Retailers Harness Big Data and Big Content for Big Profits

Many savvy retailers have already implemented tools and strategies for turning their Big Data into more sales and more efficient operations, which in turn leads to more profit. There are countless case studies of major brands using their structured data (from POS systems, CRM’s, and marketing databases) to provide faster, more personalized, and more lucrative customer experiences.

Unfortunately, most of them are only tapping into a fraction of the vast amounts of information available to them, and in the process, minimizing their ability to harness their information assets in manner that helps to increase their bottom line.

Big Data Only Scratches the Surface

Retailers today are sitting on massive amounts of unstructured content, like notes from call center agents, results from satisfaction surveys, and even emails from customers. It’s called Big Content, and it’s largely ignored in most Big Data projects since unstructured content is more difficult to organize and analyze because it does not reside within a structured data system (such as a CRM or POS system).

Plus, the Big Content universe is massive, including just about anything that doesn’t adhere to a pre-defined data model or fit cleanly into your database tables. Emails, videos, spreadsheets, call center recording, reports, presentations, documents such as purchase orders and invoices, audio files, and images are all examples of common types of unstructured content.

In fact, it’s widely believed that as much as seventy to ninety percent of the information that most companies collect is unstructured, and it’s growing by leaps and bounds. In fact, IDC estimates that the worldwide volume of digital information will grow by a factor of 10 between 2013 and 2020.

When Content Becomes Unmanageable, the Customer Experience Suffers

As consumers continue to demand for retailers to provide seamless, highly personalized omnichannel customer experiences, being unable to find, evaluate, and act upon all of your data can not only hinder the effort, it can be detrimental.

Consider this example: A customer buys a major appliance online, and two months later mails in a warranty claim. The claim document (unstructured content) gets saved to the company's network drive, which isn’t connected to the company's CRM system (a structured data system). As a result, the claim document isn’t readily visible to customer service agents, and they are unable to check on the status when the customer calls the contact center.

In this scenario, the retailer had critical data in hand that could have made the experience a better one for the customer — but since the data was unstructured and siloed, it was effectively invisible to the people who arguably needed it the most.

But it’s not just the customer experience that can suffer, or that can be improved. Just about any department, function, or process can boost efficiency and effectiveness by making Big Content more accessible and more intelligent.

The Ultimate Solution: Harmonize Big Data and Big Content

Forward-thinking retailers are looking beyond simply providing a better way to organize and access unstructured content. Instead, they’re bringing Big Data and Big Content together — and as a result, they’re able to get a true picture of customer and channel performance, improve operational efficiency, and ultimately, provide faster and better service to customers.

Here’s an example. An email arrives to a general customer service inbox with a photo of damaged merchandise (unstructured content). By connecting this information to their structured data, customer service gets more insight of the customer and their purchase history and knows, whether the item is eligible for a return or warranty claim.

How EIM Makes it Possible

Enterprise information management (EIM) systems focus on connecting unstructured content with structured data systems. How? The most sophisticated EIM systems use metadata, or “data about the data,” to serve as the bridge that connects unstructured content with structured data systems. For example, using metadata, an EIM system can intelligently link a customer feedback email to the right customer account in a CRM system. As a result, sales people can see how the customer has been interacting with others in the organization. This information is often buried in employees’ email boxes even though it is often critical information that sales reps need to have before contacting the client next time.