Abstract
Market basket analysis provides an insight into customer consumption patterns and trends in the industry. These will be achieved by analyzing and studying the performance of the large datasets of transactions made by consumers held in retail stores. These commercial transactions will be analyzed using the Machine Learning technique called the A priori algorithm by establishing association rules and determining those groups of items in a market basket whose association could represent better economic benefits for companies. This study will analyze the historical sales data of the product groups, in order to identify relationships that al-low companies in the sector to generate patterns to propose the increase of their portfolio based on the products with the greatest purchasing trends. At the end of this investigation, commercial strategies will be proposed to improve sales, take advantage of spaces in stores and implement more effective strategic offers, based on the groups of articles with the best associations found.
| Translated title of the contribution | Un análisis de la red de productos utilizando un algoritmo a priori para ampliar la cesta de la compra en el comercio minorista: Un análisis de la red de productos utilizando un algoritmo a priori para ampliar la cesta de la compra en el comercio minorista |
|---|---|
| Original language | American English |
| Title of host publication | A Product Network Analysis Using A Priori Algorithm for Extending the Market Basket in Retail |
| Subtitle of host publication | A Product Network Analysis Using A Priori Algorithm for Extending the Market Basket in Retail |
| Editors | Md Mizanur Rahman |
| Place of Publication | Sydney, Australia |
| Publisher | IEOM Society International |
| Pages | 2025- 2034 |
| Number of pages | 10 |
| Volume | 12 |
| Edition | 7 |
| ISBN (Electronic) | 979-8-3507-0542-3 |
| ISBN (Print) | 2169-8767 |
| State | Published - 22 Dec 2022 |
Publication series
| Name | IOEM Society |
|---|---|
| Publisher | IOEM Society |
| Number | 7 |
| Volume | 12 |
| ISSN (Print) | 2169-8767 |
| ISSN (Electronic) | 2169-8767 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 12 Responsible Consumption and Production
OECD Category
- Ingeniería industrial
Ulima Repository Category
- Ingeniería industrial / Logística
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