Power BI Masterclass - DAX Cohort Customer OrderDate Analysis

The video tutorial explores cohort analysis, focusing on understanding customer behavior over time. It demonstrates how to create a unique customer table using DAX, establish relationships in a data model, and calculate the first order date for customers. The tutorial concludes with visualizing cohort analysis using a stacked column chart to gain insights into customer retention and acquisition patterns.

What is the primary goal of cohort analysis?

• To analyze customer patterns over time

• To increase sales numbers

• To reduce operational costs

• To improve product quality

Which DAX function is used to create a new table with unique values?

• VALUES

• AVERAGE

• FILTER

• SUM

What type of relationship is established between the customer and sales tables?

• Many-to-One

• One-to-Many

• One-to-One

• Many-to-Many

Which DAX function is used to iterate over a table to find the minimum order date?

• SUMX

• MINX

• MAXX

• AVERAGEX

What is the purpose of using the YEAR function in the context of this analysis?

• To calculate the total sales

• To extract the year from a date

• To find the maximum order date

• To determine the number of orders

What type of chart is used to visualize the cohort data?

• 100% Stacked Column Chart

• Bar Chart

• Line Chart

• Pie Chart

In 2020, what percentage of sales came from customers who first ordered in 2019?

• 50%

• 75%

• 100%

• 91%

Why might a high percentage of returning customers be considered a red flag?

• It indicates a lack of new customer acquisition

• It suggests an increase in operational costs

• It implies a drop in sales numbers

• It shows a decrease in product quality

What insight can be derived from the cohort analysis regarding customer loyalty?

• Customers are not loyal and switch frequently

• New customers are increasing significantly

• A steady customer base is maintained

• Sales are declining rapidly

What is the significance of the first order date in cohort analysis?

• It helps in calculating total revenue

• It identifies the year a customer first made a purchase

• It determines the most popular product

• It tracks the number of orders per customer