Using Transaction-Based Customer Segmentation to Formulate Sales Strategies: Evidence from a Motorcycle Dealer in Indonesia
Abstract
This study examines the problem of inconsistent sales target achievement faced by a motorcycle dealer in Indonesia, where sales targets were achieved in only a limited number of months. One of the main causes identified is the absence of clear customer segmentation, which leads to undifferentiated sales strategies and inefficient allocation of sales resources. This study aims to identify distinct customer segments based on transaction-based purchasing behavior and to demonstrate how these segments can be utilized to formulate more targeted sales strategies. A quantitative case study approach was employed using the CRISP-DM framework and transaction data from 4,289 motorcycle sales recorded between January 2022 and October 2025. Customer segmentation was conducted using the K-Modes clustering algorithm based on categorical transaction attributes, resulting in three distinct customer segments with different purchasing characteristics. The findings show that each segment exhibits unique behavioral and economic profiles, requiring differentiated sales approaches rather than a uniform strategy. Based on the segmentation results, this study proposes segment-specific sales strategies that support more focused resource allocation and improved sales effectiveness. The results highlight the importance of transaction-based customer segmentation as a practical decision-support tool for enhancing sales performance in motorcycle retail.
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References
Ali, N., & Shabn, O. S. (2024). Customer lifetime value (CLV) insights for strategic marketing success and its impact on organizational financial performance. Cogent Business and Management, 11(1). https://doi.org/10.1080/23311975.2024.2361321
Alves Gomes, M., & Meisen, T. (2023). A review on customer segmentation methods for personalized customer targeting in e-commerce use cases. Information Systems and E-Business Management, 21(3), 527–570. https://doi.org/10.1007/s10257-023-00640-4
Beane, T. P., Ennis, D. M., & Morris, P. (1987). Market Segmentation: A Review. European Journal of Marketing, 21(5), 20–42. https://doi.org/10.1108/EUM0000000004695
Bilgic, E., Cakir, O., Kantardzic, M., Duan, Y., & Cao, G. (2021). Retail analytics: store segmentation using Rule-Based Purchasing behavior analysis. International Review of Retail, Distribution and Consumer Research, 31(4), 457–480. https://doi.org/10.1080/09593969.2021.1915847
Bogacki, S., Rymarczyk, P., Smutek, T., Rutkowski, M., & Chmielowska-Marmucka, A. (2024). Advanced methods for target audience identification: enhancing marketing strategies through machine learning and data analytics. Journal of Modern Science, 57(3), 417–435. https://doi.org/10.13166/jms/191180
Chen, Q., Choi, B. J., & Lee, S. J. (2025). Tailoring customer segmentation strategies for luxury brands in the NFT market – The case of SUPERGUCCI. Journal of Retailing and Consumer Services, 82. https://doi.org/10.1016/j.jretconser.2024.104121
Freeman, R. E., Dmytriyev, S. D., & Phillips, R. A. (2021). Stakeholder Theory and the Resource-Based View of the Firm. Journal of Management, 47(7), 1757–1770. https://doi.org/10.1177/0149206321993576
Haenlein, M., Kaplan, A. M., & Schoder, D. (2006). Valuing the Real Option of Abandoning Unprofitable Customers When Calculating Customer Lifetime Value. Journal of Marketing, 70, 5–20. http://www.marketingpower.com/jmblog.
Hristov, I., & Appolloni, A. (2022). Stakeholders’ engagement in the business strategy as a key driver to increase companies’ performance: Evidence from managerial and stakeholders’ practices. Business Strategy and the Environment, 31(4), 1488–1503. https://doi.org/10.1002/bse.2965
Huang, Z. (1998). Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. In Data Mining and Knowledge Discovery (Vol. 12).
Jain, D., & Singh, S. S. (2002). Customer lifetime value research in marketing: A review and future directions. Journal of Interactive Marketing, 16(2), 34–46. https://doi.org/10.1002/dir.10032
Kacprzak, D. (2020). An extended TOPSIS method based on ordered fuzzy numbers for group decision making. Artificial Intelligence Review, 53(3), 2099–2129. https://doi.org/10.1007/s10462-019-09728-1
Kasem, M. S. E., Hamada, M., & Taj-Eddin, I. (2024). Customer profiling, segmentation, and sales prediction using AI in direct marketing. Neural Computing and Applications, 36(9), 4995–5005. https://doi.org/10.1007/s00521-023-09339-6
Li, Y., & Wu, B. (2025). A Fuzzy MCDM-Based Deep Multi-View Clustering Approach for Large-Scale Multi-View Data Analysis. Symmetry, 17(8). https://doi.org/10.3390/sym17081253
Lu, J., Cairns, L., & Smith, L. (2020). Data science in the business environment: customer analytics case studies in SMEs. Journal of Modelling in Management, 16(2), 689–713. https://doi.org/10.1108/JM2-11-2019-0274
Lubowiecki-Vikuk, A., & Michalska-Dudek, I. (2025). Customer Insight in Tourism. In Customer Insight in Tourism. Routledge. https://doi.org/10.4324/9781003638193
Malthouse, E. C., & Blattberg, R. C. (2005). Can we predict customer lifetime value? Journal of Interactive Marketing, 19(1), 2–16. https://doi.org/10.1002/dir.20027
Plotnikova, V., Dumas, M., & Milani, F. P. (2022). Applying the CRISP-DM data mining processin the financial servicesindustry- Elicitation of adaptation requirements copy. Data & Knowledge Engineering, 139.
Schröer, C., Kruse, F., & Gómez, J. M. (2021). A systematic literature review on applying CRISP-DM process model. Procedia Computer Science, 181, 526–534. https://doi.org/10.1016/j.procs.2021.01.199
Sun, Y., Liu, H., & Gao, Y. (2023). Research on customer lifetime value based on machine learning algorithms and customer relationship management analysis model. Heliyon, 9(2). https://doi.org/10.1016/j.heliyon.2023.e13384
Tabianan, K., Velu, S., & Ravi, V. (2022). K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data. Sustainability (Switzerland), 14(12). https://doi.org/10.3390/su14127243
Trivedi, P., Shah, J., Cep, R., Abualigah, L., & Kalita, K. (2024). A Hybrid Best-Worst Method (BWM) - Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Approach for Prioritizing Road Safety Improvements. IEEE Access, 12, 30054–30065. https://doi.org/10.1109/ACCESS.2024.3368395
van Lieshout, J. W. F. C., Nijhof, A. H. J., Naarding, G. J. W., & Blomme, R. J. (2021). Connecting strategic orientation, innovation strategy, and corporate sustainability: A model for sustainable development through stakeholder engagement. Business Strategy and the Environment, 30(8), 4068–4080. https://doi.org/10.1002/bse.2857
Varghese, J., Edward, M., & Sahadev, S. (2017). A multipath model of salesperson performance in the financial services industry. South Asian Journal of Business Studies, 6(3), 195–213. https://doi.org/10.1108/SAJBS-02-2017-0017
Wu, W., Xu, Z., Kou, G., & Shi, Y. (2020). Decision-Making Support for the Evaluation of Clustering Algorithms Based on MCDM. Complexity, 2020. https://doi.org/10.1155/2020/9602526
Yoseph, F., Ahamed Hassain Malim, N. H., Heikkilä, M., Brezulianu, A., Geman, O., & Paskhal Rostam, N. A. (2020). The impact of big data market segmentation using data mining and clustering techniques. Journal of Intelligent and Fuzzy Systems, 38(5), 6159–6173. https://doi.org/10.3233/JIFS-179698
You, Z., Si, Y. W., Zhang, D., Zeng, X., Leung, S. C. H., & Li, T. (2015). A decision-making framework for precision marketing. Expert Systems with Applications, 42(7), 3357–3367. https://doi.org/10.1016/j.eswa.2014.12.022
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