It wasn’t too long ago that technology companies were using the initial software-as-a-service (SaaS) programs to (hopefully) provide analysis through computed statistics. The data was manually inputted and mined through by professionals who then produced infographics to show their marketing executives and CMOs. It was a long, labor-intensive process. Luckily, programmers have not stopped creating and upgrading their algorithms and are bringing us a bright new era in premium programmatic.
Those early days of SaaS didn’t take big data into account. Today, we can use a bigger and smarter software trading systems (DSPs and SSP) to provide real-time analysis on data and media performance, but also make predictions, prescribe solutions, and take action to optimize media.
Big Data has opened us up to the world of SaaS 2.0 (or DaaS: data-as-a-service).
These new media trading tools (DMPs and exchanges) are helping with data-driven decisions for media buying. Think of the book “Moneyball.” Instead of relying on scouts to predict who would be the best player to sign onto the Oakland Athletics, the team turned to the numbers and signed players based on regression analysis. The math didn’t lie and the team started playing better than they had in years.
Smart media-trading software does not only predict, it can learn. With the implementation of learning loops, it can take incoming data to continually improve advertising. Many recent IPOs for ad technology companies have mentioned a secret sauce made up of ads that can learn and perform smart retargeting, making both the media and the data one and the same.
Prescriptive analytics automatically synthesizes the big data with predictions, or makes decisions to take advantage of the analysis when buying future media or premium media. Answering the question of what will happen is always harder when we really care about media attribution at the top of the sales funnel as well as the bottom.
The next step in transforming the programmatic space is by going deep and wide with business intelligence to solve problems not just with digital, but cross-platform as well. Smart software or platforms have opened us up to the world of SaaS 3.0 (or PaaS: platform-as-a-service).
Over the past year, both large and small ad technology companies have increasingly adopted PaaS strategies for programmatic, with a focus on big data (of course), along with the full media solution. By starting with the full programmatic media ecosystem, those using PaaS have evolved to function faster and more effectively, powering the full advertising marketplace.
The PaaS platforms are built to scale “horizontally” (across-media formats), and “vertically” (functional areas such as ad serving, RTB, programmatic direct, and private exchanges). Exciting opportunities exist for those who automate both horizontally and vertically, since today’s biggest digital media players are buying and building these platforms to automate non-digital media.
The most interesting change has been the significant programmatic movement into mobile. If mobile RTB was last year’s focus, then building intelligent platforms for premium mobile trading is today’s. New mobile programmatic media buys can take advantage of local, native, and responsive design ads to capture the branding opportunities that exist in a new connected-device world.
The Internet of Things (IoT) advertising phase is the next industrial revolution, with estimates of over 50 billion connected devices and thousands of IoT solutions coming. The IoT market will reach approximately $8 trillion dollars by 2020. While traditional ideals of SaaS, DaaS, and now PaaS will be around for a longer time than their acronyms, a premium programmatic marketplace will need to encompass smarter software, data and platforms.
To get to a futuristic “Minority Report” type premium programmatic environment, we have to evolve our software, data, and platforms holistically. This will provide innovative marketing to create value exchanges that consumers understand to align seamlessly with new applications.