Managing customer outflow in a digital economy
Mkhitaryan S.V.1, Tultaev T. A.1, Tultaeva I.V.1, Andreev S.N.1
1 Российский экономический университет им. Г.В. Плеханова, Russia
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Journal paper
Creative Economy (РИНЦ, ВАК)
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Volume 12, Number 10 (October 2018)
Indexed in Russian Science Citation Index: https://elibrary.ru/item.asp?id=36455313
Cited: 9 by 07.12.2023
Abstract:
The article is devoted to the application of statistical methods of multivariate classification in the conditions of outflow of customers from companies operating in the market, and their transition to competing organizations.
The existing problem of customer outflow in Russia may seem ordinary and quite natural for the subjects of modern business only at first glance. In fact, the loss of even one buyer directly erodes the organization's once-strong market position and competitiveness. Dissatisfied with the quality or level of service, the buyer often moves to other companies, thereby causing double damage to the enterprise, the client of which he was for a long period of time: there is not only a significant weakening of the competitive positions of the organization that has lost its support, but also in parallel with this there is a strengthening of the position of its market opponent, the client of which becomes an unsatisfied consumer. In this regard, the processes of customer outflow management using statistical methods of multivariate classification are of particular relevance.
The paper determines the degree of influence of unstructured information flows that cause complications in the management of consumer behavior, develops algorithms for managing the outflow of customers and justifies their possible practical application in the activities of modern organizations, identifies the possibility of using statistical methods of multidimensional classification to manage the outflow of customers in the company.
Keywords: marketing communications, consumer behavior, customer loyalty, discriminant analysis, logistic regression, customer leaving, statistical methods of multidimensional classification
JEL-classification: M31, M39
References:
Mkhitaryan S.V. (2017). Stsenarnoe prognozirovanie prodazh s pomoschyu adaptivnoy trend-sezonnoy modeli [Scenario sales forecasting using an adaptive trend-seasonal model]. Aktualnye voprosy ekonomiki i marketinga. 37-42. (in Russian).
Prosvirkin B.L., Grineva O.O. (2016). Povedenie potrebiteley [Consumer behavior] Moscow: Rossiyskiy ekonomicheskiy universitet imeni G.V. Plekhanova. (in Russian).
Seyfullaeva M.E., Kapitsyn V.M. (2006). Eksportnyy potentsial rossiyskikh regionov v usloviyakh globalizatsii mirovoy ekonomiki [Export potential of Russian regions in the context of globalization of the world economy]. Region. (3). 93. (in Russian).
Skorobogatyh I.I., Sidorchuk R.R., Efimova D.M. (2015). Problemy i perspektivy razvitiya transgranichnoy torgovli zarubezhnyh onlayn-magazinov v Rossii [Problems and prospects of development of cross-border trade foreign online stores in Russia]. Journal of Marketing in Russia and Abroad. (5). 30-42. (in Russian).
Solntsev M.A. (2009). Osobennosti strategicheskogo analiza na B-2-B-rynke v usloviyakh krizisa [Features of strategic analysis in the B-2-B-market in the crisis]. Industrialnyy i b2b marketing. (1). 2-11. (in Russian).
Tsvetkova A.B., Musatova Zh.B., Markin I.M. (2016). Upravlenie mediakanalami [Media channels management] M.: Rossiyskiy ekonomicheskiy universitet imeni G.V. Plekhanova. (in Russian).
Urintsov A.I. (2011). Elektronnyy obmen dannymi [Electronic data interchange] M.: Evraziyskiy otkrytyy institut. (in Russian).
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