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Journal ArticleDOI

Machine Learning and Marketing: A Systematic Literature Review

TL;DR: It was found that the main marketing problems solved with machine learning were related to consumer behavior, recommender systems, forecasting, marketing segmentation, and text analysis—content analysis.
Abstract: Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing. This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008–2022. In this period, the adoption of ML in marketing has grown significantly. This growth has been quite heterogeneous, varying from the use of classical methods such as artificial neural networks to hybrid methods that combine different techniques to improve results. Generally, maturity in the use of ML in marketing and increasing specialization in the type of problems that are solved were observed. Strikingly, the types of ML methods used to solve marketing problems vary wildly, including deep learning, supervised learning, reinforcement learning, unsupervised learning, and hybrid methods. Finally, we found that the main marketing problems solved with machine learning were related to consumer behavior, recommender systems, forecasting, marketing segmentation, and text analysis—content analysis.
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Journal ArticleDOI
TL;DR: In this paper , the authors discuss the possible applications, drawbacks, and research directions using advanced language Chatbots (e.g., ChatGPT) in each of these fields, then they go through possible applications of such models, after that they discuss limitations and drawbacks of the current technological state of the art, and finally they point out future possible research directions.
Abstract: ChatGPT is a type of artificial intelligence language model that uses deep learning algorithms to generate human-like responses to text-based prompts. The introduction of the latest ChatGPT version in November of 2022 has caused shockwaves in the industrial and academic communities for its powerful capabilities, plethora of possible applications, and the great possibility for abuse. At the time of writing this work, several other language models (e.g., Google Bard and Meta LLaMA) just came out in an attempt to get a foothold in the vast possible market. These models have the ability to revolutionize the way we interact with computers and have potential applications in many fields, including education, software engineering, healthcare, and marketing. In this paper, we will discuss the possible applications, drawbacks, and research directions using advanced language Chatbots (e.g., ChatGPT) in each of these fields. We first start with a brief introduction and the development timeline of artificial intelligence based language models, then we go through possible applications of such models, after that we discuss the limitations and drawbacks of the current technological state of the art, and finally we point out future possible research directions.

2 citations

Journal ArticleDOI
TL;DR: In this article , a genetic algorithm-based classifier was proposed to select the best discriminating features and tune classifier parameters simultaneously, which achieved an average of 89.07% and 0.059 in terms of geometric mean and type I error respectively.
Abstract: Currently, almost all direct marketing activities take place virtually rather than in person, weakening interpersonal skills at an alarming pace. Furthermore, businesses have been striving to sense and foster the tendency of their clients to accept a marketing offer. The digital transformation and the increased virtual presence forced firms to seek novel marketing research approaches. This research aims at leveraging the power of telemarketing data in modeling the willingness of clients to make a term deposit and finding the most significant characteristics of the clients. Real-world data from a Portuguese bank and national socio-economic metrics are used to model the telemarketing decision-making process. This research makes two key contributions. First, propose a novel genetic algorithm-based classifier to select the best discriminating features and tune classifier parameters simultaneously. Second, build an explainable prediction model. The best-generated classification models were intensively validated using 50 times repeated 10-fold stratified cross-validation and the selected features have been analyzed. The models significantly outperform the related works in terms of class of interest accuracy, they attained an average of 89.07% and 0.059 in terms of geometric mean and type I error respectively. The model is expected to maximize the potential profit margin at the least possible cost and provide more insights to support marketing decision-making.
References
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Journal Article
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

47,974 citations

Book ChapterDOI
21 Jun 2000
TL;DR: Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.
Abstract: Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.

5,679 citations

Journal ArticleDOI
TL;DR: To better understand the impact of Big Data on various marketing activities, enabling firms to better exploit its benefits, a conceptual framework that builds on resource-based theory is proposed.

835 citations

Journal ArticleDOI
TL;DR: This article examines the impact of machine learning and artificial intelligence and their impact on personal selling and sales management on a small area of sales practice and research based on the seven steps of the selling process.

371 citations