Da Perceção ao Impacto: Análise Avançada para Melhorar a Eficiência Operacional
Overview
A multiyear collaboration with a client in the Energy sector focused on improving operational efficiency through advanced analytics and AI. Over more than four years, we worked directly with the client’s business units to develop a set of analytical solutions and agentic AI components implementing high impact business use cases that drive value
Background
Challenge
The main challenge of the project was to leverage the client’s existing data to generate practical and measurable improvements. A key point was ensuring that analytical solutions were not developed in isolation but aligned with real operational need (identified with clear business case supported by cost-benefit analysis) in order to balance development time with the expected savings and performance gains.
To overcome the challenges the team maintained strong collaboration across IT, business teams and external partners to ensure alignment and efficiency.
Approach
To ensure that resources were focused on initiatives with clear and measurable impact, while maintaining speed and adaptability, the project was delivered through an iterative and collaborative approach designed to keep analytical development closely alligned with operational needs. Work was organized in short, agile cycles that allowed teams to refine requirements, validate assumptions and adjust priorities as new insights emerged.
A comprehensive portfolio of advanced analytics solutions was developed, ranging from predictive churn-prevention models to more advanced conversational and agentic AI capabilities. These solutions were created in close collaboration with the relevant business units, ensuring a high level of customization and seamless integration into existing workflow
We saw an opportunity to introduce a less expensive version of the Hydrow Classic rower to capture new sales and also address some logistics challenges we faced
Results
Results Achieved
The project delivered measurable improvements across multiple operational and commercial areas. More than 15 advanced analytics solutions were developed across key departments including Sales, Operations, Finance, Procurement, and Marketing, supporting a broad transformation toward data-driven processes.
One of the most tangible outcomes was a significant reduction in debt collection costs, with consistent monthly savings enabled by better targeting and more efficient workflows. On the commercial side, analytical and AI driven models supported substantial increases in both commodity and extra-commodity sales, as well as improved effectiveness of marketing campaigns through refined customer segmentation and personalized engagement.
Beyond financial results, the initiative strengthened the organization’s overall decision-making capabilities. By embedding predictive, conversational, and agentic AI tools into existing processes, teams were able to operate with greater efficiency and rely more confidently on data-supported insights.
~20k
€ /month saved in debt collection fees
+100%
increase in commodity sales; +150% in extra commodities in marketing campaigns