Global Store Customer Data Analysis

Goals: The primary goal of this project was to analyze customer data from a global store to uncover meaningful insights that could drive business decisions. Specifically, we aimed to:

1. Identify key customer segments and their purchasing behaviors.
2. Understand sales trends over time.
3. Highlight areas for potential growth and improvement.

Business Needs: The business needed a comprehensive analysis to:

1. Increase sales by targeting high-value customers.
2. Improve customer retention by understanding and addressing the needs of different customer segments.
3. Optimize inventory management by analyzing returned items and sales trends.

Discovering and Presenting Insights:

1. Data Collection and Cleaning:
   • Sources: We collected data from various sources, including sales records, customer feedback, and return logs.
   • Cleaning: The data was cleaned to remove duplicates, correct errors, and fill in missing values. This step ensured the accuracy and reliability of our analysis.
2. Segmentation Analysis:
   • Techniques: We used clustering techniques such as K-means clustering to segment customers into categories like high-value, medium-value, and low-value customers.
   • Insights: This segmentation helped us identify the most profitable customer groups and tailor marketing strategies accordingly.
3. Sales Trend Analysis:
   • Visualization: We visualized sales data over multiple years using line graphs to identify trends and seasonal patterns.
   • Comparisons: We compared sales across different product categories (e.g., Office Supplies, Technology, Furniture) to understand which categories were performing well and which needed attention.
4. Return Analysis:
   • Metrics: We calculated the return rate for different product categories and identified patterns in returned items.
   • Insights: The analysis highlighted that the ‘Office Supplies’ segment had a low purchase value, suggesting potential issues with product quality or customer satisfaction.
5. Customer Behavior Analysis:
   • Top Customers: We identified the top 5 customers by sales, which helped in focusing marketing efforts on these key customers.
   • Loyalty Programs: Insights from customer behavior analysis were used to design loyalty programs aimed at retaining high-value customers.
6. Visualization and Reporting:
   • Tools: We used tools like Power BI to create interactive dashboards that presented our findings in a visually appealing and easily understandable format.
   • Reports: Detailed reports were generated to provide stakeholders with actionable insights. These reports included recommendations for targeting high-value customers, improving product quality, and optimizing inventory management.

Key Insights:

• High-Value Customers: Identified top 5 customers by sales, which helped in focusing marketing efforts on these key customers.
• Sales Trends: Noted a significant increase in sales during certain periods, indicating successful promotional campaigns.
• Returned Items: Highlighted the ‘Office Supplies’ segment as having a low purchase value, suggesting a need for further investigation into product quality or customer satisfaction in this category.
• Customer Segmentation: Segmented customers into high-value, medium-value, and low-value categories, allowing for targeted marketing strategies.

Conclusion: This project provided valuable insights into customer behavior and sales trends, enabling the business to make data-driven decisions. By targeting high-value customers and addressing issues in the ‘Office Supplies’ segment, the business can improve sales and customer satisfaction.