| Título |
Competing for Amazon\'s Buy Box: A Machine-Learning Approach |
| Autores |
GÓMEZ LOSADA, ALVARO, Duch-Brown, Nestor |
| Publicación externa |
Si |
| Medio |
Lecture Notes in Business Information Processing |
| Alcance |
Proceedings Paper |
| Naturaleza |
Científica |
| Cuartil SJR |
3 |
| Impacto SJR |
0.26 |
| Fecha de publicacion |
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. |
| Palabras clave |
Buy-box; Amazon; Machine learning; Classification; Data science |
| Miembros de la Universidad Loyola |
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