Your entire organization is creating data that could enable you to better market your brand and products. Unfortunately, that data is being used rarely, if at all. The term for it is “dark data.” Its official definition from Gartner is: “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing). Similar to dark matter in physics, dark data often comprises most organizations’ universe of information assets. Thus, organizations often retain dark data for compliance purposes only.”
Today’s dark data isn’t just data that’s sitting around waiting for an audit
Gartner focuses on how dark data is stored for audit and compliance purposes, but the definition has expanded beyond that so it encompasses all kinds of things that often aren’t even necessarily thought of as data.
Examples include recorded conversations, emails and messaging chats with customers; financial transactions; digital marketing campaign information; CPC rates and SEO, brand health tracking and more. But that’s not all—there’s also the data related to the different relationships you have with customers that’s existing separately somewhere in your organization, some of which probably isn’t associated with the customer data you are currently analyzing.
From eco-friendly to a dancing bear
The effects of not including dark data in the analytics used for marketing tactics and strategy really began to be felt in 2013. This was after companies became savvier at applying analytics to other kinds of big data to help them make their marketing decisions. In a 2014 Adweek article, Elena Klau wrote about what can happen when dark data analytics isn’t part of a strategy based on analytics.
Klau described a person who sees a commercial for a favorite brand of soap, uses the soap in a commercial, and sees a promotion for the soap in an online ad feed that does not resemble the customary appeal to refined scent and ecological friendliness she usually sees. Instead, the ad has a dancing cartoon bear. Later that day, the consumer looks for the soap and notices that it has a new pink and brown package with a bear on it. The consumer questions whether this is the right brand to use after all.
In this soap opera, brand loyalty becomes brand questioning
In this story, a loyal customer is now questioning whether she should continue buying the soap because she is seeing a story that’s not connected and doesn’t resonate well. A repackaging strategy was executed without a complete picture of the customer and that customer might now be lost.
Fast-forward to today, where dark data is gaining recognition as a source to be reckoned with. Blogs and articles have stories of how Netflix, Vigilant, some oil and gas providers, Oncam and other companies are analyzing dark data to improve customer satisfaction, experiences and loyalty.
The five types of dark to Datawatch
So, what kinds of data are they focusing on? What kinds should you be investigating?
According to an e-book from Datawatch, there are five types:
- Salesforce data trends over time
- Lead segmentation data
- Comprehensive spend analysis
- Competitive benchmarks
- Market and industry research trends.
By aggregating everything from salesforce daily snapshots to quarterly financial reporting, you can fill in some big blanks while getting critical insight for improving performance and maximizing ROI on marketing dollars spent.
What’s in it for you?
A lot. For example, you improve demand generation and lead nurture because you have accurate response rates and the laser customer focus that results in more conversions. You get a much better sense of the costs of activities because you have a deeper view into more expenditures. And, you gain competitive advantage because your holistic view of customer and competitor online behavior and trends help you pinpoint your sweet spots and areas of attack with precision.
How can you get started?
The challenge with dark data analytics is that it comes with perceived roadblocks, namely that only data scientists can unlock the potential and that sophisticated and expensive systems are needed.
Enter IBM Watson Analytics and Cognos Analytics. Both have the capabilities that enable you to benefit from data analytics without going to a data scientist or investing in a new system. You can spot patterns and trends with the automatic visualizations created by Watson Analytics and that inspire you to dig deeper into analysis using Cognos Analytics. And, when you use Datawatch Monarch for IBM Analytics for self-service automated dark data preparation, you can get to important insights from your dark data faster.
To sign up for Watson Analytics, visit www.watsonanalytics.com.
Learn more about Datawatch Monarch for IBM Analytics here.