Andy Nibley is the CEO of Yieldex, an Internet advertising software firm. In this interview Andy talks about how Yieldex helps its customers achieve a higher return on their advertising expenditures and how the company applies big data analytics to achieve those goals. Furthermore, he gives insights as to how the market is developing and what entrepreneurs should be looking for in this space.
Sramana Mitra: Andy, let’s start with some context about Yieldex. What do you do? Who is the customer and what is the big data angle?
Andy Nibley: Yieldex is a big data analytics firm. Our vision is to help digital publishers increase their revenue by better forecasting, better inventory management, and better pricing.
SM: Where in the publisher’s life cycle or infrastructure do you get plugged in? How does this work? Are you working with the advertising side, or are you working directly with the publisher’s side?
AN: We work entirely with the publisher’s side. We provide business intelligence about their advertising inventory so their direct sales force can do a better job at selling it at higher CPMs.
SM: So, you only work with sides that directly sell their inventory to advertisers?
AN: That is correct. We work with the really big publishers. Currently, we have more than 30% of the comScore 100. We are talking about publishers that have billions of impression per month that they are trying to keep track of. This turns out to be extremely difficult with any other tools that are out there in the market, with the exception of Yieldex.
SM: Let’s go deeper into that. I would like to do three use cases. You may choose whichever cases best illustrate your technology and your value propositions.
AN: We usually don’t name our customers. However, I can give you three scenarios of how we would help large publishers. Let’s talk about a traditional publisher that has a large online presence.
SM: What kind of publisher are we talking about?
AN: I am talking about a large news publisher based in New York. The problem they have is that before we came along, they were trying to keep track of their advertising inventory through spreadsheets. That was barely easy to do in the early days, because the inventory was sold by site and section. Men went to the sports section; women went to the fashion section. The general news section was a combination of both. As advertisers began to want to buy on the basis of audience, it became increasingly difficult for publishers to keep track of that entire inventory. Let me give you an example: Salesman A would sell to 18- to 24-year-olds, Salesman B would sell to females. Salesman C would sell in the New York area, and Salesman D would sell cars. As it turns, out there is an 18-year-old woman from Staten Island who wants to buy a Ford Focus. Basically, you sold four ads to one person.
Obviously you can only show one ad, which means for the other three ads you would get money back or replace it with another ad the advertisers are willing to accept. That becomes extremely difficult to keep track of when you are dealing with billions of impressions per month. What we do is follow every ad impression. Nobody else does that at this point. There are the ads services that do a bit of forecasting themselves, but the forecasting is done only a couple of months out, and it is basic and uses sampling. When you sample, you frequently miss a more targeted and granular inventory, which tends to be the most valuable inventory.