We’ve heard the buzz around machine learning and its highly predictive trend in marketing, but what is its impact on digital advertising today? In this blog post we will cover the ideas behind artificial intelligence, examples of machine learning in a digital marketing setting, and the concept of data modelling and algorithmic learning.
The key to understanding the concept behind machine learning and how it is contributing to decisions in advertising requires a closer look at the ideas behind artificial intelligence.
Let’s take the example of how certain foods based on their colours, textures and tastes provide rewarding and nourishing experiences to individuals. The human brain embraces certain patterns and then applies them to future circumstances in areas of recognition and decision-making for the best possible outcome. In a cross-comparison with AI, this pattern of recognition and decision-making could be compared to the idea of machine learning and the process of automated reasoning. These types of decisions, made possible by algorithms which are self-contained sequences of executions, eventually determine the appropriate output. Rapid advancements in the application of machine learning processes to leading technologies are increasingly playing a role in unlocking the true potential of such systems, affecting the contexts in which they operate. With the continued progression of automated marketing in the advertising world, there are various opportunities in which machine learning algorithms are tested to increase efficiency, performance and success with data utilization.
A basic, practical example of machine learning in digital marketing can be demonstrated in the use of dynamic creative optimization. In this process, features within the UIs of ad-serving tech can be preselected to increase the impression output of creatives which appear to be driving higher click-through rates. More precise types of targeting can be accomplished by using feeds for dynamic ads that are called by variables depending on the required output. An airliner that is advertising rates for multiple destinations would benefit from having users with specific travel requirements being retargeted with the relevant creative.
Automated bidding or budgeting is another example of the intersections with machine learning. With a programmatic display advertising campaign for example, features can be activated to automatically distribute budget toward line items within an order based on a success metric. If this success is based on a particular goal such as CPA or CTR, budget is automatically distributed according to the success rate of an item in contributing to such a goal.
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Additionally, bids can also be determined through machine learning processes in an automated way such as optimizing towards viewable impressions. This function would determine viewable online spaces and execute bids to have the corresponding impressions served to those placements. As a result, the ad is better placed to reach the advertiser’s particular goal. Other types of automated bidding might also be applied so that over time, the bid is decreased as much as possible while still meeting the overall campaign goal. This in-turn allows advertisers to meet performance goals while reducing CPM.
Further use in the kind of predictive analysis central to machine learning is observed in audience insights and data mining. In an industry where data-driven marketing is becoming more and more important, methods in which automated reasoning can be carried out to expand or define valuable data has become increasingly available to marketers. The possibility to create lookalike or affinity audiences is an example of this process. Campaigns for particular brands will typically allow for the collection of first-party audiences, a user-list comprised of cookie IDs that have shown interest with a product or service. Algorithms are used in creating pools of new audiences that correspond to the attributes of the original list by drawing comparisons in online activity. The benefit is then that the advertiser will have access to a wider audience that is more likely to engage with their product based on predictive analytics.
One of the more innovative ways in which machine learning can be applied to data has recently surfaced within attribution modeling. With Google’s new data-driven attribution model, sets of algorithms determine how marketing touch-points increase the likelihood of conversion given a particular sequence of exposures. This type of attribution gives credit by using data from marketing activity to determine which ads, keywords and campaigns have the greatest impact on the overall goal. From an attribution standpoint, this allows for more precise data at an upper level, providing the opportunity to respond to particular successes or failures as they occur. Sophisticated machine learning algorithms are also assisting companies with issues such as ad fraud prevention and by systematically eradicating this problem at a large scale, it’s clear to see that there is significant potential for development in this area.
While machine learning is undoubtedly expanding the parameters within digital advertising, the argument remains that the process will always require a human decision. Even though the patterns are increasingly inspired by capabilities of the brain, at some point, there will need to be an affirmation that the process is heading in the right direction. As such, humans remain essential for the confirmation and interpretation of these patterns.