Model selection for direct marketing: performance criteria and validation methods

2008 ◽  
Vol 26 (3) ◽  
pp. 275-292 ◽  
Author(s):  
Geng Cui ◽  
Man Leung Wong ◽  
Guichang Zhang ◽  
Lin Li
Keyword(s):  
Neural Networks ◽  
Model Selection ◽  
Cross Validation ◽  
Direct Marketing ◽  
Latent Class Models ◽  
Performance Criteria ◽  
Content Type ◽  
Validation Methods ◽  
Practical Implications ◽  
Fold Cross Validation

PurposeThe purpose of this paper is to assess the performance of competing methods and model selection, which are non‐trivial issues given the financial implications. Researchers have adopted various methods including statistical models and machine learning methods such as neural networks to assist decision making in direct marketing. However, due to the different performance criteria and validation techniques currently in practice, comparing different methods is often not straightforward.Design/methodology/approachThis study compares the performance of neural networks with that of classification and regression tree, latent class models and logistic regression using three criteria – simple error rate, area under the receiver operating characteristic curve (AUROC), and cumulative lift – and two validation methods, i.e. bootstrap and stratified k‐fold cross‐validation. Systematic experiments are conducted to compare their performance.FindingsThe results suggest that these methods vary in performance across different criteria and validation methods. Overall, neural networks outperform the others in AUROC value and cumulative lifts, and the stratified ten‐fold cross‐validation produces more accurate results than bootstrap validation.Practical implicationsTo select predictive models to support direct marketing decisions, researchers need to adopt appropriate performance criteria and validation procedures.Originality/valueThe study addresses the key issues in model selection, i.e. performance criteria and validation methods, and conducts systematic analyses to generate the findings and practical implications.

Agritourism consumers’ participation in wine tasting events

2014 ◽  
Vol 26 (2) ◽  
pp. 120-138 ◽  
Author(s):  
Ramu Govindasamy ◽  
Kathleen Kelley
Keyword(s):  
Design Methodology ◽  
Direct Marketing ◽  
Internet Survey ◽  
Behavioral Characteristics ◽  
Graduate Degree ◽  
Atlantic Region ◽  
Content Type ◽  
Wine Tourism ◽  
On Farm ◽  
Practical Implications

Purpose – The purpose of this study is to determine the likelihood of a USA Mid-Atlantic region consumers’ willingness to partake in a wine tasting event, an example of an agritourism activity, based on their responses to an Internet survey conducted from June 22 to 29, 2010. Design/methodology/approach – Potential participants were screened and asked to participate if they resided in one of the states targeted (Delaware, New Jersey or Pennsylvania); were aged 21 years and older; were the primary food shopper for the household; and had previously attended an agritourism and/or direct marketing events or activities. Findings – A logit model was developed based on responses from 972 consumers who participated in the 15-minute Internet survey to predict participation in wine tasting activity. Consumers who are more likely to attend an on-farm wine tasting event include those who learn about agritourism events through newspapers, think that the variety and price of produce is better at direct markets than supermarkets, are older than 50 years, have a graduate degree and are self-employed. Research limitations/implications – Empirical results will help agritourism operators enhance marketing efforts and develop profitable on-farm agricultural activities by identifying consumer segments likely to participate in wine tourism activities. Practical implications – This paper helps identify consumer segments that are more likely to participate in a wine tasting event and provides marketers with the ability to target likely buyers based on corresponding demographic characteristics. Originality/value – This paper identifies likely wine tasting participants based on demographics, psychographics and behavioral characteristics.

scholarly journals Selling to enabled purchasers: the effect of perceived supply risk and profit impact on the buyer–seller interaction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bert Paesbrugghe ◽  
Johanna Vuori ◽  
Heidi Kock
Keyword(s):  
Direct Marketing ◽  
Personal Selling ◽  
Marketing Channels ◽  
Future Research ◽  
Content Type ◽  
Sales Organization ◽  
Sales Organizations ◽  
Business To Business ◽  
Depth Interviews ◽  
Practical Implications

Purpose Based on insights from the buying process, the purpose of this study is to align selling firms to the buyer’s efficiency needs that are grounded on the different types of purchases. Design/methodology/approach Using thematic analysis, this study conducted 35 in-depth interviews with business-to-business buyers and salespeople on the changing buyers’ sourcing needs. Findings In line with buyer enablement, buyers prefer personal selling when they perceive the sales offer as highly risky for the buying organization, whereas they have a strong preference for a direct marketing approach by the selling firm when they are purchasing low-risk purchases. Research limitations/implications This paper is a qualitative study. Future research should collect secondary company data to validate the results. Practical implications This paper addresses the buyer’s sourcing needs and presents how direct marketing channels and personal selling should be balanced to increase the return on salesforce resources. Originality/value This is one of the first studies to examine how sales organizations can create value by facilitating the buying process. Depending on the buyer’s categorization of the sales offer, this study highlights how a choice between direct marketing or personal selling improves the buyer’s perception of the sales organization.

scholarly journals Modeling Count Data for Healthcare Utilization: an Empirical Study of Outpatient Visits Among Vietnamese Older People

2020 ◽  
Author(s):  
Dung Duc Le ◽  
Roberto Leon-Gonzalez ◽  
Joseph Upile Matola
Keyword(s):  
Model Selection ◽  
Older People ◽  
Healthcare Utilization ◽  
Count Data ◽  
Aging Process ◽  
Cross Validation ◽  
Outpatient Visits ◽  
Best Fitting ◽  
Fold Cross Validation ◽  
Fitting Model

Abstract Background Vietnam is undergoing an unprecedented pace of aging process and is expected to experience the fastest aging process in region. Association between increasing age and health deterioration has been well-documented across settings. Consequently, demand for healthcare utilization is rising among older people. However, healthcare utilization, here measured as count data, creates challenges for modeling because such data typically has distributions that are skewed with a large mass at zero. This study compares empirical econometric strategies for the modeling of healthcare utilization (measured as the number of outpatient visits in the last 12 months), and identifies the determinants of healthcare utilization among Vietnamese older people based on the best-fitting model identified. Methods Using the Vietnam Household Living Standard Survey in 2006 (N=2426), nine econometric regression models for count data were examined to identify the best-fitting one. We used model selection criteria; statistical tests; and goodness-of-fit for in-sample model selection. In addition, we conducted 10-fold cross-validation checks to examine reliability of in-sample model selection. Finally, we utilized marginal effects to identify the factors associated with number of outpatient visits among Vietnamese older people based on the best-fitting model identified. Results We found strong evidence in favor of hurdle negative binomial model 2 (HNB2) for both in-sample selection and 10-fold cross-validation checks. The marginal effect results of the HNB2 showed that predisposing, enabling, need, and lifestyle factors were significantly associated with number of outpatient visits. The predicted probabilities for each count event showed the distinct trends of healthcare utilization among specific groups: at low count events, women and people in younger age group used more healthcare utilization than did men and their counterparts in older age groups, but a reversed trend was found at higher count events. Conclusions The findings here suggest that the HNB2 model should be considered for use in modeling counts of healthcare use. This study’s findings lay the groundwork for future research on the modeling of healthcare utilization in developing countries and those findings could be used to forecast on healthcare demand and making provisions for healthcare costs.

scholarly journals Stiffness Modulus and Marshall Parameters of Hot Mix Asphalts: Laboratory Data Modeling by Artificial Neural Networks Characterized by Cross-Validation

2019 ◽  
Vol 9 (17) ◽  
pp. 3502 ◽  
Author(s):  
Nicola Baldo ◽  
Evangelos Manthos ◽  
Matteo Miani
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cross Validation ◽  
Laboratory Data ◽  
Stiffness Modulus ◽  
Artificial Neural ◽  
Marshall Stability ◽  
Mean Square Errors ◽  
Fold Cross Validation

The present paper discusses the analysis and modeling of laboratory data regarding the mechanical characterization of hot mix asphalt (HMA) mixtures for road pavements, by means of artificial neural networks (ANNs). The HMAs investigated were produced using aggregate and bitumen of different types. Stiffness modulus (ITSM) and Marshall stability (MS) and quotient (MQ) were assumed as mechanical parameters to analyze and predict. The ANN modeling approach was characterized by multiple layers, the k-fold cross validation (CV) method, and the positive linear transfer function. The effectiveness of such an approach was verified in terms of the coefficients of correlation ( R ) and mean square errors; in particular, R values were within the range 0.965 – 0.919 in the training phase and 0.881 – 0.834 in the CV testing phase, depending on the predicted parameters.

A neural observer for sensorless speed control of servomotors

2014 ◽  
Vol 31 (8) ◽  
pp. 1668-1678 ◽  
Author(s):  
Jenq-Ruey Horng ◽  
Ming-Shyan Wang ◽  
Tai-Rung Lai ◽  
Sergiu Berinde
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Design Methodology ◽  
Computation Time ◽  
Elman Neural Network ◽  
Content Type ◽  
On Line ◽  
Future Work ◽  
Practical Implications ◽  
Neural Speed

Purpose – Extensive efforts have been conducted on the elimination of position sensors in servomotor control. The purpose of this paper is to aim at estimating the servomotor speed without using position sensors and the knowledge of its parameters by artificial neural networks (ANNs). Design/methodology/approach – A neural speed observer based on the Elman neural network (NN) structure takes only motor voltages and currents as inputs. Findings – After offline NNs training, the observer is incorporated into a DSP-based drive and sensorless control is achieved. Research limitations/implications – Future work will consider to reduce the computation time for NNs training and to adaptively tune parameters on line. Practical implications – The experimental results of the proposed method are presented to show the effectiveness. Originality/value – This paper achieves sensorless servomotor control by ANNs which are seldom studied.

Predictor and optimizer system on selective catalytic reduction of NO in activated carbons based on experiment and computational intelligence technique

2020 ◽  
Vol 37 (5) ◽  
pp. 1737-1756
Author(s):  
Zhen Yang ◽  
Kangning Song ◽  
Xingsheng Gu ◽  
Zhi Wang ◽  
Xiaoyi Liang
Keyword(s):  
Experimental Data ◽  
Selective Catalytic Reduction ◽  
Cross Validation ◽  
Activated Carbons ◽  
Catalytic Reduction ◽  
Data Driven ◽  
Support Vector ◽  
Content Type ◽  
Reduction Of No ◽  
Fold Cross Validation

Purpose Nitrogen oxides (NOx) have been considered as primarily responsible for many serious environmental problems. Removing NO is the key task to remove NOx hazards. To clarify, NO removal process for pitch-based spherical-activated carbons (PSACs), an online prediction and optimization technique in real-time based on support vector machine algorithm in regression (support vector regression [SVR]) is discussed. The purpose of this paper is to develop a predictor and optimizer system on selective catalytic reduction of NO (SCRN) using experimental data and data-driven SVR intelligence methods. Design/methodology/approach Predictor and optimizer using developed SVR have been proposed. To modify the training efficiency of SVR, the authors especially customize batch normalization and k-fold cross-validation techniques according to the unique characteristics of PSACs model. Findings The results present that SVR provides a property regression model since it can linkage linear and non-linear process and property relationships in few experimental data sets. Also, the integrated normalization and k-fold cross-validation show a satisfying improvement and results for SVR optimization. The predicted results of predictor and optimizer in single and double factor systems are in excellent agreement with the experimental data. Originality/value SCRN-PO for predicting and optimization SCRN problems is developed by data-driven methods. The outperformed SCRN-PO system is used to predict multiple-factors property parameters and obtain optimum technological parameters in real-time. Also, experiment duration is greatly shortened.

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