Data Science in Retail and E-commerce
In such a competitive and fast-paced environment as retail and e-commerce, data has become a key resource for understanding customer behavior, optimizing campaigns, and making fast, evidence-based decisions. Data Science enables retail businesses to improve efficiency, personalize their offerings, and anticipate market demand.
In this section, I present some of the projects I have worked on applying Big Data, Machine Learning, and advanced analytics to both physical and digital businesses.
What types of projects have I developed?
- Customer analysis and segmentation: Classifying users based on purchase patterns, frequency, value, and preferences to personalize marketing actions and improve retention.
- Demand forecasting and inventory management: Models that anticipate sales behavior and help adjust stock according to seasons, trends, and external events.
- Product recommendation systems: Algorithms that suggest products based on customer history and the behavior of similar users, increasing average order value and conversion rates.
- Campaign analysis and funnel optimization: Evaluating the performance of marketing actions across various channels, using interactive dashboards that support real-time decision-making.
Tools and technologies I use
For these projects, I use tools such as Power BI, Tableau, Google Analytics, and models developed in Python and deployed on cloud platforms like Google Cloud, AWS, or Azure. Combining data from sales, CRM, web navigation, and logistics provides a full 360º view of the business.
