ESTUDIO DE CASO — SERVICIOS FINANCIEROS
Sistema de Recomendación de Productos
Provide your customers with instant product insights
Sector:
Financial Services, Retail, Consumer Goods
Enfoque:
Sistema de Recomendación de Productos
DESAFÍO EMPRESARIAL
Lack of advanced systems to predict customer needs
Lack of advanced systems to process large volumes of customer data made it challenging to identify behavior patterns, as traditional methods couldn’t accurately predict customer needs, limiting the effectiveness of personalized marketing y cross-selling opportunities.
SOLUCIÓN IMPLEMENTADA
A platform aggregating data and predicting product interest
Implementamos a solution to aggregate and analyze customer data, visualize key behavior metrics mediante paneles interactivos, and apply AI models to predict customer product interests based on buying patterns.
TECNOLOGÍA
Built to aggregate, visualize, and predict customer product interest
PLATAFORMA DE DATOS
Azure Stack
Aggregates and cleans customer data, storing it in a database for efficient analysis of behavior patterns y purchase history.
ENTREGA DE DATOS
Power BI
Creates paneles interactivos that visualize key insights about customer behavior, lo que permite data-driven marketing strategies.
IA
Azure FOUNDRY
Builds predictive models that assess the likelihood of customers purchasing new products, analyzing behavior patterns and influential features.
RESULTADOS Y ROI
Lo que cambió tras la puesta en marcha del modelo
TARGETED CAMPAIGNS
Selects the top clients most likely to respond positively to specific marketing campaigns, based on ML predictions.
ROI OPTIMIZATION
Focuses marketing resources sobre high-potential leads, reducing waste and increasing return on investment.
SALES & RETENTION
Mejora sales y las tasas de retención de clientes mediante tailored marketing efforts, strengthening overall market position.
