The retail industry has to contend with a monumental shift in the outlook of customers toward selecting and purchasing goods and services more and more. Millennials and the middle-aged segment of buyers, in particular, lean more toward e-retail options than swanky stores. Quick deliveries, lucrative discounts, and personalized shopping recommendations are fast becoming the basic yardsticks of buyer expectations.

This may often lead to the impression that retail businesses are dying out. However, this perception couldn’t be any farther removed from the truth. The retail industry is booming more than ever before, just in a different sphere – the digital space. The e-commerce revolution ushered in by the likes of eBay and Amazon has certainly led to massive challenges for the good old brick and mortar model of businesses but at the same time also opened up massive potential for retailers. This paradigm shift also dictates the need to move away from the culture of waste and excess and toward efficiency and optimization. It is in this realm that retail businesses stand to benefit the most from big data, predictive analytics, and the advancement of artificial intelligence.

Evolution of Analytics in Businesses
A bird’s eye view of the evolution of analytics in the decision-making process in retail businesses reveals that we have come a long way, and yet, there is a lot left to be achieved. In the 1990s, there was a clear disconnect between number-based reporting at the back office and front-office approach of businesses. These statistics were primarily used for governing internal operational decisions back in the day.

During the first few years of the 21st century, tech giants like Google started focussing on the large amounts of unstructured data lying all around us. Thus began the advent of data gathering, management, and analytics. In a span of a decade, big data started to take form and retail businesses, among other sectors, began acknowledging the power of data analytics.