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Industry
Skills
Tools
Learning Objectives
Demonstrating how to integrate and preprocess multiple datasets related to pricing, sales, and customer behavior for a regression problem.
Apply feature engineering techniques to create meaningful predictors for revenue optimization.
Interpret model results and optimization outputs to provide actionable insights for business decision-making.
Perform Exploratory Data Analysis (EDA) to identify key insights from pricing, competitor, and inventory data.
Build and evaluate machine learning models to optimize pricing strategies.
Overview
GlobalMart, a leading retailer in the fast-moving consumer goods (FMCG) sector, offers a wide range of products to its diverse customer base. Among its extensive portfolio, Tide stands out as one of the prominent detergent brands.
However the product faces increasing competition in the detergent market due to aggressive pricing strategies by rival brands. This has led to fluctuating customer demand and inventory inefficiencies.
The company seeks to implement a data-driven Dynamic Pricing Strategy to optimize Tide’s pricing decisions.
Your task as a data scientist is to analyze sales, competitor, customer behavior, and inventory data to :
- Identify key factors influencing Tide's sales and demand.
- Optimize pricing to maximize revenue while maintaining demand and inventory balance. By solving this challenge, you will enable GlobalMart to regain its competitive edge, improve profitability, and align pricing with market dynamics.
Prerequisites
- Basic knowledge of Python programming (e.g., variables, loops, functions and data structures).
- Experience with libraries such as Pandas, Matplotlib/Seaborn, and Scikit-learn. Knowledge of optimization libraries (e.g., Scipy or Pulp) is a plus.
- Familiarity with foundational concepts in machine learning (e.g., regression models, train-test splits, model evaluation metrics).
- Understanding of optimization principles