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Manufacturing / Beverage
Azure Data Lake, Azure Databricks, Snowflake, Azure Machine Learning, Power BI
Optimized data, improved models, boosted engagement, and enhanced forecasting.
The company leverages advanced technologies and data-driven strategies to optimize its operations, enhance customer engagement, and deliver superior products. By doing so, AB-InBev positions itself as a leader in the competitive global market, driving growth and ensuring long-term sustainability.
Improvement in ML Model Performance
Increase in Customer Engagement
Boost in Sales Forecast Precision
The client struggled with managing and analyzing large volumes of sales data to optimize promotional strategies and pricing models. They faced difficulties in merging and processing data from multiple sources in Snowflake, improving the accuracy of ML models through feature selection, and using clustering techniques to segment customers effectively. Additionally, they needed more precise demand estimation models to enhance sales forecasting and optimize pricing. To address these challenges, the client wanted a scalable, cost-efficient solution to boost ROI and improve decision-making.
• Merged and cleaned data from Snowflake paths to create a unified dataset.
• Conducted feature engineering to enhance model performance.
• Applied DBSCAN and Hierarchical Clustering for customer-product segmentation.
• Developed and tuned ML classification models for each cluster.
• Built demand estimation models to predict sales and understand demand elasticity.
• Created optimization models to determine optimal product pricing for revenue maximization.
• Visualized all KPIs using Power BI dashboards.
Improved data processing and ML models, boosting sales forecasting by 30%, optimized pricing for a 15% sales increase, and enhanced decision-making with Power BI.
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Ondemand Webinar
AWS
May 2024
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