Authored by Arpit Chaudhary, CTO of GreedyGame

AI has the potential to drive unprecedented growth in the digital industry by facilitating experimentation and unlocking hidden insights. The AdTech landscape is one such industry that has been enriched by AI’s influence.

By analyzing copious amounts of historic data, AI can uncover trends and patterns that may not be apparent to human observation. This capability has opened up a world of possibilities for the AdTech industry by allowing them to optimize advertising strategies.

AI also presents a promising solution to reduce operational costs. By automating numerous tasks and streamlining processes, the AdTech sphere has become significantly profitable.

In this article, we will explore the multifaceted impact AI has on the AdTech industry and shed light on how publishers and advertisers can navigate the landscape.

How has AI transformed AdTech

 Hyper-Personalization and Engagement:

AI has held a firm foothold on behavioral analysis and has helped brands collect, analyze and take action with data. It has enabled brands to target users personally and deliver highly tailored ads by using first-party data and algorithms.

Take for instance a retailer who uses AI to analyze a buyer’s browsing history or purchase patterns. If buyers are on the lookout for a laptop, they are likely to be served ads that show laptops from different brands. A laptop owner is also likely to invest in a laptop cover. So, AI tools can help ads become smarter by making this analysis without human intervention and end up automatically serving ads of laptop covers to the buyer. This will in turn result in lesser ad spending for the advertiser.

Similarly, to keep users engaged, publishers have begun using AI chatbots in their apps or websites. Users can converse with the bot, clarify doubts, and more, making them stay on the app or website for longer by default. This, in turn, increases engagement exponentially. 

By automating the personalization process, AI can start generating ads for a business on its own without creative input from humans. AI can analyze a person’s profile and morph itself to become more relevant.

 Targeting and optimizing media campaigns:

AI has made it possible to analyze huge datasets and segment audiences based on demographics, preferences, geography, age, gender, and more. This has helped segment the audience and serve tailored ads that will appeal to the target demographic.
AI does not just serve tailored ads but has also optimized ad campaigns to yield stellar results.

 Creative ideation and generation:

AI has expedited the process of creative iterations and helps advertisers refine their campaigns faster. Generative AI will help create impactful advertisements by creating headlines, ad copies, and visuals with pre-prescribed inputs. These tools shell out compelling ad variations with ease.

For instance, AI can take a pre-existing template and generate many variations by creatively reworking the design or copy. AI can also analyze the visual elements that resonate the most with the audience. It has the ability to identify objects, images, and scenes and help replicate such creatives.

Although the content generated needs scrutiny regarding accuracy and appropriateness, it has saved a lot of time for advertisers who can get ready-to-use templates from AI.

 Predictive modeling:

AI has enabled advertisers to stay ahead of the game with the help of predictive analytics and modeling. AI uses historic data and behavior patterns to estimate various creative elements' impact. This has ultimately guided advertisers by helping them make informed decisions. By analyzing how various aspects of a campaign (creatives, colors, and call-to-actions) affect user engagement, AI has minimized the trial-and-error process that is involved in running ad campaigns.

Predictive modeling helps anticipate consumer behavior. This does not only consider consumers’ previous browsing patterns but also every touchpoint they have with a brand. AI takes into account the whole user experience for every impression they deliver. This has helped with frequency management and sequential storytelling across channels and platforms.

Additionally, measuring the effectiveness of advertising campaigns and calculating ROIs has proved to be a complex and daunting task. But, AI can usher in vast improvements in this process. Through predictive analytics, AI can forecast the ROI of future advertising campaigns and can help advertisers decide where to allocate their ad budgets and optimize their strategies.

Fraud detection and prevention:

By leveraging advanced algorithms and machine learning techniques, AI has displayed immense capabilities to detect and prevent fraud in the adtech industry. With the help of AI, advertisers can identify and mitigate unlawful activities with ease and maintain the integrity of their campaigns.

As AI can momentarily analyze vast amounts of data like user behavior, click patterns, and conversions, it can baseline them and identify anomalies in the data. These anomalies will indicate fraudulent activities which AI algorithms can flag as malicious players.

At present, AI also recognizes patterns and tracks down click farms, ad stacking, or pixel stuffing. AI algorithms detect and prevent such activities from intruding into the ad scape in real-time. Eventually, this will become a solely AI-run operation, with no human intervention.

For instance, AI can easily pick out a high number of ad clicks from a single IP address or bot clicks that do not align with the usual behavior of a user. AI also tracks down fraudulent patterns and helps adtech professionals block or filter such activities.

These fraud detection and prevention activities,  if done by traditional data analysis methods, can take longer to yield tangible results. AI has simplified the process of running secure ad campaigns.

 Optimal ad placements:

By ensuring that ads are served in the most strategic and optimal placements, AI can play a huge role in helping ads reach their target audience. AI can enable real-time bidding in programmatic advertising and help in buying and selling ads based on bidding algorithms. In fact, AI leverages bidding data and user profiles to determine the optimal bid for each ad placement. This helps advertisers optimize their bids and secure an ideal position in real-time auctions.

AI algorithms can also help in contextual targeting. This is a targeted advertising technique that considers keywords or app or website content to show relevant ads. Instead of manually deciding what ads to display, AI has ensured that it analyzes the app or webpage content faster and positions the relevant ads.

Furthermore, AI can also help one to select the right ad networks. Ad networks provide different kinds of ads to the app or website developer. It is up to them to choose ads relevant to their target audience. AI can reduce human efforts by doing the analysis and finding the best ad-user match. This in turn helps serve relevant ads to the right audience.

 Enhanced supply path optimization:

At present, when advertisers want to buy ad space, they educate themselves on the different options to choose the best advertising platform. But this is a time-consuming and complex process to carry out manually.

But in the future, AI can help advertisers optimize these decisions with ease. AI will take the lead in analyzing information in an in-depth manner. It will consider aspects like price, performance, and any possible fraud that could take place to help advertisers make an informed decision.

For example, if an advertiser wants to place an ad on CNN.com, AI can predict which advertising platform can yield the best results. By considering factors like Cost Per Impression (CPM) and Cost Per Click (CPC), AI can help advertisers make an informed decision. It does so by analyzing the type of ads that perform well in the domain. By giving insights into the ads that could gain the most traction, AI will simplify the process of running ad campaigns.

Currently, certain AdTech firms already use AI to optimize ad buying based on signals, but there is still a lot of potential for further growth.

Challenges with AI in AdTech

Despite the potential AI has shown, it also poses particular challenges that one needs to look out for. There is a lack of regulation when it comes to AI and machine learning models. No boundary indicates how much one can rely on AI and for what. So, it makes it difficult for businesses to determine which solution is effective or trustworthy.

Most AI tools make use of black-box algorithms. Since all the research and drafting of responses take place in the backend, it is not possible to determine how the machine came to a specific conclusion, or what datasets it analyzed. This leaves the accuracy of the results in question.

Additionally, to fine-tune the AI algorithms, there is a need for reliable data from various organizations that need to be incorporated and tested. But many organizations are reluctant to share their data with third parties.

Having a reliable AI partner to integrate into your AdTech platform is important. It is also essential to tackle the initial costs of setting up an AI tool with the platform.

AI already excels at creating landing pages, social ads, and more. It will soon display its potential with video ads as well. But AI is not beyond providing error-free outputs. It is also difficult for AI to stick to a brand identity. It could prove disastrous if the AI tool copies or replicates pre-existing formats, creating a brand safety issue.

Key takeaways

AI has made the process of generating content effortless since it has rendered manual work obsolete. By creating numerous variations based on hypothesis and determining the traction a piece of content gains, AI can transform the AdTech space tremendously.

Many Adtech companies have started leveraging AI to experiment with various ad forms and create ads that captivate users. By understanding the types of ads users respond to, we seek to provide a better user experience.

Another aspect that AI has brought to the forefront is transparency. Through technologies like real-time bidding, users can gain visibility into the entire process. People with AI-backed or AI-led approaches can quickly determine which inventory works for them, causing less wastage.

Generative AI has also democratized the process of content creation by allowing the general public to access or work with content. By creating a baseline for technical content, like website creation, AI has managed to decentralize the work of developers.

But, on the flip side, AI has the potential to create entry barriers for various professionals because of its numerous capabilities. To understand AI’s potential better, tech leaders of the world need to stay tuned to the various technological advancements that come in every day. AI has become an essential part of our lives so to harness it better, we need to embrace it.