Role of Generative AI in Google Ads Part 2: Automation, personalisation and challenges

Generative AI in Google Ads represents a revolutionary approach to ad creation that significantly differs from traditional methods in myriad ways. Generative AI makes use of vast amounts of data and training models on various advertising patterns to create compelling and highly relevant ad content. One of the key distinctions lies in the level of personalisation offered by generative AI as against traditional methods that often struggle to tailor ads to individual users, leading to generic and content that can be less impactful. In contrast, generative AI can analyse user behaviour, preferences, and demographics in real-time to generate ads that resonate with specific audiences, ultimately improving ad performance.

By embracing generative AI in Google Ads, advertisers can optimise their creative efforts, save time, and allocate resources more efficiently, leading to improved ROI and a better user experience for their target audience. This technology has the potential to revolutionise the advertising landscape, taking it to new heights of innovation and effectiveness.

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Google’s AI tool for ad creation - Part 1: A game-changer for marketers?

The automation game

Generative AI in Google Ads differs from traditional ad creation methods in many ways. While traditional ad creation relies on manual efforts, generative AI automates the process, says Shubit Rakshit, Business Director, FoxyMoron, [Zoo Media]. “It leverages advanced language models and machine learning algorithms to analyse data and generate various ad assets. This automation speeds up the process, enables personalisation at scale, and allows for real-time optimisation. Generative AI continuously learns and adapts to audience preferences, leading to more relevant and engaging ads,” he adds.

Marketers’ role

Experts are of the opinion that the adoption of generative AI in ad creation will affect the roles and responsibilities of marketers and advertisers in the days to come.

The adoption of generative AI in ad creation is likely to reshape the roles and responsibilities of marketers and advertisers, points out Ayatiworks Technologies Founder-CEO Upendran. According to him, the adoption of generative AI will elevate marketers and advertisers to be strategic thinkers and creative visionaries.

“With AI handling tasks like automated content generation, ad optimisation, and audience targeting, marketers can shift their focus from manual, repetitive tasks to strategic thinking and creativity. They will play a more critical role in defining campaign objectives, understanding audience insights, and setting broader marketing strategies. As AI takes care of data analysis and performance optimisation, marketers can use the generated insights to make informed decisions and refine their approach. Advertisers will need to become more adept at leveraging AI tools effectively, understanding the algorithms' outputs, and interpreting data-driven recommendations. While AI streamlines and enhances the ad creation process, the human touch remains crucial in brand storytelling, creative direction, and aligning campaigns with the company's overall vision and values. Overall, the adoption of generative AI will elevate marketers and advertisers to be strategic thinkers and creative visionaries rather than solely execution-focused professionals,” Upendran explains.

The roles for digital marketers will evolve and certainly not disappear, asserts Shubit Rakshit. “While AI speeds up ad creation, marketers will play a pivotal role. Creative strategy remains their expertise, giving a heart and soul to the campaigns. Through data analysis, marketers derive insights that inspire ad optimisation. They still will be instrumental in creating audience segmentation, aligning the brand voice and forging meaningful connections with customers. Collaborating with AI specialists amplifies marketers’ impact in the digital marketing landscape,” says Rakshit.

Let us explore the way Google’s generative AI model works in order to create ads automatically. What is the role of data and algorithms in the process?

This will involve training the model on large datasets of successful ad campaigns, marketing insights, and creative content, says Upendran.

“The data can include text, images, video elements, and audience behaviour patterns. The model learns to recognise patterns and generate new ad variations based on input parameters like target audience, campaign goals, and budget. Algorithms in the generative AI model use techniques like deep learning, natural language processing (NLP), and computer vision to understand and recreate the visual and textual elements of the ads. The system undergoes continuous optimisation through multivariate testing, ensuring the most effective ad variants are generated for different platforms and audiences,” he explains.

With generative AI being used to create ads, Upendran anticipates a significant positive impact on ad personalisation and targeting capabilities. According to him, the AI algorithms can analyse vast amounts of user data, including browsing behaviour, interests, demographics, and historical interactions with ads. This enhanced understanding, he adds, enables advertisers to create highly personalised and relevant ads that align with individual user preferences and needs.

“As a result, users are more likely to see ads that are genuinely useful and interesting to them, leading to a more positive user experience. The tailored approach can also reduce the feeling of intrusive or irrelevant advertising, thereby fostering a sense of trust between users and advertisers. Additionally, the AI’s ability to dynamically optimize ad content in real-time based on user interactions ensures that users are exposed to the most engaging and compelling ads, further enhancing their overall experience with online advertising,” he says.

Potential risks

Marketers, however, should be mindful of potential risks and concerns when using auto-generated ads. It is equally important to mitigate any possible negative outcomes.

Mitesh Kothari, Co-founder and Chief Creative Officer, White Rivers Media, feels that it is important to stay mindful of ethical considerations like data privacy. “With AI as our ally, the advertising landscape will be dynamic, data-centric, and creatively charged,” he says.

According to Shubit Rakshi, a few of the potential risks are ad quality, policy compliance, lack of originality, audience perception, and data privacy. He suggests that maintaining human oversight is crucial, especially for ensuring brand alignment and creative excellence, in order to mitigate these risks.

Testing AI-generated ads in smaller batches can help evaluate their performance before full-scale deployment. Continuously monitoring ad performance and user feedback allows for quick adjustments. Combining AI-generated creatives with human creativity can add a unique touch to the ads and address any potential lack of originality. Robust data governance practices ensure data security and compliance.

Advertising policies

It is important for Google to ensure that auto-generated ads comply with advertising policies and do not inadvertently violate any guidelines.

Google employs robust policy filters and machine learning algorithms to screen the ads and detect potential violations, points out Shubit Rakshit. “Human reviewers also play a vital role in assessing complex cases and ensuring ad quality. Also, Google regularly updates its policies to address emerging issues. Brands and advertisers can do their bit to support compliance by maintaining transparent messaging and adhering to guidelines in their business prompts,” he adds.

To ensure that auto-generated ads comply with advertising policies and guidelines, Google may implement several layers of checks and measures, says Upendran.

“First, before generating ads, the AI system would be trained on a diverse dataset that includes both successful and compliant ad examples. This training aims to familiarise the AI with the boundaries of acceptable content. Additionally, Google would have a team of human reviewers who continuously monitor and update the guidelines to reflect changing advertising standards. These reviewers would validate and fine-tune the AI-generated ads to ensure compliance with policies. Moreover, Google may employ natural language processing and image recognition algorithms to scan and filter ad content for potential policy violations before serving them to users. If any issues arise post-launch, user feedback and reporting mechanisms would also help identify and rectify non-compliant ads promptly. This multi-layered approach combines AI capabilities with human oversight to ensure that auto-generated ads meet advertising policies and uphold a safe and respectful advertising environment for users,” he explains.

According to Upendran, advertisers and businesses should be aware of several potential risks and concerns when using auto-generated ads. One major concern is the lack of direct human oversight during the ad creation process, which might lead to unintended or inappropriate content being generated.

He adds that there is also a risk of over-reliance on AI-generated ads, potentially leading to a loss of brand identity and uniqueness. Additionally, he explains, the automated nature of the process could make it challenging to ensure complete compliance with advertising policies and legal guidelines.

“To mitigate these negative outcomes, advertisers should maintain human involvement by reviewing and approving generated ad content before deployment. Regular monitoring and analysis of performance metrics can help identify and address any issues promptly. Advertisers should also invest in proper training and education for their AI tools to ensure they align with brand values and produce ads that resonate with the target audience. By combining the efficiency of AI with human judgment and creative input, businesses can strike the right balance and leverage auto-generated ads effectively and responsibly,” he says.

Personalisation and targeting

And what kind of impact will it have on ad personalisation and targeting capabilities, including user experience? Experts feel that AI can create a much more enriching user experience when compared to traditional methods.

AI processes user data, unlocking deep insights for laser-focused targeting, says Shubit Rakshit. “Advertisers can reach audiences with tailored messages, enhancing relevance and resonating with users. With AI-driven personalisation, you can build tribes that trust your brand even more. It is not just about chasing numbers, but about creating enriching experiences that resonate with your audience,” he concludes.