Palo Alto, Calif.-based Apigee has developed Apigee Insights, a big-data analytics platform designed let enterprises better understand how customers are using their applications.
The ability to leverage data is good. Even better is the ability to leverage data that has some context attached to it. So says Anant Jhingran, vice president of products for Apigee, a Palo Alto, Calif.-based developer of application interface (API) technology and services. To create that context, the company developed Apigee Insights, a big-platform designed to let enterprises better understand how customers are using their applications.
Jhingran is used to helping machines make sense of data and apply it appropriately. While at IBM, he helped develop "Watson," the computer system that leverages artificial intelligence to the point that it is capable of answering verbally posed questions — an ability demonstrated in dramatic fashion when Watson won $1 million playing the popular television quiz show Jeopardy.
Today, one of the biggest challenges that the app economy faces is that far too much of what enterprises — particularly retailers — believe they know is based on what previously has occurred in their brick-and-mortar locations, Jhingran said.
"The API channel, if I'm a retailer, is something completely different," he said. "I have no idea what the engagement models are. I have no idea of how that correlates to the rest of what I do."
In other words, how customers make buying decisions when they connect to a retailer through an application or website can be very different than the in-store experience; this also holds true in terms of customers' deciding whether to make a return visit. To help such entities better understand this dynamic, Apigee created the Insights platform, which melds and analyzes data from an enterprise's API programs with information culled from other internal and external sources to generate much-needed context that, in turn, leads to better business decisions, according to Jhingran.
"Let's say that you're [a soft-drink company]," he said. "Obviously, you're going to be interested in whether people buy six packs or liter bottles. … Knowing what's happening on your primary channel helps you figure out promotions.
"But what if you were to layer on top of that data what else people buy when they buy [soft drinks]?" Jhingran continued. "If you knew that they also bought chips, for example, you would figure out that there might be strategy for you to either get into the chips business [yourself] or to do some cross-promotions. Putting context around the core data flow helps you to better understand and build out your retailing strategy."