Evaluation and Business Analysis

Sometimes brainstorming on a business situation cannot lead to effective decision making, and therefore, effective decision making calls for more valid, credible, and logical data that can be analyzed. To obtain that data, business management should perform evidence-based research either qualitative or quantitative to find first-hand data to make informed decisions. The decisions made should aim at improving the condition of the business. In this paper, I will analyze the data that I gathered after conducting quantitative research and show how the analyzed data can be used to make informed decisions to improve the restaurant’s operations.

Burger Joint is a fast food restaurant specializing in making and selling burgers. It is located at N Shepherd Dr Houston, in the United States. Burger Joint is not a medium-size restaurant without much developments. However, the restaurant can be developed if effective decisions are made from analyzing data gathered from quantitative research. The analysis can make the business more efficient in different ways.

Firstly, it can be used to maximize customer value. Data such as what customers like, their behaviour towards the burgers, the type of burger they like, and the time they visit the restaurant can be used to align the business with their preferences. The more customers discover that the restaurant aligns its services to their likes, the more they feel valued. Secondly, the analysis can be used to better product management. Considering the restaurant deals with burgers, there are different types of burgers, data of what type of burger moves fast, and why it can help improve the product. Some burgers are not moving fast because they are expensive or have a weird taste that many customers do not like. Such data can be very crucial in defining the product.

Lastly, the analysis can help in improving advertising. As a manager, one primary goal of conducting the research would be to improve how the restaurant is advertised. Public Relations practitioners have it that advertising cannot influence if research on the business’s status is not conducted (Ferguson, 2018). Data such as the most common gender at the restaurant, whether customers prefer to purchase a takeaway burger or sit at the restaurant, can be used to make an effective advertisement. For instance, targeting the female gender makes a higher percentage of the customers or sensitizes online purchasing in the advert.

I used several quantitative processes to analyze the business. The research involved comparing a weekday and a weekend. Firstly, I asked the manager how many customers enter the restaurant on a typical weekday and a weekend. This question later helped me conclude some factors that may lead to more clients and fewer customers. Secondly, I enquired about the time of the day most of the customers visit the restaurant. It was a crucial question because the restaurant can know the time to make more burgers so that customers get them fresh and hot to maximize customers’ value.

Another question was whether most of the customers eat from the restaurant or prefer to take away. Depending on the number of those who prefer takeaway, if it outweighed those who eat from the restaurant, improving packaging would be beneficial. Another way was by determining the type of burger that is in high demand. There are different types, such as beef, cheese, vegetable, etc. Knowing the most liked one, the business can increase the number of the type it makes each day. This will see many customers being satisfied and served.

Depending on the findings, the restaurant exhibits direct distribution. The findings indicated that it is directly due to several reasons connected to findings. Firstly, it deals with hamburger, an easily perishable product. Hamburgers may not require intermediaries because of the lengthy process involved in indirect distribution. If there are intermediaries, customers may receive the product while in bad condition, such as breaking or going bad.

Secondly, depending on the number of customers that the restaurant receives, direct distribution can work the best. The restaurant receives a relatively small number of customers, making its market size small. A small market may not require indirect distribution because too much money and time can be used, which may bring the profits down (Christopher, 2011). Thirdly, the cost of other distribution types, like indirect distribution, is too high, and the restaurant may not afford it. Considering the number of customers that it gets, its size, and the number of products it sells, it may not opt for indirect distribution. Direct distribution fit the restaurant because of such analysis.

To make decisions that will impact the restaurant positively, I will first define the problem based on the findings. In this case, the problem may be poor customer service, high prices etc. The second step would involve analyzing the problem based on the facts I have collected. For instance, many customers prefer to take away because of the poor arrangement of the restaurant. Thirdly, I would develop possible solutions for the problem. The solutions may be brainstormed by various staff within the restaurant. I would then select the most viable solution to see the restaurant operating efficiently and at a profit. The following steps are to implement, do a follow-up, and monitor the outcomes to conclude lessons learnt.

There was a correlation between the findings. Because I compared a weekday and a weekend, results indicated that the number of customers increases on weekends and reduces on weekdays. Although the cause of the increase and decrease is not identified, it implies a positive relationship between weekends and the number of customers and a negative relationship between weekdays and the number of customers.

The coefficient of determination (R2) is the proportion of variance predicted from the independent variable. It is how the dependent variable varies from the independent variable as predicted (Nakagawa et al., 2017). On the other hand, coefficient or correlation is the proportion of strength between the dependent and independent variables. Values of coefficient correlation rages from -1.0 to 1.0. (-1.0) indicates a perfect negative correlation, while (1.0) indicates a perfect positive correlation. (0.0) indicates that there is no linear relationship between the two variables. Greater than (1.0) ad less than (-1.0) indicates an error.

To sum up, the data findings imply that business operations can be improved and maximize the restaurant’s profit. The findings indicated that the number of customers is relatively small and can be used to improve customer value. If the customers are valued enough, the number may increase. The research found that most customers visit the restaurant in the evening; therefore, this finding can predict the number of burgers to produce to avoid running out of stock when the demand is high. Also, it was found that females and children constitute a more significant percentage of customers. This finding is essential in designing advertisements that can attract more customers. The advertisements can be designed to appeal to female’s and children’s needs. Lastly, weekends have more customers than weekdays, and this is a crucial finding that can help maximize the restaurant’s profits by making more burgers on weekends to make up for weekdays. If the findings concluded from quantitative analyses are utilized correctly, they can improve a company’s operations and maximize profits.

References

Christopher, M. (2011). Logistics and supply chain management ePub eBook (4th ed.). Pearson UK.

Ferguson, M. (2018). Building theory in public relations: Interorganizational relationships as a public relations paradigm. Journal Of Public Relations Research, 30(4), 164-178. https://doi.org/10.1080/1062726x.2018.1514810

Nakagawa, S., Johnson, P., & Schielzeth, H. (2017). The coefficient of determination R 2 and the intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal Of The Royal Society Interface, 14(134), 20170213. https://doi.org/10.1098/rsif.2017.0213