1- Dashboard :

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2- About the Dataset :

Sales & inventory data for a fictitious chain of toy stores in Mexico called Maven Toys, including information about products, stores, daily transactions, and current inventory levels at each location.

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3- Tables Description :

Products Table:

Field	Description
Product_ID	Product ID
Product_Name	Product name
Product_Category	Product Category
Product_Cost	Product cost ($USD)
Product_Price	Product retail price ($USD)

Inventory Table:

Field	Description
Store_ID	Store ID
Product_ID	Product ID
Stock_On_Hand	Stock quantity of the product in the store (inventory)

Stores Table:

Field	Description
Store_ID	Store ID
Store_Name	Store name
Store_City	City in Mexico where the store is located
Store_Location	Location in the city where the store is located
Store_Open_Date	Date when the store was opened

Sales Table:

Field	Description
Sale_ID	Sale ID
Date	Date of the transaction
Store_ID	Store ID
Product_ID	Product ID
Units	Units sold

Calendar Table:

Field	Description
Calendar	Date

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4- Database Digrams

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5- In Mind Questions :

• Project Objective :

  • Connect to SQL Server and Check Tables.
  • Create a relational model.
  • Add calculated measures & fields
  • Build an interactive report

• Business Questions :

  📌1️⃣ Which product categories drive the biggest profits? Is this the same across store locations?

  • Total profit among the five product categories was observed in Toys, reaching $1,079,527. This figure was 113.46% higher than the lowest total profit, which was in the Sports & Outdoors category at $505,718.

  • Toys accounted for a significant portion of the total profit, contributing to 26.89% of the overall profits across all five product categories.

  • Total profits across the entire set of product categories ranged from $505,718 to $1,079,527. This indicates a considerable variation in profitability across the different product categories.

  • 🛑 profit percentage varies from one location to another:

    • ➡️ In Residential

      • Toys recorded the highest total profit at $136,214, demonstrating a 125.68% increase compared to the lowest total profit in Sports & Outdoors, which was $60,358.

      • Toys contributed significantly to the overall profitability, representing 29.59% of the total profit across all five product categories.

      • Total profit across the five product categories varied, ranging from $60,358 to $136,214, showcasing distinct levels of profitability within the product portfolio.

    • ➡️ In Downtown

      • Toys led with the highest total profit at $630,029, marking a 114.68% increase compared to the lowest total profit in Sports & Outdoors, which amounted to $293,468.

      • Toys significantly contributed to the overall profitability, accounting for 28.02% of the total profit across all five product categories.

      • Total profit across the five product categories showed variation, ranging from $293,468 to $630,029, illustrating diverse levels of profitability within the product portfolio.

    • ➡️ In Commercial

      • Electronics led with the highest total profit at $287,574, demonstrating a 155.62% increase compared to the lowest total profit in Sports & Outdoors, which amounted to $112,499.

      • Electronics made a substantial contribution to the overall profitability, representing 31.03% of the total profit across all five product categories.

      • Total profit across the five product categories displayed variation, ranging from $112,499 to $287,574, highlighting diverse levels of profitability within the product portfolio.

    • ➡️ In Airport

      • Electronics led with the highest total profit at $108,197, showing a substantial 174.66% increase compared to the lowest total profit in Sports & Outdoors, which amounted to $39,393.

      • Electronics contributed significantly to the overall profitability, representing 28.62% of the total profit across all five product categories.

      • Total profit across the five product categories varied, ranging from $39,393 to $108,197, indicating diverse levels of profitability within the product portfolio.

  📌2️⃣ Can you find any seasonal trends or patterns in the sales data?

  • ➡️ In Sprint

    • Total orders for Toys reached 58,806, making it the product category with the highest demand.
    • This was 93.35% higher than Electronics, which had the lowest total orders at 30,414.
    • Toys accounted for a significant portion, representing 26.46% of the total orders.
    • Across all five product categories, the total orders varied, ranging from 30,414 to 58,806.

  • ➡️ In Summer

    • Toys had the highest total orders, reaching 72,085.
    • This total was 162.58% higher than Electronics, which had the lowest total orders at 27,453
    • Toys accounted for 28.48% of the total orders, indicating a significant share of overall demand.
    • Across all five product categories, the total orders ranged from 27,453 to 72,085, highlighting the variability in demand across different product types.

  • ➡️ In Autumn

    • Art & Crafts had the highest total orders, reaching 64,157.
    • This total was 138.05% higher than Electronics, which had the lowest total orders of 26,951.
    • Art & Crafts accounted for 27.88% of the total orders, indicating a substantial share of overall demand.
    • Across all five product categories, the total orders ranged from 26,951 to 64,157, showcasing the variability in demand across different product types.

  • ➡️ In Winter

    • Art & Crafts had the highest total orders, amounting to 35,559.
    • This total was 150.29% higher than Electronics, which had the lowest total orders at 14,207.
    • Art & Crafts accounted for 28.73% of the total orders, indicating a significant proportion of overall demand.
    • Across all five product categories, the total orders ranged from 14,207 to 35,559, demonstrating the variability in demand across different product types.

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Project Link in Maven : Mexico Toy Sales