Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_1ee399b195ab799a34588772ccc1ebfa/view

In the case study titled "Blinkit: Grocery Product Analysis," a dataset called 'Grocery Sales' contains 12 columns with information on sales of grocery items across different outlets. Using Tableau, you as a data analyst can uncover customer behavior insights, track sales trends, and gather feedback. These insights will drive operational improvements, enhance customer satisfaction, and optimize product offerings and store layout. Tableau enables data-driven decision-making for positive outcomes at Blinkit.

The table Grocery Sales is a .CSV file and has the following columns, details of which are as follows:

• Item_Identifier: A unique ID for each product in the dataset. • Item_Weight: The weight of the product. • Item_Fat_Content: Indicates whether the product is low fat or not. • Item_Visibility: The percentage of the total display area in the store that is allocated to the specific product. • Item_Type: The category or type of product. • Item_MRP: The maximum retail price (list price) of the product. • Outlet_Identifier: A unique ID for each store in the dataset. • Outlet_Establishment_Year: The year in which the store was established. • Outlet_Size: The size of the store in terms of ground area covered. • Outlet_Location_Type: The type of city or region in which the store is located. • Outlet_Type: Indicates whether the store is a grocery store or a supermarket. • Item_Outlet_Sales: The sales of the product in the particular store. This is the outcome variable that we want to predict.

Comprehensive dataset tracking Amazon's market share and competitor performance in US e-commerce from 2020-2024, including revenue figures, market trends, and category breakdowns.

In the end, you should only measure and look at the numbers that drive action, meaning that the data tells you what you should do next.🥰

Please do upvote if you love the work.♥️🥰 For more related datasets: https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report

Description: This dataset captures sales transactions from a local restaurant near my home. It includes details such as the order ID, date of the transaction, item names (representing various food and beverage items), item types (categorized as Fast-food or Beverages), item prices, quantities ordered, transaction amounts, transaction types (cash, online, or others), the gender of the staff member who received the order, and the time of the sale (Morning, Evening, Afternoon, Night, Midnight). The dataset offers a valuable snapshot of the restaurant's daily operations and customer behavior.

Columns: 1. order_id: a unique identifier for each order. 2. date: date of the transaction. 3. item_name: name of the food. 4. item_type: category of item (Fastfood or Beverages). 5. item_price: price of the item for 1 quantity. 6. Quantity: how much quantity the customer orders. 7. transaction_amount: the total amount paid by customers. 8. transaction_type: payment method (cash, online, others). 9. received_by: gender of the person handling the transaction. 10. time_of_sale: different times of the day (Morning, Evening, Afternoon, Night, Midnight).

Potential Uses: - Analyzing sales trends over time. - Understanding customer preferences for different items. - Evaluating the impact of payment methods on revenue. - Investigating the performance of staff members based on gender. - Exploring the popularity of items at different times of the day.

Makeyourhandsdirtyonit

In 2024, Target Corporation's food and beverage product segment generated sales of approximately 23.8 billion U.S. dollars. In contrast, the hardline segment, which include electronics, toys, entertainment, sporting goods, and luggage, registered sales of 15.8 billion U.S. dollars. Target Corporation had revenues amounting to around 106.6 billion U.S. dollars that year.

In 2025, there were 68.3 million Costco Gold Star members all over the world, up from 63.7 million the previous year. The company had net sales of close to 270 billion U.S. dollars in 2025.