Name
Abstract | ||
|---|---|---|
The paper describes a data-mining case study devoted to an analysis of the users buying behaviour with the aim to improve the effectiveness of the relevant coupon marketing campaign. A coupon represents a ticket or number in an electronic form that we can use for a financial discount when purchasing a product. We can use this type of marketing to increase the number of the new customers and to reward the current ones. In our case, we used the datasets available within DMC 2015 and implemented the analytical process in accordance to the CRISP-DM methodology. Based on initial form of data, we focused mainly on pre-processing phase to extract hidden information, potentially useful for better prediction. For this purpose, we used decision trees algorithms like C4.5, C5.0, Random forest, CART and Logistic model tree. The obtained results were plausible and in some cases more accurate as other already published. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-69023-0_7 | Lecture Notes in Business Information Processing |
Keywords | Field | DocType |
Transaction,Coupons,Data mining,Decision trees | Coupon,Decision tree,Computer science,Logistic model tree,Ticket,Purchasing,Random forest,Database transaction,Electronic form,Marketing | Conference |
Volume | ISSN | Citations |
303 | 1865-1348 | 0 |
PageRank | References | Authors |
0.34 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frantisek Babic | 1 | 16 | 8.02 |
| Ludmila Pusztová | 2 | 0 | 1.35 |