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.
 ? Based on this, we aim to present and discuss the main results of the relevant existing works in this field using a strictly scientific method, systematic literature review (SLR), consisting of various critical steps. ? This review seeks to treat and study existing solutions explored in this area and give the gist and suggestions from the previous works. ? This investigation represents a reference that guides and helps the researchers to discover the existing studies and provides them with plan for future direction. ? we don't treat all existing works because of the unavailability of some papers
 ? ML models are applied to data due to their ability to resolve different problems, from those that could be solved through conventional statistics and management of scientific techniques to complex problems that require a bigger analysis; in this regard, ML allows solving problems faster and better than conventional tech- niques. ? Thus, after a thorough review, a total of 320 scientific articles were obtained, allow81 ing us to observe what marketing problems are solved and the techniques used for this purpose. In this article, we presented the advantages and disadvantages of the methods and which marketing problems are more feasible to solve with specificalgorithms
 ? The methodology used in this paper adopts the guidelines proposed by Kitchenham and Brereton to perform a systematic literature review (SLR), which reposed on various well-defined stages to establish a rigorous process. ? We divided our proposed quality assessment questions into two classes. ? The first represents the questions that we have affected a high coefficient which is QA1 and QA2, that address the machine learning technique used, architecture of the proposed solution and comparison of the findings with others as shown in Table ? Therefore, 26 papers selected as final set that responds to our proposed criteria.
 ? In this regard, machine learning (ML) allows for generating useful results for com23 panies with less effort and time and it is increasingly being used in marketing research ? Therefore, ML-based techniques are used to predict the results of new data, predictions, and classifications or to help people in the process of decision-making ? As we will see, ML has been widely used to discover the most relevant needs of consumers and the relationship they have with products and their attributes segment satisfaction, recognition or recommendation, the selection of a new product or reaction to advertising
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