Why 'Dark Data' is the Key to Better Serving Customers

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Fintech icon on abstract financial technology background . - Stock image
Fintech icon on abstract financial technology background . - Stock image Credit: monsitj/iStock

Why did Under Armour recently purchase MapMyRun? Why did IBM acquire Weather.com? Both wanted to understand their audiences and, in turn, develop a better and more profitable product.

But marketers don't have to acquire disparate businesses to reap similar rewards.

According to the Veritas Global Databerg Report, "52% of all information currently stored and processed by organizations around the world is considered 'dark', whose value is unknown." Ben Gibson, CMO of Veritas, explains this phenomenon as a result of a "data-hoarding culture." While brands understand it's hugely important to collect data, they have no idea how to tap into the data they've amassed.

Customer first, data second
The key to success when mining dark data is driven by the lowest common marketing denominator, the link between all those disparate technologies and otherwise useless data points: the customer.

When it's time to decide what dark data to look for, it's best to start with a question about how to better interact with or understand a customer.

Take user-generated video, which is highly trusted by consumers but not easily analyzed by marketers. Google recently unveiled a new video intelligence API that could unlock insights from images, movements and audio.

Social media, loyalty programs and subscription data collected for one purpose can give insight into other consumer activities. Target India, the Indian subsidiary of the U.S.-based big-box retailer, uses machine learning to mine registration data for insight into life events to more deeply personalize marketing messages. Popular posh brand retailer Rue La La evaluated churn data to more deeply understand the motivations of departing customers, which they then used to create proactive engagement strategies, not just "we miss you" emails.

Even within a current system, such as a content management system, a marketer can ask, "When does a typical consumer disengage?" and then search for patterns across various consumer actions, using data that is often overlooked. For instance, a publisher could access online site activity data to see at what point people leave their pages across various template types, or a marketer could find that consumers leave a page when there are too many product choices or when the font is too small.

Asking new questions
Using data collected during an interaction with the customer can also help markers infer and test different types of behaviors for which they don't have direct answers. Take hover data. Consumers who hover their cursor over a link for a while but do not click it, or hover over a product but never put it in their cart, might have a totally different intent than someone who clicks and buys or simply scrolls on through. This "hover insight" can be the basis of exploratory remarketing campaigns to see if a consumer didn't take action because of pricing, product details or simply bad timing.

A retailer might think of ratings and reviews as content, but there is data there also. It might be possible to know the intent of a visitor by how many comments they read, or if they read positive or negative comments more. Customers using other languages might have provided companies with insights in other languages that simply need to be translated.

Dark data can be looked at as an opportunity to get more insight into unanswered questions that affect consumer interactions. The more creative the thinking, the more opportunities arise to find answers.

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