D. (2013). Examnng relatonshps between the return polcy, product qualty, and prcng strategy n onlne drect sellng. Internatonal Journal of Producton Economcs, 144(2), 451-460. do:10.1016/j.jpe.2013.03.013 Ltvn, N. (2003). Processes modelng of prce dscrmnaton n e-commerce. Proceedngs of the Far Eastern State Techncal Unversty, (135), 89-98. Lu, Y., Feng, J., & We, K. K. (2012). Negatve prce premum effect n onlne market The mpact of competton and buyer nformatveness on the prcng strateges of sellers wth dfferent reputaton levels. Decson Support Systems, 54(1), 681-690. do:10.1016/j.dss.2012.08.013 1151

The 11 th Internatonal Days of Statstcs and Economcs, Prague, September 14-16, 2017 Ma, S., Ln, J., & Zhao, X. (2016). Onlne store dscount strategy n the presence of consumer loss averson. Internatonal Journal of Producton Economcs, 171, 1-7. do:10.1016/j.jpe.2015.10.016 Pang, M., & Etzon, H. (2012). Research Note Analyzng Prcng Strateges for Onlne Servces wth Network Effects. Informaton Systems Research, 23(4), 1364-1377. do:10.1287/sre.1110.0414 Sgarev, A. (2014). Prcng Strateges for Frms n E-Commerce (Doctoral dssertaton, Moscow State Unversty, 2014) (p. 157). Moscow, Russa. Wu, J., L, L., & Xu, L. D. (2014). A randomzed prcng decson support system n electronc commerce. Decson Support Systems, 58, 43-52. do:10.1016/j.dss.2013.01.015 Zhang, J., Gou, Q., Yang, F., & Lang, L. (2015). Onlne hot sellng perod and ts mpact on e- retaler s prcng strateges. Internatonal Journal of Producton Research, 54(7), 1899-1918. do:10.1080/00207543.2015.1057300 Contact Maksm Patshn Sant Petersburg State Unversty 7/9 Unverstetskaya emb., St. Petersburg, 199034, Russa andomel-x@ya.ru Sergey Kulpn Ural Federal Unversty 13b Lenna Str., Ekaternburg, 620014, Russa s.v.kulpn@urfu.ru 1152