Top Tableau Scenario-Based Interview Questions Set -31 (1- 5)

 

1. How would you identify customers whose average order value is increasing month over month?

Calculate Average Order Value (AOV) and compare it with the previous month.

(
SUM([Sales]) / COUNTD([Order ID])
)
>
LOOKUP(
SUM([Sales]) / COUNTD([Order ID]),
-1
)

This identifies customers whose spending per order is growing.

---

2. How would you calculate the percentage of profitable orders?

Count profitable orders and divide by total orders.

COUNTD(
IF [Profit] > 0
THEN [Order ID]
END
)
/
COUNTD([Order ID])

This measures order quality rather than revenue volume.

---

3. How would you identify products that contribute more than 10% of category sales?

Compare product sales against category sales using FIXED LOD.

{ FIXED [Product Name] :
SUM([Sales])
}
/
{ FIXED [Category] :
SUM([Sales])
}
> 0.10

This helps identify key products within each category.

---

4. How would you calculate the average time between a customer's first and last purchase?

Calculate the duration of the customer relationship.

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

This is often used in customer lifecycle analysis.

---

5. How would you identify categories whose sales are growing faster than the company average?

Compare category growth rate against overall growth rate.

Category Growth:

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

Compare with Overall Average Growth:

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

This highlights categories outperforming the business average growth rate.

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.

---

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.