Predictive Analysis for Data-driven Decisions

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Determine business outcomes using predictive analysis techniques.

 

Student Success Criteria
View the grading rubric for this deliverable by selecting the “This item is graded with a rubric” link, which is located in the Details & Information pane.

 

Scenario
You are the manager for a company that sells outdoor grills. You’ve recently earned your MBA, and you want to apply what you’ve learned to your position to help with decision-making. You have developed the following estimated regression equation to help make data-driven decisions for the store. This will help you to better see how the unemployment rate, temperature, gas prices, and the price of steak impact weekly outdoor grill sales.

 

Y = 22,100 – 412×1 + 818×2 – 93×3 – 71×4

 

Where:

· Y = weekly sales

· x1 = local unemployment rate

· x2 = weekly average high temperature

· x3 = number of activities in the local community

· x4 = average price of gasoline per gallon

 

Use the above equation and information to answer the following questions in a Word document, and create a guideline to use for future business decisions:

Based on the equation above, please provide the value for x1, x2, x3, and x4. Also, explain what these values mean in the context of this question. For example: What does the value of 818 mean in the equation above (specify if it is x1 or x2 or x3 or x4, and explain what those values mean based on the equation and context)?
What are the estimated weekly sales if the unemployment rate is 3.7%, the average high temperature is 670, there are 10 activities, and the average price of gasoline is $3.39 per gallon?
Evaluate data mining techniques that could be used to enhance manager’s decision-making to increase sales.
What recommendations or decisions could you make based on the predictive analysis in question 2?

Sample Solution

  1. Gather data. The first step is to gather data on the factors that you believe are affecting your business outcomes. In this case, the factors are the unemployment rate, temperature, gas prices, and the price of steak. You can gather this data from a variety of sources, such as government websites, industry reports, and your own sales data.
  2. Clean the data. Once you have gathered your data, you need to clean it to remove any errors or inconsistencies. This is important to ensure that your analysis is accurate.
  3. Choose a predictive analysis technique. There are many different predictive analysis techniques available, such as regression analysis, decision trees, and neural networks. The best technique for you will depend on the specific factors that you are trying to predict and the amount of data that you have.
  4. Train the model. Once you have chosen a predictive analysis technique, you need to train the model on your data. This means feeding the data into the model and allowing it to learn the relationships between the different factors.
  5. Make predictions. Once the model is trained, you can use it to make predictions about future business outcomes. In this case, you could use the model to predict weekly sales based on the unemployment rate, temperature, gas prices, and the price of steak.
  6. Evaluate the results. It is important to evaluate the results of your predictions to ensure that they are accurate. You can do this by comparing your predictions to actual results.

In the case of the outdoor grill company, the estimated regression equation can be used to predict weekly sales based on the unemployment rate, temperature, gas prices, and the price of steak. For example, if the unemployment rate is 5%, the average high temperature is 80 degrees Fahrenheit, the number of activities in the local community is 100, and the average price of gasoline is $2.50 per gallon, then the predicted weekly sales would be $20,888.

This information can be used by the company to make decisions about its marketing and sales strategies. For example, if the company knows that the unemployment rate is going to increase, then it might want to focus its marketing efforts on people who are still employed. Or, if the company knows that the temperature is going to be below average, then it might want to offer discounts on its grills.

By using predictive analysis techniques, businesses can make more informed decisions about their operations and marketing strategies. This can help them to improve their profitability and efficiency.

Here are some additional tips for using predictive analysis techniques to determine business outcomes:

  • Use multiple techniques to get a more accurate picture.
  • Use a validation set to test the accuracy of your predictions.
  • Keep your models up-to-date as your data changes.
  • Be aware of the limitations of predictive analysis.

Predictive analysis is a powerful tool that can be used to improve business outcomes. However, it is important to use it wisely and to understand its limitations.

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