The project
Our methodology combined the analysis of purchasing behaviors and socio-demographic data to build a predictive model of each customer’s spending potential, using a targeted predictive data analytics approach.
Through advanced machine learning algorithms, we developed a system capable of accurately estimating share-of-wallet and recommending optimal up-selling and cross-selling strategies for each segment.
Results
The implementation of the model delivered outstanding results: a 25% increase in average share-of-wallet for the target segment and an 18% revenue growth through targeted, data-driven commercial actions.
The solution includes an advanced profiling and targeting dashboard that enables the sales team to identify the most promising opportunities in real time. This scientific approach to revenue growth, grounded in big data analytics, has transformed existing commercial relationships into drivers of sustainable business expansion.
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