Extension usage examples:
Here's how our browser extension sees the article:
Machine learning and AI in marketing – Connecting computing power to human insights - ScienceDirect
Source: webvpn.sdu.edu.cn
Article summary:
1. This paper reviews and calls for marketing research to leverage machine learning methods.
2. It provides an overview of common machine learning tasks and methods, and compares them with statistical and econometric methods traditionally used in marketing research.
3. It presents a unified conceptual framework and a multi-faceted research agenda from five key aspects of empirical marketing research: method, data, usage, issue, and theory.
Article analysis:
The article is generally reliable in its presentation of the topic at hand – machine learning and AI in marketing – as it provides an overview of common machine learning tasks and methods, compares them with traditional statistical and econometric methods used in marketing research, discusses salient AI-driven industry trends and practices, reviews the still nascent academic literature which uses machine learning methods, presents a unified conceptual framework for further research into this area, and proposes a multi-faceted research agenda from five key aspects of empirical marketing research. The article is well-researched with references to relevant sources such as Alan Turing’s famous Turing Test (1950), John McCarthy’s coinage of the term “Artificial Intelligence” (1955), Mitchell’s definition of Machine Learning (1997), LeCun et al.'s definition of Deep Learning (2015).
The article does not appear to be biased or one-sided in its reporting; it presents both sides equally by discussing both the advantages (e.g., flexible model structures that yield strong predictive performance) as well as disadvantages (e.g., lack of model transparency/interpretability) associated with using machine learning methods in marketing research. Furthermore, it does not appear to contain any promotional content or partiality towards any particular product or service related to this field; rather it focuses on providing an objective overview of the topic at hand. The article also notes potential risks associated with using AI agents driven by machine learning algorithms such as privacy concerns due to large-scale unstructured data collection; however it does not explore these risks in detail nor does it provide any evidence for its claims regarding potential risks associated with using AI agents driven by machine learning algorithms.