• Conducted hands-on SQL data analysis for a music store using PostgreSQL and PgAdmin4, revealing insights such as the dominance of the "Rock" genre and the popularity of "Queens" as the top artist.

• Explored customer behavior, revenue trends, and genre preferences to inform strategic decision-making, uncovering insights like the standout track "War Pigs" and the US as the primary market.

• Streamlined inventory management and enhanced customer engagement strategies, leveraging insights like an average album price of $1 to improve operational efficiency and satisfaction.

• Unveiled comprehensive insights into customer behaviors, preferences, and spending patterns, guiding targeted marketing campaigns and personalized experiences, including geographical preferences.

• Utilized genre-wise performance analysis to curate the music catalog, maximizing revenue potential, customer satisfaction, and repeat purchases by tailoring offerings to specific customer preferences. 

• Identified international market opportunities through analysis of purchasing trends across countries, leading to strategic expansion initiatives such as localized marketing campaigns and partnerships with international distributors. 

• Applied data-driven insights to personalize customer experiences, such as recommending similar tracks or artists based on past purchase history, leading to increased customer loyalty and lifetime value. 

Employed Subqueries for Deeper Analysis:

• Developed and deployed subqueries within SQL queries to extract subsets of data for more granular analysis within larger datasets.

• Leveraged subqueries to delve into specific user segments, campaign cohorts, or product categories, allowing for targeted analysis and insights generation.

• Utilized subqueries to investigate correlations between different marketing variables and uncover hidden patterns or trends in user behavior.

• Integrated subqueries seamlessly into larger SQL queries, maintaining query efficiency and readability while maximizing analytical depth.

• The use of subqueries facilitated the identification of nuanced insights and actionable recommendations for optimizing marketing strategies.

Implemented Logical Functions for Data Segmentation:

• Applied SQL logical functions, particularly CASE WHEN, to categorize and segment data based on predefined criteria or conditions.

• Employed logical functions to create distinct segments within the dataset, such as high-value customers, low-engagement users, or successful campaign responders.

• Utilized logical functions to conduct conditional operations on data, enabling dynamic segmentation and analysis based on changing business requirements.

• Incorporated logical functions into SQL queries to perform targeted analysis on specific segments of interest, enhancing the relevance and applicability of the insights generated.

• The use of logical functions facilitated a deeper understanding of user behavior patterns and campaign performance drivers, informing data-driven decision-making and strategy optimization efforts.