Artificial intelligence continues to rapidly transform digital marketing, becoming a strategic factor for companies seeking efficiency, personalization and scalability in your campaigns. In light of the latest innovations in the field of AI, there is room for a more in-depth analysis of the potential of two approaches that have gained more prominence lately: predictive AI and generative AI
While predictive AI focuses on pattern analysis to predict future behaviors and generate insights, generative AI elevates creative automation, producing highly personalized content tailored to the user's context. Today, she is one of the biggest focuses of attention and investment for marketing teams in companies of various sizes and segments
SecondMcKinsey data, generative AI has the potential to generate around US$ 2,6 trillion and US$ 4,4 trillion in the global economy annually, with 75% of this amount being generated in four main areas, including marketing and sales. For reference, the value is higher than the GDP of the major world economies in 2024, except United States (US$ 29,27 trillion, China (US$ 18,27 trillion) and Germany (US$ 4,71 trillion
This data alone helps to demonstrate the impact of adopting new technologies based on generative AI and how they will be predominant for advertisers seeking differentiation and maximization of ROI. But the question remains: are there other paths that can be explored? And the answer is, without a doubt, yes
Composite AI: Why combining different AI models can make a difference
Even though generative AI is in the spotlight right now, it is undeniable the importance played by predictive AI models for digital advertising so far. Your role is to transform large volumes of data into actionable insights, allowing precise segmentations, campaign optimization and forecasts 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 if combined with other models. The logic behind this is simple: the combination of different AI models can help solve various business challenges and contribute to the enhancement of cutting-edge solutions.
At RTB House, for example, we are advancing in the combination of Deep Learning algorithms (predictive AI) with generative models based on GPT and LLM languages to improve the identification of audiences with high purchase intent. This approach allows algorithms to analyze, beyond user behavior, the semantic context of the visited pages, refining the segmentation and positioning of the displayed ads. In other words, this adds an extra layer of precision, resulting in gains in the overall performance of the campaigns
With the growing concern about privacy and regulations regarding the use of personal data, solutions based on generative and predictive AI represent a strategic alternative to maintain personalization in environments where the collection of direct user information becomes more restricted. As these tools evolve, it is expected that the adoption of hybrid models will become a market standard, with applications that contribute to the optimization of campaigns and the results generated for advertisers
When integrating predictive and generative AI models, companies show how this approach can transform digital marketing, offering more precise and efficient campaigns. This is the new frontier of digital advertising – and the brands that embrace this revolution will have a significant competitive advantage in the coming years
In this context, the question that remains for advertisers is not about which AI model to adopt in their marketing strategies, but how can they combine them to achieve even more efficient results and with an approach more aligned with the future of digital advertising
