Research on Consumer Behavior Patterns and Their Precision Marketing Strategies Based on Decision Tree Algorithms: A Case Study of Superstore Customer Data
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Research on Consumer Behavior Patterns and Their Precision Marketing Strategies Based on Decision Tree Algorithms: A Case Study of Superstore Customer Data

Tiannuo Yin 1*
1 Liaoning University of International Business and Economics
*Corresponding author: tiannuo_yin@163.com
Published on 11 July 2025
Volume Cover
AEMPS Vol.201
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-259-1
ISBN (Online): 978-1-80590-260-7
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Abstract

With the rapid development of data-driven marketing, personalized and precision strategies have become essential tools for enterprises to enhance competitiveness. However, the deep mining of multidimensional consumer data in offline superstore scenarios still faces significant challenges. This study applies a decision tree algorithm to analyze consumer behavior patterns in four product categories—wine, candy, meat, and gold—based on customer consumption data from a supermarket. The findings indicate that income level is the core factor influencing consumption. Family structure also plays a differentiating role: childless families prefer wine; families with children spend more on confectionery; meat consumption is influenced by both income and number of children; and gold consumption is primarily driven by income. Accordingly, the study proposes precision marketing strategies such as promoting high-end wines to high-income childless families, recommending healthy candy options to families with children, and tailoring meat and gold promotions based on family structure. By shifting from single-variable to multifactor joint modeling, this study emphasizes the importance of family structure in consumer profiling and offers a cost-effective precision marketing solution using interpretable decision trees for traditional retail.

Keywords:

decision tree algorithm, consumer behavior analysis, precision marketing, household structure, retail

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Yin,T. (2025). Research on Consumer Behavior Patterns and Their Precision Marketing Strategies Based on Decision Tree Algorithms: A Case Study of Superstore Customer Data. Advances in Economics, Management and Political Sciences,201,53-63.

References

[1]. Kokash, N., & Makhnist, L. (2023). Using Decision Trees for Interpretable Supervised Clustering. SN Comput. Sci., 5, 268.

[2]. Afrin, K. , Nepal, B. , & Monplaisir, L. . (2018). A data-driven framework to new product demand prediction: integrating product differentiation and transfer learning approach. Expert Systems with Applications, 108(OCT.), 246-257.

[3]. Khan, M. A. , Islam, M. Z. , & Hafeez, M. . (2020). Data pre-processing and evaluating the performance of several data mining methods for predicting irrigation water requirement.

[4]. Usuga, J. P. C., Lamouri, S., & Grabot, B. (2018). Trends in Machine Learning Applied to Demand & Sales Forecasting: A Review. ILS 2018 - Information Systems, Logistics and Supply Chain, Proceedings, 307-316.

[5]. Yang, L. , Niu, X. , & Wu, J. . (2021). Rf-lighgbm: a probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce.

[6]. Kim, J.Y. (2021). Using Machine Learning to Predict Poverty Status in Costa Rican Households. SSRN Electronic Journal.

[7]. Aouad, A., Elmachtoub, A.N., Ferreira, K.J., & McNellis, R. (2019). Market Segmentation Trees. Manuf. Serv. Oper. Manag., 25, 648-667.

[8]. Xu, N., & Hu, C. (2023). Enhancing E-Commerce Recommendation using Pre-Trained Language Model and Fine-Tuning. ArXiv, abs/2302.04443.

[9]. Sirkeci, I., Dahl, S., & Zeyneloğlu, S. (2022). The Impact of Perceptions of Social Media Advertisements on Advertising Value, Brand Awareness and Brand Associations: Research on Generation Y Instagram Users. Transnational Marketing Journal.

[10]. Bodine, J., & Hochbaum, D.S. (2020). The Max-Cut Decision Tree: Improving on the Accuracy and Running Time of Decision Trees. ArXiv, abs/2006.14118.

[11]. Xu, K. , Zhou, H. , Zheng, H. , Zhu, M. , & Xin, Q. . (2024). Intelligent classification and personalized recommendation of e-commerce products based on machine learning.

[12]. Garc'ia-Gil, D., Garc'ia, S., Xiong, N., & Herrera, F. (2020). Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big Data. Cogn. Comput., 16, 1572-1588.

[13]. Johannes, R., & Alamsyah, A. (2021). Sales Prediction Model Using Classification Decision Tree Approach For Small Medium Enterprise Based on Indonesian E-Commerce Data. ArXiv, abs/2103.03117.

[14]. Moulay, M., Leiva, R.A., Maroni, P.J., Mancuso, V., Fernandez Anta, A., & Khatouni, A.S. (2021). MonTrees: Automated Detection and Classification of Networking Anomalies in Cellular Networks. ArXiv, abs/2108.13156.

[15]. Karb, T., Kühl, N., Hirt, R., & Glivici-Cotruţă, V. (2020). A network-based transfer learning approach to improve sales forecasting of new products. ArXiv, abs/2005.06978.

[16]. Madani, B., & Alshraideh, H. (2021). Predicting Consumer Purchasing Decision in The Online Food Delivery Industry. ArXiv, abs/2110.00502.

References

[1]. Kokash, N., & Makhnist, L. (2023). Using Decision Trees for Interpretable Supervised Clustering. SN Comput. Sci., 5, 268.

[2]. Afrin, K. , Nepal, B. , & Monplaisir, L. . (2018). A data-driven framework to new product demand prediction: integrating product differentiation and transfer learning approach. Expert Systems with Applications, 108(OCT.), 246-257.

[3]. Khan, M. A. , Islam, M. Z. , & Hafeez, M. . (2020). Data pre-processing and evaluating the performance of several data mining methods for predicting irrigation water requirement.

[4]. Usuga, J. P. C., Lamouri, S., & Grabot, B. (2018). Trends in Machine Learning Applied to Demand & Sales Forecasting: A Review. ILS 2018 - Information Systems, Logistics and Supply Chain, Proceedings, 307-316.

[5]. Yang, L. , Niu, X. , & Wu, J. . (2021). Rf-lighgbm: a probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce.

[6]. Kim, J.Y. (2021). Using Machine Learning to Predict Poverty Status in Costa Rican Households. SSRN Electronic Journal.

[7]. Aouad, A., Elmachtoub, A.N., Ferreira, K.J., & McNellis, R. (2019). Market Segmentation Trees. Manuf. Serv. Oper. Manag., 25, 648-667.

[8]. Xu, N., & Hu, C. (2023). Enhancing E-Commerce Recommendation using Pre-Trained Language Model and Fine-Tuning. ArXiv, abs/2302.04443.

[9]. Sirkeci, I., Dahl, S., & Zeyneloğlu, S. (2022). The Impact of Perceptions of Social Media Advertisements on Advertising Value, Brand Awareness and Brand Associations: Research on Generation Y Instagram Users. Transnational Marketing Journal.

[10]. Bodine, J., & Hochbaum, D.S. (2020). The Max-Cut Decision Tree: Improving on the Accuracy and Running Time of Decision Trees. ArXiv, abs/2006.14118.

[11]. Xu, K. , Zhou, H. , Zheng, H. , Zhu, M. , & Xin, Q. . (2024). Intelligent classification and personalized recommendation of e-commerce products based on machine learning.

[12]. Garc'ia-Gil, D., Garc'ia, S., Xiong, N., & Herrera, F. (2020). Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big Data. Cogn. Comput., 16, 1572-1588.

[13]. Johannes, R., & Alamsyah, A. (2021). Sales Prediction Model Using Classification Decision Tree Approach For Small Medium Enterprise Based on Indonesian E-Commerce Data. ArXiv, abs/2103.03117.

[14]. Moulay, M., Leiva, R.A., Maroni, P.J., Mancuso, V., Fernandez Anta, A., & Khatouni, A.S. (2021). MonTrees: Automated Detection and Classification of Networking Anomalies in Cellular Networks. ArXiv, abs/2108.13156.

[15]. Karb, T., Kühl, N., Hirt, R., & Glivici-Cotruţă, V. (2020). A network-based transfer learning approach to improve sales forecasting of new products. ArXiv, abs/2005.06978.

[16]. Madani, B., & Alshraideh, H. (2021). Predicting Consumer Purchasing Decision in The Online Food Delivery Industry. ArXiv, abs/2110.00502.

Cite this article

Yin,T. (2025). Research on Consumer Behavior Patterns and Their Precision Marketing Strategies Based on Decision Tree Algorithms: A Case Study of Superstore Customer Data. Advances in Economics, Management and Political Sciences,201,53-63.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICEMGD 2025 Symposium: Digital Transformation in Global Human Resource Management

ISBN: 978-1-80590-259-1(Print) / 978-1-80590-260-7(Online)
Editor: Florian Marcel Nuţă Nuţă, An Nguyen
Conference website: https://www.icemgd.org/committee.html
Conference date: 26 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.201
ISSN: 2754-1169(Print) / 2754-1177(Online)

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