Machine Learning and Artificial Intelligence in StackAdapt
By: Christiana Marouchos
Tags: Machine Learning, Programmatic Advertising, Artificial Intelligence, Stackadapt, Programmatic

The rapid growth of technology has connected and empowered consumers more than ever before. As consumers, our behaviour has evolved — we have multiple devices to consume content and we want to know everything instantly, which makes us more knowledgeable, curious, demanding and impatient. Marketers are embracing this evolution, and are constantly looking for ways to make instant changes to their digital strategy in response to these shifts. This is where machine learning and artificial intelligence, AI for short, comes in.

Machine learning and AI helps marketers streamline their advertising efforts by making decisions at scale and rapidly responding to pivots in how people consume information. Before we dive into how StackAdapt uses these capabilities to help advertisers reach their campaign goals, let’s quickly review what machine learning and AI are in the world of digital advertising and how they work.

A Breakdown of Machine Learning and AI

To understand machine learning, we need to start with artificial intelligence. AI is the ability for machines to digest large amounts of information and make (potentially a ton of) decisions in a short amount of time compared to the manual efforts by humans, which can be overwhelming for a person. The growth of AI in advertising is powered by data signals generated either by the time or way in which users interact with their devices, as well as the type of online content being consumed. This ability can be used to help advertising platforms deliver more relevant marketing messages to users both whenever and wherever they are.

Machine learning, on the other hand, is a method in which AI can be achieved. It involves algorithms to process the ingestion of vast amounts of data, identifying and categorizing this data to then computing useful and informative analysis. Some advertising platforms use machine learning as an added feature to help identify patterns in massive volumes of real-time data. This enables them to predict the outcomes of campaigns, and therefore determine what steps need to be taken in order to improve campaign efficiency. For example, machine learning helps marketers pinpoint where users are in the purchasing funnel, based on the actions they have completed last. It is a tool that can also be used to help find similar users, known as lookalike audiences, who have never been exposed to the brand before and introduce them into the funnel.

When used together, machine learning and AI are incredibly powerful because of the speed and scale of data processed. Understanding if AI is used effectively by your DSP is critical to keeping up with an ever-changing advertising landscape and ensuring your campaigns run as efficiently as possible.

Machine Learning and AI in StackAdapt

Machine learning and AI are the foundation on which the StackAdapt demand-side platform (DSP) is built. It is the basis of how every single campaign is executed on the platform. While human insights are needed in the initial set-up of the campaign, human interventions could be made throughout the campaign flight to guide the way of any external factors the AI may need to consider, such as a change in business goals. The StackAdapt DSP processes over 2 billion auctions each day and nearly 6 billion decisions per second — which is why the heavy lifting is left to AI. Here are the three main aspects of how the StackAdapt platform make AI happen:

Collecting data: Data is gathered from the moment a request is received from our supply partners to the real-time bidding we provide and how users interact with the ads being viewed.

Learning from the data: Machine learning is applied at the time of a bid request to analyze and identify patterns that can be used in real-time. These patterns identified are of features found on the platform, such as bidding and pacing, fraud detection, targeting parameters, and much more. For example, let’s say we have set up a campaign that has “pace evenly” enabled. In this scenario, the algorithm will calculate the budget and number of days within a flight date to ensure the campaign not only paces well throughout the duration of the campaign, but also considers that not all times within a day are the same. With that being true, you can rest assure your budget is spent efficiently and not wasted during downtimes in the day. The more data available for the AI to ingest, the better it can learn, identify patterns and predict possible outcomes.

Applying the learnings: The StackAdapt AI applies machine learning to every single campaign on the platform. Some examples include its ability to proactively predict the likelihood of fraudulent inventory, determine a user’s interest level before bidding or to optimize a campaign towards any KPI set. It is important to note that when any optimizations are made to a campaign, it could take anywhere from 2 to 3 days for the machine to re-learn and identify the most efficient method to reach your KPIs with the adjustment implemented.

Practice Makes Perfect

Algorithms need to be constantly trained in order for them to adapt to real-time changes in traffic and inventory sources. With all the features and capabilities offered on the platform, StackAdapt’s algorithms are constantly learning when it comes to making decisions.

Marketers who are aware of how AI is applied in their marketing efforts are one step closer to finding the audience that has an affinity for their brand. With attention spans decreasing and ads getting lost in all the content-noise, machine learning and AI can work to your favour to increase precision in reaching your audience at the right time, in the right place and with the right message. Having this workhorse on your team ensures you are effectively spending your media dollars and getting the most rewarding result for your investment. Most importantly, it will enable you to better strategize and plan for future campaigns.