Video produced by Steve Nathans-Kelly
At Data Summit Connect 2020, The AI-Powered Enterprise author and CEO of Earley Information Science, Seth Earley explained how to look at data initiatives as CEOs see them—in terms of revenue increases and measurable business outcomes.
Full videos of Data Summit Connect 2020 presentations are available at www.dbta.com/DBTA-Downloads/WhitePapers.
"If we look at the customer experience, that is one of the things that we can measure. We can measure many, many different aspects of this. And if we can focus our data efforts on particular stages of the customer experience, we can actually solve lots of different problems. We can measure the result, but we can apply the outcome to many different types of data initiatives," Earley said.
He suggested grounding these programs so businesses discover the impact on their revenue, the customer, and the experience.
"And what's happening is, we're getting signals from lots of different systems," Earley said. "So what we need to do is figure out what are those signals and what are they telling us? What are they indicating? How do we make a decision? How do we do remediation? And there's a bunch of ideas here. We can have something where people are coming to the website, and then they're leaving. They're bouncing out. They're not buying our products. They're not even investigating the products. They're not registering. So what does that mean?"
That means that there's a number of possibilities, he explained. Either the path wasn't clear, the user was in the wrong place, they didn't meet, they didn't understand the content, it didn't meet their needs, or the navigation was misleading.
"With one industrial client, we've been working on this for three years. It takes a long time. It takes a while to get a handle on this. First, you have to have good data. Then you have to have the right processes. You have to bring the right people to the table. You have to understand chains of trust when we're looking at processes, and when we're trying to leverage our data in some way, shape, or form," Earley said. "But this is critical, and people don't necessarily intuitively understand the linkage. The linkage is what's critical. We have to show why this is going to enable these processes. We may think it's obvious, but it is not."