Part 2

As a data analyst, you need to use decision analysis techniques to recommend decision alternatives or optimal decisions based on expected profit payoff for the upcoming holiday season. Respond to one of your peers' initial posts, and complete the following:

• Identify the sequence of actions you need to take to start this decision process.
• Explain each sequence and justify why it will help you with your decision.
• Propose a risk profile for each choice.

Guided Response: Respond to at least two peers. In your first response, critique one of your peers' Part 1 scenarios. In your response, critique a student's reply to the scenario for each of the following:

• Missing or out of place sequences of actions. Justify your assessment.
• Determination of essential decisions and uncertainties. Justify your reasons.
• Determination of risk profile for each choice. Explain your assessment.

This is the part I need a critique for

This week's discussion is about decision analysis and calculating risks. As the holiday season approaches for ChoiceToys, there are two different toy marketing scenarios to assess to optimize profits and mitigate risks. Toy 1 is a brand new toy that would be introduced to the market for the first time during the holiday toy rush. Toy 2 is an established toy that has two other companies also selling the product. Part of the decision making process consists of identifying the pros and cons of each scenario. The data analyst must evaluate statistical probabilities and projections to ensure maximum success for the marketing efforts.

Advantages for Toy 1 would be that there is no competition while disadvantages include little or no familiarity of the toy to consumers. Advantages to Toy 2 would be the high demand of the familiar toy while disadvantages include higher competition due to two other companies selling the product. With the information currently presented, I would opt to market for Toy 1 since there is potential for greater profits given the gamble of a 50/50 success rate. Without competition, I would be able to decide on fair retail price as oppose to lowering the prices due to market competition. For example, I can try to market Toy 1 at $20 initially and if that does not sell, I can decrease to $18, then $16, etc. With Toy 2, I would be forced to price compete with other retailers.

In order to make the best business decision to market Toy 1 or Toy 2, I would need more data from the analyst. The outcome (or payoff), profit margins and the return to risk ratio (RRR) must be measured and can be looked at on a Payoff table and decision trees where the sate of nature and action are calculated per combination of the variables (Sharpe et. al., 2019). Simulations and probability models can help yield best predictions of action but data sensitivity must also be considered. This would lead to best business decision since it looks at monetary outcomes or profits. Forecasting is never perfect, as decision makers, we can only make the best informed decision we can with the data we have.

References

Sharpe, N. D., De Veaux, R. D., & Velleman, P. F. (2019).Business statistics(4th ed.). Retrieved from https://www.redshelf.com