Top Tableau Scenario-Based Interview Questions Set -33 (1- 5) - Important

 

1. How would you calculate Customer Lifetime Value (CLV)?

Calculate total revenue generated by a customer throughout their relationship with the company.

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

For Average CLV:

AVG(
{ FIXED [Customer ID] :
SUM([Sales])
}
)

Interview Follow-up: Why use FIXED LOD instead of SUM(Sales)? Because CLV should be calculated at Customer level regardless of view granularity.

---

2. How would you identify customers likely to churn?

Find customers who haven't purchased in the last 180 days.

DATEDIFF(
'day',
{ FIXED [Customer ID] :
MAX([Order Date])
},
TODAY()
) > 180

These customers can be targeted with retention campaigns.

---

3. How would you calculate Year-to-Date (YTD) Sales?

Calculate sales from January 1st until today.

IF YEAR([Order Date]) = YEAR(TODAY())
AND [Order Date] <= TODAY()
THEN [Sales]
END

Then aggregate:

SUM(
IF YEAR([Order Date]) = YEAR(TODAY())
AND [Order Date] <= TODAY()
THEN [Sales]
END
)

---

4. How would you calculate Same Period Last Year (SPLY) Sales?

Compare current YTD against previous year's YTD.

IF YEAR([Order Date]) = YEAR(TODAY()) - 1
AND DATEPART('dayofyear',[Order Date])
<= DATEPART('dayofyear',TODAY())
THEN [Sales]
END

This is frequently asked in Tableau and Power BI interviews.

---

5. How would you identify the Top Customer in each Region and Category simultaneously?

Rank customers within Region and Category.

RANK(SUM([Sales])) = 1

Compute Using: Customer Name
Partition By: Region, Category

This returns the highest revenue-generating customer for every Region-Category combination.

---

Bonus Architect-Level Question

How would you calculate Repeat Purchase Rate?

Percentage of customers who placed more than one order.

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

This is one of the most commonly used KPIs in Retail, E-commerce, and Customer Analytics projects.

Top Tableau Scenario-Based Interview Questions Set -32 (1- 10) - Important

 

1. How would you identify customers who purchased in consecutive months?

Find customers whose purchase month difference is exactly 1.

DATEDIFF(
'month',
LOOKUP(MIN([Order Date]),-1),
MIN([Order Date])
) = 1

This helps analyze customer engagement and retention.

---

2. How would you identify customers whose latest purchase amount is greater than their first purchase amount?

Compare sales from first and latest purchase.

First Purchase Date

{ FIXED [Customer ID] :
MIN([Order Date])
}

Latest Purchase Date

{ FIXED [Customer ID] :
MAX([Order Date])
}

Flag customers where latest purchase value exceeds first purchase value.

---

3. How would you calculate the percentage of customers acquired each month?

Count first-time customers in a month divided by total customers.

COUNTD(
IF DATETRUNC('month',[Order Date]) =
{ FIXED [Customer ID] :
DATETRUNC('month',MIN([Order Date]))
}
THEN [Customer ID]
END
)
/
COUNTD([Customer ID])

---

4. How would you identify products that are sold together frequently?

Create combinations using Order ID.

COUNTD([Order ID])

Then analyze Product A and Product B combinations using self-join in the data source.

This is commonly called Market Basket Analysis.

---

5. How would you identify customers whose profit is increasing for 3 consecutive months?

Compare profit values across months.

SUM([Profit])
>
LOOKUP(SUM([Profit]),-1)

AND

LOOKUP(SUM([Profit]),-1)
>
LOOKUP(SUM([Profit]),-2)

Useful for identifying growing customers.

---

6. How would you calculate customer concentration risk?

Measure sales dependency on top customers.

WINDOW_SUM(
IF RANK(SUM([Sales])) <= 5
THEN SUM([Sales])
END
)
/
WINDOW_SUM(SUM([Sales]))

Shows % of revenue coming from Top 5 customers.

---

7. How would you identify customers whose order frequency is decreasing?

Compare current period order count with previous period.

COUNTD([Order ID])
<
LOOKUP(COUNTD([Order ID]),-1)

Helps identify customers at risk of churn.

---

8. How would you calculate the average discount given per customer?

Aggregate discount at customer level.

{ FIXED [Customer ID] :
AVG([Discount])
}

Useful for discount optimization analysis.

---

9. How would you identify products that are profitable but have declining sales?

Combine profit and sales trend.

SUM([Profit]) > 0

AND

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

These products may need marketing support rather than pricing changes.

---

10. How would you identify regions where sales are increasing but profits are decreasing?

Compare sales growth and profit growth together.

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

AND

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

This often indicates excessive discounting or rising costs.

---

These are closer to the real Tableau interview scenarios asked in Deloitte, Accenture, Cognizant, TCS, Infosys, Capgemini, EY, KPMG, EXL, Tiger Analytics, Fractal, and BI Architect interviews than the basic Top-N and Running Total questions.