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Churn Customer Management in Retail Industry: A Case Study

Yıl 2022, Cilt: 37 Sayı: 4, 1094 - 1118, 10.11.2022
https://doi.org/10.24988/ije.1070830

Öz

Retail industry is amongst the emerging industries globally, and has attracted increasing attention from practitioners and academicians. The retail environment is changing rapidly and characterized by huge competition from both domestic and foreign companies. Most of the companies produce identical goods and try to sell them at competitive prices. In this regard, finding new customers and make them a loyal one is one of the most difficult things for the retail sector. It costs five times more than keeping the old one (Idris et al., 2012). That is why, the concept of customer retention led to the emergence of a new term in the academic literature that is “Churn Management”. The aim of this study is to analyse the low and high efficient stores of Retailer X that are located in different parts of İzmir by conducting data envelopment analysis, and then examine the reasons of the churn customers in these stores both from customers and store managers perspective. Data was collected from Retailer X to conduct data envelopment analysis to find out low and high efficient stores. In the next stage, semi-structured interviews were conducted with both store managers and customers to be able to compare the perceptions of both sides. As a result of these interviews, the reasons of churn customers are classified into 7 groups that are product and stock level, price, promotions, physical atmosphere, interaction of sales personnel, after sales services and competitors.

Kaynakça

  • Amin, A., Anwar, S., Adnan, A ., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, 242-254.
  • Arslan, İ. K., & Ersun, N. (2011). Moda sektöründe faaliyet gösteren mağazalarda müşterilerin mağaza tercihinde mağaza tasarımının önemi ve tasarım kriterleri, Istanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi. 10(19), 221-245.
  • Bagul, N., Surana, P., Berad, P., & Khachane, C. (2021).Retail Customer Churn Analysis using RFM Model and K-Means Clustering, International Journal of Engineering Research & Technology (IJERT), 10(3).
  • Barros, C. P., & Alves, C. A. (2003). Hypermarket retail store efficiency in Portugal. International Journal of Retail & Distribution Management, 31, 549–560.
  • Berman, B. and Evans, J.R. (2004). Retail Management: A Strategic Perspective, Pearson Prentice Hall, Upper Saddle River, NJ.
  • Bharti, A. (2017). Customer churn management. ACADEMICIA: An International Multidisciplinary Research Journal, 7(5), 96-102.
  • Bi, W., Cai, M., Liu, M., & Li, G. (2016). A big data clustering algorithm for mitigating the risk of customer churn. IEEE Transactions on Industrial Informatics, 12(3), 1270-1281.
  • Buttle, F. (2004). Customer relationship management. Routledge.
  • Chan, K., & Li, Q. (2022). Attributes of young adults’ favorite retail shops: a qualitative study. Young Consumers, (ahead-of-print).
  • Dabholkar, P. A., Thorpe, D. I., & Rentz, J. O. (1996). A measure of service quality for retail stores: scale development and validation. Journal of the Academy of Marketing Science, 24(1), 3.
  • Deekshitha, M. A. Udaya Kumar & M. D. Pradeep (2017). A Study on Changing Consumer Behaviour towards Fast Moving Consumable Goods in India. International Journal of Multidisciplinary Research and Modern Education (IJMRME), 3(1), 392-398.
  • Donthu, N., & Yoo, B. (1998). Retail productivity assessment using data envelopment analysis. Journal of Retailing, 74(1), 89-105.
  • Filimonau, V., Zhang, H. and Wang, L. (2020). Food waste management in Shanghai full-service restaurants: a senior managers’ perspective. Journal of Cleaner Production, Vol. 258, pp. 1-13.
  • Gagliano, K. B., & Hathcote, J. (1994). Customer expectations and perceptions of service quality in retail apparel specialty stores. Journal of Services Marketing, 8(1), 60-69.
  • Gülpinar, V. (2013). Yapay Sinir Ağlari Ve Sosyal Ağ Analizi Yardimi İle Türk Telekomünikasyon Piyasasinda Müşteri Kaybi Analizi. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 34(1), 331-350.
  • Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2007). Computer assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research, 34(10), 2902-2917.
  • Huang, Y., Zhu, F., Yuan, M., Deng, K., Li, Y., Ni, B., Dai, W., Yang, Q. & Zeng, J. (2015) Telco Churn Prediction with Big Data. SIGMOD Conference 2015.
  • Hung, S. Y., Yen, D. C., & Wang, H. Y. (2006). Applying data mining to telecom churn Management. Expert Systems with Applications, 31(3), 515-524.
  • Idris, A., Rizwan, M. and Khan, A. (2012) Churn Prediction in Telecom Using Random Forest and PSO Based Data Balancing in Combination with Various Feature Selection Strategies. Computers & Electrical Engineering, 38, 1808- 1819.
  • Johny, C. P., & Mathai, P. P. (2017). Customer churn prediction: A survey. International Journal of Advanced Research in Computer Science, 8(5), 2178-2181.
  • Karakaya, F., & Ganim Barnes, N. (2010). Impact of online reviews of customer care experience on brand or company selection. Journal of Consumer Marketing, 27(5), 447-457.
  • Kaya, S., Williams, B. (2005). Effective churn management for business. Journal of Corporate Real Estate, 7(2), 154-163.
  • Keramati, A., Ghaneei, H., & Mirmohammadi, S. M. (2016). Developing a prediction model for customer churn from electronic banking services using data mining. Financial Innovation, 2(1), 1-13.
  • Keramati, A., Jafari-Marandi, R., Aliannejadi, M., et al. (2014).Improved Churn Prediction in Telecommunication Industry Using Data Mining Techniques. Applied Soft Computing, 24, 994-1012.
  • Kim, S.Y., Staelin, R., (1999). Manufacturer allowances and retailer pass-through rates in a competitive environment. Marketing Science 18 (1), 59–76.
  • Khan, A.A; Jamwal, S. & Sepehri, M.M. (2010). Applying Data Mining to Customer Churn Prediction in an Internet Service Provider. International Journal of Computer Applications, 9(7), 8-14.
  • Ko, K., Chang, M., Bae, E. S., & Kim, D. (2017). Efficiency analysis of retail chain stores in Korea. Sustainability, 9(9), 1-14.
  • Koca Y., Söğüt, B. E., ve Mardikyan, S. (2019). Sadakat Programında Müşteri Kayıp Tahmini: Bir Vaka Çalışması. Journal of Information Systems and Management Research, 1(1), 59-66.
  • Lau, K. H. (2012). Distribution network rationalisation through benchmarking with DEA. Benchmarking: An International Journal, 19(6), 668-689.
  • Lejeune, M. A. (2001). Measuring the impact of data mining on churn management. Internet Research&quot, 11(5), 375-387.
  • Leroi-Werelds, S. (2021). Conceptualising Customer Value in Physical Retail: A Marketing Perspective. In The Value of Design in Retail and Branding. Emerald Publishing Limited.
  • Lindlof, T.R. & Taylor, B. C. (2002). Qualitative Communication Research Methods. (2nd Ed.) California: Sage Publication.
  • Liu, Y., Cheng, S., Liu, X., Cao, X., Xue, L. and Liu, G. (2016). Plate waste in school lunch programs in Beijing, China, Sustainability, 8(12), 1288-1300.
  • Magaldi, D. and Berler, M. (2020).Semi-structured Interviews. In: Zeigler-Hill V., Shackelford T.K. (eds) Encyclopedia of Personality and Individual Differences, Springer.
  • McDonald, L. M., & Rundle‐Thiele, S. (2008). Corporate social responsibility and bank customer satisfaction: a research agenda. International Journal of Bank Marketing, 26(3), pp. 170-182.
  • Miguéis, V. L., Camanho, A., & e Cunha, J. F. (2013). Customer attrition in retailing: an application of multivariate adaptive regression splines. Expert Systems with Applications, 40(16), 6225-6232.
  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage.
  • Mutanen, T. (2006). Customer churn analysis–a case study. Journal of Product and Brand Management, 14(1), 4-13.
  • Oghojafor, B., Mesike, G., Bakarea, R., Omoera, C., & Adeleke, I. (2012). Discriminant analysis of factors affecting telecoms customer churn. International Journal of Business Administration, 3(2), 59-67.
  • Okumus, B. (2020).How do hotels manage food waste? Evidence from hotels in Orlando, Florida, Journal of Hospitality Marketing and Management, 29(3), 291-309.
  • Orac, R. (2019). Churn prediction: Learn how to train a decision tree model for churn prediction, https://towardsdatascience.com/churn-prediction-770d6cb582a5
  • Patil, A. P., Deepshika, M. P., Mittal, S., Shetty, S., Hiremath, S. S., & Patil, Y. E. (2017, August). Customer churn prediction for retail business. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 845-851). IEEE.
  • Perdikaki, O., Kesavan, S., & Swaminathan, J. M. (2012). Effect of traffic on sales and conversion rates of retail stores. Manufacturing & Service Operations Management, 14(1), 145-162.
  • Perrigot, R., & Barros, C. P. (2008). Technical efficiency of French retailers. Journal of Retailing and Consumer Services, 15(4), 296-305.
  • Petermans, A., & Kent, T. (2017). Retail design: Theoretical perspectives. Oxon: Routledge.
  • Rao, S., Goldsby, T. J., & Iyengar, D. (2009). The marketing and logistics efficacy of online sales channels. International Journal of Physical Distribution & Logistics Management, 39(2), 106-130.
  • Ridge, M., Johnston, K.A & O'Donovan, B. (2015). The use of big data analytics in the retail industries in South Africa, 9(19), 688-703.
  • Saricam, C. (2022). Analysing Service Quality and Its Relation to Customer Satisfaction and Loyalty in Sportswear Retail Market. Autex Research Journal, 22(2), 184-193.
  • Seker, S. E. (2016). Müşteri Kayıp Analizi (Customer Churn Analysis). YBS Ansiklopedi, 3(1), 26-29.
  • Sellers Rubio, R., & MasRuiz, F. (2006). Economic efficiency in supermarkets: evidences in Spain. International Journal of Retail & Distribution Management, 34, 155–171.
  • Shapiro, C.,(1982). Consumer information, product quality, and seller reputation. 13(1), 20-35.
  • Sherman, H. D., Zhu, J.(2006). Service Productivity Management; Improving Service Performance using Data Envelopment Analysis (DEA). 49-89.
  • Subramanya, K.B. (2016). Enhanced feature mining and classifier models to predict customer churn for an e-retailer".Graduate Theses and Dissertations. Iowa State University, 16023.
  • Thomas, R. R., Barr, R. S., Cron, W. L., & Slocum Jr, J. W. (1998). A process for evaluating retail store efficiency: a restricted DEA approach. International Journal of Research in Marketing, 15(5), 487-503.
  • Tsai C-F, Chen M-Y (2010). Variable selection by association rules for customer churn prediction of multimedia on demand. Expert Syst Appl 37:2006–2015
  • Uyar, A., Bayyurt, N., Dilber, M., & Karaca, V. (2013). Evaluating operational efficiency of a bookshop chain in Turkey and identifying efficiency drivers. International Journal of Retail & Distribution Management, 41, 331–347.
  • Veningston, K., Rao, P. V., Selvan, C., & Ronalda, M. (2022). Investigation on Customer Churn Prediction Using Machine Learning Techniques. In Proceedings of International Conference on Data Science and Applications (pp. 109-119). Springer, Singapore.
  • Yu, W., & Ramanathan, R. (2008). An assessment of operational efficiencies in the UK retail sector. International Journal of Retail & Distribution Management.
  • Zhang, T., Feng, X., & Wang, N. (2021). Manufacturer encroachment and product assortment under vertical differentiation. European Journal of Operational Research, 293(1), 120-132.
  • Zhang, T., Moro, S., & Ramos, R. F. (2022). A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation. Future Internet 2022, 14, 94.
  • Zhu, J. (2008). Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets. Springer.

Perakende Sektöründe Kayıp Müşteri Yönetimi: Bir Vaka Çalışması

Yıl 2022, Cilt: 37 Sayı: 4, 1094 - 1118, 10.11.2022
https://doi.org/10.24988/ije.1070830

Öz

Perakende sektörü, küresel olarak gelişmekte olan endüstriler arasında yer almakta, uygulayıcılar ve akademisyenler tarafından artan bir ilgi görmektedir. Perakende çevresi hızla değişmekte ve hem yerli hem de yabancı şirketlerden gelen büyük rekabet ile karakterize edilmektedir. Firmaların çoğu özdeş mallar üretmekte ve bunları rekabetçi fiyatlarla satmaya çalışmaktadır. Bu bağlamda yeni müşteriler bulmak ve onları sadık kılmak perakende sektörünün en zor işlerinden biridir. Firmalar için yeni müşteri bulmak eski müşteriyi elde tutmaktan beş kat daha pahalıya mal olmaktadır. Bu nedenle müşteriyi elde tutma kavramı akademik literatürde yeni bir terim olan “Kayıp Müşteri Yönetimi” nin ortaya çıkmasına neden olmuştur. Bu çalışmanın amacı, Perakendeci X'in İzmir'in farklı bölgelerinde bulunan düşük ve yüksek verimli mağazalarını veri zarflama analizi yaparak analiz etmek ve ardından bu mağazalardaki müşteri kaybının nedenlerini hem müşteriler hem de mağaza yöneticileri açısından incelemektir. Düşük ve yüksek verimli mağazaları bulmak için veri zarflama analizi yapmak üzere Perakendeci X'ten veriler toplanmıştır. Bir sonraki aşamada, her iki tarafın algılarını karşılaştırabilmek için hem mağaza yöneticileri hem de müşterilerle yarı yapılandırılmış görüşmeler yapılmıştır. Bu görüşmeler sonucunda müşteri kaybı nedenleri ürün ve stok düzeyi, fiyat, promosyonlar, fiziksel mağaza atmosferi, satış personelinin etkileşimi, satış sonrası hizmetler ve rakipler olmak üzere 7 grupta sınıflandırılmıştır.

Kaynakça

  • Amin, A., Anwar, S., Adnan, A ., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237, 242-254.
  • Arslan, İ. K., & Ersun, N. (2011). Moda sektöründe faaliyet gösteren mağazalarda müşterilerin mağaza tercihinde mağaza tasarımının önemi ve tasarım kriterleri, Istanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi. 10(19), 221-245.
  • Bagul, N., Surana, P., Berad, P., & Khachane, C. (2021).Retail Customer Churn Analysis using RFM Model and K-Means Clustering, International Journal of Engineering Research & Technology (IJERT), 10(3).
  • Barros, C. P., & Alves, C. A. (2003). Hypermarket retail store efficiency in Portugal. International Journal of Retail & Distribution Management, 31, 549–560.
  • Berman, B. and Evans, J.R. (2004). Retail Management: A Strategic Perspective, Pearson Prentice Hall, Upper Saddle River, NJ.
  • Bharti, A. (2017). Customer churn management. ACADEMICIA: An International Multidisciplinary Research Journal, 7(5), 96-102.
  • Bi, W., Cai, M., Liu, M., & Li, G. (2016). A big data clustering algorithm for mitigating the risk of customer churn. IEEE Transactions on Industrial Informatics, 12(3), 1270-1281.
  • Buttle, F. (2004). Customer relationship management. Routledge.
  • Chan, K., & Li, Q. (2022). Attributes of young adults’ favorite retail shops: a qualitative study. Young Consumers, (ahead-of-print).
  • Dabholkar, P. A., Thorpe, D. I., & Rentz, J. O. (1996). A measure of service quality for retail stores: scale development and validation. Journal of the Academy of Marketing Science, 24(1), 3.
  • Deekshitha, M. A. Udaya Kumar & M. D. Pradeep (2017). A Study on Changing Consumer Behaviour towards Fast Moving Consumable Goods in India. International Journal of Multidisciplinary Research and Modern Education (IJMRME), 3(1), 392-398.
  • Donthu, N., & Yoo, B. (1998). Retail productivity assessment using data envelopment analysis. Journal of Retailing, 74(1), 89-105.
  • Filimonau, V., Zhang, H. and Wang, L. (2020). Food waste management in Shanghai full-service restaurants: a senior managers’ perspective. Journal of Cleaner Production, Vol. 258, pp. 1-13.
  • Gagliano, K. B., & Hathcote, J. (1994). Customer expectations and perceptions of service quality in retail apparel specialty stores. Journal of Services Marketing, 8(1), 60-69.
  • Gülpinar, V. (2013). Yapay Sinir Ağlari Ve Sosyal Ağ Analizi Yardimi İle Türk Telekomünikasyon Piyasasinda Müşteri Kaybi Analizi. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 34(1), 331-350.
  • Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2007). Computer assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research, 34(10), 2902-2917.
  • Huang, Y., Zhu, F., Yuan, M., Deng, K., Li, Y., Ni, B., Dai, W., Yang, Q. & Zeng, J. (2015) Telco Churn Prediction with Big Data. SIGMOD Conference 2015.
  • Hung, S. Y., Yen, D. C., & Wang, H. Y. (2006). Applying data mining to telecom churn Management. Expert Systems with Applications, 31(3), 515-524.
  • Idris, A., Rizwan, M. and Khan, A. (2012) Churn Prediction in Telecom Using Random Forest and PSO Based Data Balancing in Combination with Various Feature Selection Strategies. Computers & Electrical Engineering, 38, 1808- 1819.
  • Johny, C. P., & Mathai, P. P. (2017). Customer churn prediction: A survey. International Journal of Advanced Research in Computer Science, 8(5), 2178-2181.
  • Karakaya, F., & Ganim Barnes, N. (2010). Impact of online reviews of customer care experience on brand or company selection. Journal of Consumer Marketing, 27(5), 447-457.
  • Kaya, S., Williams, B. (2005). Effective churn management for business. Journal of Corporate Real Estate, 7(2), 154-163.
  • Keramati, A., Ghaneei, H., & Mirmohammadi, S. M. (2016). Developing a prediction model for customer churn from electronic banking services using data mining. Financial Innovation, 2(1), 1-13.
  • Keramati, A., Jafari-Marandi, R., Aliannejadi, M., et al. (2014).Improved Churn Prediction in Telecommunication Industry Using Data Mining Techniques. Applied Soft Computing, 24, 994-1012.
  • Kim, S.Y., Staelin, R., (1999). Manufacturer allowances and retailer pass-through rates in a competitive environment. Marketing Science 18 (1), 59–76.
  • Khan, A.A; Jamwal, S. & Sepehri, M.M. (2010). Applying Data Mining to Customer Churn Prediction in an Internet Service Provider. International Journal of Computer Applications, 9(7), 8-14.
  • Ko, K., Chang, M., Bae, E. S., & Kim, D. (2017). Efficiency analysis of retail chain stores in Korea. Sustainability, 9(9), 1-14.
  • Koca Y., Söğüt, B. E., ve Mardikyan, S. (2019). Sadakat Programında Müşteri Kayıp Tahmini: Bir Vaka Çalışması. Journal of Information Systems and Management Research, 1(1), 59-66.
  • Lau, K. H. (2012). Distribution network rationalisation through benchmarking with DEA. Benchmarking: An International Journal, 19(6), 668-689.
  • Lejeune, M. A. (2001). Measuring the impact of data mining on churn management. Internet Research&quot, 11(5), 375-387.
  • Leroi-Werelds, S. (2021). Conceptualising Customer Value in Physical Retail: A Marketing Perspective. In The Value of Design in Retail and Branding. Emerald Publishing Limited.
  • Lindlof, T.R. & Taylor, B. C. (2002). Qualitative Communication Research Methods. (2nd Ed.) California: Sage Publication.
  • Liu, Y., Cheng, S., Liu, X., Cao, X., Xue, L. and Liu, G. (2016). Plate waste in school lunch programs in Beijing, China, Sustainability, 8(12), 1288-1300.
  • Magaldi, D. and Berler, M. (2020).Semi-structured Interviews. In: Zeigler-Hill V., Shackelford T.K. (eds) Encyclopedia of Personality and Individual Differences, Springer.
  • McDonald, L. M., & Rundle‐Thiele, S. (2008). Corporate social responsibility and bank customer satisfaction: a research agenda. International Journal of Bank Marketing, 26(3), pp. 170-182.
  • Miguéis, V. L., Camanho, A., & e Cunha, J. F. (2013). Customer attrition in retailing: an application of multivariate adaptive regression splines. Expert Systems with Applications, 40(16), 6225-6232.
  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage.
  • Mutanen, T. (2006). Customer churn analysis–a case study. Journal of Product and Brand Management, 14(1), 4-13.
  • Oghojafor, B., Mesike, G., Bakarea, R., Omoera, C., & Adeleke, I. (2012). Discriminant analysis of factors affecting telecoms customer churn. International Journal of Business Administration, 3(2), 59-67.
  • Okumus, B. (2020).How do hotels manage food waste? Evidence from hotels in Orlando, Florida, Journal of Hospitality Marketing and Management, 29(3), 291-309.
  • Orac, R. (2019). Churn prediction: Learn how to train a decision tree model for churn prediction, https://towardsdatascience.com/churn-prediction-770d6cb582a5
  • Patil, A. P., Deepshika, M. P., Mittal, S., Shetty, S., Hiremath, S. S., & Patil, Y. E. (2017, August). Customer churn prediction for retail business. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 845-851). IEEE.
  • Perdikaki, O., Kesavan, S., & Swaminathan, J. M. (2012). Effect of traffic on sales and conversion rates of retail stores. Manufacturing & Service Operations Management, 14(1), 145-162.
  • Perrigot, R., & Barros, C. P. (2008). Technical efficiency of French retailers. Journal of Retailing and Consumer Services, 15(4), 296-305.
  • Petermans, A., & Kent, T. (2017). Retail design: Theoretical perspectives. Oxon: Routledge.
  • Rao, S., Goldsby, T. J., & Iyengar, D. (2009). The marketing and logistics efficacy of online sales channels. International Journal of Physical Distribution & Logistics Management, 39(2), 106-130.
  • Ridge, M., Johnston, K.A & O'Donovan, B. (2015). The use of big data analytics in the retail industries in South Africa, 9(19), 688-703.
  • Saricam, C. (2022). Analysing Service Quality and Its Relation to Customer Satisfaction and Loyalty in Sportswear Retail Market. Autex Research Journal, 22(2), 184-193.
  • Seker, S. E. (2016). Müşteri Kayıp Analizi (Customer Churn Analysis). YBS Ansiklopedi, 3(1), 26-29.
  • Sellers Rubio, R., & MasRuiz, F. (2006). Economic efficiency in supermarkets: evidences in Spain. International Journal of Retail & Distribution Management, 34, 155–171.
  • Shapiro, C.,(1982). Consumer information, product quality, and seller reputation. 13(1), 20-35.
  • Sherman, H. D., Zhu, J.(2006). Service Productivity Management; Improving Service Performance using Data Envelopment Analysis (DEA). 49-89.
  • Subramanya, K.B. (2016). Enhanced feature mining and classifier models to predict customer churn for an e-retailer".Graduate Theses and Dissertations. Iowa State University, 16023.
  • Thomas, R. R., Barr, R. S., Cron, W. L., & Slocum Jr, J. W. (1998). A process for evaluating retail store efficiency: a restricted DEA approach. International Journal of Research in Marketing, 15(5), 487-503.
  • Tsai C-F, Chen M-Y (2010). Variable selection by association rules for customer churn prediction of multimedia on demand. Expert Syst Appl 37:2006–2015
  • Uyar, A., Bayyurt, N., Dilber, M., & Karaca, V. (2013). Evaluating operational efficiency of a bookshop chain in Turkey and identifying efficiency drivers. International Journal of Retail & Distribution Management, 41, 331–347.
  • Veningston, K., Rao, P. V., Selvan, C., & Ronalda, M. (2022). Investigation on Customer Churn Prediction Using Machine Learning Techniques. In Proceedings of International Conference on Data Science and Applications (pp. 109-119). Springer, Singapore.
  • Yu, W., & Ramanathan, R. (2008). An assessment of operational efficiencies in the UK retail sector. International Journal of Retail & Distribution Management.
  • Zhang, T., Feng, X., & Wang, N. (2021). Manufacturer encroachment and product assortment under vertical differentiation. European Journal of Operational Research, 293(1), 120-132.
  • Zhang, T., Moro, S., & Ramos, R. F. (2022). A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation. Future Internet 2022, 14, 94.
  • Zhu, J. (2008). Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets. Springer.
Toplam 61 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Makaleler
Yazarlar

Gülmüş Börühan 0000-0003-0347-3463

Erken Görünüm Tarihi 9 Eylül 2022
Yayımlanma Tarihi 10 Kasım 2022
Gönderilme Tarihi 9 Şubat 2022
Kabul Tarihi 20 Ağustos 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 37 Sayı: 4

Kaynak Göster

APA Börühan, G. (2022). Churn Customer Management in Retail Industry: A Case Study. İzmir İktisat Dergisi, 37(4), 1094-1118. https://doi.org/10.24988/ije.1070830

İzmir İktisat Dergisi
TR-DİZİN, DOAJ, EBSCO, ERIH PLUS, Index Copernicus, Ulrich’s Periodicals Directory, EconLit, Harvard Hollis, Google Scholar, OAJI, SOBIAD, CiteFactor, OJOP, Araştırmax, WordCat, OpenAIRE, Base, IAD, Academindex
tarafından taranmaktadır.

Dokuz Eylül Üniversitesi Yayınevi Web Sitesi
https://kutuphane.deu.edu.tr/yayinevi/

Dergi İletişim Bilgileri Sayfası
https://dergipark.org.tr/tr/pub/ije/contacts


İZMİR İKTİSAT DERGİSİ 2022 yılı 37. cilt 1. sayı ile birlikte sadece elektronik olarak yayınlanmaya başlamıştır.