Artificial Intelligence (AI) has become a key enabler for companies to drive competitive value. Advanced technologies’ ability to process large amounts of information and detect patterns and trends faster and more accurately than humans has revolutionized how businesses make decisions. In this article, we will explore this transformation through success stories of AI implementations by Clariba SEIDOR.
Success Stories with AI in Business Analytics
Success Stories with AI in Advanced Demand Prediction
AI enables the analysis of enormous amounts of data, identifying patterns in customer behavior, and accurately predicting future demand. This includes not only the data from the business itself but also external factors such as weather or sociopolitical factors. As a result, more accurate predictions can optimize the supply chain and reduce costs but also prevent imbalances in inventory.
SEIDOR developed a predictive model based on historical sales data to assist a pharmaceutical sector client facing the challenge of optimizing their product demand prediction process. This model allowed predicting unit demand for all products up to 12 months in advance, transforming a manual process into an automated and efficient operation.
Success Stories with AI in Optimal Resource Allocation
By analyzing large volumes of data, AI also reveals hidden patterns and trends. This empowers companies to distribute resources more intelligently and strategically, maximizing performance and minimizing unnecessary costs. Additionally, it provides real-time information for precise and quick operational adjustments and identifies and resolves bottlenecks in business processes.
In a practical AI success case, we applied this principle to the Catalonia 112 Emergency Response Service, in the autonomous community of Spain with Barcelona at its capital. The challenge was to ensure the constant availability of resources in the contact center to ensure the most effective response time to its citizens. Through an analysis of correlations between incidents and external data, such as weather information and patterns during important events (Mobile World Congress, Sports Events, Concerts,…), we developed a comprehensive management system. This system includes two applications for forecasting and sizing, as well as simulations.
As a result, resource utilization was optimized. The continuously evolving algorithm achieved an accuracy of 93%, leading to a significant increase in citizen satisfaction.
Fig. 1 Alternative chart with predicted results
Success Stories with AI in Optimizing Product Recommendations at the Point of Sale
Another aspect to consider with AI is that, by analyzing large amounts of information, it can generate personalized recommendations aligned with the needs and preferences of each customer. This personalization not only improves the shopping experience but also increases the likelihood of sales. Additionally, AI can identify patterns in customer behavior and predict future product interests.
In a specific AI success case, we applied this technology to a company in the automotive and construction chemicals sector. We developed a data analysis solution with AI that provided a more comprehensive view to the sales team. Clariba SEIDOR's recommendation algorithm was integrated into both the website and the CRM system, providing sales representatives with updated information on customer behavior patterns. As a result, the average order volume increased, and there was a significant boost in sales.
In conclusion, the success stories of Clariba SEIDOR highlight the transformative impact of AI in a variety of use cases. From optimizing demand prediction to enhancing resource allocation and personalized recommendations, AI offers tangible benefits for organizations. As we delve deeper into this topic, stay tuned for Part 2 of our article, where we'll explore more case studies and insights.
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