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Doğrudan Pazarlama Amaçlı Hedef Kitle Analizi
Abstract
Dog ̆rudan pazarlama, uygun ürünleri uygun kis ̧ilerle en kısa yoldan bulus ̧turma sürecidir. Son yılların en popüler pazarlama yaklas ̧ımlarından birisidir. Bu çalıs ̧mada turizm sek- törüne ait isimsizles ̧tirilmis ̧ bir veri tabanını kullandık. Bir otel zinciri için yapılan kampanya kapsamında veri madencilig ̆i tekniklerini uygulayarak hedef kitle seçimi yaptık. Çalıs ̧mada birçok makine ög ̆renmesi yöntemini denedik. Sonuç olarak; geçmis ̧te yapılan ve herhangi bir makine ög ̆renmesi yöntemi kullanılmadan hazırlanan kampanya sonuçlarına göre daha iyi sonuçlar elde ederken benzer analizlerde kullanılabilecek bir altyapı olus ̧turmus ̧ olduk.
... Keçeci vd., son yıllarda popüler olan doğrudan pazarlama sürecini veri madenciliği yöntemleri uygulayarak incelemişlerdir. Turizm sektörüne ait bir firmanın kampanyalar ile ilgili veri setine Yapay Sinir Ağları, Naive Bayes, Rassal Orman tekniklerini uygulamışlardır [5]. Kaneko ve Yada müşteri mağaza içi hareketlerinin satın alma davranışlarına etkilerini inceleyen bir model önermişlerdir. ...
All bank marketing campaigns are dependent on
customers’ huge electronic data. The size of these data source is
impossible for a human analyst to come up with interesting
information that will help in the decision-making process. Data
mining models are completely helping in performance of these
campaigns. This paper introduces applications of recent and
important models of data mining; Multilayer perceptron neural
network (MLPNN) and Ross Quinlan new decision tree model
(C5.0). The objective is to examine the performance of MLPNN
and C5.0 models on a real-world data of bank deposit
subscription. The purpose is increasing the campaign
effectiveness by identifying the main characteristics that affect a
success (the deposit subscribed by the client) based on MLPNN
and C5.0. The experimental results demonstrate, with higher
accuracies, the success of these models in predicting the best
campaign contact with the clients for subscribing deposit. The
performances are measured by three statistical measures;
classification accuracy, sensitivity, and specificity.
Identifying customers who are more likely to respond to new product offers is an important issue in direct marketing. In direct marketing, data mining has been used extensively to identify potential customers for a new product (target selection). Using historical purchase data, a predictive response model with data mining techniques was developed to predict a probability that a customer in Ebedi Microfinance bank will respond to a promotion or an offer. To achieve this purpose, a predictive response model using customers' historical purchase data was built with data mining techniques. The data were stored in a data warehouse to serve as management decision support system. The response model was built from customers' historic purchases and demographic dataset. Bayesian algorithm precisely Naïve Bayes algorithm was employed in constructing the classifier system. Both filter and wrapper feature selection techniques were employed in determining inputs to the model. The results obtained shows that Ebedi Microfinance bank can plan effective marketing of their products and services by obtaining a guiding report on the status of their customers which will go a long way in assisting management in saving significant amount of money that could have been spent on wasteful promotional campaigns.
Most of the service providers and product based companies while launching brand new products, services or releasing new versions of existent products need to campaign to reach at the potential customers. While doing so they target their already existing customers who are the ambassadors of their company. To address the existing customers, they maintain the detailed customer data at all levels as customer maser data [9]. In this paper, we have built a prediction model to identify the customers who would most likely respond to the prospective offerings of the company basing on their past purchasing trends. Experiments have been conducted using the well known classifiers, viz., Naive Bayes, KNN and SVM to classify a bank customer data. Subsequently, we have compared the effectiveness of these techniques and found out which one produces the maximum accuracy for the existing data set.
Data Mining Framework For Direct Marketing: A Case Study of Bank Marketing
- L Sing'oei
- J Wang
L. Sing'oei, J. Wang, "Data Mining Framework For Direct Marketing: A
Case Study of Bank Marketing," IJCSI International Journal of Computer
Science Issues, Vol. 10, Issue 2, No 2, March 2013
Knowledge-Oriented Applications in Data Mining
- Derya Birant
Derya Birant, "Data Mining Using RFM Analysis" Knowledge-Oriented
Applications in Data Mining, InTech, 2011.