Name
Title | ||
|---|---|---|
Understand the Buying Behavior of E-Shop Customers Through Appropriate Analytical Methods. |
Abstract | ||
|---|---|---|
Customer satisfaction represents a crucial goal for every seller. In e-commerce, it is possible to increase this factor by a better understanding of customers purchasing behavior based on collected historical data. In a period of a continually growing amount of data, it is not an easy task to effectively preprocess and analyses. Our motivation was to understand the buying behavior of the on-line e-shop customer through appropriate analytical methods. The result is a knowledge set that retailers could use to deliver products to specific customers, to meet their expectations, and to increase his revenues and reputation. For recommendations generation, we used a collaborative filtering method and matrix factorization associated with Singular Value Decomposition (SVD) algorithm. For segmentation, we selected the K-Means algorithm and the RFM method. All methods produced interesting and potentially useful results that will be evaluated and deployed into practice. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-22999-3_27 | ADVANCES AND TRENDS IN ARTIFICIAL INTELLIGENCE: FROM THEORY TO PRACTICE |
Keywords | Field | DocType |
E-shop,Transactions,Recommendations,Segmentation | Revenue,Singular value decomposition,Customer satisfaction,Collaborative filtering,Computer science,Segmentation,Matrix decomposition,Operations research,Purchasing,Reputation | Conference |
Volume | ISSN | Citations |
11606 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaroslav Olejár | 1 | 1 | 0.69 |
| Frantisek Babic | 2 | 16 | 8.02 |
| Ludmila Pusztová | 3 | 0 | 1.35 |