Data Convergence; The Role of Machine Learning in Retail

  • October 10, 2017 at 1:38 PM EDT
  • By Bill Peterson, MapR Technologies
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0aaBill Peterson MapRBig-data driven changes are sweeping through the retail industry. Though the retail sector is no stranger to big data, much of the focus has been on analyzing historical data. While this has been useful in creating efficiencies and identifying consumer trends, retailers need to go further if they want to remain competitive and increase their margins. Using artificial intelligence (AI) and machine learning in real time is the key to unlocking deeper insights in all aspects of retail operations.

Big data and machine learning promise payoffs at nearly every stage in the retail process, ranging from supply chain optimization to inventory management to workforce scheduling to customer personalization. McKinsey has estimated that retailers can use big data and machine learning to increase their operating margins by up to 24%. By harnessing big data and real-time analytics, retailers can deliver customized experiences that anticipate and reward customer behavior in a way that creates not only long-term bonds but powerful word-of-mouth advocacy. In addition to improving the customer’s overall shopping experience, machine learning can be used in retail to manage operations and inventory.

What Does This Look Like Today?

Customers are more tech savvy than ever. Armed with smartphones, they have the entire Internet at their fingertips, which can be either a hazard or an opportunity for retailers. The last thing a brick-and-mortar store wants is to lose a sale because a customer scans a barcode and finds the item cheaper elsewhere.


To combat this, retailers are using computing on the edge and Internet of Things (IoT)-connected devices to turn potential showrooming into sales. When a customer scans a pack of printer ink, connecting through the store’s WiFi, the retailer can use this as an opportunity to serve a coupon or make personalized recommendations for complementary products such as printer paper or a computer mouse, upselling and cross-selling additional items. This needs to be done in real time, which is where machine learning comes in.

Retailers can use IoT devices such as sensors, beacons and RFID with WiFi to understand customer and product movement throughout the store, both enhancing the customer experience and maximizing profits. Machine learning can leverage historical data, current behavior and even social media platforms to analyze customers’ profiles in real time while customers shop, to send personalized promotions to their smartphones via in-store beacons. Retailers often know more about customers’ preferences than customers know themselves.

These IoT devices can be used to automate efficiencies both in operations and the supply chain too. RFID tags can track inventory and ensure availability by using machine learning to significantly reduce out of stock items. Sensors can determine when store shelves are low and need to be restocked. Retailers can use machine learning to dynamically update pricing based on inventory, competitors’ pricing and even weather-related events. Through AI and machine learning, retailers can improve conversion rates and increase customer visits.

What about the customers who have searched the web for product specs, pricing and availability before even walking through the door of a brick-and-mortar store? Ensuring the experience is the same across all channels is important in developing brand loyalty. The last thing retailers want is for potential customers to walk out the door because pricing on that printer ink is different in the store than it is on the retailer’s web site. Unified commerce is key, and customers’ experiences need to be consistent across all channels.

What Could This Look Like In The Future?

AI, machine learning, and computer vision could mean grab and go shopping, where consumers take what they want off the shelves, leave without checking out, and payment is made automatically. Someday soon facial recognition software and machine learning will allow retailers to personally greet customers at the door, anticipate orders and direct customers to appropriate locations. Still other possibilities include autonomous shopping carts that follow customers around the store and automatically deliver goods to cars or to homes via drones.

The Promise Of Big Data In Retail

Understanding and winning customers in today’s competitive retail environment requires analyzing multiple layers of customer data. The challenge is to understand why shoppers are buying in such a way that influences sales. By leveraging accurate and timely data, retailers can act on insights and augment human activities to improve both sales and the shopping experience. Retailers must overcome today’s data challenges with the right data platform in order to create more targeted promotions, tailor store assortments to specific clientele and create a personalized shopping experience for all stages of the buying cycle.

The promise of AI and machine learning in retail is big, but in order to achieve this reality, retailers need to be able to manage all their disparate data from a multitude of sources. Retailers need a big data platform in which data is available and consistent regardless of where it originates and where it is accessed. By harnessing the power of big data, retailers can achieve one view of a customer across all channels and one view of inventory across their organization. To make all this possible, retailers need a cost-effective solution to store and share data, and translate it into actionable insights.


William “Bill” Peterson is Senior Director, New Business Initiatives, Solutions and Vertical Marketing for MapR. Prior to MapR, Peterson was the Director of Product and Solutions Marketing for CenturyLink. Prior to CenturyLink, he ran Product and Solutions Marketing for NetApp’s Analytics and Hadoop solutions. In addition to his marketing role at NetApp, Peterson was the Marketing Co-Chair for the Analytics and Big Data committee, SNIA. He has also served as a research analyst at IDC and The Hurwitz Group, covering the operating environments, content management and business intelligence markets.

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