Which data types could be included in an online sales dataset?

• A. City, state, customer, auto parts, quantity, and campaign code
• B. Weather data
• C. Favorite color and pet name
• D. Employee salaries and payroll numbers

Answer: (A)

In online sales analytics, you want data that capture who bought, what was bought, where they are, and how the sale happened. City and state provide geographic context, helping regional analyses. The customer field identifies the buyer, which is essential for segmentation and tracking repeat purchases. Auto parts represents the product category or items sold. Quantity records how many units were purchased. Campaign code shows how the sale was driven by marketing efforts, enabling attribution and ROI analysis. Together, these data types cover the key dimensions of a typical online sales dataset: who, what, where, and how the sale occurred, making them the most aligned with standard sales analytics needs.

Weather data, while potentially useful for exploratory questions about demand, isn’t a core part of a standard sales dataset. Favorite color and pet name are not relevant for analyzing sales performance and raise privacy concerns. Employee salaries and payroll numbers belong to HR data, not sales data.