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Optimizing Online Sports Retail Revenue Through SQL

Analyze product data for an online sports retail company to optimize revenue.

Objective :

The goal of this project is to improve revenue of online Retail Company and produce recommendations for its marketing and sales teams.

In this project, i’ll will work with numeric, string, and timestamp data on pricing and revenue, ratings, reviews, descriptions, and website traffic.

SQL FUNCTION :

SQL functions that I used in this project:

  • Aggregate functions: These functions facilitate the calculation of values across multiple rows of data, including operations like sum, average, and count.
  • Date functions: These functions provide the means to manipulate and extract information from date columns within the database.
  • Join functions: These functions empower me to merge data from different tables based on shared columns.
  • Union functions: These functions permit the merging of outcomes from various queries into a single result set.
  • CTE (Common Table Expression): This function empowers me to establish a temporary result set within a SELECT statement.
  • Subqueries: These represent SELECT statements executed within another SELECT statement.
  • Window functions: These functions empower me to conduct calculations across a group of rows.

Analysis & Insights

Detailed analysis and insights found on my Blog Post : here

Suggestions :

  • The brand needs to explore opportunities to develop products in the “Expensive” and “Elite” categories that have higher revenue potential.
  • Also, highest revenue generated Products are from footwear section, brand should focus on giving less discounts on footwear and more discounts on clothing that will increase sales and revenue for clothing section as well.
  • Continuously monitoring product section like footwear and clothing and making relevant price adjustments or marketing strategies.
  • Focusing on product quality, customer service, and holistic marketing strategies can help improve reviews and revenue.
  • Analyzing factors that influence monthly review fluctuations and planning appropriate marketing strategies.
  • Using this data as a foundation to design more effective and customer-oriented business strategies.
  • All of these recommendations can assist the brand in enhancing product performance, increasing revenue, and providing a better experience to customers.