Market Basket Analysis
Market Basket Analysis is one of the revolutionary advanced data technologies that have enabled online retailers to understand consumer behaviors. This technology involves obtaining invaluable consumer buying variable data and demographics, which provides companies with crucial insights to drive overall marketing initiatives, product promotions, placement, and staffing. According to the IGD SupplyChainAnalysis news, online retailers such as Wal-Mart have implemented market basket analysis to obtain real-time information about customers including details such as geospatial location, demographics, and buying patterns. This news article relates to the applied analytics frameworks and methods arguing that the implementation of advanced data technologies, such as facial recognition, has facilitated the acquisition of intelligence regarding demographic information about customers, their in-store movement patterns, and store traffic. This invaluable information has empowered Wal-Mart to make effective evidence-based decisions related to real customer value.
According to the IOT Feature News, World market online leaders such as Amazon and Netflix have implemented recommendation systems to enhance quality and planning, reduce transaction costs, and increase revenues. The evolution of advanced data systems has led to significant challenges regarding information overload and the hindrance to the accessibility of real-time data. The above news article confirms that the revolutionary recommendation systems have evolved as a solution to the problem of information overload. The emergence of new data management technologies and applied analytics has facilitated the leveraging of information in organizations’ operations. The technology has empowered internet providers and users to retrieve relevant information from vast amounts of data crucial for evidence-based planning and real-time analytics. Recommendation systems have facilitated the filtering of meaningful information fragments from high volumes of perishable data to suit customers’ preferences and interests. The article is relevant to applied analytics frameworks and methods because it discusses the approaches employed by large companies, such as Amazon, to capture and manipulate data. These companies generate information from the data and use it to enhance their services by providing users with personalized, exclusive content and services.
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