In 2022, the marketing industry is being compelled to develop a new way of thinking: one which recognises and respects consumer concerns around data privacy and the global environment.
Earlier this summer, Remi Lémonnier, Co-Founder and President of Scibids coined a new term, ‘Virtuous Advertising’, to describe the future that marketers should be moving towards. But what is it? And why does Artificial Intelligence (AI) play a big role in making Virtuous Advertising the new norm?
Virtuous Advertising acknowledges that marketing organisations are under increasing pressure due to the heightened economic, environmental and regulatory pressures surrounding digital marketing. At the heart of the concept of Virtuous Advertising is respecting resources, i.e. being efficient so that we only use resources that we absolutely need.
In a digital marketing context, this means removing as much ‘wastage’ from ad campaigns as possible, and the best way to do this is by not serving digital ads to people who are unlikely to convert. AI is conducive to Virtuous Advertising, as it is constantly adapting to avoid irrelevant targets, and reallocating budgets in real time to achieve waste-free campaigns.
Opening the door to AI
According to the latest research, which consulted contemporary brand marketers, many have doubts over whether their marketing stacks are delivering adequate scale and performance. Alongside this major challenge, today’s decision makers are dealing with an array of external issues, including increased privacy regulation, a shortage of human expertise, the environmental impacts of digital marketing, a fragmented technology landscape, and a volatile macro-economic climate.
How is it possible to reflect these multiple considerations in your marketing tech stack, while still generating business growth and strong ROI for advertisers? Well-engineered AI, designed to tackle the mounting challenges facing digital marketing, provides a way forward.
One consideration here is the type of AI used: an off-the-shelf algorithm for ad decisioning provides standard lift for all its users. By contrast, advanced, sophisticated AI provides customisable algorithms that can optimise to differentiated outcomes whilst leveraging and growing proprietary data sets. When advertisers use their proprietary data for advanced optimisation, they are building a competitive advantage that is unique to their campaigns.
How is AI being used today?
Currently, in the UK, it’s estimated that around 40% of programmatic ad spend uses algorithmic decisioning or artificial intelligence. Agencies are generally using algorithmic/AI systems more than brands and, interestingly, agencies and brands are often deploying AI for different reasons. Revenue gains are the driving factor in agency use of AI, while brands are more motivated by the prospect of privacy-compliant targeting.
Overall, operational efficiencies, convenience, and revenue gains are the most commonly cited factors driving the use of algorithmic decisioning and/or AI in digital marketing, while scale and campaign optimisation are less prominent factors. ‘Complexity’ is often given as the leading barrier to further use of AI-enabled tools.
When considering the use of AI inside your own organisation, it’s crucial to consider the extent to which automation allows your traders to be effective. AI designed to compute optimal media prices with all available data will outpace and outperform human guesswork. This increases the quality of insights that professionals can leverage, providing clear paths to scaling efficiencies across more media opportunities. When people are equipped with the right tools for a job, they can do their job more effectively, rather than feeling outsmarted by the tech they are using.
AI in context
In today’s privacy-focused climate, marketers can no longer rely on cross-site tracking and personal identifiers for ad decisioning. Promisingly, though, where results from contextual signals are available, UK marketing professionals are reporting that these are performing just as well as traditional identifiers.
Despite these encouraging results, a minority of brand marketers are currently using all the contextual signals present in the bid request (log level data), which is one of the most scalable and privacy-preserving signals on the web today. Through the smart use of AI, this freely available log level data can be made actionable to enable privacy-friendly media buying practices.
While there are many commercial metrics advertisers need to focus on, marketers must prioritise insights available from data sets such as sales data and warehouse stock level data. A good piece of advice is to focus on these data sets, and then partner with a company that can ingest that data and produce performance metrics that can be codified into the AI to optimise media buying.
Advertisers that lack advanced data science and engineering capabilities should plan to leverage AI to bring business metrics into the buying process. For more advanced buyers, AI can be used to tailor media buying strategies for specific business outcomes.
When a whole industry cites scarcity of expertise as their main challenge, every manager should give their team the tools they need to ensure that their work is meaningful, insightful and responsible. AI is not intended to automate most of what industry experts do. Instead, it is a vital and incredibly powerful tool that can help us build a responsible, ‘virtuous’ future for the digital marketing industry.