Managerial Accounting Example Essay Example

options. The Indian coffee will also need to overcome competition from convenient options like vending machines and ready-to-drink products. Coffee shops play a key role in educating consumers about different types of coffee and expanding their preferences.

The environment provided by certain food service brands is attracting younger, affluent, working population consumers away from traditional retail stores, resulting in decreased retail category usage. These brands also have a presence in the food service segment. Analyzing market trends and brand performance, as well as correlating coffee consumption with regional trends, will aid in positioning the product in a competitive market.

With the rise of computer databases, integrated networks, and the internet, accessing data sources is more feasible than ever before. Prefixes on data sources are not practical. The sources may include annual statistics that summarize detailed data on various fields, such as Population Census Reports, Economic Trends, Family Expenditure Survey, Monthly Digest of Statistics, Regional Trends, and Social Trends. The collected data should aid in providing business solutions by representing a statistical population devoid of bias.

To ensure accurate analysis, sorting primary and secondary data is vital. It's crucial to only analyze a portion of the collected data and understand why it was gathered and how it was obtained. The means of collecting samples vary based on the type of data needed, with each technique having its own benefits and drawbacks depending on the situation.

Coffee marketers in the UK provide a wide range of products. A large sample size is needed to understand the consumption habits of coffee drinkers in different areas of the country. This sample size helps determine the market's size and value over

previous years, as well as how brands perform in the UK market. We access this information through Mintel research website using Athens login. The 2002-2007 coffee market survey uses data from this source and includes responses from 25,000 individuals aged 15 or older.

The extensive research and surveys conducted by a panel of Mintel experts has provided valuable data on UK market activities. This data includes insights on market segmentation, size, value, consumption trends, consumer repertoires, and consumer typologies across various sectors. The use of Minitab software for analysis has been instrumental in identifying correlations between coffee consumption and regional consumption using linear regression (Wisniewski, 2002).

To ensure clarity, decimal data is sometimes rounded. This involves establishing the connection between two variables - Y as the dependent variable and X as the independent or explanatory variable - represented by Y=f(x). Assuming a linear relationship between the two (Wisniewski, 2002) produces a straight line when graphed.

It should be noted that relying solely on context may not always be appropriate. The general form of the relationship between variables can be expressed as Y=a+bX, where a and b are known as parameters and represent numerical values in the equation. A is also known as the function's intercept, while b is referred to as its slope. Understanding the principles of intercept and slope is necessary before drawing conclusions about forecasting (Wisniewski, 2002). The b term in the equation indicates the slope of a linear function and represents how changes in X affect Y (Wisniewski, 2002).

The gradient, or b term, represents the slope of the function's linear representation and should be considered in a contextual rather than mathematical sense during analysis (Wisniewski,

2002). The specific business interpretation of a and b parameters will vary depending on the situation (Wisniewski, 2002).

According to Wisniewski (2002), the b term represents the slope or gradient of a straight line, which reflects the change in Y resulting from a change in X. This can be illustrated by changes in profit resulting from changes in sales. Specifically, a positive b term represents an increase in profits as sales increase, indicating the profit margin on sales. The a term, on the other hand, serves as the intercept and indicates where the line intersects with the Y axis graphically. In other words, a represents the profit when sales are zero.

Wisniewski (2002) suggests that encountering a situation where sales=0 and profit is positive is unlikely. Realistically, the profit would be zero or negative due to overhead expenses likely being incurred even without sales. Therefore, there may be a relationship between the two variables.

Region: London has trends in user usage categorized as: All users, Heavy users, Medium users, Light users and Non-users. The percentages for each category are: 38% for All users; 61% for Heavy users; 12% for Medium users; 21% for Light users and Non-users.

761.4, Southeast, 39.39.712.615.

860.7 South West 41.411.711.

317.758.6 Wales 29.86.88.

812. 470. 2 Midlands32. 56. 89. 814.

967.5, North West33.67.810.613.566.

Yorkshire has four values: 31, 66, 39, and 315.

368.4 North 27.35.98.312.472.

The graph depicted above displays a compelling pattern in coffee consumption among different regions of the United Kingdom. It classifies users into heavy, medium, light, and non users of coffee. The graph reveals that the Southeast and Southwest of the UK are major coffee consumption hubs, making them potential target markets for our upcoming

coffee product.

The company could focus less on North and Wales as they are low consumption areas and prioritize placement in regions with potential such as London, Northwest, and Midlands. These regions show a rise in consumption trends and London, with its diverse population, economic drive, and cultural exchange, could be a key market. The company could also study the beverage preferences of non-users to explore avenues for introducing the product and creating new consumers.

Coffee consumption is analyzed using linear regression to find a correlation between region-wise consumption and the consumption of coffee. A comparative analysis is done with heavy, medium, light, and non-users as independent variables and total users as the dependent variable. Figure 1 below explores the relationship between all users and the percentage of heavy users which shows a strong correlation with an R square value of 0.86, close to the ideal R square value of 1.

According to Figure1, there is a significant correlation between all users and the percentage of heavy coffee consumers. The regression analysis displays a formula of All users = 18. 8 + 1. 87 heavy users. Figure2 depicts the relationship between age and the percentage of medium coffee drinkers, and the linear regression graph indicates an inverse correlation between the x-axis and y-axis.

With an R square value of 0.85, which is close to 1, there is a strong likelihood that the percentage of medium coffee users is decreasing as there is a linear relationship between the region and percentage of medium users. Figure2 Regression Analysis: All users versus medium users displays the regression equation as All users = 5.6 + 2.83 medium users. Furthermore, Figure3 depicts

the correlation between age and percentage of light users with an R square value of 0.

The data shows a slight correlation between the x-axis and y-axis variables, indicating a decrease in the percentage of light coffee consumption in relation to the regional trend. Figure 3 presents the Regression Analysis for All users versus light users, with the equation All users = 7.8 + 1.

Figure 4 displays the linear regression graph illustrating the correlation between region-wise coffee consumption and the percentage of non-coffee drinkers among 79 light users. The R square value of 100 indicates a steady decline in the percentage of non-users in a linear fashion. The regression equation is All users = 100 - 1.00 nonusers. As a coffee marketer, this approach presents significant opportunities to penetrate high-intensity regions with potential connoisseurs and save marketing expenses by targeting niche markets. The linear regression tool establishes distinct patterns among coffee drinkers in different UK regions, providing an avenue for targeted product promotion to key consumers.

One potential target market for the company's product is Indian restaurants in specific geographic areas. It is important to focus on key retail chains in those areas, as consumers often try coffee in the food court before purchasing. Due to limited opportunities for niche brands, it is crucial to leverage strengths rather than compete with larger brands for market share.

REFERENCE LIST: The following sources were consulted for quantitative methods in business:

- Mik Wisniewski and Richard Stead
- Mik Wisniewski
All sources were used in the publication of Foundations Quantitative methods for business by Wisniewski, M. and R. S. in 1996.

The website "www.academic" provides information on the book "Quantitative

Methods for Decision Makers" published in 2002.The contents of the state:

Visit mintel.com (1) and www.academic.mintel.com.