Through a special arrangement, presented here for discussion is a summary of a current article from the M Squared Group blog.
Last week, I met with a company that had the most complete data that I have ever seen. From web traffic to email/direct mail to transactions and so on, this company had invested in their data infrastructure as a key asset. But they were still dissatisfied. They weren't making money from the data.
The complaints of that digital marketing team were the same as companies with less complete data:
"Management keeps asking questions that are interesting but not actionable."
"We haven't had time to create the measurement approach."
"Control groups are not part of the company culture here," and so on.
The main complaint boiled down to one thing: "We don't know the right questions to ask."
This issue is not uncommon. When you look at companies who are the most successful at leveraging Big Data, they have the following characteristics:
- Fewer questions to start, rather than more. Rather than investigate everything under the sun, these companies have taken a "step-by-step" approach focusing on identifying opportunities for quick wins from relatively quick analysis. The goal is to increase company confidence in the accuracy of the data and analysis as well as to demonstrate how many profitable actions can be identified and taken based on data-driven insights.
- Begin with the end in mind. Rather than research "why people buy more red t-shirts in Tulsa on Tuesday," each analysis should begin with a hypothesis of what the answer might be, and an explanation of the action that could be taken based on that finding. That way, the ROI of data-mining Big Data can be clear and meaningful.
- Measure and publicize the results. The value of Big Data is based strictly on the actions that can be taken by analyzing it. The actions must be driven cleanly by the insights (so others can see how the analysis led to conclusions which led to marketing programs) and must result in a change in customer behavior that drives incremental revenue and profit. Whether pre/post, vs. prior year or vs. control group, results must be measured to ensure they are valid.
Organizing data to answer questions is one of the challenges of Big Data but the greater one is focus. We will, more than ever, be able to ask almost any question about our businesses and the data will be there to answer.
But the key is — which questions to ask.