Understanding your individual customers and being prepared to meet their current needs is essential for business success, but it’s also important to be prepared to meet their needs in the future.
By running analytics on your collected customer data, it becomes possible to predict customers’ behavior, in terms of what, when, how, where, and why they buy. With this foresight, companies can develop strategies to help maintain and grow resilient, active business relationships throughout the entire customer life cycle. This kind of insight-driven decision-making is fundamental to an effective, customer-centric marketing strategy.

Most likely, your business is already capturing plenty of useful information that is transactional data, or the basic information customers supply when they purchase a product or service—names, addresses, credit card information, SKU, and so on. However, your business likely collects, or has access to, additional customer information that you might not know how to use. This non-transactional data is commonly referred to as “dark data.”
Barton Goldenberg, founder and president of ISM, a premier customer-centric strategic advisor for organizations, describes customer inputs to dark data this way: “It's things like, ‘What have I said in the survey that was sent to me by the company? What have I said I'd be interested in learning more about? Or what marketing pieces have I responded to? Or what other companies am I buying products from?’ And then there's the big one ... and that is what have you said about us in any of the social media spheres, whether it's Facebook or Twitter or on a private social community? What have you said about us that gives us some insight into what you think about us?”

Some other examples of dark data include customer demographics, purchasing histories, product data, how customers use products, trends in their usage levels of services, how satisfied they are with customer-care services or what kind of complaints they might have raised, and information from traditional market research. As the field of data analytics has grown, experts are finding that this dark data can be valuable for predicting customer behavior and incentivizing new purchases as well. Put simply, the more data you have and the more accurately you can understand your individual customers, the more likely you can earn their business and loyalty over the long term.

Data analytics is big business

The idea of studying customers to deliver products and services that resonate with their specific interests and meet their specific needs is not new. For years, businesses have been using targeted advertising based on analytics to make themselves more visible to their specific audiences. But high-level data analytics can tell you much more than just what particular customers are looking to buy right now. It can also tell you what they will be interested in buying at different points in the future, approximately when they will be ready to purchase again, how to effectively incentivize those purchases, and how to reach others with similar interests.

To do this sort of customer behavior modeling, however, it’s important to be looking at long-range customer purchase histories and other factors beyond simple transactional data, which is where dark data comes in. For example, hotel chains have used dark data-driven insights to gauge and respond to customer loyalty trends. If a frequent traveller stays at a particular hotel chain numerous times, he or she develops a purchase history pattern, and this pattern can be analyzed to determine the best time to offer that customer a company credit card or rewards program.

The potential of dark data

To get a truly comprehensive profile of a customer, it’s necessary to mine dark data for its true potential. But that’s not as easy as it sounds, says Paul Thompson, director of analytical services for Pitney Bowes Software U.K. He explains that, when it comes to dark data, a common problem is that nobody in a company knows what to do with it or it doesn’t have the staff to deal with it. Or you may be using it, but not analyzing correctly. Sometimes, it’s even hiding in plain sight, which is why Thompson recommends Uplift Modeling, which can mine a well-established data source in a different way, generating new insight.

Thompson’s colleague, Dr. Gerhard Heide, Pitney Bowes’ director of global market strategy, who consults on Portrait Software, adds that this customer data is often in a raw state. “It’s not cataloged; it’s not being used; it’s not being combined,” he explains. “There’s a huge amount of information in it, and a number of organizations are not aware of it yet.” Heide agrees that customer insight can be obtained from a great variety of data. “On some level, you can use almost anything – the more you have the better,” he points out. “So, any data that allows you to make connections to your customers and also connections between your customers. You can see the potential for information there is huge.”

The analysis of customer dark data should lead to insight-driven action. Goldenberg says, “The question becomes ‘How do I use that information to better sell, service, or market to my customer?’” By collecting a more detailed picture of its customers, a business can offer them targeted incentives on new products and services—or “next best offers”—to see if they respond. One of the most promising methods involves tapping into the daily habits of your customers via geolocated services and marketing to them accordingly. Whatever feedback you get, that information is then added back into the calculations. “Now we have a closed-loop analytical capability. So we learn as you buy or not buy, as you respond or not respond.”

The future of data analytics

In a recent webinar entitled “Big Data Analytics & Insight,” Goldenberg and Thompson point out that there are 2.8 billion people online today, and another billion will be online in the next three years. Additionally, there will be 200 billion connected devices by the year 2020. This much interconnectivity will lead to an explosion of data collection. Thompson adds that the data itself will improve, becoming more available, accessible, prevalent, and easier to use for creating an enriched, single-customer view.

Pitney Bowes has been leading the way in business data analytics. Thompson highlights the company’s work in helping other businesses provide customer-engagement solutions, build loyalty, and grow revenue. He points to PB’s Uplift software platform, which is designed to look at data and ask, “Who can I actually persuade to change their behavior, based on history?” With these tools, he adds, businesspeople don’t need to be data scientists themselves to derive valuable insight from their customer information.

Turning customer data (including dark data) into actionable intelligence can help businesses compete by being more attuned to the present and future wants and needs of individual customers. But it’s not just about getting the big data right, emphasizes Goldenberg. “It’s about the people-process-technology components that allow you to really utilize that data to make insight-driven business decisions.”

Thompson agrees, and sums up why understanding dark data is so important for future success: “A wealth of connected data will help businesses become more efficient, selective, and relevant.”

Note: Depending on your jurisdiction, there might be laws or regulations that govern your use of dark data, so every company should think about working with an experienced, knowledgeable vendor to help maximize the benefit your unused and untapped dark data can bring your organization.