Retail Case Studies

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Churn prediction, distribution optimization modeling, segmentation and cohort analytics

Client: Tus Trgovine

With the introduction and increased use of digital technologies in the retail industry, retail shops experience massive volumes of data. The data stores use consists of information on customer demographics, transactional data, sales quantity, geographical data, coupon and discount usage, registry telemetry, supply chain logistics, and more. This provides retailers with an opportunity to gain insights into customer preferences, trends, shopping behaviors, pricing adjustments, inventory management, and other advantages, and holds great potential for the businesses to improve or optimize processes, increase sales, enhance customer experience, and other desired strategies. The goal was to use Machine Learning and advanced analytics to identify important segments and cohorts of customers, predict customer churn, calculate the quality of items, analyze telemetry, and optimize the distribution logistics of item delivery to the stores. This resulted in conducting several analyses, creating a knowledge graph database, building several Machine Learning models, putting them in production, and creating multiple dashboards and visualizations. 

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