Retail industry collects large amount of data on sales and customer shopping history. The quantity of data collected continues to expand rapidly, especially due to the increasing ease, availability and popularity of the business conducted at a Supermarket/Walk in Store or through an Online Store/E-Retailing website.
Retail industry provides a rich source for data mining. Retail data mining can help identify customer behavior, discover customer shopping patterns and trends, improve the quality of customer service, achieve better customer retention and satisfaction, enhance goods consumption ratios, design more effective goods transportation and distribution policies and also reduce the cost to business.
Retail is one of the most important business domains for data science and data mining applications because of its prolific data and numerous optimization problems such as optimal prices, discounts, recommendations, and stock levels that can be solved using data analysis methods.
The rise of omni-channel retail that integrates marketing, customer relationship management, and inventory management across all online and offline channels has produced a plethora of correlated data which increases both the importance and capabilities of data-driven decisions.