Top Tableau Scenario-Based Interview Questions Set -30 (1- 6)

 

1. How would you identify customers whose sales doubled compared to the previous year?

Compare current year sales with previous year sales and flag customers with >100% growth.

(
SUM([Sales])
-
LOOKUP(SUM([Sales]),-1)
)
/
ABS(LOOKUP(SUM([Sales]),-1))
>= 1

This identifies customers whose sales increased by 100% or more.

---

2. How would you calculate the percentage of products contributing to 80% of sales (Pareto Analysis)?

Calculate cumulative sales percentage and identify products within the 80% threshold.

RUNNING_SUM(SUM([Sales]))
/
TOTAL(SUM([Sales]))
<= 0.8

Sort products by Sales descending before applying the calculation.

---

3. How would you identify customers who purchased from all product categories?

Count distinct categories purchased by a customer and compare with total categories.

{ FIXED [Customer ID] :
COUNTD([Category])
}
=
{ FIXED :
COUNTD([Category])
}

This finds customers with purchases across every category.

---

4. How would you calculate average profit per active month for each product?

Divide total product profit by the number of months in which the product was sold.

{ FIXED [Product Name] :
SUM([Profit])
}
/
{ FIXED [Product Name] :
COUNTD(
DATETRUNC('month',[Order Date])
)
}

This helps compare products fairly regardless of sales duration.

---

5. How would you identify orders where sales are above the average order sales?

Compare each order's sales against the average order sales.

SUM([Sales])
>
WINDOW_AVG(SUM([Sales]))

Compute using Order ID.

This highlights unusually large orders.

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6. How would you calculate the percentage of customers who are repeat customers?

Divide customers with more than one order by total customers.

COUNTD(
IF
{ FIXED [Customer ID] :
COUNTD([Order ID])
} > 1
THEN [Customer ID]
END
)
/
COUNTD([Customer ID])

This is a common customer loyalty metric.

Top Tableau Scenario-Based Interview Questions Set -29 (1- 5)

 

1. How would you identify customers whose profit margin is higher than the company average?

Compare customer profit margin against overall company profit margin.

{ FIXED [Customer ID] :
SUM([Profit]) / SUM([Sales])
}
>
{ FIXED :
SUM([Profit]) / SUM([Sales])
}

This helps identify highly profitable customers.

---

2. How would you calculate the percentage of sales contributed by the Top 10 customers?

First identify Top 10 customers, then divide their sales by total sales.

IF RANK(SUM([Sales])) <= 10
THEN SUM([Sales])
END

Contribution %:

WINDOW_SUM(
IF RANK(SUM([Sales])) <= 10
THEN SUM([Sales])
END
)
/
WINDOW_SUM(SUM([Sales]))

---

3. How would you identify products whose current month sales are lower than last month?

Compare current month sales with previous month sales.

SUM([Sales])
<
LOOKUP(SUM([Sales]),-1)

Compute using Month.

This highlights declining products.

---

4. How would you calculate customer tenure in days?

Calculate the number of days from first purchase until today.

DATEDIFF(
'day',
{ FIXED [Customer ID] :
MIN([Order Date])
},
TODAY()
)

This is commonly used in Customer Lifetime Value analysis.

---

5. How would you identify regions where profit growth is negative?

Compare current period profit against previous period profit.

(
SUM([Profit])
-
LOOKUP(SUM([Profit]),-1)
)
/
ABS(LOOKUP(SUM([Profit]),-1))
< 0

This identifies regions experiencing profit decline.