Open AccessFinding the most potential customer in bundling products by using reverse k-skyband query: current research progress and challenges
Author(s) -
L. A. Gumilar,
Annisa Annisa,
Taufik Djatna
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/472/1/012052
Subject(s) - computer science , skyline , product (mathematics) , set (abstract data type) , loyalty business model , business , customer relationship management , database , marketing , data mining , mathematics , geometry , service quality , programming language , service (business)
Online shopping nowadays is one of the most popular ways to make transactions. Customer transactions recorded on the online shopping application can be used by a retail company’s Customer Relationship Management (CRM) manager to make sales strategies and increase customer loyalty. Sales strategies are needed to identify products that will be of interest in the market as well as potential customers who will be interested in products. The implementation of skyline queries produces the most interesting and preferred set of products. Whereas to search for the potential set of customers who are interested in certain products, a reverse skyline query can be used. To find a larger set of customers, a reverse k-skyband query can be implemented. Reverse k-skyband queries can also be applied to determine the level of interest of each customer in each product in the product bundling strategy. Product bundling is a sale of two or more products in one package commonly used by CRM manager to increase customer loyalty. This research proposes reverse k-skyband query to find the most potential customers who like bundling products. This study evaluates the performance of the reverse kskyband query in terms of computation time.