📊 Predictive Modeling and Customer Intelligence for Enterprises. Ep 6. 🚀🔍

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Companies can transform their performance and reduce churn by applying customer intelligence models and predictive strategies. In this video, we explore how AI and data analytics enable improved retention and maximize customer value.

📌 What will you learn in this video?
✔️ Predictive models for customer retention: Using algorithms such as logistic regression, decision trees, random forests and neural networks to detect patterns and predict churn.
✔️ Building a predictive model: From data collection and cleaning to key feature selection, training and model validation.
✔️ Key benefits of prediction:
🔹 Proactive customer retention.
🔹 Resource optimization by targeting customers with high probability of churn.
🔹 Improved customer experience by anticipating and resolving issues before they abandon.

✔️ Analysis of trends and consumption patterns: How to anticipate changes in demand and find cross-selling and up-selling opportunities.
✔️ Automation of cross-selling and up-selling strategies: Using AI to recommend complementary products and services in real time.
✔️ Technologies to automate recommendations: Integration with CRM, ERP and content management systems (CMS).
✔️ B2B vs. B2C in customer intelligence: Key differences in customer personalization and segmentation.
✔️ Success stories: Companies such as Amazon, Bankinter, Repsol and Sanitas have implemented these strategies with great impact.
✔️ Implementation challenges: Data quality, variable selection, model overfitting, speed of change in trends and integration with business systems.

🚀 If you want to optimize customer retention and improve commercial strategy with AI, this video is for you.

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📌 #PredictiveModels #CustomerIntelligence #CustomerRetention #CRM #ERP #CrossSelling #UpSelling #DigitalTransformation.