Unlocking business value for CPGs through AI

The Business Problem A critical question in the CPG industry is determining the true return on investment (ROI) of trade promotions. Traditional analysis often relies on simple revenue lift, failing to account for product margins, baseline sales, or the initial plan. This leads to an incomplete and often misleading picture of a promotion’s real impact on the bottom line. The Solution (No AI here) This accelerator is a professional-grade analytics tool that provides a complete Plan vs. Actual analysis for promotional events, with a strategic focus on Margin-Based ROI. ...

The Business Problem In the CPG industry, inaccurate demand forecasting leads to two costly problems: stockouts (lost sales) and overstocks (wasted capital and expired product). Traditional forecasting often relies on simple historical averages and fails to capture complex patterns like holidays, seasonality, and recent trends. The AI Solution This project showcases a predictive analytics tool that uses a time-series AI model (Meta’s Prophet) to generate accurate, SKU-level demand forecasts. The model was trained on a hyper-realistic, multi-year dataset that includes patterns for: ...

The Business Problem CPG companies generate and consume vast amounts of market research, trend reports, and internal competitive analyses. However, this wealth of information often sits in static documents, making it difficult for teams to quickly extract specific answers, synthesize insights, or find information relevant to a new strategic question without sifting through hundreds of pages. The AI Solution This project introduces an AI-powered conversational agent that transforms static market research documents into an interactive knowledge base. Leveraging Retrieval-Augmented Generation (RAG), this application allows users to ask natural language questions and receive precise, contextually relevant answers directly from a curated collection of documents. ...

The Business Problem Traditional retailer segmentation is often one-dimensional and static, grouping stores by last year’s sales volume alone over longer range data. This simplistic approach fails to capture the true nature of a retail partner, especially as it goes through several demand side patterns. It cannot distinguish between a large, stagnant store and a smaller, high-growth store that is rapidly adopting new product trends. The AI Solution This project builds a sophisticated, multi-layered segmentation engine that analyzes retailers through three independent strategic lenses: ...

The Business Problem Even with advanced retailer segmentation, a common challenge remains: how to translate high-level strategic personas into concrete, daily actions for the sales team. Sales representatives are often overwhelmed with a long list of tasks and limited time during a store visit, leading to inconsistent execution of strategy. The AI Solution This project directly solves that problem by operationalizing the insights from our multi-layered segmentation model. It is a prescriptive analytics tool that acts as a “smart assistant” for sales representatives. ...

This project is an end-to-end CPG analytics application built with Streamlit. It includes a high-performance data pipeline, predictive sales forecasting, and a generative AI chatbot that can query the database in plain English. ➡️ Try the Live Application Here