By Berina Colakovic, StackAdapt APAC Sales Director
Digital advertising increasingly requires the ability to digest large amounts of information quickly by using advanced computation and machine learning to reduce manual decision making on campaign execution.
The StackAdapt AI tools, baked into our platform since our launch in 2014, has implemented data collection, processing, and applied machine learning to campaigns for nearly a decade. The benefits of automation are clear: optimised ad spend, increased ad relevance, and higher click-through rates. Add in continuous A/B testing and optimisation, and brands can see their performance soar.
With machine learning and AI, the StackAdapt platform is able to identify patterns across all campaigns and make it possible for marketers to run highly optimised programmatic ads.
However, algorithms are not all made the same. For example, in the context of click-through rate (CTR) prediction, a human-based approach that layers on historical site domain data, on top of device data, exchange data, and geographic data can work well. However, its prediction potential is limited compared to the AI algorithm, which is able to find hidden patterns and signals in thousands of other attributes that the non-AI model cannot.
Furthermore, our experience after a decade of fine-tuning our AI-driven advertising platform is that constant testing is crucial.
Testing and Learning is Vital
Because AI models must update themselves constantly, it is dangerous to put them into practice immediately after having trained in a historical environment. Your AI must prove themselves gradually through a robust A/B testing system.
This will ensure optimisation will actually increase performance over time, rather than the other way around. A strong engineering backbone to incorporate a learning cycle is critical to a mature AI system.
Machine learning can also enable better user acquisition cost. Bid price optimisation is a key algorithm regardless of the KPIs of your campaign. Whether it be brand awareness or direct response, you should always be bidding to the perceived value of your user—and each individual user is unique - so AI is important.
AI can also assist with a complex media brief. It’s one thing to know that the AI is optimising toward a single goal - but if the AI can optimise toward multiple goals in tandem, you can really reach the best campaign outcomes. Ask your DSP about layering KPIs during optimisation - that will begin to demonstrate how strong their AI capabilities are.
Market conditions are always changing, and to be accurate, the AI you’re using needs to adapt to those changes. That may include changing bidding strategies and ad frequency for media sites that are experiencing an uptick in user visits.
Practice makes perfect. If you want to be a master chef, you need to get in the kitchen and cook often. AI is no different. AI models need to be trained often, to make them more adaptable to changes in traffic and inventory, and ensure higher accuracy.
AI Can Identify your Target Market
So how will AI affect the programmatic market in the near term? There is no doubt that AI technology is improving year over year, with less need for manual hand-holding of campaigns as we go along. That question is more likely to focus on the question of how active or proactive a DSP is in the evolution of AI.
The models and algorithms that we develop as a team can capture information that could get lost in a traditional advertising setting. These models and algorithms process hyperdimensional data that often transcends the practical understanding of humans at a speed that would be impractical for us to replicate in most cases.
As the web gets more fragmented and new platforms and channels emerge, speed and complexity will need to be tamed. Machine learning and AI algorithms can filter through the billions of pages on the internet to identify those that are most closely aligned with a company’s offering, all with just a small input of information, such as in-content or out-of-content words and phrases.
Not only is AI streamlined and more precise than ever before, but it also operates across channels and is highly scalable - it is now an invaluable resource to all marketers, and even more so to those that require highly specific or niche targeting.
Finally, as we move to a post-cookie world, AI grows in importance due to signal loss on the leading browser in the world, Google’s Chrome. That will also drive more informed contextual targeting, underpinned by AI. Crucially, the blend of AI and contextual advertising tools means the advertiser is in control of identifying where they would like to appear across the web.
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