Journal of Quality Engineering and Management

Prediction of the impact and performance of FinTech companies' advertisements on customer acquisition and loyalty using metaheuristic algorithms

Document Type : Original Article

Authors

Samad Bandari 1
Farhad Hossein Zadeh Lotfi 2
Seyyed Esmaeil Najafi 1, 3
Seyyed Ahmad Edalatpanah 4

1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Department of Mathematics and Computer Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Morvarid Intelligent Industrial Systems Research Group, Iran.

4 Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran.

https://doi.org/10.48313/jqem.2024.219125
Abstract
Purpose: With the rapid growth of the financial technology (FinTech) industry, digital advertising has become one of the key tools for attracting new customers and increasing the loyalty of existing ones. In environments where uncertainty and decision-making complexity play significant roles, the use of metaheuristic algorithms can help optimize digital advertising efforts.
Methodology: This study proposes a three-level model in an intuitionistic fuzzy environment and utilizes the Stackelberg game to examine the impact of advertising on performance, customer acquisition, and customer loyalty. In this study, the advertising process of FinTech companies is modeled as a three-level decision-making process encompassing customer acquisition, advertising performance, and customer loyalty. To solve this model, Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to optimize advertising strategies.
Findings: The results indicated that the proposed model accurately predicted customer loyalty and that metaheuristic algorithms effectively optimized advertising parameters. The analysis of the results showed that conversion rate and purchase amount are the most influential factors affecting customer loyalty. Furthermore, the findings revealed that using hybrid algorithms can reduce advertising costs and increase Return on Investment (ROI). Comparing the proposed algorithms showed that the hybrid approach, combining genetic algorithms and particle swarm optimization, outperformed the individual methods in predicting customer behavior.
Originality/Value: Based on the findings, it is recommended that FinTech companies adopt metaheuristic algorithms to optimize digital advertising and achieve precise customer targeting. These approaches can enhance advertising effectiveness, reduce marketing costs, and improve customer loyalty within the FinTech industry.

Keywords

FinTech
Digital advertising
Metaheuristic algorithms
Customer loyalty
Advertising optimization
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Journal of Quality Engineering and Management
Volume 14, Issue 3
Autumn 2024
Pages 199-216

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