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Retail: 360-Degree View of the Customer

This article is the third in an editorial series with a goal of directing line of business leaders in conjunction with enterprise technologists with a focus on 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.

In the last article, we had a goal of directing line of business leaders in conjunction with enterprise technologists with a focus on opportunities for retailers and how Dell can help them get started. The complete insideBIGDATA Guide to Retail is available for download from the insideBIGDATA White Paper Library.

insideBIGDATA_Guide_Retail360-Degree View of the Customer

The 360-degree customer view demonstrates that retailers can get a complete view of customers by aggregating data from the various touch points in which consumers interact with retailers. One way to think of this customer view is the “what,” “how” and “why” that makes up this perspective. To gain a 360-degree view, the retailer now needs to sift through and analyze mountains of structured, unstructured, and semi-structured data from on-premises and off-premises sources (the “what” part of the view) to understand customer behavior and patterns. We can now gain familiarity with
customers through much of that unstructured data—we not only have their name, address, zip code, age, but also have their buying habits, their sentiments toward companies, search history, product preferences, and emotional responses, and more—details based on social media data, sensor data, etc. Additional insights can be gleaned from this unstructured data when you begin to recognize and track the importance of the nuances and the details.

As a quick aside, semi-structured data is a class of structured data that does not conform with the formal structure of data models associated with relational databases or other forms of data tables, but nonetheless contains tags or other markers to separate semantic elements and enforce  hierarchies of records and fields within the data. Unstructured data can be thought of as a hybrid form that can be transformed to relationally  structured data, but it can equally be loaded directly into Hadoop HDFS where it can be processed in raw form.

For the “how” and “why” of the equation, retailers are uniquely positioned to clearly define the use cases that enable them to take advantage of data—
click stream data, internal structured customer databases that can be used for modeling, combined with like datasets that are available to the public  and that can enable data-driven decisions for staying relevant in front of their customers. Retailers can use their 360° view of the customer to help drive bottom-line results, e.g. build opportunities for cross-sell/up-sell, deliver point-of-sale coupons so that customers don’t abandon carts, focus on
the importance of personalized communications and more.

Some retail devices collect structured data that retailers may or may not be using. Sales and inventory data are naturally always tracked, but it can be surprising how many retailers don’t use the data they collect from loyalty programs for a lack of a way to correlate it to anything. Fortunately, there have been some exemplary success stories like the case where the use of loyalty cards potentially saved lives—a product recall triggered phone call and
e-mail alerts directed to the purchasers of the item.

Big data also includes unstructured data that is variable in nature and comes in many formats, including text, document, image, video, and more. This unstructured data is growing faster than structured data. According to a 2011 IDC study, it will account for 90 percent of all data created in the next  decade. As a new, relatively untapped source of insight, unstructured data analytics can reveal important interrelationships that were previously difficult or impossible to determine. In retail this could be a chance to see why a sale didn’t occur—whether it was product selection, pricing, store display, or ineffective promotional material. It could also point to new ways to attract and keep customers, as well as move product.

Retailers have been watching their transactional data for many years, the next step is incorporating unstructured data like social media data along with sentiment analysis. This has been a natural progression for retail. Retail companies have been collecting data in a structured manner— transactional data—but now they’re building systems that use disparate data sources including other types of structured data such as sensor data. This is where sentiment analysis comes into play —a benefit afforded by unstructured data which doesn’t fit into a traditional relational database. As one  example, retailers are employing conversion rate optimization (CRO) technology using social intelligence by tracking conversions at scale via social media (unstructured data sets).

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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