If I were hired into a company to do research on the change in their customer service protocol. I would mostly use a rating scale. Allowing the customer to rate their overall

175 words each 

discussion 1:

If I were hired into a company to do research on the change in their customer service protocol. I would mostly use a rating scale. Allowing the customer to rate their overall experience in a series of questions. Customer service is often researched through observation of the customer base within the company. By doing so I would ask a series of questions with the Likert scale to assess the impact of the customer experience from recent encounters. Then I would use a 1-5 rating on each question to best rate each answer. The ratings would be as follows 1- Very Dissatisfied, 2- Dissatisfied, 3- Neither Dissatisfied or Satisfied, 4- Satisfied and 5- Very Satisfied. This would allow the company to collect the information needed to complete their research with a simple approach to each customer allowing the company to better service their customers.

After working in customer service for many years I feel this is the best approach to collect the data needed for the research  within the company. A short yet simple questionnaire would be more likely to not intimidate the customer. In return having a better chance of the customer completing the survey with honest answers. With the outcome satisfying both the customer and the company.

discussion 2:

In evaluating the impact of a recent change in customer service protocols on customer loyalty, the variable most commonly used is ordinal. Customer loyalty is often measured using survey tools such as Likert scales, where respondents rate their likelihood to recommend the company or continue using its services (e.g., from 1 = “Very Unlikely” to 5 = “Very Likely”). These responses show a clear order—some responses indicate higher loyalty than others—but the difference between each point on the scale is not necessarily equal. For example, the jump from “Neutral” to “Likely” may not reflect the same increase in loyalty as from “Likely” to “Very Likely.” Because of this, the data must be analyzed using appropriate methods for ordinal data. This means using medians or modes for central tendency, and non-parametric tests such as the Mann-Whitney U or Kruskal-Wallis tests instead of statistical methods like t-tests or ANOVA that assume equal intervals. Treating the variable as ordinal helps maintain the integrity of the data and ensures more accurate conclusions. Understanding the nature of the variable is crucial because it directly influences how we design the study, collect responses, and ultimately interpret the impact of service changes on customer loyalty.