Purnamasari, Dian Indri and Saefudin, Asep and Permadi, Vynska Amalia and Agusdin, Riza Prapascatama Consumer Behavior Analysis of Leathercraft Small and Medium- Sized Enterprises (SME) Using Market Basket Analysis and Clustering Algorithms. An International Interdisciplinary Journal, 24 (3). pp. 143-154. ISSN 1343-4500
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Abstract
Customers are essential to a business’s existence so did effective customer management, which may increase companies’ revenue. On the contrary, managing consumer preferences is not straightforward. Companies must prove their capacity to apply the most successful business strategies to match customer preferences. Thus, they will require a procedure for doing business research that is both compliant and capable of developing customer-driven resolutions. Consumer behavior analysis is a frequently used analytical technique in the business area to enhance marketing strategies or determine future corporate intentions. Consumer behavior can be investigated for various objectives, including customer transaction analysis and profiling. Consumer behavior analysis may be complicated for businesses that deal with enormous amounts of customer transactions history data. Thus, a data mining method might be required to aid in the inquiry. This study used the Apriori data mining technique to conduct a market basket analysis towards uncovering product transaction association rules. The K- Means clustering technique is then applied, as is the Elbow approach for identifying the ideal K to use in the customer segmentation clustering process. K-Means clustering has been able to find two dominant consumer groups through this research, whereas Apriori association identifies two product association rules with the highest possible confidence level.
| Item Type: | Article |
|---|---|
| Subjects: | H Social Sciences > H Social Sciences (General) |
| Divisions: | Faculty of Law, Arts and Social Sciences > School of Social Sciences |
| Depositing User: | DR PURNAMASARI DIAN INDRI |
| Date Deposited: | 27 Nov 2021 16:51 |
| Last Modified: | 27 Nov 2021 16:54 |
| URI: | http://eprints.upnyk.ac.id/id/eprint/27152 |
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