Calculate And Interpret The Relative Frequency Of Types Of C

Calculate and interpret the relative frequency of types of clients and reasons for returning

The owners of Mi Casa Front Porch restaurant seek to understand customer loyalty and repeat business by analyzing client behavior. They have collected sample data from their Downtown Phoenix location to determine the reasons customers return and the types of clients who visit. To do this effectively, they need to compute the relative frequency of different client types and reasons for returning. This analysis will help identify patterns, highlight areas for improvement, and foster strategies to increase customer retention.

The first step involves categorizing the data into relevant client types—such as local residents, tourists, or business professionals—and reasons for returning—including food quality, service, ambiance, or price. For each category, the frequency is counted and then expressed as a percentage of the total customer visits to calculate their relative frequencies. Understanding these percentages enables the restaurant owners to identify the most common client profiles and drivers of repeat visits.

Calculating the relative frequency is straightforward: divide the count of each category by the total number of customer responses and multiply by 100 to express as a percentage. For example, if 150 out of 500 customers cited food quality as a reason for returning, the relative frequency would be (150/500) × 100 = 30%. Similarly, if 200 clients are local residents, then their relative frequency is (200/ Total customers) × 100. This data can be presented visually through pie charts or bar graphs to illustrate the proportion of each client type and reason, making patterns more evident for strategic planning.

Interpreting the findings involves analyzing which client types and reasons are most prevalent. A high relative frequency of local residents returning for specific reasons could indicate strong community loyalty, which the owners can reinforce through targeted promotions or loyalty programs. Conversely, a low percentage of repeat customers due to reasons such as poor service or high prices signals areas needing intervention. For example, if the majority of customers return because of ambiance but few due to service quality, management might focus on training staff to improve repeat patronage based on service experience.

This analysis offers crucial insights into customer preferences and behavior, guiding strategic improvements to enhance customer satisfaction, loyalty, and ultimately increasing repeat business. By monitoring shifts in relative frequencies over time, the restaurant can evaluate the effectiveness of new initiatives or changes in their service to foster more consistent customer return rates.

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