Penerapan Association Rules untuk Elemen Cross Selling Pada Sistem Informasi Customer Development
DOI:
https://doi.org/10.33633/tc.v20i3.4750Keywords:
Association Rules, CRM, Cross Selling, HPAIAbstract
Tingkat persaingan yang tinggi dalam dunia bisnis, membuat HPAI harus melakukan strategi yang kuat dalam pemasaran dan penjualan produk. Semakin hari, data transaksi penjualan semakin banyak, dikarenakan ada 1500 transaksi setiap bulan yang terdiri dari 49 produk. Selama ini, data transaksi penjualan dilaporkan kepada pimpinan dan kemudian diarsipkan. Setelah itu, data tersebut tidak diolah dengan benar. Untuk itu diperlukannya sebuah strategi bisnis untuk dapat mengenal pelanggan secara lebih detail dan melayani mereka sesuai kebutuhannya. Customer Relationship Management (CRM) adalah sebuah strategi bisnis untuk memahami, mengantisipasi dan mengelola kebutuhan pelanggan yang potensial. Salah satu bagian dari CRM yakni cross- selling, yang harus didahului oleh analisis yang mendalam mengenai data transaksi pelanggan dengan menggunakan konsep data mining yang melibatkan proses pengambilan sumber informasi dari sebuah transaksi pelanggan. Association rules merupakan salah satu metode dalam data mining yang dapat menghasilkan beberapa pola yang bisa dijadikan strategi penjualan cross selling dalam merekomendasikan suatu produk kepada pelanggan. Nilai minimun support yang digunakan yakni 9% dan 60% yang menghasilkan 3 rules yang nantinya akan direkomendasikan kepada pelanggan melalui email.References
Beheshtian-Ardakani, A., Fathian, M., & Gholamian, M., 2018. A novel model for product bundling and direct marketing in e-commerce based on market segmentation. Decision Science Letters, 7(1),39–54. https://doi.org/10.5267/j.dsl.2017.4.005
Buttle, F., & Stan, M., 2015. Customer Relationship Management (Third edit). Butterworth-Heinemann.
Cruz-Jesus, F., Pinheiro, A., & Oliveira, T., 2019. Understanding CRM adoption stages: empirical analysis building on the TOE framework. Computers in Industry, 109, 1–13. https://doi.org/10.1016/j.compind.2019.03.007
Gordon., 2013. For Marketing, Sales, and Customer Relationship Management. In Journal of Chemical Information and Modeling (Vol. 53). https://doi.org/10.1017/CBO9781107415324.004
Li, S., Sun, B., & Montgomery, A. L., 2011. Cross-selling the right product to the right customer at the right time. Journal of Marketing Research, 48(4), 683–700. https://doi.org/10.1509/jmkr.48.4.683
Lim, A. H. L., & Lee, C. S., 2010. Processing online analytics with classification and association rule mining. Knowledge-Based Systems, 23(3), 248–255. https://doi.org/10.1016/j.knosys.2010.01.006
Ngai, E. W. T., Xiu, L., & Chau, D. C. K., 2009. Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2 PART 2), 2592–2602. https://doi.org/10.1016/j.eswa.2008.02.021
Park, C. H., & Kim, Y. G., 2003. A framework of dynamic CRM: Linking marketing with information strategy. Business Process Management Journal, 9(5), 652–671. https://doi.org/10.1108/14637150310496749
Rababah, K., 2011. Customer Relationship Management (CRM) Processesfrom Theory to Practice: The Pre-implementation Plan ofCRM System. International Journal of E-Education, e-Business, e-Management and e-Learning, 1(1). https://doi.org/10.7763/ijeeee.2011.v1.4
Richards, K. A., & Jones, E., 2008. Customer relationship management: Finding value drivers. Industrial Marketing Management, 37(2), 120–130. https://doi.org/10.1016/j.indmarman.2006.08.005
Smita, S., & Sharma, P., 2014. Use of Data Mining in Various Field: A Survey Paper. IOSR Journal of Computer Engineering, 16(3), 18–21. https://doi.org/10.9790/0661-16351821
Timalsina, A. K., 2013. A framework for personalized dynamic cross-selling in E-commerce retailing. Dissertation Abstracts International: Section B: The Sciences and Engineering, 73.
Downloads
Published
Issue
Section
License
Copyright (c) 2021 Siti Monalisa Monalisa
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
License Terms
All articles published in Techno.COM Journal are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This means:
1. Attribution
Readers and users are free to:
-
Share – Copy and redistribute the material in any medium or format.
-
Adapt – Remix, transform, and build upon the material.
As long as proper credit is given to the original work by citing the author(s) and the journal.
2. Non-Commercial Use
-
The material cannot be used for commercial purposes.
-
Commercial use includes selling the content, using it in commercial advertising, or integrating it into products/services for profit.
3. Rights of Authors
-
Authors retain copyright and grant Techno.COM Journal the right to publish the article.
-
Authors can distribute their work (e.g., in institutional repositories or personal websites) with proper acknowledgment of the journal.
4. No Additional Restrictions
-
The journal cannot apply legal terms or technological measures that restrict others from using the material in ways allowed by the license.
5. Disclaimer
-
The journal is not responsible for how the published content is used by third parties.
-
The opinions expressed in the articles are solely those of the authors.
For more details, visit the Creative Commons License Page:
? https://creativecommons.org/licenses/by-nc/4.0/
