از بازاریابی انبوه تا بازاریابی الکترونیکی یکبهیک
محورهای موضوعی : مديريت تکنولوژيآرش بهشتیان اردکانی 1 , محمد فتحيان 2
1 - دانشگاه علم و صنعت ایران
2 - دانشگاه علم و صنعت ایران
کلید واژه: بازاریابی انبوه بازاریابی الکترونیکی بازاریابی یکبهیک مدل سازی پاسخ فناوری اطلاعات,
چکیده مقاله :
روشهای سنتی بازاریابی، تمرکز بر پوشش عده زیادی از مشتریان در مقیاس وسیع و با تبلیغات یکسان دارند و نظرات مشتریان مختلف را در نظر نمیگیرند. این تبلیغات با وجود هزینه زیاد، تعداد اندکی از مشتریان هدف را جذب مینمایند. این بدان معنا است که قسمت اعظمی از بودجه بازاریابی اتلاف میشود. با پیشرفت فناوری اطلاعات، اینترنت، تجارت الکترونیکی، کسبوکارهای الکترونیکی و توسعهی انواع مدلهای بازاریابی الکترونیکی، شرکتها موفق به دریافت اطلاعات کاربران بهصورت بلادرنگ و با هزینه کمتر شدند. رشد روزافزون دادههای مشتریان، افزایش فضای رقابت و قابلیتهای جدید فناوری اطلاعات، شرکتها را به سمت بهبود استراتژیهای بازاریابی با در نظر گرفتن ارتباط مستقیم با مشتریان و اجرای بازاریابی یکبهیک سوق داده است. همچنین برای افزایش موفقیت بازاریابی یکبهیک و شناسایی مشتریانی که با احتمال بیشتری به فعالیتهای بازاریابی پاسخ مثبت میدهند، میتوان از مدلسازی پاسخ استفاده کرد. در این مقاله، ابتدا مشکلات ناشی از بازاریابی انبوه و لزوم حرکت به سمت روشهای نوین بازاریابی مطرح شده است. سپس سیر تحول استراتژیهای بازاریابی، از بازاریابی انبوه و سنتی تا بازاریابی الکترونیکی یکبهیک با استفاده مؤثر از فناوری اطلاعات، مورد بررسی قرار گرفتهشدهاند. در ادامه آن نیز به تشریح اجزای اصلی بازاریابی الکترونیکی یک به یک پرداخته شده است. شرکتها با شناسایی و اعمال مراحل اصلی بازاریابی یکبهیک الکترونیکی، که در این مقاله ارائه شدهاند، میتوانند در راستای جذب مشتریان جدید، ترغیب مشتریان فعلی به خرید و حفظ مشتریان وفادار و درنتیجه افزایش سودآوری خود گام بردارند.
Traditional methods of marketing used to focus on covering a large number of customers with the same advertising methods and did not consider the various customer preferences. Despite the high cost of advertising, a small number of target customers were being attracted by these methods. With the advancement of information technology, Internet, e-commerce, e-business and e-marketing development models, companies can collect user, information in real-time and at a lower cost. The rapid growth of customer data, competition and new IT capabilities have forced companies to improve marketing strategies by taking direct relationship with customers and execute One-to-One marketing. Response modeling can also be employed to increase the chance of success in one-to-one marketing by identifying the customers who are more likely to respond to marketing activities. In this paper, the weaknesses of mass marketing and the need to move towards new methods of marketing have been proposed. After that, the evolution of marketing strategies from mass and traditional marketing to One-to-One electronic marketing by using information technology has been presented. Then it outlines the main components of one-to-one marketing. By applying the concepts that have been presented in this paper, Companies can identify and apply the main components of one-to-one marketing. Therefore, they can attract new customers, encourage existing customers to buy and maintain loyal customers. As a result, they can be more successful in their marketing activities and increase their profits.
1- S. Hossein Javaheri, M. M. Sepehri, B. Teimourpour, Y. Zhao, and Y. Cen, Data Mining Applications with R,Chapter 6. Elsevier, 2014.
2- S. Moro, “Enhancing Bank Direct Marketing through Data Mining,” EMAC Conf., no. May, 2012.
3- A. Beheshtian-Ardakani, M. Fathian, and M. R. Gholamian, “A novel model for product bundling and direct marketing in e-commerce based on market segmentation,” Decis. Sci. Lett., vol. 7, no. 1, pp. 39–54, 2018.
4- E. Turban, R. Sharda, and D. Delen, Decision Support and Business Intelligence Systems. Prentice Hall, 2011.
5- F. Talla Nobibon, R. Leus, and F. C. R. Spieksma, “Optimization models for targeted offers in direct marketing: Exact and heuristic algorithms,” Eur. J. Oper. Res., vol. 210, no. 3, pp. 670–683, 2011.
6- P. C. Verhoef, P. N. Spring, J. C. Hoekstra, and P. S. H. Leeflang, “The commercial use of segmentation and predictive modeling techniques for database marketing in the Netherlands,” Decis. Support Syst., vol. 34, no. 4, pp. 471–481, 2003.
7- K.-W. Cheung, J. T. Kwok, M. H. Law, and K.-C. Tsui, “Mining customer product ratings for personalized marketing,” Decis. Support Syst., vol. 35, no. 2, pp. 231–243, 2003.
8- Y. Chen, C. Narasimhan, and Z. J. Zhang, “Individual Marketing with Imperfect Targetability,” Mark. Sci., vol. 20, no. 1, pp. 23–41, 2001.
9- W. A. Kamakura, M. Wedel, F. de Rosa, and J. A. Mazzon, “Cross-selling through database marketing: a mixed data factor analyzer for data augmentation and prediction,” Int. J. Res. Mark., vol. 20, no. 1, pp. 45–65, 2003.
10- D. Peppers, M. Rogers, and B. Dorf, “Is your company ready for one-to-one marketing?,” Harv. Bus. Rev., vol. 77, no. 1, pp. 151–160, 1999.
11- N. Arora, X. Dreze, A. Ghose, J. D. Hess, R. Iyengar, B. Jing, Y. Joshi, V. Kumar, N. Lurie, S. Neslin, S. Sajeesh, M. Su, N. Syam, J. Thomas, and Z. J. Zhang, “Putting one-to-one marketing to work: Personalization, customization, and choice,” Mark. Lett., vol. 19, no. 3, pp. 305–321, 2008.
12- D. Fowler, D. Pitta, and R. C. Leventhal, “Technological advancements and social challenges for one-to-one marketing,” J. Consum. Mark., vol. 30, no. 6, pp. 509–516, 2013.
13- H. Tang, S. S. Liao, and S. X. Sun, “A prediction framework based on contextual data to support Mobile Personalized Marketing,” Decis. Support Syst., vol. 56, no. 1, pp. 234–246, 2013.
14- P. Luarn, I. J. Chen, and K. Y. Lo, “An Exploratory Study of the Critical Success Factors of One-to-One Web-Marketing: User Perspectives,” J. Internet Commer., vol. 5, no. 3, pp. 147–178, Oct. 2006.
15- D. Peppers and M. Rogers, The One to One Manager: Real-World Lessons in Customer Relationship Management. Currency/Doubleday, 2002.
16- C. Liu and K. P. Arnett, “Exploring the factors associated with Web site success in the context of electronic commerce,” Inf. Manag., vol. 38, no. 1, pp. 23–33, 2000.
17- M. Jun and S. Cai, “The key determinants of Internet banking service quality: a content analysis,” Int. J. Bank Mark., vol. 19, pp. 276–291, 2001.
18- T. Jelassi and S. Leenen, “An E-Commerce Sales Model for Manufacturing Companies:,” Eur. Manag. J., vol. 21, no. 1, pp. 38–47, 2003.
19- D. Jarach, “The digitalisation of market relationships in the airline business: The impact and prospects of e-business,” J. Air Transp. Manag., vol. 8, no. 2, pp. 115–120, 2002.
20- T. S. H. Teo and Y. D. Yeong, “Assessing the consumer decision process in the digital marketplace,” Omega, vol. 31, no. 5, pp. 349–363, 2003.
21- W. Lassar, C. Manolis, and J. A. F. Nicholls, “Antecedents to Online Purchasing–An Exploratory Comparison of Anglo versus Hispanic Consumers in the United States,” J. Internet Commer., vol. 4, no. 1, pp. 27–61, Mar. 2005.
22- C. Allen, D. Kania, and B. Yaeckel, One-to-One Web Marketing: Build a Relationship Marketing Strategy One Customer at a Time. Wiley, 2001.
23- N. Ind and M. C. Riondino, “Branding on the Web: A real revolution?,” Journal of Brand Management, vol. 9. pp. 8–19, 2001.
24- E. Lepkowska-White, C. Page, and M. Youndt, “Web Perception and Young Consumers: An Empirical Investigation of Factors Influencing Perceptions of Online Companies,” J. Internet Commer., vol. 3, no. 2, pp. 53–77, Jul. 2004.
25- S. Menon and B. Kahn, “Cross-category effects of induced arousal and pleasure on the Internet shopping experience,” J. Retail., vol. 78, no. 1, pp. 31–40, 2002.
26- N. V Flor and P. P. Maglio, “Modeling business representational activity online: A case study of a customer-centered business,” Knowledge-Based Syst., vol. 17, no. 1, pp. 39–56, 2004.
27- R. K. Rajamma and C. R. Neeley, “Antecedents to Shopping Online: A Shopping Preference Perspective,” J. Internet Commer., vol. 2861, no. October 2014, pp. 37–41, 2008.
28- M. (Veronica) Chae and B. Lee, “Transforming an Online Portal Site into a Playground for Netizen,” J. Internet Commer., vol. 4, no. 2, pp. 95–114, Jul. 2005.
29- K. Coussement, P. Harrigan, and D. F. Benoit, “Improving direct mail targeting through customer response modeling,” Expert Syst. Appl., vol. 42, no. 22, pp. 8403–8412, 2015.
30- H. SHIN and S. CHO, “Response modeling with support vector machines,” Expert Syst. Appl., vol. 30, no. 4, pp. 746–760, 2006.
31- S. Moro and R. M. S. Laureano, “Using Data Mining for Bank Direct Marketing: An application of the CRISP-DM methodology,” Eur. Simul. Model. Conf., no. Figure 1, pp. 117–121, 2011.
32- S. Moro, P. Cortez, and P. Rita, “A data-driven approach to predict the success of bank telemarketing,” Decis. Support Syst., vol. 62, pp. 22–31, 2014.
33- S. Moro, P. Cortez, and P. Rita, “Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns,” Neural Comput. Appl., vol. 26, no. 1, pp. 131–139, 2015.
34- A. Dursun and M. Caber, “Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis,” Tour. Manag. Perspect., vol. 18, pp. 153–160, 2016.
35- P. A. Sarvari, A. Ustundag, and H. Takci, “Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis,” Kybernetes, vol. 45, no. 7, pp. 1129–1157, 2016.
36- C. Howard, D. C. Plummer, Y. Genovese, J. Mann, D. A. Willis, and D. M. Smith, “The Nexus of Forces: Social, Mobile, Cloud and Information,” Gartner, vol. 14, no. June, pp. 1–16, 2012.
37- A. Cataldo and J. Ferrer, “Optimal pricing and composition of multiple bundles: A two-step approach,” Eur. J. Oper. Res., vol. 259, no. 2, pp. 766–777, 2017.