Bi in the Retail Sector...a Comprehensive Analysis
1. Abstract (One page)
One of the challenges for companies that have invested heavily in customer data collection is how to extract important information from their vast customer databases and product feature databases, in order to gain competitive advantage. The retail industry collects huge amounts of data on sales, customer buying history, goods transportation, consumption, and service. With increased availability and ease of use of modern computing technology and e-commerce, the availability and popularity of such businesses has grown rapidly. Many retail stores have websites where customers can make online purchases. These factors have resulted in increase in the quantity of the data collected. For this reason, the retail industry is a major application area for data mining. This paper elaborates upon the use of the data mining technique of clustering to segment customer profiles for a retail store. Retail data mining can help identify customer buying patterns and behaviours, improve customer service for better customer satisfaction and hence retention. The retail industry collects huge amounts of data on sales, customer buying history, goods transportation, consumption, and service. With increased availability and ease of use of modern computing technology and e-commerce, the availability and popularity of such businesses has grown rapidly. Many retail stores have websites where customers can make online purchases. These factors have resulted in increase in the quantity of the data collected. For this reason, the retail industry is a major application area for data mining. This paper elaborates upon the use of the data mining technique of clustering to segment customer profiles for a retail store. Retail data mining can help identify customer buying patterns and behaviours, improve customer service for better customer satisfaction and hence retention.
Case Background Note a. Industry Overview
The Indian
One of the challenges for companies that have invested heavily in customer data collection is how to extract important information from their vast customer databases and product feature databases, in order to gain competitive advantage. The retail industry collects huge amounts of data on sales, customer buying history, goods transportation, consumption, and service. With increased availability and ease of use of modern computing technology and e-commerce, the availability and popularity of such businesses has grown rapidly. Many retail stores have websites where customers can make online purchases. These factors have resulted in increase in the quantity of the data collected. For this reason, the retail industry is a major application area for data mining. This paper elaborates upon the use of the data mining technique of clustering to segment customer profiles for a retail store. Retail data mining can help identify customer buying patterns and behaviours, improve customer service for better customer satisfaction and hence retention. The retail industry collects huge amounts of data on sales, customer buying history, goods transportation, consumption, and service. With increased availability and ease of use of modern computing technology and e-commerce, the availability and popularity of such businesses has grown rapidly. Many retail stores have websites where customers can make online purchases. These factors have resulted in increase in the quantity of the data collected. For this reason, the retail industry is a major application area for data mining. This paper elaborates upon the use of the data mining technique of clustering to segment customer profiles for a retail store. Retail data mining can help identify customer buying patterns and behaviours, improve customer service for better customer satisfaction and hence retention.
Case Background Note a. Industry Overview
The Indian
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