Case Study Title: Learning Business Operations with Business Analytics Background You are hired as a Business Analytics Consultant for "GlobalMart," a mid-sized retail company facing challenges in maintaining profitability amidst growing competition. The company operates online and offline, with a diverse product portfolio from electronics to groceries. They aim to optimize their operations, forecast demand, and improve customer satisfaction by embedding analytics into their decision-making processes. Project Overview Objective: Leverage data analytics to identify, analyze, and resolve key business challenges at GlobalMart. The focus will be on: Understanding the role of analytics in modern business decisions. Applying descriptive, predictive, and prescriptive analytics to tackle real business problems. Using Business Intelligence (BI) tools to generate actionable insights. Case Study Tasks 1. Understanding the Business Problem Conduct a stakeholder interview to understand GlobalMart's challenges. Define key objectives: Increase customer satisfaction by identifying reasons for delayed deliveries. Forecast demand for better inventory management. Optimize marketing spend by identifying high-performing campaigns. Map the business problems into data problems using the Business Analytics Process. 2. Collecting and Analyzing Data Data Sources: Obtain structured (transaction data) and unstructured data (customer reviews). Summarize data using: Central tendency measures (mean, median, mode). Categorical data summaries (top-selling products, regional trends). Visual summaries (charts, graphs). Perform Descriptive Analytics: Identify patterns in sales and delivery timelines. Highlight KPIs like customer satisfaction scores, delivery lead times, and product return rates. Deliverables Data Analysis Report: Present insights derived from descriptive and predictive analytics. Evaluation Criteria Application of Concepts: Depth of understanding and application of analytics techniques. Problem-Solving: Creativity in addressing business problems with data-driven insights.