AI, No Lie: A Definition And Review Of Marketing Use Cases – AdExchanger

Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.

Today’s column is by Sarah Rose, SVP International Digital Operations, Data & Platform Ops at IPG’s Kinesso.

Artificial intelligence is a heavy and complex topic with tons of deep ethical complications, confusing applications and unknown impacts on many industries. It brings panic to some and sci-fi-infused joy to others.

AI conceptually echoes Ray Kurzweil’s Singularity. In that scenario, humanity itself will be altered as we become “one” with AI. With any new technology there is fear and reluctance. In the case of AI, many industries have cleaved to the old ways while lightly playing with the buzzword as a pretense to progress. Our first human instinct is to protect the known, while not going full throttle on integration.

There is no question AI will change industries, markets, company valuations, jobs and our status quo. On the question of legality alone, with little to no federal or state regulation, most industries are perplexed on standard applications and uncertain where to begin.

Even so, the technology has advanced.

Ad tech and mar tech companies now commonly boast of AI-powered optimization tools and bidding methodologies that fuel brand engagement and ROAS. It may not look like a Spielberg film yet, but advertising and marketing technology is starting to integrate the beginnings of technology evolutions that employ self-learning decisioning.

By deconstructing and unpacking artificial intelligence into smaller packets, we can make it more accessible and applicable – and provide ourselves with a selection menu on where to start and what to start with.

Types Of AI

There are really three categories of AI technology that can lead us to integrated systems and self-learning tech. The first is Robotic Process Automation (RPA), the second is Machine Learning (ML), and the third is AI (Artificial Intelligence) that is truly self-learning and actualizing.

Robotic process automation (RPA) is built by scripting languages (Python, for example) and is useful in repetitive, simplistic and linear tasks that produce a standard output. This is super basic and widely applied today. For the advertising ecosystem, RPA is great for operational tasks where there are “copy-and-paste” and server-to-server integrations requiring linear data ingestion. We can find one example in ad trafficking, where APIs between third-party platforms already exist and steps can be standardized. Operationally, this can save time, ensure data accuracy with fewer trafficking errors and conserve resources on quality assurance, campaign management, relationship management and data governance.

Machine learning (ML) is the first step in optimized data science applications, where a human would usually attempt to analyze large data sets to come up to some simple conclusions on patterns. It is difficult for us humans to look at tons of data points in real time and make statistical conclusions that, however minute they be, could be statistically relevant from a Bayesian logic perspective. It is timely and costly to any organization to throw bodies at the problem and find inherent value. However, ML will thankfully set rules for us and look for triggers and flags to meet defined criteria and find value in data. One example is evaluating inventory performance and ROI on long-tail SSP sources and/or to optimize DSP delivery to provide the best ROI in even low-value inventory sources. This is how most AI-powered optimization works and where the bulk of companies are spending data science resources.

When we attain AI, it is a combination of operational RPA and ML technologies. Artificial intelligence in definition is self-learning and making decisions “on its own” for the benefit of reaching a brand’s audience and meeting client ROAS deliverables. By integrating RPA, ML and self-learning programming, addressable media plans can shift in real time without human interaction.

AI self-learning technologies have not fully arrived in our industry at scale, but major players have started this journey in simple ways to bring automation (RPA/ML) to the fore. Whether they are startups or well-funded players focused solely on AI applications, companies are beginning to test efficiency gains. Some agencies, publishers and ad technology companies simply license this tech, and it is no surprise that Apple, Google, and Amazon are also innovating advertising practices to set the tone for automation.

While it is not gravity modifying, and we have not reached warp speed, the path has been set. By approaching this journey step by step and knowing what type of tech to integrate at what time and in what way, it becomes less head-spinning.

Let our industry be careful, cognizant, self-aware and available for change to boldly go where no one has gone before. 

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