The combination of different AI models in digital marketing

Artificial intelligence continues to rapidly transform digital marketing, becoming a strategic factor for companies seeking efficiency, personalization, and scalability in their campaigns. Given the latest innovations in AI, it's worth taking a deeper look at the potential of two approaches that have gained increasing attention recently: predictive AI and generative AI.

While predictive AI focuses on analyzing patterns to predict future behaviors and generate insights, generative AI elevates creative automation, producing highly personalized content tailored to the user's context. Today, it is a major focus of attention and investment for marketing teams in companies of all sizes and segments.

According to McKinsey data, generative AI has the potential to generate between US$2.6 trillion and US$4.4 trillion in revenue annually in the global economy, with US$751 trillion of this value generated in four main areas, including marketing and sales. For reference, this figure is greater than the GDP of the world's major economies in 2024, excluding the United States (US$29.27 trillion), China (US$18.27 trillion), and Germany (US$4.71 trillion).

This data alone helps demonstrate the impact of adopting new generative AI-based technologies and how they will be crucial for advertisers seeking differentiation and maximizing ROI. But the question remains: are there other avenues that can be explored? And the answer is undoubtedly yes.

Composite AI: Why Combining Different AI Models Can Make a Difference

Even though generative AI is currently in the spotlight, the importance of predictive AI models in digital advertising is undeniable. Their role is to transform large volumes of data into actionable insights, enabling precise targeting, campaign optimization, and predictions about consumer behavior. Data from RTB House indicates that solutions based on Deep Learning, one of the most advanced fields of predictive AI, are up to 50% more efficient in retargeting campaigns and 41% more effective in product recommendations compared to less advanced technologies.

However, Deep Learning algorithms can be improved by combining them with other models. The logic behind this is simple: combining different AI models can help solve different business challenges and contribute to the development of cutting-edge solutions. 

At RTB House, for example, we're advancing the combination of Deep Learning algorithms (predictive AI) with generative models based on GPT and LLM to improve the identification of audiences with high purchase intent. This approach allows algorithms to analyze not only user behavior but also the semantic context of the pages visited, refining the targeting and positioning of displayed ads. In other words, this adds an additional layer of precision, resulting in gains in overall campaign performance.

With growing concerns about privacy and regulations regarding the use of personal data, solutions based on generative and predictive AI represent a strategic alternative for maintaining personalization in environments where the collection of direct user information becomes more restricted. As these tools evolve, the adoption of hybrid models is expected to become a market standard, with applications that contribute to the optimization of campaigns and the results generated for advertisers.

By integrating predictive and generative AI models, companies demonstrate how this approach can transform digital marketing, delivering more accurate and efficient campaigns. This is the new frontier of digital advertising—and brands that embrace this revolution will have a significant competitive advantage in the coming years.

In this context, the question for advertisers is not which AI model to adopt in their marketing strategies, but how they can combine them to achieve even more efficient results and with an approach more aligned with the future of digital advertising.