insideBIGDATA Guide to Retail

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In this new insideBIGDATA Guide to Retail, the goal is directed toward line of business leaders in conjunction with enterprise technologists with a focus on the above opportunities for retailers and how Dell can help them get started. The guide also will serve as a resource for retailers that are farther along the big data path and have more  advanced technology requirements. This article is the first in a series that explores a high-level view of how the retail industry has been influenced by big data technologies. The complete inside Big Data Guide to Retail is available for download from the insideBIGDATA White Paper Library.


Download the insideBIGDATA Guide to Retail

Big Data for Retail – An Overview

Few industries have greater access to data around consumers, products, and channels than the retail industry. Data coupled with insights are at the heart of what drives this business. It’s a logical consequence then that retail is the vertical market that adopted big data and technologies like Hadoop earlier than many other industries. Retail started with diverse transactional data but is now much more sophisticated in the way technology is being applied toward gaining competitive advantage. Big data refers to huge data sets characterized by larger volumes (by orders of  magnitude) and greater variety and complexity, and generated at a higher velocity than companies have faced before. The use of big data is all about deriving real meaning from the increasing amount of data everywhere and providing  richer insights into business patterns and trends, to drive operational efficiencies, and to improve advantage in a competitive marketplace.

So what is big data in terms of its relevance to the retail industry? In the simplest terms, big data offers a means to understand shoppers via a myriad of digital touch points—from their online purchases, to their presence on social networks, to their visits to brick and mortar stores (using the shopper’s cell phone Wi-Fi to track physical movements). But while big data might hold the key to newfound consumer insights, retailers are grappling with how to meaningfully process—and ultimately monetize—huge amounts of unstructured data in the form of consumer tweets, images, video, and more. In addition, machine generated structured data adds yet another dimension, e.g. sensor data like RFID tags and GPS data, web log data and point-of-sale data.

Retailers that understand how to analyze the multitude of digital bread crumbs left behind by unknowing browsers and buyers are finding themselves at a major advantage over competitors that solely rely on intuition. Big data enables companies to create comprehensive customer profiles and precise product recommendations. Today, customers are finding the products they want quicker than ever before, and in many cases the items are finding them, thanks to targeted ads.

By placing buying habits under the microscope, companies are perfecting the science of impulse buying. Amazon and Alibaba are renowned for their ability to suggest products based on who you are, what you look at and what you’ve bought. It was reported that in the first year the big data technology stack was deployed on, there was an increase of 20% in the number of shoppers completing a purchase after searching for a product because they were able to quickly find what they were looking for (Bloomberg Business, April 2014). Physical retailers are now using big data, storing massive amounts of information on severs and using software to search for trends, to drive more people into their stores.

Whether the targeted ads are online or off, the challenge for retailers is to avoid seeming invasive. We’ve all heard the anecdote of the retailer who knew a female shopper was pregnant before her family based on her purchase patterns. Seemingly invasive or not, stockpiling information isn’t  effective if you don’t have enough of it or the means to properly sift through it.

One important aspect of big data in retail is the more data you collect and act on, the greater the benefit. Being on the front end of the customer relationship, it’s important for a retailer to understand everything about a customer, engage them in every way possible, and continuously build on that relationship. This is how big data provides benefit to the retailer in terms of customer intelligence leading to extending customer life-time value (LTV).

Retail generates a flood of complex structured and unstructured data. There is a vast number of sources of this data, but for a short-list we can consider the following:

  • Point-of-sale (POS) devices
  • Websites
  • Mobile commerce solutions
  • Social media sites like Twitter and Facebook
  • Customer loyalty programs
  • Video surveillance systems with video analytics that record store traffic patterns, employee-customer interactions, and customer-merchandise interactions (such as the dwell time around an end cap)
  • UPC and RFID readers
  • Employee devices, including PCs, smartphones and other handheld devices
  • Sensors: Near Field Communication (NFC) and Bluetooth Low-Energy (BLE) on customer smartphones, real-time location systems (RTLS), Wi-Fi and GPS—electronic asset protection at the point of sale (POS) and point of exit, bar codes and application specific sensors.

Connecting these individual pieces of data is what the future of retail looks like. In fact, online companies like Amazon are already heavily invested in finding the connecting points—the company’s product recommendations are a result of big data analysis, and Walmart and other large retailers are busy putting in place similar big data tools. But big data depends on the collection and transfer of data, including initial analysis or analytics, and then the ability to deploy solutions based on big data business intelligence.

Over the next few weeks we will explore these big data retail topics:

If you prefer the complete insideBIGDATA Guide to Retail is available for download in PDF from the insideBIGDATA White Paper Library, courtesy of Dell and Intel.


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