Who will be your next customer: A machine learning approach to customer return visits in airline services

Hwang, Shu-Yuen; Kim, Jina; Park, Eunil; Kwon, Sang Jib

doi:10.1016/j.jbusres.2020.08.025

Cited by 43 publications

(30 citation statements)

References 39 publications

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“…ML is expected to completely change the business environment in the near future as using ML has a positive impact on strategic performance of businesses (Reis et al, 2020). ML techniques are used for many functions of a company; among these functions, marketing stands out because it aims to create value and satisfaction by focusing on phenomena such as need, want, demand, target customers, relationship management, customer attraction, sales and competition (Hwang et al, 2020;Khokhar & Chitsimran, 2019;Mariani & Wamba, 2020;Wirtz, 2020). ML techniques are helpful to design customized services and advertisement content for shared-home rental industry (Sengupta et al, 2021).…”

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confidence: 99%

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

et al. 2022

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In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques-Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques-Artificial Neural Network (ANN), Long Short Term Memory B Yigit Kazancoglu

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confidence: 99%

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

et al. 2022

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“…Its alignment was based on MAE, which was the sum of the differences between the predicted and actual inventory. In the prediction results of 36 Specifically, the mean evaluation results of the 36 channels could be considered a bit higher than the figure reached in the existing prediction-related research. This occurs because this study had numerous over-forecasts in which the predicted value of the advertising inventory of the channels in each time zone substantially exceeded the actual value [48].…”

Section: A Developed Lstm Model Resultsmentioning

confidence: 66%

“…They adopted a deep neural network approach to forecast traffic flow and then evaluated its performance by using the mean absolute percentage error (MAPE) and charts [35]. Hwang et al [28] applied classification-based machine learning techniques to predict customer revisits in the context of airline service [36]. Liu, Y., and Xie, T. [32] predicted box office performances of films by contrasting nine econometric and machine learning algorithms.…”

Section: B Literature Reviewmentioning

confidence: 99%

Prediction of Online Video Advertising Inventory Based on TV Programs: A Deep Learning Approach

Lee

Yoon²,

Kim

2021

IEEE Access

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With the recent spread of digital content, patterns of media viewing have changed. This is especially true for programs formerly watched on TV but are now increasingly viewed through online videos. As more and more people watch online videos, the market for online video advertising is increasing. Including online video advertising, online advertising can be effective if advertisers and online service providers attract as many viewers as possible. In particular, service providers try to maximize their profits by efficiently selling advertising inventory, which indicates the volume of space available for advertisements. However, most of today's service providers use simple statistical applications to predict advertising inventory that leads to relatively inaccurate predictions. Therefore, this study aims to develop a model capable of accurately predicting advertising inventory and then validate the model. This study in predicting online video advertising inventory is based on using deep learning to analyze the raw data of online video channels and then comparing the results of these predictions with actual inventory, other results of machine learning techniques, and work-site method results. Using these techniques and approaches, future advertising inventory can be more accurately predicted. In addition, detailed strategies for the practice of online video advertising are suggested. INDEX TERMS advertising inventory, online video advertising, TV programs, deep learning, predictionThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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“…We applied the synthetic minority over-sampling technique (SMOTE) for the machine learning classifiers [28]; moreover, we adjusted class weights in the cross-entropy function of the deep neural networks to handle class imbalance (Fig. 2) [29,30].…”

Section: Classification Modelsmentioning

confidence: 99%

Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages

Ji¹,

Oh²,

Kim³

et al. 2022

Computers, Materials &Amp; Continua

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In the field of natural language processing (NLP), the advancement of neural machine translation has paved the way for cross-lingual research. Yet, most studies in NLP have evaluated the proposed language models on well-refined datasets. We investigate whether a machine translation approach is suitable for multilingual analysis of unrefined datasets, particularly, chat messages in Twitch. In order to address it, we collected the dataset, which included 7,066,854 and 3,365,569 chat messages from English and Korean streams, respectively. We employed several machine learning classifiers and neural networks with two different types of embedding: word-sequence embedding and the final layer of a pre-trained language model. The results of the employed models indicate that the accuracy difference between English, and English to Korean was relatively high, ranging from 3% to 12%. For Korean data (Korean, and Korean to English), it ranged from 0% to 2%. Therefore, the results imply that translation from a low-resource language (e.g., Korean) into a high-resource language (e.g., English) shows higher performance, in contrast to vice versa. Several implications and limitations of the presented results are also discussed. For instance, we suggest the feasibility of translation from resource-poor languages for using the tools of resource-rich languages in further analysis.

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Who will be your next customer: A machine learning approach to customer return visits in airline services

Cited by 43 publications

References 39 publications

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods

Prediction of Online Video Advertising Inventory Based on TV Programs: A Deep Learning Approach

Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages

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