Thought Leaders in Artificial Intelligence: Mike Flannagan, SVP of Analytics, SAP (Part 3)

Sramana Mitra: The Netflix example is not a right example, because Netflix is not a vendor that sells AI software to other people. I think this question is only reasonable when you take into account a vendor who sells any domain-specific AI software to a whole lot of different customers who could be competitors.

Netflix is running their own service and they collect data. They own the data. I don’t think that’s even a question that Netflix should be able to use that data to be able to power their recommendations better. I don’t think that’s up for questioning anyway. Other people may think about it. >>>

Thought Leaders in Artificial Intelligence: Mike Flannagan, SVP of Analytics, SAP (Part 2)

Sramana Mitra: The job titles become critical because all the search engines and the AI on that side operate on the basis of keywords. Using the right keyword vastly enhances the findability of certain things.

Mike Flannagan: Absolutely. Say for example I tell you, “I want you to go find me a data scientist.” You’d say, “Okay, I’ll go search for data scientist.” What I really meant was I want you to go search for somebody who has these five job skills. Our system is becoming more and more intelligent about understanding what skills underpin various job descriptions and those job descriptions may apply to different job titles.

If it’s the same five underlying skills, then recommending that if you’re considering this role and this title, you might also look >>>

Thought Leaders in Artificial Intelligence: Mike Flannagan, SVP of Analytics, SAP (Part 1)

This is a superb conversation about the trends and directions in which AI is evolving, especially in business applications.

Sramana Mitra: Let’s start by introducing our audience to SAP’s artificial intelligence activities. What trends are you seeing? What trends are you leading?

Mike Flannagan: The most significant thing that we’re seeing is that people don’t expect to interact with artificial intelligence, machine learning, advanced analytics as a side card to their core business processes. When we think about how to apply machine learning, we’re thinking about how to apply it in the context of existing workflows and how to enrich business processes >>>

Thought Leaders in Artificial Intelligence: Diaz Nesamoney, Founder and CEO of Jivox (Part 6)

Sramana Mitra: We do content-based marketing largely. Everybody that we encounter finds us through our content. We essentially run a media property and all the ancillary distribution of our media work. We put in as much finesse as possible into that work.

Diaz Nesamoney: If you think about personalization, it’s essentially content-driven. We’re just saying, “If this person is interested in this kind of car, why not share with them content around that instead of showing them generic things?”

Sramana Mitra: I think that content is becoming the brand these days. It will increasingly be that as people >>>

Thought Leaders in Artificial Intelligence: Diaz Nesamoney, Founder and CEO of Jivox (Part 5)

Sramana Mitra: Can you walk us through some use cases? You talked about Claritin and the allergy season as one set of identifiers around which you are personalizing. What are some other examples?

Diaz Nesamoney: The hotels, for example, are looking at multiple such data points. The loyalty program says a lot about you. They know how frequently you stay and what type of properties you stay in. Do you travel for business or for pleasure? They start to understand what your travel patterns are. If a hotel analyzed me, they would realize I go to New York every other week, I go to London once a month, and go to India every quarter. It’s fairly predictable. >>>

Thought Leaders in Artificial Intelligence: Diaz Nesamoney, Founder and CEO of Jivox (Part 4)

Sramana Mitra: Talk to us a little bit about all these different data sources. What are the strengths and weaknesses? What level of depth can you find?

Diaz Nesamoney: The differences and, arguably, the challenges with data is two-fold. One is some data is essentially not very accurate because it’s based on projections or modeling. They call it look-alike modeling. There, the data is often wrong. That’s one problem. It turns out that the data we collect from the brand is very precise. We tend to lean more on that than all the other data. The other data is used to supplement. >>>

Thought Leaders in Artificial Intelligence: Diaz Nesamoney, Founder and CEO of Jivox (Part 3)

Sramana Mitra: The engine that you are selling sits on your end and the customer basically buys it in a cloud mode? Is it a managed service?

Diaz Nesamoney: It can be either way. The software and the technology sits on the cloud. That’s what allows us to scale across hundreds of customers and massive amounts of data. A client can decide to use it themselves if they have the expertise to operate it themselves, or they can work with us. We have a managed service offering. A lot of marketing teams and agencies tend to outsource a lot of things. >>>

Thought Leaders in Artificial Intelligence: Diaz Nesamoney, Founder and CEO of Jivox (Part 2)

Sramana Mitra: Where does the data come from? Where are you getting the data on which you are running your personalization?

Diaz Nesamoney: We work directly with brands. Let’s take, for example, a large hotel chain. A lot of the people would go to the hotel chain website. There’s a tremendous amount of data there. What category of hotel do they look at? That tells us a lot.

There’re also third-party data platforms like Oracle’s BlueKai and other data management platforms. There are a number >>>