Müşteri Odaklı Pazarlama Stratejileri için Veri Madenciliği Teknikleri Kapsamında Perakende Sektöründe Kümeleme Analizi Uygulaması
Abstract
Günümüz
rekabetçi ortamında pazarlama kavramının sadece satış ve reklam anlayışı ile
sınırlandırılamayacağı ekonomik göstergeler ve pazar paylarındaki değişimler
neticesinde kolaylıkla anlaşılmaktadır.
pazarlamada ki temel noktanın tüketici ihtiyaçlarının araştırılıp ortaya
çıkarılmasından, satın alma sonrası müşteri sadakati ve memnuniyetine kadar
olan geniş bir süreç olduğu yapılan bilimsel çalışmalar ve araştırmalarla
ortaya konmuştur.
Tüketicilerin
ürün veya hizmet satın alırken nasıl karar verdikleri kişisel, sosyal ve
psikolojik olarak hangi parametrelerden etkilendikleri satış sonrası
firmalardan beklentileri tüketicilerin davranışlarını belirlemektedir.
İşletmeler pazarlama stratejilerini tüketicileri çeşitli gruplara ayırarak,
davranışları benzerlik gösteren müşterilerine benzer stratejileri uygulayarak
Pazar paylarını, marka değerlerini ve karlılıklarını arttırmaya
çalışmaktadırlar. Buna ek olarak Firmalar müşteri ilişkilerini verimli ve
faydalı bir biçimde yönetebilmek için birçok bilimsel yöntem kullanarak müşteri
segmentasyonu yapmaktadırlar. Son yıllarda popülaritesi gittikçe artan veri madenciliği tekniklerinden,
sınıflandırma, kümeleme ve birliktelik analizi gibi yöntemlerin oldukça
başarılı sonuçlar verdiği görülmektedir.
Bu
çalışmada ülkemizin lokomotif sektörlerinden olan tekstil sektörü kapsamında
perakende tekstil satış verileri incelenmiştir. İki Aşamalı Kümeleme Analizi
yöntemi ve Beklenti Maksimizasyonu algoritması kullanılarak müşteri gruplarına
özel promosyon ve kampanya çalışmalarına ek olarak müşteri odaklı pazarlama
stratejileri geliştirmek için kümeleme analizi yapılmıştır. Elde edilen müşteri
segmentesyon sonuçları doğrultusunda firma için akıllı pazarlama önerileri ve
stratejiler ortaya konmuştur.
References
- Bacher, J., Wenzig, K., & Vogler, M. (2004). SPSS TwoStep Cluster-a first evaluation.
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- Biscarri, F., Monedero, I., García, A., Guerrero, J. I., & León, C. (2017). Electricity clustering framework for automatic classification of customer loads. Expert Systems with Applications, 86, 54-63.
- Bradley, P. S., Fayyad, U., & Reina, C. (1998). Scaling EM (expectation-maximization) clustering to large databases.
- Chiu, T., Fang, D., Chen, J., Wang, Y., & Jeris, C. (2001, August). A robust and scalable clustering algorithm for mixed type attributes in large database environment. In Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining (pp. 263-268). ACM.
- Chong, A. Y. L., Ch’ng, E., Liu, M. J., & Li, B. (2017). Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews. International Journal of Production Research, 55(17), 5142-5156.
- Claveria, O., & Poluzzi, A. (2017). Positioning and clustering of the world’s top tourist destinations by means of dimensionality reduction techniques for categorical data. Journal of Destination Marketing & Management, 6(1), 22-32.
- Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904.
- Fuentes, I., Nápoles, G., Arco, L., & Vanhoof, K. (2018, September). Customer Segmentation Using Multiple Instance Clustering and Purchasing Behaviors. In International Workshop on Artificial Intelligence and Pattern Recognition (pp. 193-200). Springer, Cham.
- Gordini, N., & Veglio, V. (2017). Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry. Industrial Marketing Management, 62, 100-107.
- Hol, V., & Sokol, O. (2017). Clustering retail products based on customer behaviour. Applied Soft Computing, 60(C), 752-762.
- Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing. California Management Review, 61(4), 135-155.
- Kotler, P., Burton, S., Deans, K., Brown, L., & Armstrong, G. (2015). Marketing. Pearson Higher Education AU.
- Shih, M. Y., Jheng, J. W., & Lai, L. F. (2010). A two-step method for clustering mixed categroical and numeric data. Tamkang Journal of science and Engineering, 13(1), 11-19.
- Ünlü, R., & Xanthopoulos, P. (2019a). A weighted framework for unsupervised ensemble learning based on internal quality measures. Annals of Operations Research, 276(1-2), 229-247.
- Ünlü, R., & Xanthopoulos, P. (2019b). Estimating the number of clusters in a dataset via consensus clustering. Expert Systems with Applications, 125, 33-39.
- Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Application of Clustering Analysis in Retail Sector within the Scope of Data Mining Techniques for Customer Oriented Marketing Strategies
Abstract
In today's competitive
environment, marketing concept can not be limited only by sales and advertising
understanding, as a result of economic indicators and changes in market shares
is easily understood. The main point in marketing is a wide process from
exploring and revealing consumer needs, post-purchase service, customer loyalty
and satisfaction. is laid out by studies and research
How consumers decide when buying
products or services, which parameters are affected by personal, socially and
psychologically, the expectations of after-sales firms are consumers'
determines their behavior. Business marketing strategies by dividing consumers
into a variety of groups, applying similar strategies to their customers whose
behavior is similar to their customers, market shares, brand values and are
trying to increase their profitability. In addition, companies are engaged in
customer segmentation using many scientific methods to manage customer relationships
efficiently and beneficially. From the increasingly popular data mining
techniques in recent years, methods such as classification, clustering and
association analysis show quite successful results.
In this study, retail textile
sales data were analyzed within the scope of the textile sector, which is one
of the locomotive sectors of our country. Customer-oriented marketing
strategies in addition to promotional and campaign work for customer groups
using Two-Step Clustering Analysis method and Expectation Maximization
algorithm was conducted clustering analysis to improve. Intelligent marketing
proposals and strategies for the company are presented in accordance with the
results of the customer segmentation.
Keywords
References
- Bacher, J., Wenzig, K., & Vogler, M. (2004). SPSS TwoStep Cluster-a first evaluation.
- Bahari, T. F., & Elayidom, M. S. (2015). An efficient CRM-data mining framework for the prediction of customer behaviour. Procedia computer science, 46, 725-731.
- Biscarri, F., Monedero, I., García, A., Guerrero, J. I., & León, C. (2017). Electricity clustering framework for automatic classification of customer loads. Expert Systems with Applications, 86, 54-63.
- Bradley, P. S., Fayyad, U., & Reina, C. (1998). Scaling EM (expectation-maximization) clustering to large databases.
- Chiu, T., Fang, D., Chen, J., Wang, Y., & Jeris, C. (2001, August). A robust and scalable clustering algorithm for mixed type attributes in large database environment. In Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining (pp. 263-268). ACM.
- Chong, A. Y. L., Ch’ng, E., Liu, M. J., & Li, B. (2017). Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews. International Journal of Production Research, 55(17), 5142-5156.
- Claveria, O., & Poluzzi, A. (2017). Positioning and clustering of the world’s top tourist destinations by means of dimensionality reduction techniques for categorical data. Journal of Destination Marketing & Management, 6(1), 22-32.
- Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904.
- Fuentes, I., Nápoles, G., Arco, L., & Vanhoof, K. (2018, September). Customer Segmentation Using Multiple Instance Clustering and Purchasing Behaviors. In International Workshop on Artificial Intelligence and Pattern Recognition (pp. 193-200). Springer, Cham.
- Gordini, N., & Veglio, V. (2017). Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry. Industrial Marketing Management, 62, 100-107.
- Hol, V., & Sokol, O. (2017). Clustering retail products based on customer behaviour. Applied Soft Computing, 60(C), 752-762.
- Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing. California Management Review, 61(4), 135-155.
- Kotler, P., Burton, S., Deans, K., Brown, L., & Armstrong, G. (2015). Marketing. Pearson Higher Education AU.
- Shih, M. Y., Jheng, J. W., & Lai, L. F. (2010). A two-step method for clustering mixed categroical and numeric data. Tamkang Journal of science and Engineering, 13(1), 11-19.
- Ünlü, R., & Xanthopoulos, P. (2019a). A weighted framework for unsupervised ensemble learning based on internal quality measures. Annals of Operations Research, 276(1-2), 229-247.
- Ünlü, R., & Xanthopoulos, P. (2019b). Estimating the number of clusters in a dataset via consensus clustering. Expert Systems with Applications, 125, 33-39.
- Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | October 27, 2019 |
| Submission Date | September 16, 2019 |
| Published in Issue | Year 2019 2019 Additional Number |
