Project Overview
Trade Promotion Management (TPM) in consumer packaged goods (CPG) refers to companies' strategies to plan, execute, and analyze promotional activities with their retail partners. These promotions are designed to drive sales, increase brand visibility, and ultimately boost the market share of consumer packaged goods.
In this project, I constructed predictive models using R to forecast trade promotions' impact on sales, optimizing promotional strategies for a leading global food brand. Simultaneously, a strategic migration was executed, transitioning intermediate storage operations from SAS Grid to AWS S3. This resulted in substantial savings on licensing costs and improvement in efficiency.
To further enhance the efficiency of the pipeline, I implemented data seasonality analysis using Facebook's Prophet library. This extraction of temporal patterns and trends allowed for a deeper understanding of the underlying sales data, leading to a significant 20% improvement in the accuracy of the promotional impact forecasts. The amalgamation of advanced statistical modeling, cloud migration, and data-driven insights optimized the global food brand's promotional strategies. It demonstrated a proactive approach to resource management and predictive analytics within the dynamic realm of consumer-packaged goods.