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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19346843 |
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| _version_ | 1866902182929891328 |
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| author | DEBAYAN MAL |
| author_facet | DEBAYAN MAL |
| contents | <p>This paper explores the application of data science methodologies to retail transactional data. By leveraging a dataset of over 200,000 transactions, we developed a modular Business Intelligence (BI) dashboard to identify revenue drivers and optimize menu offerings through Pareto analysis and quadrant-based categorization. The analysis successfully identified a 42.9% revenue concentration among core products, providing a data-driven roadmap for menu simplification.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19346843 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Debayanmal2002-official/Afficionado-Roasters-BI-Dashboard: End-to-End Business Intelligence Solutions for Retail Management: A Case Study on Specialty Coffee Roasters DEBAYAN MAL <p>This paper explores the application of data science methodologies to retail transactional data. By leveraging a dataset of over 200,000 transactions, we developed a modular Business Intelligence (BI) dashboard to identify revenue drivers and optimize menu offerings through Pareto analysis and quadrant-based categorization. The analysis successfully identified a 42.9% revenue concentration among core products, providing a data-driven roadmap for menu simplification.</p> |
| title | Debayanmal2002-official/Afficionado-Roasters-BI-Dashboard: End-to-End Business Intelligence Solutions for Retail Management: A Case Study on Specialty Coffee Roasters |
| url | https://doi.org/10.5281/zenodo.19346843 |