Artificial intelligence (AI) has been a popular phrase in various businesses over the last decade, and the ad tech industry is no exception.
If you go across the ad tech market, it’s difficult to find a platform that doesn’t claim to have AI. As a result, it’s impossible to tell which platforms actually have it and which don’t.
So, how can one tell the difference between powerful and not-so-powerful AI?
Here are three factors to think about while considering ad tech AI
What is the data source?
AI algorithms are only as good as the data on which they operate. AI that relies on stale third-party data makes choices based on outdated, frequently weakly classified inputs. This results in poor decision-making and, eventually, ineffective advertising. To get it properly, systems must operate on first-party real-time data and have the infrastructure to handle this real-time data–that is, infrastructure capable of handling large-scale (big data) with very low latency (minimal delay).
What kind of AI is it?
Not all AI techniques are created equal. The term artificial intelligence (AI) encompasses a wide range of technologies, including robotic automation and image identification, as well as natural language processing, machine learning, and others. Some in the ad tech business believe that a collection of automated scripts constitutes AI.
However, these scripts can only improve campaigns by adjusting a few input factors. In contrast, modern techniques like machine learning can first learn and then alter thousands of variables to produce desired campaign outcomes at scale.
To use a simplistic analogy, the former is similar to installing cruise control in a car, whereas the latter is more equivalent to developing a self-driving car. Influencing human decision-making through perfectly timed, uniquely appropriate advertising is a complex, nuanced subject that cannot be reduced to a few input factors.
This means that machine learning approaches must be used for AI engines in ad tech to actually produce greater commercial outcomes (e.g., neural networks).
How much interaction does the AI have with the raw data?
When AI operates on raw data as directly as possible, it is far more effective. Manually abstracting the input down to a few variables before AI processes it can diminish its efficacy significantly. Instead, functioning directly on raw data allows algorithms to self-learn relevant characteristics (a technique known as training) and then optimize those features to meet the desired aim.
For example, an AI picture recognition system designed to categorize different cats would perform far better if the algorithms utilized the raw input photographs with all of the data rather than manually abstracting the pictures down to just a few factors such as color and size, and characteristics.
Similarly, AI that adjusts a handful of arbitrary bid factors like age, gender, and geography to evaluate the worth of a bid is not as successful as AI that acts directly on raw data and allows machine learning algorithms to learn and adjust the relevant factors.
The 3 primary capabilities that make it a potent AI engine are as follows:
AI runs on real-time data, which implies it can handle huge sizes (up to 20-40 petabytes of data processed a day) and operate with low latency or little delay. This is made feasible by a data analytics system developed from the ground up–one that turns massive volumes of data into a playground for insights.
To give an interactive and instantaneous experience, it queries a database of over a trillion internet signals in less than 100 milliseconds. This type of surrounding infrastructure enables the genuine benefits of AI to be realized.
Real-time predictive modeling: AI can react to the most recent internet occurrences to record ever-changing consumer behavior, understand potential clients interest, and infer consumer intent. AI uses advanced machine learning algorithms to create individual predictive models for each campaign in order to achieve that level of intelligence and sophistication.
In addition to campaign models, AI creates media models for viewability and brand safety and generic models such as an open internet topic model. All of this modeling is accomplished through the use of advanced machine learning techniques and tricks such as neural networks and deep learning (topic modeling).