This project involved a deep-dive analysis of CGV Cinema’s raw operational and customer data, encompassing over 40,000 rows. I transformed a complex, real-world dataset into actionable business insights, culminating in 8+ strategic recommendations aimed at boosting revenue, enhancing customer experience, and optimizing operational efficiency. My Process: I followed a structured, end-to-end analytical process to ensure the integrity of the findings and the practicality of the recommendations. Step 1: Data Wrangling & Cleaning My first priority was to create a reliable foundation for analysis. This meticulous process in Excel was crucial and involved: Handling Large-Scale Data: Managing and combining customer and ticket tables, totaling over 40,000 records. Data Cleansing: Systematically correcting formatting inconsistencies, handling NULL values, and identifying/removing significant outliers (e.g., customers with over 7,000 transactions) to ensure data accuracy. Data Transformation: Creating new, valuable columns, such as calculating customer age from raw date-of-birth data, to enable deeper segmentation and analysis. Step 2: Exploratory Data Analysis (EDA) With a clean dataset, I performed exploratory analysis to understand the business landscape. I created various visualizations and pivot tables to answer key questions: Who are our most valuable customer segments? (by age, gender, job) What are the top-performing movie genres and peak showtimes? How does spending behavior correlate with customer demographics? Are there significant performance gaps in our operations (e.g., by cashier)? Step 3: Insight Generation & Strategic Recommendations The final step was to connect the dots. I focused on translating the "what" (the data) into the "so what" (the business implication), leading to actionable recommendations.