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 when I met with them, they were still dissatisfied…
They weren’t making money from the data.
The complaints that the digital marketing team had were the same ones that similar teams face in 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. Despite having Big Data in better shape than most companies, they were still bogged down in the same issues as their less successful brethren.
The main complaint boiled down to one thing: “We don’t know the right questions to ask.“
This issue is not uncommon. And very frustrating. And as Yogi Berra once said, “If you don’t know where you are going, you will wind up somewhere else.”
When you look at companies who are the most successful at leveraging Big Data, they have the following characteristics:
1. 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 of this effort 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.
- One example of this type of analysis include product gap analysis, to identify customers who have purchased Product A without the accompanying Product B. Cross-sell promotions of this type are usually immediately profitable.
2. 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.
- This approach does not mean that the analytics team should be the arbiter of whether or not a proposed action is valid; rather, the analytics team should work WITH the marketing teams to help make sure that the proposed action is as valuable as possible.
3. Measure and publicize the results. Analytics does not exist in a vacuum; the value of Big Data is based strictly on the actions that can be taken by analyzing it. For that value to be recognized, the actions must be driven cleanly by the insights (so others can see how the analysis led to conclusions which lead to marketing programs) and must result in a change in customer behavior that drives incremental revenue and profit.
- To ensure that the results are valid, they must be measured. Whether pre/post, vs. prior year or vs. control group depends on the approaches in your company (BTW — control group is the most valid, but many of the best companies use all three).
Big Data presents us all with challenges. Organizing the data to be able to answer questions is one of the challenges. But the greater challenge is one of focus. We will, more than ever, be able to ask almost any question about our businesses and the data will be there to answer.
What are we going to ask and what are we going to do with the answers?
The answer to that question may be the most important one of all.
