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Our main objective is to identify the most profitable product sub-categories across each region and the least profitable (most loss-making) product subcategories and if needed, stop selling those product subcategories in the regions where they are the least profitable.

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Super_store_Sales-Analysis-

Our main objective is to identify the most profitable product sub-categories across each region and the least profitable (most loss-making) product subcategories and if needed, stop selling those product subcategories in the regions where they are the least profitable.

Problem Statement

  1. High-Profit Categories

In this section, you want to identify the most profitable product sub-categories across each region.

  • Using the raw data, find the top 3 profitable Product Sub-Categories in each region.
  • Using PIVOT Tables, compare the profit for each of the top 3 profitable product subcategories by region:
  • Which subcategories are the profitable in most regions?
  • Sort the rows and columns by profit and apply Conditional Formatting. Does this throw up some exceptions?
  1. Loss Making Categories

Say you want to identify the least profitable (most loss-making) product subcategories and if needed, stop selling those product subcategories in the regions where they are the least profitable.

  • Find the two most loss-making Product Sub-Categories across all regions.
  • For these subcategories, identify the regions where they are the least profitable.
  • Articulate your observations and identify any anomalies that you observe.
  • Food for thought: People mostly buy tables and chairs together; carefully analyse the business angle of these product categories and form hypotheses to explain your observations.

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Our main objective is to identify the most profitable product sub-categories across each region and the least profitable (most loss-making) product subcategories and if needed, stop selling those product subcategories in the regions where they are the least profitable.

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