As the ad industry realises that getting the most out of automated technologies requires artificial intelligence (AI) in addition to a human brain, it seems the phrase “it takes one to know one” applies as much to machines as it does to people.

Programmatic advertising may be booming – with the UK spend growing 26% last year to account for almost three-quarters (72%) of display ad spend – but on-going issues such as ad wastage and poor placement make achieving maximum ROI on programmatic a real pain point for marketers.

AI – including the use of machine learning and cognitive technologies – is one solution being used to resolve these issues. Whether it’s Elon Musk working on an interface to integrate AI with human consciousness or retailers replacing their digital ad agencies with AI, this buzzword is rarely out of the headlines. The technologies are permeating every aspect of our lives, so it’s only natural that marketers are looking to AI to optimise programmatic strategies and make the most of their budgets.  

So how can AI be used to effectively inform programmatic ad spend?

Reaching the right audiences

To make the most of the programmatic budgets, marketers must be sure their message reaches the right audience at the right time. Through AI-based technologies such as semantic analysis and behavioural analytics, marketers can uncover insights hidden in their first-party data, including audience interests, content consumption patterns, and propensity to interact with specific messaging.

These insights can be used to create incredibly accurate 360-degree profiles of existing and future customers, ensuring marketers only target consumers who already have the propensity to buy or take another specific action. This tactic maximises the performance of each and every impression and limits ad wastage.

Avoiding the wrong placements

Getting messaging in front of the right audience at the right time is vital, but this shouldn’t mean sacrificing brand safety and integrity. The automated nature of programmatic puts brands at risk of their ads being placed alongside inappropriate or undesirable content such as hate speech or fake news, as illustrated by the recent YouTube ad placement scandal. Marketers must put robust brand safety measures in place to prevent this situation from occurring, as the potential loss of revenue from damaged brand reputation could be extremely high.

Brands impacted by the YouTube controversy have responded by pulling ad spend, but by using AI-based technologies they can take a far more proactive approach. Natural language processing is a form of AI that analyses online content at the page level to deliver a granular understanding of its true context and sentiment, helping marketers avoid any placements that might potentially damage their brand.   

Limiting the heavy lifting

Despite being an automated process, programmatic can still be labour intensive if data analysis and campaign optimisation are deemed hands-on tasks. Employing AI technology in the form of machine learning can reduce the workload by aggregating, filtering, processing, and analysing vast volumes of performance data, and using the insights gleaned to close the programmatic loop and optimise campaigns in real time. Limiting the heavy lifting involved in data analysis and campaign optimisation frees up the marketing team to focus on areas where they can add more value such as the creative ad experience.

While AI is clearly a vital tool in making the most of the programmatic budgets, it does have some limitations and should not be seen as a magic bullet that will miraculously solve all issues. AI can only work well if quality data is used to fuel the process. There are already examples of AI exhibiting bias when data is skewed, and the importance of human input should not be ignored. Determining brand-specific definitions of safe placements, setting campaign goals or understanding what is meant by fake news are examples of subjective concepts that humans must still oversee. Algorithms alone cannot make these decisions and the combined efforts of man and machine are required to maximise the return on programmatic investments.

It should come as no surprise that AI-based technologies such as machine learning, semantic analysis, and behavioural analytics can maximise ROI on programmatic more effectively than humans. As long as marketers don’t forget the importance of the human touch, they can use AI to target the right audiences, avoid poor ad placements, and reduce resource requirements, making their programmatic budgets work far more effectively.