Enhanced Marketing Decision Making for Consumer Behaviour Classification Using Binary Decision Trees and a Genetic Algorithm Wrapper

doi:10.3390/informatics9020045

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Enhanced Marketing Decision Making for Consumer Behaviour Classification Using Binary Decision Trees and a Genetic Algorithm Wrapper

2022-05-31

Dimitris C. Gkikas *

Prokopis K. Theodoridis

Grigorios N. Beligiannis

DOI：10.3390/informatics9020045

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Abstract

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An excessive amount of data is generated daily. A consumer's journey has become extremely complicated due to the number of electronic platforms, the number of devices, the information provided, and the number of providers. The need for artificial intelligence (AI) models that combine marketing data and computer science methods is imperative to classify users' needs. This work bridges the gap between computer and marketing science by introducing the current trends of AI models on marketing data. It examines consumers' behaviour by using a decision-making model, which analyses the consumer's choices and helps the decision-makers to understand their potential clients' needs. This model is able to predict consumer behaviour both in the digital and physical shopping environments. It combines decision trees (DTs) and genetic algorithms (GAs) through one wrapping technique, known as the GA wrapper method. Consumer data from surveys are collected and categorised based on the research objectives. The GA wrapper was found to perform exceptionally well, reaching classification accuracies above 90%. With regard to the Gender, the Household Size, and Household Monthly Income classes, it manages to indicate the best subsets of specific genes that affect decision making. These classes were found to be associated with a specific set of variables, providing a clear roadmap for marketing decision-making.

Keywords:

marketing

consumer behaviour

artificial intelligence

decision making

predictive analytics

machine learning

data mining

genetic algorithm wrapper

decision trees

optimal feature selection