A Data Mining Framework for Response Modelling in Direct Marketing

2021 ◽  
pp. 357-366
Author(s):  
Fátima Rodrigues ◽  
Tiago Oliveira
Keyword(s):  
Data Mining ◽  
Direct Marketing ◽  
Response Modelling

scholarly journals Prescription Data Mining and the Protection of Patients' Interests

2010 ◽  
Vol 38 (1) ◽  
pp. 74-84 ◽  
Author(s):  
David Orentlicher
Keyword(s):  
Data Mining ◽  
Medical Care ◽  
Medical Literature ◽  
Direct Marketing ◽  
Pharmaceutical Companies ◽  
Prescription Data ◽  
Sales Representatives ◽  
Patient Health ◽  
Drug Detailing

Pharmaceutical companies have long relied on direct marketing of their drugs to physicians through one-on-one meetings with sales representatives. This practice of “detailing” is substantial in its costs and its number of participants. Every year, pharmaceutical companies spend billions of dollars on millions of visits to physicians by tens of thousands of sales representatives.Critics have argued that drug detailing results in sub-optimal prescribing decisions by physicians, compromising patient health and driving up spending on medical care. In this view, physicians often are unduly influenced both by marketing presentations that do not accurately reflect evidence from the medical literature and by the gifts that sales representatives deliver in conjunction with their presentations.

Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset

2021 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Maulida Ayu Fitriani ◽  
Dany Candra Febrianto
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Direct Marketing ◽  
Class Imbalance ◽  
Processing Technique ◽  
Customer Segmentation ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Mining Methods ◽  
Bank Marketing

Direct marketing is an effort made by the Bank to increase sales of its products and services, but the Bank sometimes has to contact a customer or prospective customer more than once to ascertain whether the customer or prospective customer is willing to subscribe to a product or service. To overcome this ineffective process several data mining methods are proposed. This study compares several data mining methods such as Naïve Bayes, K-NN, Random Forest, SVM, J48, AdaBoost J48 which prior to classification the SMOTE pre-processing technique was done in order to eliminate the class imbalance problem in the Bank Marketing dataset instance. The SMOTE + Random Forest method in this study produced the highest accuracy value of 92.61%.

On Data Mining for Direct Marketing

2007 ◽  
pp. 491-498 ◽  
Author(s):  
Chuangxin Ou ◽  
Chunnian Liu ◽  
Jiajing Huang ◽  
Ning Zhong
Keyword(s):  
Data Mining ◽  
Direct Marketing

A Hybrid Multilayer Perceptron Neural Network for Direct Marketing

Data Mining ◽  
2013 ◽  
pp. 1534-1544
Author(s):  
M. Govindarajan ◽  
RM. Chandrasekaran
Keyword(s):  
Data Mining ◽  
Model Selection ◽  
Multilayer Perceptron ◽  
Direct Marketing ◽  
Customer Relationship ◽  
Classification Problems ◽  
Validation Data ◽  
Data Set ◽  
Knowledge Discovery In Database ◽  
Learning Data

Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes feature selection and model selection simultaneously for Multilayer Perceptron (MLP) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the classifier significantly. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: Direct Marketing in Customer Relationship Management. It is shown that, compared to earlier MLP technique, the run time is reduced with respect to learning data and with validation data for the proposed Multilayer Perceptron (MLP) classifiers. Similarly, the error rate is relatively low with respect to learning data and with validation data in direct marketing dataset. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

Segmentation Approach for Athleisure and Performance Sport Retailers Based on Data Mining Techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 71-85
Author(s):  
Sunčica Rogić ◽  
Ljiljana Kašćelan
Keyword(s):  
Data Mining ◽  
Direct Marketing ◽  
Business Environment ◽  
Customer Segmentation ◽  
Data Sets ◽  
Data Mining Techniques ◽  
The Future ◽  
Segmentation Approach ◽  
Promotional Materials ◽  
And Performance

This paper seeks to compare certain customer segments from two sport footwear, apparel, and equipment retailers and to examine an objective market segmentation method, based on the recency, frequency, monetary (RFM) and the decision tree (DT) models. The case study is based on two data sets, aiming to compare the different customer segments, both from sport retail industry, and represents an application of data mining techniques in a business environment. The customer segmentation enables the customer selection for the future direct marketing campaigns based on the previous purchasing behavior. Analyzing the customers' purchasing history can help the company determine the value of each customer and therefore target or not target such customers in the future with promotional materials, based on both the customers' interests and their value. Thus, based on the results, personalized offers can be created for each of the defined customer groups, which may increase the efficiency of the overall campaign, reduce costs, and increase profitability.

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