# Data Analysts must have a keen sight for decision alternatives to optimize rules on expects profit payoffs

In your first response, critique one of your peers’ Part 1 scenarios.

BELOW IS PART 1 SCENARIO:

PLEASE RESPOND TO THIS POST BELOW CRITIQUING THE SCENARIO..

Data Analysts must have a keen sight for decision alternatives to optimize rules on expects profit payoffs. The upcoming holiday season has two toys that gained my attention as a business executive for ChoiceToys, conducting data analysis for expected profit payoff and associated probabilities.

As the information is provided above, Toy 1 is new to the market, and it is believed that no competitor will be able to bring a similar toy to the market in time. Because of the no market competition, the company is not sure it will be a success or a bust.

Toy 2 has been in the market long enough to have two competitors. With ChoiceToys joining the market, it will make three company’s competing in the holiday season. The profit payoff could either be highly successful, successful, and not successful. Being all in or not, we are only projecting either highly successful or not profitable.

Before we continue the rabbit hole, as a business executive for ChoiceToys, it is crucial for us as a company to list what the definition of success is to us. Because the data that considerer success is missing, we would need to know so we can use it to see if it is achievable. Would it be the maximum profit? Would it be breaking even with the cost? Because we are a company, it is easy to assume that we are in the business to make money, and making money constitutes success. So, we would need to gauge how much needs to be produced and sold to be out of the red. Sarpe et al. (2019) state that determining costs and price is based on an expert’s probability. In the scenario, we only have three choices: highly successful, successful, and not successful. Based on this probability has a risk that is associated.

The use of a decision tree would be found most beneficial. There are no data related to the supply or demand for the toys. Based on the data, we can create a payoff table that shows a 33.33% chance of achieving either choice. Toy 2 only wants to project either highly successful or not, increasing both the risk and the reward. The Expected Value for and standard deviation would reflect more uncertainty than compared to Toy 1. Toy 1 would have less risk and has more chance of measuring success through more choices. Unlike the all-or-nothing approach in Toy 2, the successful variable is included in the measurement in Toy 1. Based on the gain or loss approach, success can be measured as receiving profit, but not the maximum profit as highly successful would establish variable.

Based on expected value, Sharpe et al. (2019) state minimax reflect the safest choice using minimal cost while trying to receive the maximum return. Toy 1 shows the more conservative approach towards recovery. In contrast, because the market offers two competitors are already in the field, and ChoiceToys is joining, the maximum return is in that market. I would recommend investing more in Toy 2 because of the already established market and the large pool of customers.