Top Tableau Scenario-Based Interview Questions Set - 23 (1- 5)

 

1. How would you identify customers whose average order value is greater than the overall average order value?

Calculate customer AOV and compare it with the overall AOV.

Example:

SUM([Sales]) / COUNTD([Order ID])
>
{ FIXED : SUM([Sales]) / COUNTD([Order ID]) }

This highlights customers spending more per order than the average customer.

---

2. How would you calculate the percentage of sales generated by new customers?

Identify first-time customers and divide their sales by total sales.

Example:

SUM(
IF [Order Date] =
{ FIXED [Customer ID] : MIN([Order Date]) }
THEN [Sales]
END
)
/
SUM([Sales])

This shows how much revenue comes from newly acquired customers.

---

3. How would you identify products with no sales in the last 12 months?

Compare the latest sale date for each product against today's date.

Example:

DATEDIFF(
'month',
{ FIXED [Product ID] : MAX([Order Date]) },
TODAY()
) > 12

This helps identify obsolete or inactive products.

---

4. How would you calculate the running count of distinct customers over time?

Use a running total on customer acquisitions.

Example:

RUNNING_SUM(
COUNTD([Customer ID])
)

Place Month on Columns and compute using Month.

---

5. How would you identify categories where profit margin is below the company average?

Compare category profit margin against overall profit margin.

Example:

SUM([Profit]) / SUM([Sales])
<
{ FIXED : SUM([Profit]) / SUM([Sales]) }

This quickly highlights underperforming categories that need attention.

Top Tableau Scenario-Based Interview Questions Set - 22 (1- 5)

 

1. How would you identify customers who have not placed any order in the last 90 days?

Calculate the difference between today's date and the customer's last order date.

Example:

DATEDIFF(
'day',
{ FIXED [Customer ID] : MAX([Order Date]) },
TODAY()
) > 90

---

2. How would you calculate the average sales of the Top 10 customers?

Rank customers by sales and average the Top 10.

Example:

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

Then place:

AVG([Top 10 Sales])

in the view.

---

3. How would you identify products whose sales are below the overall average sales?

Compare product sales against overall average sales.

Example:

SUM([Sales])
<
WINDOW_AVG(SUM([Sales]))

Compute using Product.

---

4. How would you calculate customer recency score?

Measure the number of days since the last purchase.

Example:

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

Lower values indicate more recent customers.

---

5. How would you identify the top-performing sub-category within each category?

Rank sub-categories by sales within each category.

Example:

RANK(SUM([Sales])) = 1

Compute Using: Sub-Category
Partition By: Category

This returns the highest-selling sub-category for each category.