HW9 Solution

Question 12.1 Describe a situation or problem from your job, everyday life, current events, etc., for which a design of experiments approach would be appropriate. Answer 12.1: In my role as an analytics manager for a software and services company, I might encounter various situations where a design of experiments (DOE) approach can be highly beneficial. One such scenario is optimizing the customer support process to improve customer experience, service quality, and ultimately, customer retention. Here's how I can use a DOE approach for this: Scenario: Optimizing Customer Support Problem: My company has been experiencing challenges with customer retention, and customer complaints about the support process have been increasing. The service team is overwhelmed, and there's a need to enhance the customer support process to improve satisfaction and retain customers. However, I’m not sure which specific changes will yield the best results. How to Apply Design of Experiments (DOE): Identify Factors : Begin by identifying the critical factors that could affect the customer support process. These factors could include response time, the knowledge of support agents, communication channels, and the effectiveness of self-service resources. Select Levels: For each factor, choose different levels or variations that I want to test. For instance, response time levels could be "24 hours," "12 hours," and "6 hours." The knowledge of support agents could have levels like "basic," "intermediate," and "advanced." Create a Matrix: Create a matrix of experiments where I systematically combine the different levels of these factors. For example, one experiment could involve using "12 hours response time" with "intermediate agent knowledge," and another could involve "24 hours response time" with "advanced agent knowledge." Conduct Experiments: Implement these combinations in my customer support process. Ensure that I have a controlled and random approach to assigning customer support requests to these experimental groups to minimize bias. Collect Data: Gather data on key metrics such as customer satisfaction scores, resolution time, and customer retention rates for each experiment. Analyze Results: Use statistical analysis to determine which combination of factors leads to the most significant improvements in customer experience, service quality, and customer retention. Optimize: Based on the analysis, I can identify the optimal combination of factors. Implement these changes in my customer support process and continuously monitor the impact on customer retention and satisfaction. By applying a DOE approach in this scenario, I can systematically test and identify the most effective changes to my customer support process, leading to improved customer experience, higher retention rates, and potentially justifying higher charges for the enhanced service quality. This approach allows me to make data-driven decisions to optimize your operations.

Question 12.2 To determine the value of 10 different yes/no features to the market value of a house (large yard, solar roof, etc.), a real estate agent plans to survey 50 potential buyers, showing a fictitious house with different combinations of features. To reduce the survey size, the agent wants to show just 16 fictitious houses. Use R’s FrF2 function (in the FrF2 package) to find a fractional factorial design for this experiment: what set of features should each of the 16 fictitious houses have? Note: the output of FrF2 is “1” (include) or “-1” (don’t include) for each feature. Answer 12.2: A fractional factorial design is a structured approach to experimentation where you systematically study a subset of possible combinations of factors or variables to understand their effects. In my case, I have 11 fictitious features (variables) that could potentially impact the market value of a house, and I want to identify which features have the most significant impact on this need. But instead of conducting a larger survey we are using fractional factorial design to identify a subset of fictitious houses for the survey. In my code R's FrF2 function is used to generate a fractional factorial design for an experiment aimed at determining the set of features from a list of 16 fictitious features that should be included or excluded from each house. The objective is to reduce the number of surveys that a real estate agent needs to conduct. For ex: in our output row 1 for house 1 The combination of features is represented as property_ size number_of_ bedrooms number_of_ bathrooms garage_size backyard_ space kitchen _size living_room_ size solar_panel _installed electric_car _charging corner_lot lot_size 1 -1 -1 1 -1 -1 1 1 -1 -1 1 which suggests that this combination: 1. includes the features property_size, garage_size, living_room_size, solar_panel_installed, lot_size , and 2. excudes the following features: number_of_bedrooms, number_of_bathrooms, backyard_space, kitchen_size, electric_car_charging, corner_lot. property_ size number_of_ bedrooms number_of_ bathrooms garage_size backyard_ space kitchen _size living_room_ size solar_panel _installed electric_car _charging corner_lot lot_size 1 1 -1 -1 1 -1 -1 1 1 -1 -1 1 2 -1 1 1 -1 -1 -1 1 1 -1 1 -1 3 -1 -1 -1 1 1 1 1 -1 -1 1 1 4 -1 -1 1 -1 1 -1 -1 1 1 1 1 5 -1 -1 1 1 1 -1 -1 -1 -1 -1 -1 6 1 -1 1 1 -1 1 -1 1 -1 1 -1 7 -1 1 -1 1 -1 1 -1 -1 1 1 -1 8 1 1 -1 1 1 -1 -1 1 1 -1 -1 9 1 1 1 1 1 1 1 1 1 1 1 10 1 -1 -1 -1 -1 -1 1 -1 1 1 -1 11 -1 1 1 1 -1 -1 1 -1 1 -1 1 12 -1 -1 -1 -1 1 1 1 1 1 -1 -1 13 1 1 -1 -1 1 -1 -1 -1 -1 1 1 14 -1 1 -1 -1 -1 1 -1 1 -1 -1 1 15 1 1 1 -1 1 1 1 -1 -1 -1 -1 16 1 -1 1 -1 -1 1 -1 -1 1 -1 1 10 different yes/no features House no. Below you can find the code for the question. You can also find the code file and excel file attached