Artificial Intelligence (AI) is the buzzword on everyone’s lips and the technology is expected to almost double the rate of innovation and employee productivity by 2021, according to a Microsoft and IDC study.
AI has widespread business benefits, not only in automating time-consuming and resource-heavy tasks but also in using predictive wisdom to inform smart decisions and increase efficiency. It is particularly beneficial to digital advertising, where it is becoming an essential differentiator.
AI can be used in a variety of ways to enhance an ad campaign, from detecting fraud and powering the programmatic bidding process to delivering precisely targeted, highly personalised data-driven messaging that resonates with brand audiences.
Using AI to personalise and localise ad messaging is particularly beneficial in a region characterised by diversity, allowing brands and advertisers to dynamically tailor their strategy to individual markets rather than adopting a one-size-fits-all approach. The use of AI in digital advertising is essentially joining multiple data points and interpreting the resulting patterns to identify and act on opportunities.
With AI for digital advertising still in its infancy in Asia, now is the ideal time for the industry to establish best practices and ensure its approach to implementing AI-powered ad campaigns allows it to make the best use of the technology as adoption inevitably accelerates.
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Implementing robust algorithmic architecture
The first step in implementing AI in advertising is to ensure robust algorithmic architecture. Traditional algorithms use step-by-step processes to achieve a particular result or solve a specific problem.
In theory, algorithms should be able to make better decisions than humans because they can factor in more variables and analyse them all in milliseconds to reach the right conclusion.
AI algorithms take this ability one step further because they have the capacity to learn from previous outcomes and therefore make smarter decisions, continually improving their performance through machine learning. Powerful AI can learn in real-time, refining processes, improving organically, training, learning and then adapting with minimal human intervention, enabling intelligent and accurate forecasting as well as data-enriched decisions.
For instance, a programmatic advertising AI– such as Adform’s Odin– can oversee the trading process, analysing when to bid as well as how much to bid, and learning from failed bids to continuously improve towards the perfect strategy.
Naturally, this ability to make increasingly intelligent decisions depends on the underlying structure or architecture of the algorithm. With AI, programmers do not need to code for every possible action and reaction as the system will identify all potential patterns for itself, but they do need to create sophisticated machine-learning algorithms that are fit for purpose, as well as impartial and free from unintended bias, which requires a high level of experience and expertise.
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AI algorithms must be continually monitored, and their output verified, to ensure their function and subsequent learning does not become distorted.
Ensuring the quality and diversity of data
In addition to establishing robust algorithmic architecture, the advertising industry also needs to ensure data quality and diversity as the output from AI is only ever as good as the information that trains and feeds its algorithms.
AI algorithms become increasingly effective as they are exposed to more data, so the advertising industry needs access to large volumes of information that are typically held in siloes.
It needs the technological capacity and infrastructure to handle these vast volumes of data, ensuring information can be accessed, unified, aggregated and analysed quickly and accurately enough to provide actionable insight.
But volume alone is not enough; the information must be sourced from across the entire digital advertising and marketing ecosystem and should incorporate multiple data streams to make it as diverse and representative as possible, enabling powerful, broad-based decision making.
And of course, the data used to feed AI algorithms in advertising needs to be of the highest quality. In a world where misinformation, inaccurate reporting, fraud and obscured signals are all too common, it is essential data is accurate, ethically collected, sourced from reliable providers and free from the bias of all kinds.
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Data must be current and refreshed regularly to ensure recommendations are based on the latest intelligence, not information that is hours or days old, allowing effective, real-time optimisation.
The use of AI for digital advertising in the APAC region is still relatively nascent, but it is about to escalate. AI is key to advertising’s data-driven future, but to make the most of its many benefits, marketers need to implement best practices now, leveraging knowledge, expertise and technological infrastructure to ensure the algorithmic architecture is robust and the data that feeds it is plentiful, diverse and of exceptionally high quality
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