This combination enables personalised experiences and data-driven decision-making, which is critical to staying competitive in a context where nearly 55% of organisations are expected to use AI by 2025.
The predictive analytics market is expected to reach around €37 billion by 2028, positioning itself as an essential tool for identifying behavioral patterns and optimizing campaigns.
Today, Artificial Intelligence and Business Intelligence are two of the main compasses that guide marketing and sales. These technologies enhance real-time data analysis, offering insights that help organizations create personalized strategies and make more accurate decisions. This combination generates more efficient campaigns with tangible and improved results.
These tools become key drivers due to their ability to prepare costa rica telegram phone numbers for market demands. This transformation is not only an advantage, but a necessity to remain relevant and competitive in the future. In fact, the value of the predictive analytics market will amount to approximately 37 billion euros by 2028, according to industry data. On the other hand, almost 55% of organizations globally are expected to use AI by 2025.
For José Luis Pascual, CEO of Convertia, “In the digital age, where data is the new fuel, predictive analysis goes beyond identifying behaviour patterns. It allows us to anticipate customer needs and adjust advertising campaigns in real time. This not only improves efficiency, but transforms the relationship with customers by offering truly personalised experiences. The ability to anticipate and react quickly is what makes AI and BI indispensable tools for modern marketing.”
Behavioral prediction: “the holy grail” of the future of marketing
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AI in marketing identifies patterns and automates campaigns that adjust in real time to user responses, improving customer satisfaction and loyalty. Business intelligence, meanwhile, turns data into valuable information, helping companies anticipate behavior and improve their segmentation and personalization strategies based on consumer trends.
A clear example is the use of predictive analytics in e-commerce platforms, which recommend products based on users’ purchasing and browsing history. This not only increases conversion rates by more than 10%, according to McKinsey & Company, but also improves customer experience, strengthening long-term loyalty.