بخش‏بندی مشتریان صنعت دارو بر‌اساس مدل RFML

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 استاد گروه مدیریت فناوری اطلاعات، دانشکدۀ مدیریت دانشگاه تهران، تهران، ایران

2 استادیار گروه مدیریت، دانشگاه علامه طباطبایی، تهران، ایران

3 کارشناس‎ارشد مدیریت فناوری اطلاعات ـ گرایش هوش تجاری، دانشکدۀ مدیریت دانشگاه تهران، تهران، ایران

چکیده

در صنعت دارو مدیران بازاریابی و فروش با حجم انبوهی از داده‏های فروش شرکت‏های پخش، به داروخانه‏های مشتری خود مواجه‎اند. یکی از روش‎هایی که به آنان در کنترل وضعیت بازار، رقابت با سایر رقبا، برنامه‏ریزی هر چه بهتر برای افزایش فروش محصولات خود و در نتیجه هدفمند‌کردن فعالیت‎های بازاریابی کمک خواهد کرد، آگاهی از بخش‏بندی‏های مختلف مشتریان و سیاست‏گذاری بازاریابی و فروش بر‌مبنای آن خواهد بود. هدف اصلی این مقاله، کمک به مدیران بازاریابی و فروش صنعت دارو، از طریق تعیین و تحلیل بخش‏های مختلف مشتریان و ارائۀ پیشنهاد‏های متناسب با هر بخش، به‌منظور حفظ و افزایش خرید آنان به‎کمک روش‎های داده‏کاوی است. در این تحقیق، بر‌اساس متغیرهای تازگی، تکرار، ارزش پولی و مدت زمان خرید در مدل RFML، داروخانه‏ها در خوشه‏های مختلف قرار گرفته و تحلیل شده‏اند. در نتیجۀ این بخش‏بندی، سه دسته داروخانه‏ به نام‏های: داروخانه‏های کم‌خرید و کم‌سود، با میزان خرید و سود متوسط و وفادار و پُر‌سود از نظر روند فروش شناسایی شدند و بر‌اساس این بخش‏بندی، تحلیل‏های مربوط به آن ارائه شده است

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Customer Segmentation in Pharmaceutical Industry Assisting the Decision Making of Marketing and Sales Managers Based on RFML Model.

نویسندگان [English]

  • Babak Sohrabi 1
  • Iman Raiesi 2
  • Nastaran Nik Aein 3
1
2
3
چکیده [English]

In pharmaceutical industries, marketing and sales managers face many sales data from distributor companies. One of the methods that might help them to control the market, competing with rivals, devising the best plan to increase the sales and finally organizing the business activities, is to know about possible segmentations of costumers. The main purpose of this paper is to help the pharmaceutical industry marketing and sales managers, by the way of setting and analyzing different segments of costumers and to give proper suggestions that are appropriate for each segment, in order to maintain and increase the purchase trend using data mining methods.
In this research based on regency, frequency, monetary and the purchase duration variables in RFML model, pharmacies are divided into different clusters and then analyzed. As a result of this segmentation, three categories of pharmacies will be recognized: premium, normal and cold and accordingly, the related analysis is provided.

کلیدواژه‌ها [English]

  • customer segmentation
  • Data Mining
  • Pharmaceutical industry
  • RFML Model
Allahyari Soeini, R. & Fathalizadeh, E. (2012). Customer segmentation based on modified RFM model in insurance industry. International Journal of Modeling and Optimization, 25, 101-104.
Arora, P., Deepali, D. & Shipra, V. (2015). Analysis of K-Means and K-Medoids Algorithm for Big Data. Procedia Computer Science, 78, 507-512.
Asosheha, A., Bagherpour, S. & Yahyapour, N. (2008). Extended acceptance models for recommender system adaption, case of retail and banking service in Iran. WSEAS transactions on business and economics, 5(5), 189-200. (in Persian)
Baradaran, V.  Farokhi, Z. (2014). "Customer Segmentation in the Banking Industry by Extended Model of RFMC. Journal of Brand Management, 1(2), 135-154. (in Persian)
Baradaran, V. & Biglari, B. (2015). Customer Segmentation in the Distribution Industry. Journal of Brand Management, 1(7), 23-42. (in Persian)
Behboudi, M., Minae, B. & Ebrahimpour, H. (2012). A data mining method for customer segmentation According to Buy behavior. International Conference on Management and Economics Industrian engineering. Mahshahr. (in Persian)
Blocker, C. & Flint, D. J. (2007). Customer segments as moving targets: integrating customer value dynamism into segment instability logic. Industrial Marketing Management, 36(6), 810– 822.
Chang, H-C. & Tsai, H-P. (2011). Group RFM analysis as a novel framework to discover better customer consumption behavior.  Expert Systems with Applications, 38(12), 14499–14513.
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. & Wirth, R. )2000(. CRISP-DM 1.0 Step-by-step data mining guide. USA.
Chen, C. & Chen, A. (2007). Using data mining technology to provide a recommendation service in the digital library. The Electronic Library, 25(6), 711-724.
Chiu, C. Y., Chen, Y.F., Kuo, I.T. & Ku, H.C. (2009). An intelligent market segmentation system using k-means and particle swarm optimization. Expert Systems with Applications, 36(3), 4558-4565.
Csikosova, A., Antosova, A. M. & Mihalcova, B. (2015). Segmentation of Airports´ Customers in Slovakia. Procedia Economics and Finance, )23(, 1068-1083.
Cuadros, A. D. & Domínguez, V. (2014). Customer segmentation model based on value generation for marketing strategies formulation. Estudios Gerenciales )30(, 25-30.
Dehdashti, SH. & Pourhossein, A. (2012). Performance Implications of Sales & Marketing Strategy. Quarterly Journal of Business Management, (5), 61-84. (in Persian)
Dianjun, F. & Weibing, W. (2011). Sales Forecasting System for Chinese Tobacco Wholesalers. Procedia Environmental Sciences, (11(, 180 – 186.
Esfidani, R. M., Mohamadi, M., Keimasi, H., Parsafar, M. (2014). Retail banking market segmentation based on the expected benefits of Bank Mellat customers. Quarterly Journal of Business Management, 2(6), 227-250. (in Persian)
Heesters, D. M. (2009). An assault on the business of pharmaceutical data mining. journal of pennsylvania journal of business law, 11(3), 789-821.
Hosseinzade, SH., Karami, M. & Mehrabani, M. (2013). Customers segmentation in chain restaurants based on nutrition style. Quarterly Journal of Business Management, 7, 83-99. (in Persian)
Hu, Y. & Yeh, W. (2014). Discovering valuable frequent patterns based on RFM analysis without customer identification information. Knowledge-Based Systems, )61(, 76-88.
Jianying, M., Yongjian, F. & Yanguang, S. (2009). A neural networks-based clustering collaborative filtering algorithm in E-commerce recommendation system, Paper presented at the Web Information Systems and Mining. WISM 2009. International Conference.IEEE, PP. 616-619.
Johnson, G. (2005). Sales Forecasting for Pharmaceuticals: An Evidence Based Approach. London. Scientific Publishing.
Khatami-Firuzabadi, M. & Sadaghiani, J. S. (2015). A developing model for clustering and ranking bank customers.  International Journal of Electronic Customer Relationship Management, 9(1), 73-86.
Kolarovszki, P. Tengler, J. Majerčáková, M. (2016). The New Model of Customer Segmentation in Postal Enterprise. Procedia - Social and Behavioral Sciences,  (230), 121-127.
Kotler, F. (2006). Principles of Marketing.  (5, Ed., & Parsian, Trans.) Tehran, Tehran: Dabestan. (in Persian)
Li, D. Dai, W. & Tseng, W. (2011). A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business.  Expert Systems with Applications, 38(6), 7186–7191.
Maleki, M. Hajilu, Z. (2016). Identifying & Segmenting Key Customers for Prioritizing them Based on Lifetime Value using RFM Model (Case study: Internet customer of Qom Telecommunications Company).  Quarterly Journal of Business Management, 2(6), 461-478. (in Persian)
Mehta, J. S. Gawande, A. (2015). A purpose of data mining in banking sector, International.  Journal of Advance Research in Computer Science and Management Studies, 3(3), 407-411.
Ngai, E. Hu, Y. Wong, Y. Chen, Y. Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature.  Decision Support Systems, 50(3), 559-569.
Padashi, A. (2013). Customer Segmentation and definition of the strategy in each sector Based on RFML Model. Master of MBA Thesis, University of Guilan. (in Persian)
Patak, M., Lestacowa, H., Curdova, C. &  Vlckova, V. (2014). The E-Pharmacy Customer Segmentation Based on the Perceived Importance of the Retention Support Tools.  Procedia – Social and Behavioral Sciences, (150), 552-262.
Pradeep, R. & Singh, S. (2010). A survey of clustering technigues. International Journal of Computer Application, 7(12).
Prins, S. & Stegwee, R.A. (2000). Zorgproducten en geintegreerde infromatie systemen. (in Dutch) Handboek sturen met zorgproducten, F3100-3, December.
Rai, S. & Shubha, P. (2010). A Survey of Clustering Techniques.  International Journal of Computer Applications, (1), 0915-8881.
Ramos, S., Duarte, J., Duarte, F. & Vale, Z. (2015). A data-mining-based methodology to support MV electricity customers’ characterization.  Energy and Buildings, (91), 16-25.
Ranjan, J. (2009). Data mining in pharma sector: benefits.  International journal of health care quality assurance, 22(1), 92-82.
Sadarina, P., Kothari, M. & Gondaliya, J. (2013). Implementing Data Mining Techniques for Marketing of Pharmaceutical Products. International Journal of Computer Applications & Information Technology, 2(1). (ISSN: 2278-7720).
Saleabadi, S. (2011). Customers segmentation in the broadcast industry using data mining.  International Conference on Management And Economics Industrian engineering, (6), 1-20. (in Persian)
Salehabadi, S. (2015). Classification of pharmaceutical distribution industry customers Based on data mining.  International conference on management economics and industrial engineering. Tehran. http://www.civilica.com/ Paper-ICMEI01-ICMEI01_383.html.
Senthilkumaran, U., Manikdan, N. & Senthilkumar, N. (2016). Role of data mining on pharmaceutical industry lanni.  International Journal of Pharmacy & Technology, 8(3), 16100-16106.
Seyed Hosseini, M., Maleki, A. & Gholamian, M. R. (2010). Cluster analysis using data mining approach to develop CRM methodology to asses the customer loyalty. Expert System with Applications, (37), 5259-5264.
(in Persian)
Soeini, A. & Fathalizadeh, E. (2012). Customer segmentation based onmodified RFM model in insurance industry.  International Journal of Modeling and Optimization, 25, 101-104.
Tsiptsis, K. & Chorianopoulos, A. (2009). Data mining techniques in CRM.  Inside customer segmentation. John Wiley & Sons, Chichester.
Velmurugan, T. & Santhanam, T. (2011). A survey of partition based clustering algorithms in data mining: An experimental approach. Information Technology Journal, (3), 478-484.
Wei, J., Lin, Sh. & Weng, Ch. (2012). A case study of applying LRFM model in market segmentation of a children’s dental clinic.  Expert Systems with Applications, (39), 5529-5533.
Zare Hosseini, Z. & Mohammadzadeh, M. (2016). Knowledge discovery from patients’ behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services. Iranian Journal of Pharmaceutical Research, 15 (1), 355-367.