Thought Leaders in Artificial Intelligence: Kevin O’Malley and Patrick Shea, Co-founders of AdDaptive Intelligence (Part 4)

Sramana Mitra: When it comes to publishers, how much revenue are publishers making by plugging you in? Are they doubling revenues or tripling revenues? What kind of ROI are you showing them?

Kevin O’Malley: A lot of times, they are doubling the amount of revenue they’re bringing in from a traditional display advertising. Obviously, different publishers charge different amounts. We have seen, on many cases, that we have clients that are literally doubling the amount of display advertising revenue from plugging our solution in.

Sramana Mitra: What is your sweet spot in terms of publishers? Are we talking large publishers like New York Times and Mail Online or are we talking about smaller publishers? Where is your clientele? >>>

Thought Leaders in Artificial Intelligence: Kevin O’Malley and Patrick Shea, Co-founders of AdDaptive Intelligence (Part 3)

Sramana Mitra: Can you talk about the workflow of this? Where is the data collection happening? Where is the data integration with the algorithm happening? Is this happening in real-time, batch, or pre-setting of some sort? How is this setup? If I’m a publisher who wants to plug in your system into my site, how does this work?

Kevin O’Malley:  Everything is real-time, so is our data management platform, especially the technology that helps segment the data. That is done through real-time transactions. We’ll essentially place our technology on the publisher’s website. From there, we’re collecting, segmenting, and analyzing all that data in real-time. When the publisher has an advertising campaign to run, they’ll select certain segments of users and certain characteristics that they would like to target. We then would run the campaign on our platform.

The algorithm is obviously built into our media buying engine. Essentially, we’re using their data but we’re also using the data that we receive back from all of the impressions that we’re running. As Patrick mentioned earlier, there’s dozens, if not thousands, of different parameters – time >>>

Thought Leaders in Artificial Intelligence: Kevin O’Malley and Patrick Shea, Co-founders of AdDaptive Intelligence (Part 2)

Sramana Mitra: Maybe take one or two of your customers ideally from different segments of publishing, and walk us through what kind of data you are collecting, and what specifically are you able to do using artificial intelligence. What is the specific artificial intelligence application here?

Patrick Shea: I think there are two use cases that identify key value propositions on different ends of the spectrum. On the one hand, we work with a few large newspaper companies that have holdings scattered throughout the country. Because of that, they have tremendous relationships with thousands of local advertisers. The challenge is taking that relationship and turning it into a scalable product at a national level because it involves running thousands of very small hyper-targeted campaigns.

When you look at the amount of revenue that each campaign, by itself, is able to bring that company, it becomes very difficult to have humans do all of that work. The system that we’ve built, on the one hand, automates all the repetitive tasks that are involved with launching a media campaign. It also puts a lot of the decision-making power into the algorithms that we’ve developed specifically for that purpose. However, one of the challenges that Kevin mentioned earlier with the black box style of managing campaigns is that you typically need tremendous scale for the tens of millions of impressions. >>>

Thought Leaders in Artificial Intelligence: Kevin O’Malley and Patrick Shea, Co-founders of AdDaptive Intelligence (Part 1)

This discussion delves into the applications of Artificial Intelligence in the advertising and publishing industry.

Sramana Mitra: If one of you could get started and introduce the company as well as yourselves, that would be great.

Patrick Shea: I’m one of the co-founders of AdDaptive Intelligence. Kevin is my co-founder here. We started the company as Data Point Media back in 2010 with the goal to bring some of the tools, which were coming into the marketplace primarily aimed at advertisers and agencies, to the publisher side of the business. Our universe is really the advertising technology ecosystem.

Kevin and I both have backgrounds coming from the publisher side. That’s why we set to solve some of those initial challenges. Over the past five years, our company has evolved a great deal. We’ve placed huge focus on our technology. What initially started as creating systems to automate a lot of repetitive tasks became more and more sophisticated. They started to grow into real decision makers in their own right. That’s how we got into the artificial intelligence space. >>>

Thought Leaders in Big Data: Mikko Jarva, CTO of Comptel (Part 3)

Mikko Jarva: There is a prediction that in 2016, the world won’t have enough data storage space to store all the data that is currently being generated. Some of the challenges that we need to think about also is how we manage that data and what do we do with it.

Third challenge that we see is data preparation. Getting from data to insights requires a lot of data preparation. The data scientists would be better off actually helping generate the revenue generating function rather than preparing the data. Those are the challenges that we’ve identified.

Maybe, the fourth challenge we have is while Big Data has insights, it’s mainly utilized in batch mode. We have an approach in Big Data which we call intelligent fast data. The idea is to automate the data analysis in real-time so that we refine different kinds of data in real-time towards recommended actions. Data only stops after that. It doesn’t stop during the analysis. >>>

Thought Leaders in Big Data: Mikko Jarva, CTO of Comptel (Part 2)

Sramana Mitra: When you say you want to monetize the data that you have, what does that mean? I can understand what kind of data your clients have. Are you saying that your clients are already collecting a certain level of data on your system that you have not yet built applications on top of?

Mikko Jarva: What our solutions traditionally do is they support the process of billing and charging of data. Billing and charging of data has been done on the data generated by networks and channels that end users utilize. Traditionally, that data is only utilized for billing and charging of telco services. There are more ways of using the data for intelligent recommendations and further monetization of services. The first step of monetization of telco customer data is to provide better recommendation of the telco services to get the end users to use the services more. This can be done through Big Data and intelligence.

Of course, the parallel step is to utilize that data for monetization of third-party services. As I mentioned earlier, telcos have a holistic view of their customers and end users. They know their customer’s location at any given time. They know what kind of applications they are utilizing. They >>>

Thought Leaders in Big Data: Mikko Jarva, CTO of Comptel (Part 1)

Here, we discuss Big Data in the telecom industry.

Sramana Mitra: Why don’t we start with some introduction about yourself as well as your company?

Mikko Jarva: I represent Comptel Intelligence Data business unit as the CTO. Comptel is almost 30 years old. We are traditionally a provider of telco IT. We have launched a new product called Nexterday. Nexterday takes us beyond software and also helps us in telco to move from Big Data towards intelligent data, and move from traditional buying experience towards digital buying experience, and help monetize more.

Sramana Mitra: When you say you represent Comptel’s intelligence work, what does that work entail? What do you do? What kind of Big Data work does Comptel do?
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Thought Leaders in Big Data: Pubmatic CTO Anand Das and VP Engineering Sri Gopalsamy (Part 6)

Sri Gopalsamy: Forester has a research report based on the survey of over 110 publishers. They came back and said that this is one of their biggest problems in terms of aggregating multiple streams of data to get a holistic view. In fact, 56% of publishers agree that this is a big challenge for them. They’re looking for opportunities for someone who can pull all of this data together and provide them a single view. This is something that everyone will benefit from.

Anand Das: Say you take a publishing house, there are different systems that they utilize. Some people will use Salesforce. Some people will use other management solutions, integrate a particular server, and work with multiple SSPs and DSPs. Everybody has a different view or format in which they provide data. By getting all this data at an aggregate level, you can basically figure out how much you make at the end of the day. To figure out what it is that you’re selling better, you need to go down to granular analytics at the user level. Things get more complicated because when you look at the user level, you then have to overlay the audience data, which is not coming from one source. You need to overlay pricing data. >>>