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In this project, I analyzed Target company's data using SQL in BigQuery, focusing on data extraction, manipulation, and performing various analytical queries to derive insights.

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Target E-commerce Analysis (Brazil, 2016-2018)

Context

Target is one of the world’s most recognized brands and a leading retailer in America. Known for its outstanding value, inspiration, and exceptional guest experience, Target strives to be the preferred shopping destination. This project focuses on the business case involving 100,000 orders made at Target in Brazil between 2016 and 2018. The dataset includes multiple dimensions of analysis such as order status, price, payment and freight performance, customer location, product attributes, and customer reviews.

Dataset

The dataset is provided in the following 8 CSV files:

  • customers.csv
  • geolocation.csv
  • order_items.csv
  • payments.csv
  • reviews.csv
  • orders.csv
  • products.csv
  • sellers.csv

Dataset Link: Target Dataset - Google Drive

Key Insights and Recommendations from SQL Analysis

1. Growing E-commerce Trend in Brazil

Insight: The e-commerce market shows consistent growth over time with peaks in May and November, driven by sales events like Black Friday.
Recommendation:

  • Plan major promotional campaigns around May and November.
  • Focus on customer acquisition strategies during non-peak months to ensure sustained growth.

2. Customer Purchase Behavior

Insight: Most purchases occur in the afternoon and evening, aligning with customer free time or reminders during work hours.
Recommendation:

  • Schedule push notifications and email campaigns in the early afternoon to maximize engagement.
  • Optimize website performance during high-traffic hours to enhance the user experience.

3. Regional Insights

Insight: Sao Paulo (SP) and Rio de Janeiro (RJ) dominate orders due to high populations and purchasing power.
Recommendation:

  • Enhance inventory and logistics in SP and RJ.
  • Launch targeted marketing efforts to penetrate Tier-2 and Tier-3 cities.

4. Delivery Performance

Insight: Delivery times vary significantly, with delays risking customer trust in certain regions.
Recommendation:

  • Invest in better logistics infrastructure in underperforming states to reduce delays.
  • Provide realistic delivery timelines to manage customer expectations effectively.

5. Economic Analysis

Insight: There was a 137.26% increase in order costs from 2017 to 2018, likely due to inflation and rising operational expenses.
Recommendation:

  • Introduce cost-effective product ranges to attract price-sensitive customers.
  • Monitor market trends and adjust pricing strategies accordingly.

6. Freight and Delivery Costs

Insight: States with higher freight costs also experience longer delivery times, indicating inefficiencies.
Recommendation:

  • Optimize supply chain routes and warehouse distribution to reduce freight costs in high-expense regions.

7. Payment Behavior

Insight: Credit cards dominate payment methods (77%), followed by UPI (19%). Vouchers and debit cards are underutilized.
Recommendation:

  • Introduce exclusive offers for UPI users to increase adoption.
  • Promote the use of debit cards and vouchers with special discounts to diversify payment preferences.

8. Seasonal Trends

Insight: Sales peaks are event-driven rather than seasonal.
Recommendation:

  • Experiment with new sales events in non-peak months to identify additional growth opportunities.

Conclusion

This analysis reveals key trends and opportunities for Target in Brazil, including optimizing promotional campaigns, enhancing logistics infrastructure, managing payment methods, and identifying seasonal demand shifts. The insights can drive targeted strategies to improve customer satisfaction, drive growth, and reduce operational inefficiencies.

About

In this project, I analyzed Target company's data using SQL in BigQuery, focusing on data extraction, manipulation, and performing various analytical queries to derive insights.

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