Publicação
Data Science in Business: Understanding Growth from a Data-Driven Perspective: A Case Study of a Lisbon-Based Bakery
| Resumo: | In the highly competitive food industry, data analytics has become an essential tool for driving strategic growth and expansion. Leveraging data insights allows businesses to make informed decisions, optimize operations, and enhance customer experiences, thereby building a strong foundation for business growth. This work analyzes customer and sales data from a renowned bakery in Lisbon, Portugal, which contains data from 16 points of sale across four years. The primary objective of this research is to leverage advanced analytics to identify underlying patterns in the data that help the brand to grow. In order to achieve this, four main categories were identified that build the foundation of growth: profit generation, cost reduction, risk mitigation, and improving innovation life cycles. The insights of the analysis aim to facilitate and optimize strategic business decisions. The underlying patterns were identified by using association rule mining, utilizing the apriori algorithm, with which every shop and every year of that bakery was analyzed. Subsequently, the identified rules were further explored through a Meta-Analysis, utilizing K-means clustering to investigate similarities across points of sale based on their association rules. Additionally, Kruskal-Wallis tests were employed to assess the significance of seasonal variations, followed by Dunn’s post-hoc test for a more in-depth analysis. The clusters were further analyzed by examining the coefficient of variation within each cluster to gain deeper insights. The analysis revealed various association patterns that were consistently found across different shops and years. Significance could be found in sales behaviour across the seasons, weekdays, and months. Significant seasonal variations were also observed for the metrics of the association rules. The clusters formed based on the association rules did not exhibit significant differences in sales growth rates. A deeper analysis of the clusters unveiled a more complex structure, underscoring the need for careful decision-making across clusters. Furthermore, the analysis identified business-relevant patterns, such as strong product bundles and resulted in the development of a recommendation system. The results could be integrated into four foundational fields for growth: profit generation, cost reduction, risk mitigation and the improvement of innovation life cycles. The findings of this work were culminated in a strategic framework designed to help businesses leverage their data through an incremental and visual association rule mining approach. Thus, the outcomes can be utilized by other brands to enhance their business processes as demonstrated in this work. |
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| Autores principais: | Jerschov, Marc |
| Assunto: | Business Growth Advanced Data Analytics Association Rule Mining K-means Clustering Strategic Decision Making Python Seasonal Analysis SDG 8 - Decent work and economic growth |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | In the highly competitive food industry, data analytics has become an essential tool for driving strategic growth and expansion. Leveraging data insights allows businesses to make informed decisions, optimize operations, and enhance customer experiences, thereby building a strong foundation for business growth. This work analyzes customer and sales data from a renowned bakery in Lisbon, Portugal, which contains data from 16 points of sale across four years. The primary objective of this research is to leverage advanced analytics to identify underlying patterns in the data that help the brand to grow. In order to achieve this, four main categories were identified that build the foundation of growth: profit generation, cost reduction, risk mitigation, and improving innovation life cycles. The insights of the analysis aim to facilitate and optimize strategic business decisions. The underlying patterns were identified by using association rule mining, utilizing the apriori algorithm, with which every shop and every year of that bakery was analyzed. Subsequently, the identified rules were further explored through a Meta-Analysis, utilizing K-means clustering to investigate similarities across points of sale based on their association rules. Additionally, Kruskal-Wallis tests were employed to assess the significance of seasonal variations, followed by Dunn’s post-hoc test for a more in-depth analysis. The clusters were further analyzed by examining the coefficient of variation within each cluster to gain deeper insights. The analysis revealed various association patterns that were consistently found across different shops and years. Significance could be found in sales behaviour across the seasons, weekdays, and months. Significant seasonal variations were also observed for the metrics of the association rules. The clusters formed based on the association rules did not exhibit significant differences in sales growth rates. A deeper analysis of the clusters unveiled a more complex structure, underscoring the need for careful decision-making across clusters. Furthermore, the analysis identified business-relevant patterns, such as strong product bundles and resulted in the development of a recommendation system. The results could be integrated into four foundational fields for growth: profit generation, cost reduction, risk mitigation and the improvement of innovation life cycles. The findings of this work were culminated in a strategic framework designed to help businesses leverage their data through an incremental and visual association rule mining approach. Thus, the outcomes can be utilized by other brands to enhance their business processes as demonstrated in this work. |
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