Business Metrics Analysis

Project Overview

This report presents an exploratory data analysis of key business metrics for a simulated business database. The dataset was generated using Python, analyzed with SQL, and visualized in Tableau to uncover valuable insights. The analysis focused on customer behavior, sales performance, product demand, and complaint trends, providing actionable insights into business performance and areas for improvement.

The following resources are linked below:

    The GitHub repository, which contains:

  • Database schema
  • Generated dataset
  • Business questions
  • All SQL and Python code used for analysis
  • The comprehensive business report, detailing findings and recommendations

    The Tableau dashboard, featuring an interactive view of the data

This project serves as a comprehensive business intelligence assessment, offering data-driven recommendations to enhance operational efficiency and strategic decision-making.

Tools & Technologies

  • PostgreSQL
  • Python
  • PGAdmin
  • Tableau
  • PowerPoint
  • Data Visualization

Methodology

Analysis of business data using SQL and Tableau to identify patterns for customers, products, orders, complaints, and any other key metrics.

  • Data collection and cleaning
  • Pattern identification
  • Root cause analysis
  • Solution development

Results & Impact

Summary of the results:

The analysis reveals strong customer retention, with 97% of customers placing repeat orders and a consistent demand for top-selling products. However, some months, particularly June and December, showed lower orders and revenue, signaling the need for targeted promotional strategies during these periods. Beauty products dominated sales, especially in the later months, while electronics performed poorly, suggesting the need for focused marketing to boost underperforming categories. Reorder frequency insights indicate opportunities to optimize inventory and improve reorder strategies through loyalty programs and targeted promotions. To capitalize on trends, businesses should focus on cross-category bundling, inventory adjustments, and engaging high-value customers to maintain steady growth and enhance profitability.

The following is the full set of insights:

    High Customer Retention:

  • 970 out of 1,000 customers are repeat customers, indicating strong retention and loyalty.
  • Top Customers Identified:

  • A list of top customers by order volume is displayed. Average orders per customer: 12.
  • Order Frequency Insights:

  • Customers reorder approximately every 14.2 days on average.
  • Product Reorder Trends:

  • A graph shows the number of times each product has been ordered per customer, sortable by Customer ID or Product.
  • Reorder Time Analysis:

  • A chart displays product reorder frequency, ranked from shortest to longest reorder time for optimized inventory planning.
  • Customer Spending Insights:

  • Average Order Value per Customer calculated, with a ranked list from highest to lowest to identify top spenders.
  • Year-to-Date (YTD) Revenue by Category:

  • Beauty led in total revenue, followed by Home Appliances and Furniture. Electronics had the lowest revenue.
  • Quarterly Revenue Trends:

  • Q3 performed best with $465,799 in sales and 1,031 orders. Q4 saw a slight drop, generating $463,424 and 1,006 orders.
  • Category Revenue per Quarter:

  • Beauty was the top category every quarter except Q1, where Home Appliances slightly outperformed. Electronics and Clothing had the lowest revenue contributions, around 8%.
  • Orders in the Last Month (12/24):

  • 345 orders placed in December 2024, lower compared to previous months. Only 287 out of 1,000 customers placed an order, indicating lower engagement.
  • Revenue in December 2024:

  • $158k in revenue, lower than in 9/24, 5/25, 4/24, and 2/24.
  • Monthly Orders and Revenue Trends:

  • Top months: 9/24, 10/24, 4/24, 5/24, and 12/24. June had the lowest orders and revenue, while August showed another slower month.
  • Categorical Trends by Month:

  • Beauty led most months, but with a dip early in the year. Furniture spiked in September, while other categories remained steady.
  • Top 5 Most Ordered Products:

  • Product IDs: 1046 (473 sold), 1043 (469 sold), 1025 (436 sold), 1049 (431 sold), 1073 (422 sold).
  • Bottom 3 Most Ordered Products:

  • Product IDs: 1027 (268 sold), 1088 (287 sold), 1063 (292 sold).
  • Reorder Patterns:

  • Most top 5 ordered products also top the reorder list, except for 1025, which dropped to 8th. 1063 was second last in reorders.
  • Cross-Category Purchasing Behavior:

  • 97.97% of customers have purchased products from 2 or more categories, indicating strong cross-category purchasing.
  • Average Total Quantity per Order:

  • 8.92 items per order, with a graph clearly marking above and below-average volumes.
  • Average Quantity per Product:

  • Consistent at ~3 per product, with slight variations (~3.3, ~2.8).
  • Average Quantity per Category:

  • Similar to product trends, category quantities are around 3, suggesting steady demand and aiding in inventory predictions.
  • Recommendations

    The following are recommended actions to take based on the insights:

      Focus on Top-Selling Products:

    • Increase inventory and marketing for top products (1046, 1043, 1025, 1049, 1073).
    • Promote best-selling items in bundles or subscription services.
    • Improve Reorder Strategy:

    • Create loyalty programs or special offers for products with low reorder rates (e.g., 1025, 1027).
    • Explore subscription models for high-reorder items (e.g., 1083).
    • Optimize Packaging & Logistics:

    • Right-size packaging based on average order volume (8.92) and offer bulk options for high-quantity orders.
    • Leverage Bulk Pricing & Discounts:

    • Introduce "Buy More, Save More" for products with average quantities (~3).
    • Promote category-based bundles to increase order value.
    • Adjust Inventory & Manage Demand:

    • Maintain stock levels for consistent demand and prioritize restocking for products/categories with slight spikes in sales.
    • Increase Engagement in Low-Performing Months:

    • Boost sales in slower months like June and August through promotions or targeted ads.
    • Capitalize on Seasonal Trends & Spikes:

    • Promote beauty products and seasonal bundles for end-of-year sales.
    • Replicate the furniture sales spike from September through similar campaigns:

    • Target furniture products with seasonal promotions.
    • Leverage Bulk Pricing & Discounts:

    • Introduce "Buy More, Save More" for products with average quantities (~3).
    • Promote category-based bundles to increase order value.
    • Maximize High-Value Customers:

    • Offer exclusive deals or VIP programs to increase retention and order frequency among top spenders.
    • Boost Underperforming Categories:

    • Drive sales for Electronics with promotional campaigns and bundle deals.
    • Optimize Reorder Timing & Inventory Management:

    • Send reorder reminders based on the average 14.2-day reorder cycle.
    • Use reorder data to adjust inventory restocking cycles.
    • Leverage Cross-Category Purchases:

    • Create cross-category promotions or bundles to increase sales and customer engagement.
    • Address Sales Decline in Q4:

    • Optimize Q4 holiday strategies to address slight sales drops and boost end-of-year performance.