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KARAYOLU TAŞIMACILIĞI SEKTÖRÜNDE MÜŞTERİ ANALİTİĞİ: BİR VAKA ÇALIŞMASI

Year 2020, Volume: 11 Issue: 21, 85 - 117, 30.06.2020
https://doi.org/10.36543/kauiibfd.2020.005

Abstract

Müşteri analitiği, hızlı değişen pazarlarda ve kar marjlarının
küçüldüğü alanlarda istatistiksel analiz yöntemleri ile
kârlı
müşterilerin elde tutulması için firmaya
daha hızlı, dinamik ve isabetli kararlar almalarına yardımcı olan bir
araştırma alanıdır. Karayolu taşımacılığı sektörü büyüyen, gelişen ve önemini
arttıran bir alan konumundadır. Bu yüzden lojistik firmaları anlık ve hızlı bir
şekilde veriyi kullanarak doğru kararları almak istemektedirler. Lojistik
firması müşterilerine, kamyoncudan elde ettiği fiyatın üstüne bir kar marjı
koyarak sunmaktadır.  Bu kapsamda  müşteri bazlı bir kar marjı öneri ve
tahminleme sistemi geliştirilmiştir. Uygulama Türkiye’de karayolu taşımacılığı
yapan bir firma üzerinden gerçekleştirilmiştir. Tasarlanan bu metodoloji kestirimsel
analitik (predictive analytics) yöntemlerini içermektedir. Öncelikle ilk
aşamada müşteri segmentasyonu gerçekleştirilmiştir. İkinci aşamada kar marjı
tahminlemede, CHAID, CART ve doğrusal regresyon yöntemleri kullanılmıştır. Elde
edilen en iyi küme senaryosu, değişken olarak bu modellere eklenip senaryolar
oluşturulmuştur. Bu sayede tahmin performansları karşılaştırılmıştır. CHAID
yönteminin kar marjı tahminlemede en iyi sonucu verdiği belirlenmiştir. 

Supporting Institution

TUBİTAK

Project Number

1059B141600618

Thanks

Bu çalışmanın bir kısmı TUBİTAK tarafından 1059B141600618 kodu ile desteklenmiştir.

References

  • Abbott D (2014) Chapter 8—Predictive modeling. Applied predictive analytics: principles and techniques for the professional data analyst. Wiley, Hoboken, pp 213–281 29 Han, J., Kamber, M. 2006. Data Mining: Concepts and Techniques, Morgan Kaufmann, USA.
  • Ambler, T., Bhattacharya, C.B., Edell, J., Keller, K.L., Lemon, K.N. and Mittal, V. (2002), “Relating brand and customer perspectives on marketing management”, Journal of Service Research, Vol. 5 No. 1, pp. 13-25, doi: 10.1177/1094670502005001003.
  • Azeem, M., Usman, M., Fong, A.C.M. (2017). A churn prediction model for prepaid customers in telecom using fuzzy classifiers. Telecommunication Systems, 66 (4), 603-614.Bock H. H., (2008). "Origins and Extensions of The K-Means Algorithm in Cluster Analysis", Electronic Journal for History of Probability and Statistics, 4(2): sayfalar.
  • Chen W., Xie X., Wang J., Pradhan B., Hong H., Bui D. T., Duan Z. and Ma J., (2017). "A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility", CATENA, 151: 147-160.
  • Cheng, C.H. and Chen, Y.S. (2009), “Classifying the segmentation of customer value via RFM model and RS theory”, Expert Systems with Applications, Vol. 36 No. 3/Part 1, pp. 4176-4184, doi: 10.1016/j.eswa.2008.04.003.
  • Chan C. C. H., (2008). "Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer", Expert Systems with Applications, 34 (4): 2754-2762.
  • Chen, Y., & Hao, Y. (2017). A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications, 80, 340-355.
  • Chung, Y.-C. and Chen, S.-J. (2016), “Study on customer relationship management activities in Taiwan tourism factories”, Total Quality Management & Business Excellence, Vol. 27 Nos 5-6, pp. 581-594, doi: 10.1080/14783363.2015.1019341.
  • Coussement, K., Van den Bossche, F.A.M. and De Bock, K.W. (2014), “Data accuracy’s impact on segmentation performance: benchmarking RFM analysis, logistic regression, and decision trees”, Journal of Business Research, Vol. 67 No. 1, pp. 2751-2758, doi: 10.1016/j. jbusres.2012.09.024.
  • Deconinck, E., Hancock, T., Coomans, D., Massart, D. L., & Vander Heyden, Y. (2005). Classification of drugs in absorption classes using the classification and regression trees (CART) methodology. Journal of Pharmaceutical and Biomedical Analysis, 39(1-2), 91-103.
  • 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.
  • Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22:679–688.
  • Hong T. and Kim E., (2012). "Segmenting customers in online stores based on factors that effect the customer's intention to purchase", Expert Systems with Applications, 39 (2): 2127-2131.
  • Jain VK (2017). Chapter 1—Overview of big data. Big data and Hadoop. Khanna Book Publishing Co Ltd.
  • Hwang H., Jung T. and Suh E., (2004). "An LTV model and customer segmentation based on customer value: a case study on wireless telecommunication industry", Expert System witj Applications, 26 (2): 181-188.
  • Khajvand,M., Zolfaghar, K., Ashoori, S. and Alizadeh, S. (2011), “Estimating customer lifetime value based on RFM analysis of customer purchase behavior: case study”, Procedia Computer Science, Vol. 3 No. 1, pp. 57-63.
  • Larivière, B. , & Van den Poel, D. (2005). Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems with Applications, 29 (2), 472–484 .
  • Liu H.-H. and Ong C.-S., (2008). "Variable selection in clustering for marketing segmentation using genetic algorithm", Expert Systems with Aplications, 34 (1): 502-510.
  • Martínez, A., Schmuck, C., Pereverzyev Jr, S., Pirker, C., & Haltmeier, M. (2018). A machine learning framework for customer purchase prediction in the non-contractual setting. European Journal of Operational Research.
  • McCarty, J.A. and Hastak, M. (2007), “Segmentation approaches in data-mining: a comparison of RFM, CHAID, and logistic regression”, Journal of Business Research, Vol. 60 No. 6, pp. 656-662, doi: 10.1016/j.jbusres.2006.06.015.
  • MacQueen. (1967) Some methods for classification and analysis of multivariate observations. Proc. 5th Berkeley Symp. Math. Stat. Prob. 281.
  • Ngai E., Xiu L. and Chau D., (2009). "Application of data mining techniques in customer relationship management: A literature review and classification", Expert Systems with Applications, 36 (2): 2592-2602.
  • Nakano, S., & Kondo, F. N. (2018). Customer segmentation with purchase channels and media touchpoints using single source panel data. Journal of Retailing and Consumer Services, 41, 142-152.
  • Oğuzlar, A. (2010). CART ANALİZİ İLE HANEHALKI İŞGÜCÜ ANKETİ SONUÇLARININ ÖZETLENMESİ. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 18(3-4).
  • Paker N. and Vural C. A., (2016). "Customer segmentation for marinas: Evaluating marinas as destinations", Tourism Management, 56: 156-171.
  • Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. E. (2019). Linking big data analytics and operational sustainability practices for sustainable business management. Journal of Cleaner Production, 224, 10-24.
  • Sarvari, P. A., Ustundag, A., & Takci, H. (2016). Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis. Kybernetes, 45(7), 1129-1157.
  • Singh, A., & Tucker, C. S. (2017). A machine learning approach to product review disambiguation based on function, form and behavior classification. Decision Support Systems, 97, 81-91.
  • Tanaka, T., Hamaguchi, T., Saigo, T., & Tsuda, K. (2017). Classifying and Understanding Prospective Customers via Heterogeneity of Supermarket Stores. Procedia Computer Science, 112, 956-964.
  • Vincent, O. R., Makinde, A. S., Salako, O. S., & Oluwafemi, O. D. (2018). A self-adaptive k-means classifier for business incentive in a fashion design environment. Applied Computing and Informatics, 14(1), 88-97.
  • Wei Y., Zhang X., Shi Y., Xia L., Pan S., Wu J., Han M. and Zhao X., (2018). "A Review of Data-Driven Approaches for Prediction and Classification of Building Energy Consumption" Renewable and Sustainable Energy Reviews, 82 (1): 1027-1047.
  • Xu, L. and Chu, H. (2015), “Big Data analytics toward intelligent mobile service provisions of customer relationship management in e-commerce.
Year 2020, Volume: 11 Issue: 21, 85 - 117, 30.06.2020
https://doi.org/10.36543/kauiibfd.2020.005

Abstract

Project Number

1059B141600618

References

  • Abbott D (2014) Chapter 8—Predictive modeling. Applied predictive analytics: principles and techniques for the professional data analyst. Wiley, Hoboken, pp 213–281 29 Han, J., Kamber, M. 2006. Data Mining: Concepts and Techniques, Morgan Kaufmann, USA.
  • Ambler, T., Bhattacharya, C.B., Edell, J., Keller, K.L., Lemon, K.N. and Mittal, V. (2002), “Relating brand and customer perspectives on marketing management”, Journal of Service Research, Vol. 5 No. 1, pp. 13-25, doi: 10.1177/1094670502005001003.
  • Azeem, M., Usman, M., Fong, A.C.M. (2017). A churn prediction model for prepaid customers in telecom using fuzzy classifiers. Telecommunication Systems, 66 (4), 603-614.Bock H. H., (2008). "Origins and Extensions of The K-Means Algorithm in Cluster Analysis", Electronic Journal for History of Probability and Statistics, 4(2): sayfalar.
  • Chen W., Xie X., Wang J., Pradhan B., Hong H., Bui D. T., Duan Z. and Ma J., (2017). "A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility", CATENA, 151: 147-160.
  • Cheng, C.H. and Chen, Y.S. (2009), “Classifying the segmentation of customer value via RFM model and RS theory”, Expert Systems with Applications, Vol. 36 No. 3/Part 1, pp. 4176-4184, doi: 10.1016/j.eswa.2008.04.003.
  • Chan C. C. H., (2008). "Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer", Expert Systems with Applications, 34 (4): 2754-2762.
  • Chen, Y., & Hao, Y. (2017). A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications, 80, 340-355.
  • Chung, Y.-C. and Chen, S.-J. (2016), “Study on customer relationship management activities in Taiwan tourism factories”, Total Quality Management & Business Excellence, Vol. 27 Nos 5-6, pp. 581-594, doi: 10.1080/14783363.2015.1019341.
  • Coussement, K., Van den Bossche, F.A.M. and De Bock, K.W. (2014), “Data accuracy’s impact on segmentation performance: benchmarking RFM analysis, logistic regression, and decision trees”, Journal of Business Research, Vol. 67 No. 1, pp. 2751-2758, doi: 10.1016/j. jbusres.2012.09.024.
  • Deconinck, E., Hancock, T., Coomans, D., Massart, D. L., & Vander Heyden, Y. (2005). Classification of drugs in absorption classes using the classification and regression trees (CART) methodology. Journal of Pharmaceutical and Biomedical Analysis, 39(1-2), 91-103.
  • 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.
  • Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22:679–688.
  • Hong T. and Kim E., (2012). "Segmenting customers in online stores based on factors that effect the customer's intention to purchase", Expert Systems with Applications, 39 (2): 2127-2131.
  • Jain VK (2017). Chapter 1—Overview of big data. Big data and Hadoop. Khanna Book Publishing Co Ltd.
  • Hwang H., Jung T. and Suh E., (2004). "An LTV model and customer segmentation based on customer value: a case study on wireless telecommunication industry", Expert System witj Applications, 26 (2): 181-188.
  • Khajvand,M., Zolfaghar, K., Ashoori, S. and Alizadeh, S. (2011), “Estimating customer lifetime value based on RFM analysis of customer purchase behavior: case study”, Procedia Computer Science, Vol. 3 No. 1, pp. 57-63.
  • Larivière, B. , & Van den Poel, D. (2005). Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems with Applications, 29 (2), 472–484 .
  • Liu H.-H. and Ong C.-S., (2008). "Variable selection in clustering for marketing segmentation using genetic algorithm", Expert Systems with Aplications, 34 (1): 502-510.
  • Martínez, A., Schmuck, C., Pereverzyev Jr, S., Pirker, C., & Haltmeier, M. (2018). A machine learning framework for customer purchase prediction in the non-contractual setting. European Journal of Operational Research.
  • McCarty, J.A. and Hastak, M. (2007), “Segmentation approaches in data-mining: a comparison of RFM, CHAID, and logistic regression”, Journal of Business Research, Vol. 60 No. 6, pp. 656-662, doi: 10.1016/j.jbusres.2006.06.015.
  • MacQueen. (1967) Some methods for classification and analysis of multivariate observations. Proc. 5th Berkeley Symp. Math. Stat. Prob. 281.
  • Ngai E., Xiu L. and Chau D., (2009). "Application of data mining techniques in customer relationship management: A literature review and classification", Expert Systems with Applications, 36 (2): 2592-2602.
  • Nakano, S., & Kondo, F. N. (2018). Customer segmentation with purchase channels and media touchpoints using single source panel data. Journal of Retailing and Consumer Services, 41, 142-152.
  • Oğuzlar, A. (2010). CART ANALİZİ İLE HANEHALKI İŞGÜCÜ ANKETİ SONUÇLARININ ÖZETLENMESİ. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 18(3-4).
  • Paker N. and Vural C. A., (2016). "Customer segmentation for marinas: Evaluating marinas as destinations", Tourism Management, 56: 156-171.
  • Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. E. (2019). Linking big data analytics and operational sustainability practices for sustainable business management. Journal of Cleaner Production, 224, 10-24.
  • Sarvari, P. A., Ustundag, A., & Takci, H. (2016). Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis. Kybernetes, 45(7), 1129-1157.
  • Singh, A., & Tucker, C. S. (2017). A machine learning approach to product review disambiguation based on function, form and behavior classification. Decision Support Systems, 97, 81-91.
  • Tanaka, T., Hamaguchi, T., Saigo, T., & Tsuda, K. (2017). Classifying and Understanding Prospective Customers via Heterogeneity of Supermarket Stores. Procedia Computer Science, 112, 956-964.
  • Vincent, O. R., Makinde, A. S., Salako, O. S., & Oluwafemi, O. D. (2018). A self-adaptive k-means classifier for business incentive in a fashion design environment. Applied Computing and Informatics, 14(1), 88-97.
  • Wei Y., Zhang X., Shi Y., Xia L., Pan S., Wu J., Han M. and Zhao X., (2018). "A Review of Data-Driven Approaches for Prediction and Classification of Building Energy Consumption" Renewable and Sustainable Energy Reviews, 82 (1): 1027-1047.
  • Xu, L. and Chu, H. (2015), “Big Data analytics toward intelligent mobile service provisions of customer relationship management in e-commerce.
There are 32 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Ayşenur Budak 0000-0002-6961-2414

Project Number 1059B141600618
Publication Date June 30, 2020
Acceptance Date May 10, 2020
Published in Issue Year 2020 Volume: 11 Issue: 21

Cite

APA Budak, A. (2020). KARAYOLU TAŞIMACILIĞI SEKTÖRÜNDE MÜŞTERİ ANALİTİĞİ: BİR VAKA ÇALIŞMASI. Kafkas Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 11(21), 85-117. https://doi.org/10.36543/kauiibfd.2020.005

Cited By

KAUJEASF is the corporate journal of Kafkas University, Faculty of Economics and Administrative Sciences Journal Publishing.

2024 June issue article acceptance and evaluations are ongoing.