Retail Analytics Romance with Big Data

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Two decades ago, America’s biggest Retailers—Walmart—planned to increase its market size by penetrating rural communities located in small towns with the view of bringing its services to all and sundry while maximizing its profitability. Walmart successfully pulled this off by analyzing the large sets of data available to them—gathered from its previous ventures—for understanding the consumption needs of these communities as well as selecting choice locations for setting up its stores.

Walmart’s move has been credited by most market analysts as the first use of big data in the retail industry, giving it the tag of “merchandising pioneers in big data” due to the fact that its analysts successfully analyzed the millions of customer data available to them without the use of today’s sophisticated software/hardware tools. Since then, smaller retail chains both off and online has utilized big data in understanding customer consumption needs and building adequate marketing strategies.

How Big Data is transforming the Retail Industry

Walmart, Amazon, E-bay, amongst others are just a small group of large merchandising organizations that have incorporated the use of big data into their marketing policies and this raises the question of what these large organizations in particular and smaller retail outlets/chains stand to gain or lose from the use of big data. Quoting Forbes contributor—Marianne Bickle—the input of big data in Retail are ambivalent, because why it has many benefits, it also raises a couple of un-answered questions.

The use of big data has helped retailers:

  • Optimize the pricing of merchandise—with the ability to analyze large data streams coming from competitor research, customer behavioral pattern, inventory analysis etc. setting competitive prices on services or goods produced to capture a customer base has never been easier. The use of big data in both virtual and physical retail stores helps these business organizations accurately manipulate real-time pricing according to meet market demands and its other influences.
  • Improve Marketing Campaigns—understanding the amount of time consumers spend perusing an inventory on an e-commerce site or in a store as well as receiving realistic data on how customers relate to your adverts use through the use of pay per click data, online coupons and video Ads, can help retailers tailor marketing campaigns to fit the public’s perceptions.
  • Improve Store Operations—analyzing customer care data that comes in the form of phone calls, comments and reviews on social media platforms can help retailers achieve a couple of things such as; optimizing shopping experiences, increasing inventory lists, and handling negative press coverage emanating from disgruntled users who turn to the social media to vent their frustrations.
  • Product Placement and Customer Satisfaction Analysis—with the advent of video data recording software tools such as “Smart Mannequin”—designed by Almax—and “Neoface” which can be used to analyze the faces of shoppers in a store to note either their frustration or elation, the ability to gauge customer satisfaction and product arrangement has been changed forever. Due to the possibilities, of noting how each customer views a store’s inventory, the quality of service provided as well as merchandise pricing.
  • Staffing Policies and Providing Decision Support—large retail organizations with numerous outlets, daily face the issues of providing adequate staff and technical teams saddled with the responsibilities of optimizing a customer’s shopping experiences. The use of big data has enabled these organizations spot outlets that require; more staff due to sales volume, a technical team to help customers as well as customized inventory lists to cater for a wide range of customers.

As much as professionals in the field of big data try to paint rosy pictures concerning its use in the retail industry, the fact remains that retail outlets will continue to face the challenges associated with gathering useful data and analyzing them to predict the ever changing trends in the retail industry. But this should not be a deterrent in pursuing data associated with your store and letting these data speak for themselves.

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