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:
- Database schema
- Generated dataset
- Business questions
- All SQL and Python code used for analysis
The GitHub repository, which contains:
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:
- 970 out of 1,000 customers are repeat customers, indicating strong retention and loyalty.
- A list of top customers by order volume is displayed. Average orders per customer: 12.
- Customers reorder approximately every 14.2 days on average.
- A graph shows the number of times each product has been ordered per customer, sortable by Customer ID or Product.
- A chart displays product reorder frequency, ranked from shortest to longest reorder time for optimized inventory planning.
- Average Order Value per Customer calculated, with a ranked list from highest to lowest to identify top spenders.
- Beauty led in total revenue, followed by Home Appliances and Furniture. Electronics had the lowest revenue.
- Q3 performed best with $465,799 in sales and 1,031 orders. Q4 saw a slight drop, generating $463,424 and 1,006 orders.
- Beauty was the top category every quarter except Q1, where Home Appliances slightly outperformed. Electronics and Clothing had the lowest revenue contributions, around 8%.
- 345 orders placed in December 2024, lower compared to previous months. Only 287 out of 1,000 customers placed an order, indicating lower engagement.
- $158k in revenue, lower than in 9/24, 5/25, 4/24, and 2/24.
- 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.
- Beauty led most months, but with a dip early in the year. Furniture spiked in September, while other categories remained steady.
- Product IDs: 1046 (473 sold), 1043 (469 sold), 1025 (436 sold), 1049 (431 sold), 1073 (422 sold).
- Product IDs: 1027 (268 sold), 1088 (287 sold), 1063 (292 sold).
- Most top 5 ordered products also top the reorder list, except for 1025, which dropped to 8th. 1063 was second last in reorders.
- 97.97% of customers have purchased products from 2 or more categories, indicating strong cross-category purchasing.
- 8.92 items per order, with a graph clearly marking above and below-average volumes.
- Consistent at ~3 per product, with slight variations (~3.3, ~2.8).
- Similar to product trends, category quantities are around 3, suggesting steady demand and aiding in inventory predictions.
- Increase inventory and marketing for top products (1046, 1043, 1025, 1049, 1073).
- Promote best-selling items in bundles or subscription services.
- 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).
- Right-size packaging based on average order volume (8.92) and offer bulk options for high-quantity orders.
- Introduce "Buy More, Save More" for products with average quantities (~3).
- Promote category-based bundles to increase order value.
- Maintain stock levels for consistent demand and prioritize restocking for products/categories with slight spikes in sales.
- Boost sales in slower months like June and August through promotions or targeted ads.
- Promote beauty products and seasonal bundles for end-of-year sales.
- Target furniture products with seasonal promotions.
- Introduce "Buy More, Save More" for products with average quantities (~3).
- Promote category-based bundles to increase order value.
- Offer exclusive deals or VIP programs to increase retention and order frequency among top spenders.
- Drive sales for Electronics with promotional campaigns and bundle deals.
- Send reorder reminders based on the average 14.2-day reorder cycle.
- Use reorder data to adjust inventory restocking cycles.
- Create cross-category promotions or bundles to increase sales and customer engagement.
- Optimize Q4 holiday strategies to address slight sales drops and boost end-of-year performance.
High Customer Retention:
Top Customers Identified:
Order Frequency Insights:
Product Reorder Trends:
Reorder Time Analysis:
Customer Spending Insights:
Year-to-Date (YTD) Revenue by Category:
Quarterly Revenue Trends:
Category Revenue per Quarter:
Orders in the Last Month (12/24):
Revenue in December 2024:
Monthly Orders and Revenue Trends:
Categorical Trends by Month:
Top 5 Most Ordered Products:
Bottom 3 Most Ordered Products:
Reorder Patterns:
Cross-Category Purchasing Behavior:
Average Total Quantity per Order:
Average Quantity per Product:
Average Quantity per Category:
Recommendations
The following are recommended actions to take based on the insights:
Focus on Top-Selling Products:
Improve Reorder Strategy:
Optimize Packaging & Logistics:
Leverage Bulk Pricing & Discounts:
Adjust Inventory & Manage Demand:
Increase Engagement in Low-Performing Months:
Capitalize on Seasonal Trends & Spikes:
Replicate the furniture sales spike from September through similar campaigns:
Leverage Bulk Pricing & Discounts:
Maximize High-Value Customers:
Boost Underperforming Categories:
Optimize Reorder Timing & Inventory Management:
Leverage Cross-Category Purchases:
Address Sales Decline in Q4: