Construction of Bayesian classifiers with GA for response modeling in direct marketing

2009 ◽  
Author(s):  
Hongmei Shao ◽  
Gaofeng Zheng
Keyword(s):  
Direct Marketing ◽  
Bayesian Classifiers ◽  
Response Modeling

Effective customer selection for marketing campaigns based on net scores

2017 ◽  
Vol 11 (1) ◽  
pp. 2-15 ◽  
Author(s):  
René Michel ◽  
Igor Schnakenburg ◽  
Tobias von Martens
Keyword(s):  
Decision Trees ◽  
Direct Marketing ◽  
Real Data ◽  
Business Case ◽  
Added Value ◽  
Scoring Method ◽  
Content Type ◽  
Effective Selection ◽  
Selection For ◽  
Response Modeling

Purpose This paper aims to address the effective selection of customers for direct marketing campaigns. It introduces a new method to forecast campaign-related uplifts (also known as incremental response modeling or net scoring). By means of these uplifts, only the most responsive customers are targeted by a campaign. This paper also aims at calculating the financial impact of the new approach compared to the classical (gross) scoring methods. Design/methodology/approach First, gross and net scoring approaches to customer selection for direct marketing campaigns are compared. After that, it is shown how net scoring can be applied in practice with regard to different strategical objectives. Then, a new statistic for net scoring based on decision trees is developed. Finally, a business case based on real data from the financial sector is calculated to compare gross and net scoring approaches. Findings Whereas gross scoring focuses on customers with a high probability of purchase, regardless of being targeted by a campaign, net scoring identifies those customers who are most responsive to campaigns. A common scoring procedure – decision trees – can be enhanced by the new statistic to forecast those campaign-related uplifts. The business case shows that the selected scoring method has a relevant impact on economical indicators. Practical implications The contribution of net scoring to campaign effectiveness and efficiency is shown by the business case. Furthermore, this paper suggests a framework for customer selection, given strategical objectives, e.g. minimizing costs or maximizing (gross or lift)-added value, and presents a new statistic that can be applied to common scoring procedures. Originality/value Despite its lever on the effectiveness of marketing campaigns, only few contributions address net scores up to now. The new χ2-statistic is a straightforward approach to the enhancement of decision trees for net scoring. Furthermore, this paper is the first to the application of net scoring with regard to different strategical objectives.

A Cluster-Based Data Balancing Ensemble Classifier for Response Modeling in Bank Direct Marketing

2015 ◽  
Vol 14 (04) ◽  
pp. 1550022 ◽  
Author(s):  
Mohammad Amini ◽  
Jalal Rezaeenour ◽  
Esmaeil Hadavandi
Keyword(s):  
Prediction Models ◽  
Direct Marketing ◽  
Imbalanced Data ◽  
Response Prediction ◽  
Ensemble Classifier ◽  
Ensemble Method ◽  
Ensemble Classification ◽  
Under Sampling ◽  
The Right ◽  
Response Modeling

The aim of direct marketing is to find the right customers who are most likely to respond to marketing campaign messages. In order to detect which customers are most valuable, response modeling is used to classify customers as respondent or non-respondent using their purchase history information or other behavioral characteristics. Data mining techniques, including effective classification methods, can be used to predict responsive customers. However, the inherent problem of imbalanced data in response modeling brings some difficulties into response prediction. As a result, the prediction models will be biased towards non-respondent customers. Another problem is that single models cannot provide the desired high accuracy due to their internal limitations. In this paper, we propose an ensemble classification method which removes imbalance in the data, using a combination of clustering and under-sampling. The predictions of multiple classifiers are combined in order to achieve better results. Using data from a bank’s marketing campaigns, this ensemble method is implemented on different classification techniques and the results are evaluated. We also evaluate the performance of this ensemble method against two alternative ensembles. The experimental results demonstrate that our proposed method can improve the performance of the response models for bank direct marketing by raising prediction accuracy and increasing response rate.

Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic Programming

2020 ◽  
pp. 1-28
Author(s):  
Pak-Kan Wong ◽  
Man-Leung Wong ◽  
Kwong-Sak Leung
Keyword(s):  
Genetic Programming ◽  
Direct Marketing ◽  
Machine Learning Algorithms ◽  
Benchmark Problems ◽  
Bayesian Classifiers ◽  
Complexity Measures ◽  
Fitness Evaluation ◽  
Bayesian Network Classifiers ◽  
A Minor ◽  
Grammar Based Genetic Programming

Genetic Programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by complex dependencies among components of programs is challenging. It is important because it can misguide Genetic Programming to create sub-optimal programs. Besides, a minor modification in the programs may lead to a notable change in the program behaviours and affect the final outputs. This paper presents Grammar-based Genetic Programming with Bayesian Classifiers (GBGPBC) in which the probabilistic dependencies among components of programs are captured using a set of Bayesian network classifiers. Our system was evaluated using a set of benchmark problems (the deceptive maximum problems, the royal tree problems, and the bipolar asymmetric royal tree problems). It was shown to be often more robust and more efficient in searching the best programs than other related Genetic Programming approaches in terms of the total number of fitness evaluation. We studied what factors affect the performance of GBGPBC and discovered that robust variants of GBGPBC were consistently weakly correlated with some complexity measures. Furthermore, our approach has been applied to learn a ranking program on a set of customers in direct marketing. Our suggested solutions help companies to earn significantly more when compared with other solutions produced by several well-known machine learning algorithms, such as neural networks, logistic regression, and Bayesian networks.

Response Modeling in Direct Marketing

2014 ◽  
pp. 153-180 ◽  
Author(s):  
Sadaf Hossein Javaheri ◽  
Mohammad Mehdi Sepehri ◽  
Babak Teimourpour
Keyword(s):  
Direct Marketing ◽  
Response Modeling

scholarly journals A Stacking Approach to Direct Marketing Response Modeling

2018 ◽  
pp. 1-13
Author(s):  
Ernest Kangogo Kiprop ◽  
George Okeyo ◽  
Petronilla Muriithi
Keyword(s):  
Statistical Tests ◽  
Direct Marketing ◽  
Great Promise ◽  
Support Vector ◽  
Classification Algorithms ◽  
Benchmark Experiment ◽  
Stacked Generalization ◽  
Vector Machines ◽  
Area Under Roc Curve ◽  
Response Modeling

In this work, we investigate the viability of the stacked generalization approach in predictive modeling of a direct marketing problem. We compare the performance of individual models created using different classification algorithms, and stacked ensembles of these models. The base algorithms we investigate and use to create stacked models are Neural Networks, Logistic Regression, Support Vector Machines (SVM), Naïve Bayes and Decision Tree (CART). These algorithms were selected for their popularity and good performance on similar tasks in previous studies. Using a benchmark experiment and statistical tests, we compared five single algorithm classifiers and 26 stacked ensembles of combinations these algorithms on two popular metrics: Area Under ROC Curve (AUC) and lift.  We will demonstrate a significant improvement in the AUC and lift values when the stacked generalization approach is used viz a viz the single-algorithm approach. We conclude that despite its relative obscurity in marketing applications, stacking holds great promise as an ensembling technique for direct marketing problems.

Item response modeling of longitudinal personality data

2004 ◽  
Author(s):  
Kate E. Walton ◽  
Brent W. Roberts ◽  
Avshalom Caspi ◽  
Terrie E. Moffitt
Keyword(s):  
Item Response ◽  
Item Response Modeling ◽  
Response Modeling

Integrated Marketing Communication pada Heri Pemad Management Studi Kasus International ARTJOG MMXIX

2020 ◽  
Vol 6 (1) ◽  
pp. 1-17
Author(s):  
Cut Nailil Muna
Keyword(s):  
Public Relations ◽  
Direct Marketing ◽  
Evaluation Research ◽  
Personal Selling ◽  
Sales Promotion ◽  
Primary Data ◽  
Marketing Communication ◽  
Integrated Marketing ◽  
Integrated Marketing Communication ◽  
Interactive Marketing

ABSTRAKPenelitian ini bertujuan menganalisis lebih lanjut penerapan Integrated Marketing Communication pada Festival Seni Rupa Kontemporer Internasional ARTJOG MMXIX yang diselenggarakan oleh Heri Pemad Management (selanjutnya disingkat HPM). Manfaat penelitian ini adalah memberikan kontribusi pemikiran bagi pengembangan pengelolaan manajemen seni dalam keberhasilannya meraih pasar. Untuk menjawab pokok masalah dalam penelitian ini, peneliti mengacu pada konsep komunikasi pemasaran terpadu model George dan Michael Belch (2011) yang meliputi advertising, sales promotion, personal selling, direct marketing, Public Relations and Publicity serta interactive marketing melalui tiga tahapan proses, yaitu perencanaan, implementasi, dan evaluasi. Penelitian dijalankan secara kualitatif dengan metode studi kasus. Pengumpulan data terbagi dua yaitu data primer melalui wawancara dan observasi; serta data sekunder melalui studi pustaka. Key informan dan informan yang dipilih berasal dari internal HPM dan pengunjung Festival ARTJOG MMXIX. Penelitian ini mampu menjelaskan bahwa HPM telah mengimplementasikan Integrated Marketing Communication melalui tahap perencanaan yang dimulai dari targeting, positioning, penetapan tujuan dan anggaran. Tahap implementasi, dilakukan perancangan pesan, pemilihan media, dan penerapan bauran komunikasi. Tahap evaluasi, dilakukan analisis untuk mengukur hasil akhir dari implementasi bauran IMC sekaligus mengambil tindakan korektif dalam penyelenggaraan festival tersebut. Kesimpulannya, HPM telah berhasil menerapkan konsep komunikasi pemasaran terpadu pada penyelenggaraan Festival Seni Rupa Kontemporer Internasional ARTJOG MMXIX. ABSTRACTThis research aims to further analyze the implementation of Integrated Marketing Communication at the International Contemporary Arts Festival of ARTJOG MMXIX organized by Heri Pemad Management (hereinafter abbreviated as HPM). The benefit of this research is to contribute to thinking for the development of art management in the success of achieving the market. To address the subject matter of this study, researchers refer to the concept of Integrated Marketing Communication George and Michael Belch (2011) model’s which include advertising, sales promotion, personal selling, direct marketing, Public Relations and Publicity and interactive marketing through three stages of the process, planning, implementation and evaluation. Research is conducted qualitatively with case study methods. Two data collection is the primary data through interviews and observations; and secondary data through library studies. Key informant and informant are selected from the internal HPM and visitors Festival ARTJOG MMXIX. The research can explain that HPM has implemented Integrated Marketing Communication through the planning phase starting from targeting, positioning, goal setting and budget. Implementation stage, message design, media selection and communication mix application. Evaluation stage analyzed to measure the outcome of the implementation of IMC mix and take corrective action in the implementation of the festival. In conclusion, HPM has successfully adopted the concept of integrated marketing communication at the International Contemporary Art Festival of ARTJOG MMXIX.

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