Power BI Masterclass - Learning DAX: Customer Transactions Within the Last 30 Days

The video tutorial guides viewers through analyzing customer transactions over the last 30 days. It begins with visualizing customer data, then calculates transactions within a specified timeframe using the maximum order date. The tutorial demonstrates creating a new measure to count transactions and applying filters to focus on recent data. Finally, it evaluates and visualizes the results, preparing for further customer classification in the next video.

What is the primary goal of analyzing customer transactions over the last 30 days?

• To determine the average transaction value

• To identify the most popular products

• To understand customer purchasing patterns

• To calculate the total revenue

Which function is used to find the maximum order date in the dataset?

• Min function

• Average function

• Max function

• Sum function

What is the purpose of creating a new measure for transactions in the last 30 days?

• To calculate the total number of customers

• To visualize customer demographics

• To determine the average transaction size

• To filter transactions based on a specific date range

Which function is used to modify the context for counting rows in the dataset?

• Average function

• Filter function

• Calculate function

• Sum function

How is the filtered version of the date table created?

• By selecting random dates

• By using a continuous date range

• By excluding weekends

• By including only future dates

What is the next step after evaluating transaction IDs within the timeframe?

• Classifying customers based on transaction amounts

• Analyzing customer feedback

• Calculating the total revenue

• Identifying the most popular products

What classification is given to customers with transaction amounts greater than a certain threshold?

• Low-value customers

• Top customers

• Average customers

• Inactive customers