Leveraging Data Analytics for Improving Financial Performance: A Case Study of the Retail Industry
Foudi, Larbi (2023)
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023110828879
https://urn.fi/URN:NBN:fi:amk-2023110828879
Tiivistelmä
This bachelor thesis explores the potential for data analytics to substantially improve financial outcomes and evolving retail sector. The research includes a diverse, intensive case study, serving not only to highlight theoretical background but to exemplify the practical implementation of data analytics methodologies.
These methodologies aim to stimulate financial growth, refine cost optimization processes, and boost profitability, thereby acting as a key driver for financial success in retail enterprises. In this thesis, a case study on sales data was conducted using Python, a prominent language in the field of data analytics recognized for its flexibility and strong features.
This research elaborates on essential elements of data analysis such as data cleaning to assure data precision and dependability, and investigates customer behavior, sales trends, and product performance - integral to successful retail operations.
Therefore, the thesis provides an in-depth analysis of the correlation between data analytics and financial performance in retail, revealing its immense potential for businesses in the sector. It scrutinizes the effectiveness of various analytics techniques in retail environments and highlights challenges and complications in implementing data analytics in the retail industry.
Through this thesis, a deep insight into how data analytics impacts and improves the financial performance of retail companies is expected to be gained. The results are intended to provide retail businesses with valuable knowledge about the advantages, possible obstacles, difficulties, and top approaches for using data analytics. This information will assist these businesses in making informed choices when adopting effective data analytics strategies, which have the potential to bring about significant positive changes to their financial situation and success in a highly competitive market.
These methodologies aim to stimulate financial growth, refine cost optimization processes, and boost profitability, thereby acting as a key driver for financial success in retail enterprises. In this thesis, a case study on sales data was conducted using Python, a prominent language in the field of data analytics recognized for its flexibility and strong features.
This research elaborates on essential elements of data analysis such as data cleaning to assure data precision and dependability, and investigates customer behavior, sales trends, and product performance - integral to successful retail operations.
Therefore, the thesis provides an in-depth analysis of the correlation between data analytics and financial performance in retail, revealing its immense potential for businesses in the sector. It scrutinizes the effectiveness of various analytics techniques in retail environments and highlights challenges and complications in implementing data analytics in the retail industry.
Through this thesis, a deep insight into how data analytics impacts and improves the financial performance of retail companies is expected to be gained. The results are intended to provide retail businesses with valuable knowledge about the advantages, possible obstacles, difficulties, and top approaches for using data analytics. This information will assist these businesses in making informed choices when adopting effective data analytics strategies, which have the potential to bring about significant positive changes to their financial situation and success in a highly competitive market.