As the ad tech ecosystem evolves, transforms, and grows, advertisers will be utilizing Artificial Intelligence (AI) and Machine Learning (ML) in critical ways. Indeed, the future of ad tech may depend upon it.
Ad Tech has a classic case of Artificial Intelligence fever. In such a fast-paced industry, where trends like brand safety, programmatic television, and header bidding drive innovation, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as the go-to warriors for enhancing results and increasing conversions.
The first factor that optimized this process in digital marketing was big data. Now, AI is reshaping it entirely. Additionally, Ad Tech operates on a large scale and is almost completely automated, making AI seem like a natural addition to the industry.
Here are ways Artificial intelligence can shape AdTech:
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Artificial intelligence will develop cookie-free lookalike models
If Chrome does not delay its cookie removal, 2023 will likely mark the end of the already constrained era of cookie-based targeting. While emerging solutions such as universal IDs and first-party publisher data show promise, the ad tech industry will also need to refine its ability to target and measure performance in the absence of user-level signals. This is a task that AI can assist in.
One of the most promising solutions for this future is AI’s ability to create lookalike models for brands based on smaller sets of well-performing known users. In some ways, AI has already been developing lookalike modeling for cookie-based targeting, albeit with a different set of audience data, such as demographics, behaviors, and interests that will become scarce shortly.
Also Read: Why Modern Marketers Need a Modular Content Strategy in 2022
In the future, AI will enable brands to learn about small sets of data available via user-level signals and then extrapolate more significant target segments based on non-user-level signals.
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Artificial intelligence will aid in supply chain optimization
Supply path optimization has been and will continue to be a critical tool for ad buyers looking to maximize performance by purchasing via the most efficient path. Today, this process typically entails manual analysis of a large number of quantitative and qualitative data points from SSPs (supply-side platforms) and publishers, with the result being that an advertiser or ad-buying group selects a few of its top preferred SSPs.
The reality is that exchanges do not exist in a vacuum of quality or deficiency. Advertisers selecting an extensive business cannot assume that every path will be optimal for their objectives. However, moving beyond the exchange level to each unique supply path is too complicated to analyze manually. It can result in the omission of a variety of critical but complex data signals, resulting in a data volume nearly impossible to analyze by humans alone.
The future will likely involve a combination of manual and AI optimization, with advertisers selecting a shortlist of exchanges based on broad economic and qualitative criteria. Advertisers will be relying on AI to optimize at a more detailed path level, targeting their price, performance, and fraud goals, among others.
Also Read: Three AI Slip-Ups that Hurt Customer Experience
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Artificial intelligence will bolster dynamic creative optimization
Presently, running a dynamic creative optimization (DCO) campaign is a deliberate choice, and tech partners are limited to a few DCO firms. In the not-too-distant future, AI will enhance the sophistication of this process by ingesting additional optimization parameters and creative options to automatically generate an ad for any given impression.
Advertisers could upload various images, color palettes, slogans, and headlines for inspiration, and AI could even generate similar assets. Additionally, AI could create headlines for advertisers based on keywords and past performance.
The AI will then determine and forecast which creative combination will perform best based on the parameters available at each impression call. Soon AI will play a critical role in assisting advertisers in achieving their key performance indicators (KPIs) by developing a privacy-conscious, regulatory-compliant lookalike model for a more extensive set of users. Advertisers can save money, cut down on media waste, and optimize supply chains to a certain extent. Additionally, AI can generate the optimal ad for each impression, freeing up resources for advertisers to focus on their next campaign.
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