Creating A Relative Frequency Distribution Table From A Customer Satisfaction Survey

In today's competitive telecommunications market, understanding customer satisfaction is paramount for cell phone companies. A recent survey conducted by a cell phone company, involving 200 randomly selected customers, provides valuable insights into customer satisfaction levels across different service plans. This analysis will delve into the survey results, exploring the frequency distribution of satisfaction levels across various plans. By examining the data, we can gain a comprehensive understanding of which service plans are meeting customer expectations and where improvements may be necessary. This article aims to provide a clear and concise analysis of the survey findings, highlighting key trends and patterns in customer satisfaction related to their chosen phone service plans.

The foundation of any reliable analysis lies in the methodology employed for data collection. In this case, the cell phone company surveyed a random sample of 200 customers. Random sampling is a crucial technique as it ensures that every customer has an equal chance of being included in the survey, thereby minimizing bias and enhancing the representativeness of the results. The survey focused on gauging customer satisfaction with their phone service, and this was cross-referenced with the specific service plan each customer had subscribed to. This approach allows for a detailed examination of satisfaction levels associated with different plans, providing actionable insights for the company.

The data collected was organized into a frequency table, a common method for summarizing categorical data. A frequency table displays the number of customers (frequency) who fall into each satisfaction category within each service plan. This format makes it easy to compare satisfaction levels across different plans and identify trends. For instance, we can quickly see which plan has the highest number of satisfied customers or which plan has the most dissatisfied customers. The frequency table serves as the primary data source for our analysis, enabling us to quantify and compare customer satisfaction across various service plans. The survey's meticulous methodology and the use of a frequency table for data organization lay a solid groundwork for a robust and insightful analysis of customer satisfaction.

To effectively analyze the data presented in the frequency table, it is essential to understand the key metrics that can be derived from it. The frequency table provides a breakdown of the number of customers in each satisfaction category for each service plan. From this, we can calculate several important measures, such as the percentage of satisfied customers for each plan, the percentage of dissatisfied customers, and the overall distribution of satisfaction levels. By comparing these metrics across different plans, we can identify which plans are performing well in terms of customer satisfaction and which ones may require attention.

One of the most valuable analyses we can perform is the calculation of relative frequencies. Relative frequency is the proportion or percentage of observations within a specific category relative to the total number of observations. In this context, the relative frequency of satisfied customers in a particular service plan is calculated by dividing the number of satisfied customers by the total number of customers subscribed to that plan. This provides a standardized measure that allows for direct comparison between plans, regardless of the number of customers in each plan. For example, if Plan A has 50 customers and 40 are satisfied, the relative frequency of satisfied customers is 80%. If Plan B has 100 customers and 70 are satisfied, the relative frequency is 70%. This comparison clearly shows that Plan A has a higher proportion of satisfied customers.

The frequency table also allows us to identify potential problem areas. By examining the number of dissatisfied customers for each plan, we can pinpoint plans that are consistently underperforming. It is important to consider both the absolute number of dissatisfied customers and the relative frequency. A plan with a high number of dissatisfied customers and a high relative frequency is a clear indication of an issue that needs to be addressed. Further investigation, such as qualitative feedback from customers, may be necessary to understand the root causes of dissatisfaction. The combination of frequency analysis and relative frequency calculations provides a comprehensive view of customer satisfaction, enabling the cell phone company to make data-driven decisions to improve its services.

Transforming the frequency data into a relative frequency distribution table is a crucial step in understanding customer satisfaction. The relative frequency distribution table provides a clear and concise view of the proportion of customers who fall into each satisfaction category for each service plan. This format allows for easy comparison across plans and highlights the strengths and weaknesses of each plan in terms of customer satisfaction. To construct this table, we calculate the relative frequency for each satisfaction level within each plan by dividing the frequency (number of customers) by the total number of customers in that plan. The result is then typically expressed as a percentage, making it easy to interpret and compare.

For example, consider a hypothetical scenario where Plan A has 100 customers, and 60 of them are