Merancang Data Mining untuk Mendukung Strategi Cross-Selling

Authors

Keywords:

Cross Selling, Up Selling, Data Mining, Market Baskdet Analysis, Call-Center

Abstract

Up-Selling and Cross-Selling are part of a CRM (Customer Relationship Management) strategy. Up-Selling is a way to encourage consumers to buy a better product that has a higher value than what has been purchased. Meanwhile, Cross-Selling is a method to offer complementary or additional products. In large outlets with product items that can reach thousands, it is not easy to be able to identify related products automatically and quickly. To be able to provide a solution to this problem, a literature study will be carried out in several journals regarding CRM, Data Mining and Call-Centers. In the Cross-Selling strategy, Data Mining can be used to identify a product that is related to any product by analyzing the customer's shopping basket in the transaction history, this technique is also known as MBA (Market Basket Analysis). The results of Data Mining are product/service recommendations that have a strong correlation with several other products that customers usually buy. By adding Call-Center technology and CTI (Computer Telephony Integration) recommendations from Data Mining can be followed up by Telemarketing Agents to offer products/services directly to Customers.

References

A Al-Homery, H., Asharai, H., & Ahmad, A. (2019). The core components and types of CRM. Pakistan Journal of Humanities and Social Sciences, 7(1), 121-145.

Alawadh, M. M., & Barnawi, A. M. (2022). A survey on methods and applications of intelligent market basket analysis based on association rule. Journal on Big Data, 4(1), 1-25. doi:https://doi.org/10.32604/jbd.2022.021744

Bakhshizadeh, E., Aliasghari, H., Noorossana, R., & Ghousi, R. (2022). Customer Clustering Based on Factors of Customer Lifetime Value with Data Mining Technique (Case Study: Software Industry). International Journal of Industrial Engineering & Production Research, 33(1), 1-16.

Fadillah, A. R., Yulita, I. N., Pradana, A., & Suryani, M. (2021, October). Data Mining Implementation Using Frequent Pattern Growth on Transaction Data for Determining Cross-selling and Up-selling (Case Study: Cascara Coffee). In 2021 International Conference on Artificial Intelligence and Big Data Analytics (pp. 1-6). IEEE.

H. E. Simanjuntak & W. Windarto, “Analisa Data Mining Menggunakan Frequent Pattern Growth pada Data Transaksi Penjualan PT Mora Telematika Indonesia untuk Rekomendasi Strategi Pemasaran Produk Internet,†J. Media Inform. Budidarma, vol. 4, pp. 914–923, 2020.

Jain, A., Somwanshi, D., Joshi, K., & Bhatt, S. S. (2022, April). A Review: Data Mining Classification Techniques. In 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM) (pp. 636-642). IEEE.

Kamakura, W. A. J. J. o. R. M. (2008). Cross-selling: Offering the right product to the right customer at the right time. 6(3-4), 41-58.

Kaur, M., & Kang, S. (2016). Market Basket Analysis: Identify the changing trends of market data using association rule mining. Procedia computer science, 85, 78-85.

Özdemir, Y. E., & Bayrakli, S. (2022). A Case Study on Building a Cross-Selling Model through Machine Learning in the Insurance Industry. Avrupa Bilim ve Teknoloji Dergisi, (35), 364-372.

Plotnikova, V., Dumas, M., & Milani, F. P. (2022). Applying the CRISP-DM data mining process in the financial services industry: Elicitation of adaptation requirements. Data & Knowledge Engineering, 139, 102013.

Pratt, S., Tolkach, D., & Gibson, D. (2022). Understanding Backpacker Behavior through Market Basket Analysis. Tourism Analysis.

Shmueli, G., Patel, N. R., & Bruce, P. C. (2011). Data mining for business intelligence: Concepts, techniques, and applications in Microsoft Office Excel with XLMiner. John Wiley and Sons.

Silva, M., & Lakmal, K. (2021). E-CRM on Customer Retention in E-Commerce.

Venkatachalam, L., Dhara, K. K., Krishnaswamy, V., & Vernick, M. (2008, January). Communication enabled web applications The evolution of computer telephony integration. In 2008 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE'08) (pp. 275-278). IEEE.

W. Aprianti, K. A. Hafizd, and M. R. Rizani, “Implementasi Association Rules dengan Algoritma Apriori pada Dataset Kemiskinan,†Limits J. Math. Its Appl., vol. 14, no. 2, p. 57, 2017.

Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 2 April 2023

Downloads

Published

2023-04-30

How to Cite

Handijono, A., & Suhatman, Z. (2023). Merancang Data Mining untuk Mendukung Strategi Cross-Selling. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 6(2), 72–80. Retrieved from https://openjournal.unpam.ac.id/index.php/JTSI/article/view/29245

Issue

Vol. 6 No. 2 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi

Section

Article

License

Copyright (c) 2023 Ardijan Handijono, Zaldy Suhatman

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

Jurnal Teknologi Sistem Informasi dan Aplikasi have CC BY-NC or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.

In developing strategy and setting priorities, Jurnal Teknologi Sistem Informasi dan Aplikasi recognize that free access is better than priced access, libre access is better than free access, and libre under CC BY-NC or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License

YOU ARE FREE TO:

  • Adapt - remix, transform, and build upon the material for any purpose, even commercially.
  • The licensor cannot revoke these freedoms as long as you follow the license terms