Thought Leaders in Big Data: PubMatic CTO Anand Das and VP Engineering Sri Gopalsamy (Part 3)

Sri Gopalsamy: We have done a really good job over the years to build this platform where we respond to bids in real time within few milliseconds. We provide insights to our publishers within a minute. For all the impressions that are going through our platform, how much of them actually get converted? What’s their average pay per thousand clicks? We expose all of the data in real time so that they can better manage their inventory, deals, and relationships with advertisers.

Sramana Mitra: When you’re calculating in real time what ads to show, across how many parameters is this optimization happening?

Sri Gopalsamy: Right now, it’s well over 350 parameters in real time.

Sramana Mitra: What broad categories do they fall under?

Sri Gopalsamy: They fall under various broad categories. We can talk about first party data from the publishers – what they know about the users. We don’t deal with PII. We don’t take any user-specific information, but publishers do classify users into various groups in various categories. Demand partners’ data also. They are audience data. We look at viewability as well as fraud. There’s a lot of fraud in the ad industry. Then we are also utilizing trends that we have seen before. If you’re talking about video ads, what’s the resolution of the ad to what’s the size of the ad. What kind of ads can be delivered? What’s the time slot? You look at these things that have well over thousand parameters, but you can optimize across 350.

Sramana Mitra: Do you have ROI analysis before PubMatic and after PubMatic? What value is this kind of Big Data machine learning-enabled intervention delivering for your publishers?

Sri Gopalsamy: We typically quote these numbers. Obviously, they vary from publisher to publisher depending on the content. We actually influence revenue growth from about 30% to sometimes over 100% for certain publishers. That’s the kind of optimization benefits that publishers get. I’d say revenue in line with publishers, we take revenue share. We also have a SaaS model platform wherein we charge the platform fee. Revenue share works out well because our objectives and the publisher’s objectives are aligned in that scenario. We only make money when they make money.

Sramana Mitra: What categories are your biggest successes in? Is it all across advertising? Is it lifestyle media, business media? Where do you see the maximum success?

Sri Gopalsamy: I’d say it’s in all the areas. We work with e-commerce customers. We work with entertainment. We work with technology customers. We have seen increases across all areas. It’s not like one particular area stands out in that scenario. We strive to make algorithms work for different types of content. The algorithm typically learns what works and doesn’t work for a particular content. That’s the benefit of using PubMatic. It’s not like it’s only meant for one vertical, but we go across.

This segment is part 3 in the series : Thought Leaders in Big Data: PubMatic CTO Anand Das and VP Engineering Sri Gopalsamy
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