| Title | Competing for Amazon\'s Buy Box: A Machine-Learning Approach |
|---|---|
| Authors | GÓMEZ LOSADA, ALVARO, Duch-Brown, Nestor |
| External publication | Si |
| Means | Lect. Notes Bus. Inf. Process. |
| Scope | Proceedings Paper |
| Nature | Científica |
| SJR Quartile | 3 |
| SJR Impact | 0.26 |
| Publication date | 01/01/2019 |
| ISI | 000611408800038 |
| DOI | 10.1007/978-3-030-36691-9_38 |
| Abstract | A key feature of the Amazon marketplace is that multiple sellers can sell the same product. In such cases, Amazon recommends one of the sellers to customers in the so-called \'buy-box\'. In this study, the dynamics among sellers for occupying the buy-box was modelled using a classification approach. Italy\'s Amazon webpage was crawled during ten months and features from products analyzed to estimate the more relevant ones Amazon could consider for a seller occupy the buy-box. Predictive models showed that the more relevant features are the ratio between consecutive prices in products and their number of assessment received by customers. |
| Keywords | Buy-box; Amazon; Machine learning; Classification; Data science |
| Universidad Loyola members |
|