Top Tableau Scenario-Based Interview Questions Set - 24 (1- 3)

 

How would you identify customers who buy only one product category?

Count distinct categories purchased by each customer and filter those with exactly one category.

Example:

{ FIXED [Customer ID] : COUNTD([Category]) } = 1

This helps identify customers with limited product adoption and cross-sell opportunities.

---

2. How would you calculate the percentage of customers retained from the previous year?

Count customers who purchased in both the current and previous year and divide by last year's customers.

Example:

COUNTD(
IF YEAR([Order Date]) = YEAR(TODAY())
AND
{ FIXED [Customer ID] : MIN(YEAR([Order Date])) } < YEAR(TODAY())
THEN [Customer ID]
END
)
/
COUNTD(
IF YEAR([Order Date]) = YEAR(TODAY()) - 1
THEN [Customer ID]
END
)

This provides a simple annual customer retention metric.

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3. How would you identify the category contributing the highest profit percentage?

Calculate each category's share of total profit and rank it.

Example:

SUM([Profit])
/
TOTAL(SUM([Profit]))

To get the top category:

RANK(SUM([Profit])) = 1

This highlights the category contributing the largest share of company profit.

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.