Tell us how you came to be the Head of Data Science at FreeWheel.
I have been working in analytics for my entire career and have been in the ad tech space for the past 10+ years. In 2010 I started at a company called Visible World which was best known for its development of addressable advertising technology for television. While there, I led efforts in the development of ad decisioning algorithms for some of the products we brought to market. In 2015 Visible World was acquired by Comcast and incorporated into their ad tech division, now known as FreeWheel. At FreeWheel, I have continued to lead efforts around incorporating data science into the products and solutions we are bringing to the marketplace.
What are some of the industrial sectors that FreeWheel caters to?
Free Wheel serves the premium video advertising industry by providing advanced technology that allows buyers and sellers to efficiently and effectively transact across all screens using the latest advances in ad tech innovations.
What are some of the common challenges customers approach FreeWheel with?
Free Wheel customers are looking for increasingly more effective ways to reach audiences
across a diverse set of video advertising platforms. They are looking to leverage diverse data
assets to better plan, execute, and measure video campaign impacts in an automated and
optimized manner. Publishers are looking to manage their advertising yield while connecting
with new demand channels. Free Wheel’s technology seeks to remove the friction involved in
video advertising transactions and provide clients with a greater set of capabilities for achieving their media objectives across the ecosystem.
What are some of the unique lessons you have learnt as Head of Data Science at Freewheel?
The advertising and media industry is changing quickly and every major industry player sees the use of data and data science as critical to their success. This creates a tremendous opportunity for problem solvers who are willing to move quickly to develop solutions for new challenges that arise with these industry changes. I have learned that those who are most successful in this space are willing to try new things, tolerate failure, and pivot quickly in adopting new technical approaches. This is especially true with the adoption of AI and machine learning related developments which can require experimentation and trial and error before landing on the best practical approach.
How does Free Wheel leverage data analytics?
Data analytics is a critical component of all of Free W heel’s major product offerings. Audience modeling, ad decisioning, and viewership forecasting are just some examples of where analytical methods play a critical role in how we connect buyers and sellers in the video advertising space. Artificial Intelligence, machine learning, and optimization methods are increasingly becoming core components of our product offerings in order to drive greater automation and advertising effectiveness.
What are some of the distinctive features of FreeWheel products that differentiate you from your competitors?
Few, if any, competitors have the breadth and depth of technical experience that Free W heel has across not only digital video but also traditional television. This is important because media buyers today are interested in understanding how they are reaching their target audiences across all video platforms, not just within siloed media channels. Free W heel provides market participants the ability to connect to both traditional TV and digital platforms while leveraging data and analytical capabilities that support a more holistic view of how they are reaching their audiences.
Can you give us a sneak peek into some of the upcoming product upgrades that your customers can look forward to?
In 2021 Freewheel will be delivering new powerful data enabled tools for video marketplace participants to connect with each other in a frictionless way that allows buyers to efficiently find their audience and sellers to optimize how they manage and sell their inventory. This year we can expect to see advances in how buyers can create and execute against custom audience targets using inventory across digital and traditional television. We can also expect to see greater optionality around how buyers can not only bring their own data but also bring their own algorithms to optimize how they acquire and manage inventory across a diverse set of publishers and media channels. There will also be increased capabilities designed to ensure data privacy for all market participants while providing a high level of data-driven insights and decisioning.
What are the latest capabilities of data science in media business?
There are a few key applications of data science that we expect to continue to develop over the next few years:
Contextual Advertising – Using AI to identify video metadata that will allow advertisers to
target their ads in content based on the nature of the programming. This may allow for
the ability to target ads based on the mood or content of a specific program, for example.
Differential Privacy and Federated Learning – These methods keep data in local storage
within the device it came from while using machine learning models on the devices
themselves rather than on an aggregated data set. This provides an increased level of
data security to ensure personal information is protected.
Attribution Modelling – Measuring video advertising effectiveness requires an ability to
connect the ad campaign to the desired marketer outcome. Doing so requires a
collaboration between the advertiser and those enabling the advertiser campaigns that
connects key data sources in a way that not only shows whether the advertising is
working now but supports the optimization of future campaigns.
What is that one quote that has stayed with you throughout your professional life?
I’ve found that for complex ambitious projects with a lot of uncertainty that the best strategy is almost always to start now and adjust as you go versus waiting for the perfect moment to begin. Given that, I’ve always loved the quote:
“The journey of a thousand miles begins with a single step.” -Lao Tzu
|Bob Bress Bio||Bob Bress is the Head of Data Science at Freewheel, a Comcast Company, where his work is focused on advanced advertising technologies. In that role, he leads a team of data science and analytical staff using their expertise to lead the development of the next generation of innovative advertising products for television and premium video. Bob holds a number of patents in the application of data science to advertising technology and is a frequent speaker on the intersection of analytics and media.|
|Bio of FreeWheel||FreeWheel, A Comcast Company is a provider of comprehensive ad platforms for publishers, advertisers, and media buyers. Powered by premium video content, robust data, and advanced technology, the company is revolutionizing the way publishers and marketers transact across all screens, data types, and sales channels.|