Data Analytics for Product Segmentation and Demand Forecasting of a Local Retail Store Using Python

In today's competitive business environment, understanding customers' expectations and choices is a necessity for the successful operations of a retail store. Forecasting demand also plays an important role in maintaining inventory at an optimum level. The work utilises data analytics for product segmentation and demand forecasting in a local retail store. Python is being used as a programming language for data analytics. Historical sales data of a local store has been used to categorise products into different segments. Statistical techniques and a k-means clustering algorithm have been used to understand different segments of the product. Machine learning algorithms and time series models have been used to forecast future sales trends. The business insights allow the retail store to meet customers' expectations, manage inventory at an optimum level and enhance supply chain efficiency. The present work seeks to illustrate how data-driven tactics can enhance operational decision-making in retail.

@article{Mishra2025,
  title     = {Data Analytics for Product Segmentation and Demand Forecasting of a Local Retail Store Using Python},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Arun Kumar Mishra and Megha Sinha},
  doi       = {10.14569/IJACSA.2025.0160224},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160224}
}