Driving Business Growth Through Data: A Comprehensive Sales & Profitability Analysis

🧠 Problem Statement

“How can the business optimize its sales and profitability by identifying key patterns in customer behavior, product performance, discount effectiveness, and time-based sales trends?”

Target Audience

• Business Executives & Decision-Makers

• Marketing & Sales Teams

• Product & Category Managers

• Operations & Supply Chain Teams

• Data Analytics Hiring Managers & Recruiters

📂 Dataset Description

The dataset used in this analysis is sourced from the Kaggle Superstore Dataset, which includes 9,995 rows and 21 columns. The data covers a four-year period, ranging from January 2014 to December 2017, providing a detailed record of sales transactions, customer behavior, product performance, and profitability trends over time.

🔧 Process Overview

1. Asked the business question

2. Prepared the data to be used.

3. Inspected and cleaned raw CSV using Excel & Python

4. Corrected header names and removed unused columns (e.g., Product Names)

5. Ensured data validation and consistency

6. Imported into MySQL Workbench for querying

7. Wrote modular SQL scripts:

   • Customer Segmentation & Behavior.sql

   • Sales & Profit Over Time.sql

   • Sub-Category Sales & Profitability Analysis.sql

   • Discount vs Profit.sql

   • KPIs.sql

8. Exported results to CSV

9. Visualized in Tableau for pattern discovery

10. Made insights and data-driven actions and recommendations

    
    

📊 Key Insights & Actions

1. KPIs Summary

• Total Sales: $2.3M

• Total Profit: $286K

• Overall Profit Margin: 12%

Action:

• Benchmark KPIs quarterly & yearly

• Set alerts for margin drops

• Share KPIs across departments for strategic alignment

• Use KPIs to trigger drill-down analysis

2. Customer Segment Behavior Patterns

Insight:

• Consumer segment drives $1.16M in sales (51% of total).

Action:

• Launch targeted loyalty programs

• Deploy personalized marketing for high-value segments

• Use account-based marketing for key customers

  
  

3. Product Performance Insights

Insight:

• Phones lead with $330K in sales

• Fasteners underperform with only $3K

• Labels have top profit margin (44%) but low volume

• Tables have negative margins (-9%)

Action:

• Bundle high-margin items (e.g., Labels + Phones)

• Reassess or phase out poor performers like Tables

• Reallocate inventory focus to top sellers

4. Pricing & Discount Effectiveness

Insight:

• Discounts >20% lead to negative profit

Action:

• Restructure discount tiers under 20%

• Use data-driven promotions for re-engagement

• Explore dynamic pricing based on demand & segmentation

  
  

5. Sales vs Profit Trends Over Time

Insight:

• March 2017: highest profit ($18K+)

• April 2017: steep decline to -$5.9K

• November 2017: highest sales ($89K+) but not aligned with profit peak

Action:

• Investigate anomalies (e.g., high returns, costs)

• Optimize costs during sales spikes

• Plan seasonal campaigns around proven high-demand months

🛠️ Tools & Technologies Used

• MS Excel (Data Inspection & Cleaning)

• Python (Date Formatting & Preprocessing)

• MySQL Workbench (SQL Analysis)

• Tableau (Visualization & Dashboarding)