Thought Leaders in Artificial Intelligence: Blueshift CEO Manyam Mallela (Part 3)

Sramana Mitra: Let’s take each of those use cases. It sounds like you have one bucket that is content providers. Udacity is a content business largely. The job of your platform is to enhance engagement around their content. That’s a content engagement problem.

In the content engagement problem, you understand what the users’ interests are and you package up relevant content to draw that person in. What is the use case in PayPal?

Manyam Mallela: Lending Tree and PayPal are consumer finance companies. They fit in the middle of borrowers with lenders. They’re usually a marketplace that sits in the middle that connects many borrowers to a set of lenders.

Just like matching users to the right courses in Udacity, Lending Tree and PayPal are matching the users to the right finance product. You’ve hit upon the core of this problem.

As more and more digital brands think of themselves, there’s always this notion of what do you offer. Sometimes, it’s content. Sometimes, it’s a product. Sometimes, it might just be videos. We’re working with a company that delivers meals to individual doorsteps.

In the technology world, we recognize that as a bipartite graph. It’s to say that you have consumers on one side and then a catalog of products or offers on the other side. How do you connect them such that it’s meaningful and it drives marketer goals as well as user engagement. That’s the crux of the problem that we solve for many of these large brands.

Sramana Mitra: At the beginning of this conversation, you described the lineage of where you come from. You talked about Venky and Anand and their journey with Junglee. Junglee was one of the first companies, along with Firefly, that did this collaborative filtering engine.

Talk to me, algorithmically, what is the evolution of technology from that point of where you are today?

Manyam Mallela: We have been around now for about 20+ years. We have a rich body of research from the university and industry. What happened over the last 20 years is that we used to think of the collaborative filtering in the Junglee days as rudimentary in understanding of tinkering of data.

At that time, the digital data is in orders of magnitude less than what we have today. What has happened over the last 15 years is that brands that are building these at scale are able to leverage much more data volume, much higher velocity of data, and much more longitudinal view of the user, which is to not only look at the objective of a recommendation but also to how does it drive the engagement of the user three months from now.

If you go back to the use case I was talking about, we’re not just interested in the student coming in and clicking but to understand the journey for that student. Is that student going to take this course for the first two weeks? What will it take for them to come back again and again? That realm of optimization is a multi-faceted experience. 

This segment is part 3 in the series : Thought Leaders in Artificial Intelligence: Blueshift CEO Manyam Mallela
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