Target is a globally renowned retail brand headquartered in the United States.
It has established itself as a preferred shopping destination by offering:
- Outstanding value 💰
- Inspiring innovation 💡
- Exceptional guest experiences 🌟
This business case focuses on Target’s operations in Brazil, analyzing 100,000 customer orders placed between 2016 and 2018.
The dataset provides a comprehensive view of:
- Order status and fulfillment
- Product pricing and payment behavior
- Freight and shipping performance
- Customer demographics and locations
- Product characteristics and customer reviews
Through this data, we uncover insights into:
- Operational efficiency ⚙️
- Pricing and delivery strategies 🚚
- Customer satisfaction trends 💬
- Market demand patterns 📊
Analyzed Using:
💻 Google BigQuery (SQL-based data exploration and analytics)
The dataset is provided in 8 CSV files, each representing a key dimension of Target’s operations:
| File Name | Description |
|---|---|
customers.csv |
Customer demographic information |
geolocation.csv |
Location details (latitude, longitude, and postal codes) |
order_items.csv |
Product-level details per order |
payments.csv |
Payment type and transaction values |
reviews.csv |
Customer feedback and review scores |
orders.csv |
Order lifecycle and timestamps |
products.csv |
Product details and specifications |
sellers.csv |
Seller information and location |
| Feature | Description |
|---|---|
| customer_id | Unique ID of the customer |
| customer_unique_id | Permanent unique ID of the customer |
| customer_zip_code_prefix | Zip code prefix of customer’s location |
| customer_city | City name of the customer |
| customer_state | State code (e.g., São Paulo – SP) |
| Feature | Description |
|---|---|
| seller_id | Unique ID of the seller |
| seller_zip_code_prefix | Seller’s zip code prefix |
| seller_city | Seller’s city |
| seller_state | Seller’s state code |
| Feature | Description |
|---|---|
| order_id | Unique ID of the customer order |
| order_item_id | Unique ID for each item within the order |
| product_id | Unique ID for each product |
| seller_id | Seller’s unique identifier |
| shipping_limit_date | Date before which the order must be shipped |
| price | Price of the product |
| freight_value | Freight (shipping) cost |
| Feature | Description |
|---|---|
| geolocation_zip_code_prefix | First 5 digits of the postal code |
| geolocation_lat | Latitude coordinate |
| geolocation_lng | Longitude coordinate |
| geolocation_city | City name |
| geolocation_state | State name |
| Feature | Description |
|---|---|
| order_id | Unique ID of the order |
| payment_sequential | Payment sequence number (for EMIs) |
| payment_type | Mode of payment (e.g., Credit Card) |
| payment_installments | Number of installments (if EMI) |
| payment_value | Total amount paid |
| Feature | Description |
|---|---|
| order_id | Unique ID of the order |
| customer_id | Customer’s unique identifier |
| order_purchase_timestamp | Order purchase date and time |
| order_delivered_carrier_date | Date when carrier received the order |
| order_delivered_customer_date | Date when customer received the order |
| order_estimated_delivery_date | Expected delivery date |
| Feature | Description |
|---|---|
| review_id | Unique ID of each review |
| order_id | Associated order ID |
| review_score | Rating (1–5) given by customer |
| review_comment_title | Review title |
| review_comment_message | Review text |
| review_creation_date | Date when review was created |
| review_answer_timestamp | Timestamp when review was answered |
| Feature | Description |
|---|---|
| product_id | Unique product identifier |
| product_category_name | Category of the product |
| product_name_lenght | Length of the product name string |
| product_description_lenght | Length of the product description |
| product_photos_qty | Number of product photos |
| product_weight_g | Product weight (grams) |
| product_length_cm | Product length (cm) |
| product_height_cm | Product height (cm) |
| product_width_cm | Product width (cm) |
Conducted exploratory analysis on 100,000 customer orders across Brazil using SQL in Google BigQuery.
- Analyzed city and state-level distributions of orders.
- Identified seasonal purchase trends across multiple years.
- E-commerce orders showed steady growth over time.
- January: +70% increase in order value
- February: +20% increase
- April: +10% increase
➡️ Indicates high seasonal demand at the start of the year.
- Peak shopping times: Afternoons and Nights
- Indicates potential for targeted marketing during these hours.
- Calculated total and average order value by state.
- São Paulo (SP) had the highest total sales value,
but lowest average order price, suggesting a large volume of lower-value purchases.
- Evaluated delivery efficiency by state:
- São Paulo (SP) → Fastest average delivery times
- Roraima (RR) → Longest delivery delays
- Some states experienced deliveries up to 20 days earlier than estimated.
- Credit Card emerged as the most preferred payment method, followed by Boleto (invoice).
✅ Optimize delivery operations in high-delay regions.
✅ Focus marketing campaigns during high-traffic time slots (afternoons & nights).
✅ Enhance customer retention via improved delivery tracking and satisfaction programs.
✅ Promote installment-based payment offers to drive conversions.
| Tool / Technology | Purpose |
|---|---|
| Google BigQuery (SQL) | Data querying & analysis |
| Google Cloud Platform (GCP) | Cloud data storage |
| Excel / Sheets | Supporting analysis and validation |
| Data Visualization Tools | Charts, KPIs, and dashboards for insights |
Ankit Verma
Data Analyst | SQL • Python • Power BI • Data Visualization
🔗 LinkedIn • GitHub
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