“Big retail” business includes both “brick and mortar” stores (for example – Target and TJ Maxx) as well as Internet-based businesses such as Amazon.com. Both types of big retail business models rely upon exemplary control of supply chain management, usually leveraging “just in time” processes, which requires them to have an in-depth understanding of their target customer base, what their customers want and need now, and what they will want and need in the future. To achieve performance goals in this area, big retail organizations must employ technologies fueled by extensive databases of current customer information and market trend data containing customer purchasing trends, purchasing changes, disruptive technology marketplace impact, to reveal potential future supply chain changes  triggered by a number of sources such as vendors, manufacturers, or even raw material availability. Monitoring the enormous amount of supply and demand customer/consumer data available is an enormous task made humanly possible and greatly simplified through business intelligence technologies such as big data analytics that are extremely effective at generating highly relevant and actionable information from raw customer/consumer data.

Customer/consumer data (obtained from trusted internal and external market sources) when processed using big data analytics technologies, provides big retail organizations with information essential to accurately understand, predict, and plan for future market trends in a strategic, proactive approach rather than relying primarily upon tactical responses. Processing customer/consumer data using big data analytics provides big retail organizations with a solid understanding of their customer base key demographic information correlations and associations tied to age, gender, buying habits, economic status, and more, revealing trends that indicate from where the business is gaining new customers and why; which products are in highest demand and from where; who is purchasing each product (customer profiles); which product lines to drop for lack of interest/future sales; and other new information created from customer/consumer data processing. Big data analytics enables businesses to see complex correlations and make precise inferences about their customers and potential customers that would otherwise simply not be possible.

Information generated by big data analytics also assists retail management teams in making strategic decisions about future company direction when combined with sophisticated DSS (Decision Support Systems) and ES (Expert Systems). Big data analytics helps management teams consider information that would not otherwise be visible, enabling more solid, knowledge-based decisions, less guesswork about company direction, and increased predictability which leads to greater short and long-term successes.





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