OBA Co. plans to launch a new product in its online retail store.

Before the launch of the product, OBA Co. hired a data analytics team to run some experiments to find out the maximum price that a customers is willing to pay (also know as “willingness-to-pay” in the literature). They found that each customer’s willingness-to-pay is random and follows a discrete probability distribution as given below:

Willingness-to-Pay	Probability
20	0.24
30	0.08
40	0.32
50	0.04
60	0.12
70	0.2

Construct a spreadsheet model where you calculate the firm's revenue from a random customer when the firm charges $41. Note that a customer purchases the product only when their willingness-to-pay is equal or greater than the price.

Your spreadsheet model should generate a random value for the customer's willingness-to-pay according to the distribution given above and determine the random customer's purchasing decision and the firm's revenue. As illustrated below, purchasing decision and the firm's revenue depend on the random value generated for the willingness-to-pay:

(Note: $45 is used as an example price in the above images. You need to use the price value given in the question text)

After constructing your model, run a Monte Carlo simulation study with 5000 trials using a data table.

Answer the following questions based on the results of the simulation study.

a) Calculate the mean revenue per customer generated in the simulation study. Which of the values given below in the drop-down menus is the closest to the mean you calculated?

b) Calculate the probability that a customer purchases the product. Which of the values given below in the drop-down menus is the closest to the probability you calculated?