Management Information Systems Assignment

Management Information Systems Assignment Words: 1977

The objective of this assignment is to explore the coffee market in the UK and understand the consumer preferences with aid of data resources and the outcome it would have on a new brand of Mysore coffee in the competitive UK coffee market. As the premium sectors develop in the UK, greater emphasis is placed on Arabica beans, with marketing and pack support centered on the provenance and taste credentials of specific beans.

Arabica is fast becoming synonymous with premium quality, and this is likely to lead to increased prices, particularly as some countries are seeking to trademark native bean varieties. For instant, Ethiopia applied to trademark two different Arabica beans in the US in 2006 – called Sidamo and Harar (Source Mintel, Report 2006). Arabica beans command a high price because tastes are particular to a growing location. Brazil is the world’s largest producer of Arabica beans. Erratic weather during the growing season 2006-7 led to market nervousness and prices reached around $2. 6 per kg, up 16% since 2005 (according to data from the International Coffee Association). The origin of the coffee the company Amrut distillers basically a liquor and spirits company diversify in to the coffee segment to give value addition to your single malt portfolio is planning to launch in the market a Mysore Coffee which is an Arabica bean and has it connoisseurs throughout the globe. Their channels to distribution would preference in Indian restaurants, Specialist coffee merchants, Retail channel. The idea is to create distinction for an Indian coffee rather than being used as a substitute for Irish coffee.

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There is a variety of different brands in the market and an issue of brand recognition for an Indian coffee. Consumers today are concerned about health issues and how well it delivers with low caffeine value. It would also have to face resistance from the convenience segment in the market such as vending machines and product development in the ready to drink coffee market. Coffee shops have a clear role in educating consumer taste buds and extending their preferences in to different varieties in coffee. This has stimulated consumer demand in retail.

They are luring away potentially lucrative consumers, typically those that working population, affluent, younger adults away from the retail format of stores because of the environment they provide. They are becoming lighter users of the retail category and satisfying their need through the food service segment which shows this brand to present in this segment too. This analysis will help us to find out the trends in the market and performance of leading brands . We try to create a correlation with coffee consumption and region wise coffee consumption.

This would help the positioning strategy of the product in the competitive market. Obtaining data We may be able to access data sources these days with computer databases, intergrated computer networks and the internet. It is not feasible to give a prefix on data sources . Some examples could be annual statistics which is published every and summarizes detail data on varied fields. Other such examples could be Population Census Reports, Economic Trends, Family Expenditure Survey, Monthly Digest of Statistics, Regional Trends and Social Trends. The key is how this collected data helps in giving business solutions .

Attempts should be made the data should represent statistical population which should be free from bias . Primary and secondary should be well sorted out. Analysis should represent a part of the data rather than the entire data. It is necessary to depict why this particular data is collected and the most apt way of acquiring it. Sample collection methods depend on the type of data that has to be dealt with for the required information. There are pro and cons in data collection techniques depending on the circumstances. Coffee marketers in UK provide a wide range of products .

To understand the consumption pattern of consumers a large sample size of the coffee drinking population across geographical locations in the UK. We need to understand the potential of the market in terms of size and value over a period over previous years and performance of brands in the market. The data is generated through Mintel research website accessed through Athens login . An estimate of last 5 years is taken from 2002-2007 to understand the coffee market in UK. The data survey is carried across 25,000 people in the age group of over 15 years of age.

The data is generated by panel of mintel experts through their extensive research and survey. It gives a breakdown of activities in the UK market in term of market segmentation, size, value, consumption trends, consumer repertoires, consumer typologies. They also provide a wide range of analysis for different sectors. Analysis of Data Analysis of data done by assistance of Minitab software. Linear regression is used as a tool to find co-relation between coffee consumption with region wise consumption (Wisniewski, 2002). Some data in decimals are rounded off for clarity.

The process goes as follows we specify the relationship between any two variables as Y=f(x) where Y is known as the dependant variable and X as the independent or explanatory variable. We will go a stage up and assume the relationship between the two as a linear one (Wisniewski, 2002) . It results in a straight line if plotted on a graph. We should observe that it’s not always appropriate to depend on the context of the problem. This means the general form of our relationship can be specified as Y=a+bX. where and b are known as the parameters of the linear equation: the numerical values which form the equation.

A is generally referred to as the intercept of the function and b as the slope. The implications of this forecasting should be considered only after principles of intercept and slope are clear (Wisniewski, 2002). The slope of a linear function is given by the b term in the equation and shows the relationship between a change in the X variable and a change in the y variable (Wisniewski, 2002). The b term is sometimes referred to as the gradient. It indicates the steepness of the straight line representation the function.

It is important in this kind of analysis to consider the contextual as opposed to the mathematical interpretation of a and b terms ( Wisniewski,2002). The exact business interpretation of the parameters will depend on the business scenario ( Wisniewski,2002). The b term is the slope or gradient of the straight line and in general, indicates the change in Y that occurs for a given change in X ( Wisniewski,2002). This would in context to change in profit for change in sales. The b term will be an indication of the profit margin on sales as it expected to be positive as to sales increase in profits (Wisniewski, 2002).

The a term is the intercept and indicates, graphically where the line intercepts the Yaxis . The other way to interpret it would be that y takes when X=0. Here,a will indicate the profit when sales are zero. We would never expect to encounter such a situation but there would a obvious implication (Wisniewski, 2002). It is unrealistic to expect profit to be positive when sales=0. At best we would expect profit to be zero realistically negative representing loss since overhead expenses are likely to incurred even when sales is zero . We might expect a relationship between two variables.

Region Wise Trends All usersHeavy usersMedium usersLight usersNon-users %%%%% Region: London38. 611. 812. 213. 761. 4 Southeast39. 39. 712. 615. 860. 7 South West41. 411. 711. 317. 758. 6 Wales29. 86. 88. 812. 470. 2 Midlands32. 56. 89. 814. 967. 5 North West33. 67. 810. 613. 566. 4 Yorkshire 31. 66. 39. 315. 368. 4 North27. 35. 98. 312. 472. 7 Scotland326. 39. 115. 968 The above graph shows an interesting trend in consumption of coffee across various regions in the UK on the pattern of heavy, medium, light and non users of coffee.

Its shows Southeast and Southwest of UK are high consumption bastion for coffee and potential area for our new product. Whereas the North and Wales are low consumption areas which the company could focus less as priority markets and placement in this region would be a good option in the retail channel. London, Northwest and Midlands seem to look like potential markets where the consumption trend is on a rise . London could be a key market in terms of diverse population, economic drive and cultural exchange that happens people are willing to try something unique if presented in the right scheme of things.

The company could also study the consumption trends of non users as what are the kinds of beverages they prefer to have for eg fruit juices, herbal tea, fizzy drinks etc. Which are the avenues that one could explore to get the non users to experience the product and create a new base of consumers. Coffee consumption-By users By using linear regression for analysis we try to create a co-relation with region wise consumption and consumption of coffee .

Here the total of category of coffee users heavy users, medium users, light users and non users a comparative analysis is done with total users as the dependant variable and these 4 categories as the independent variable to get conclusive evidence. Figure 1 below tries to explore the relation between allusers and percentage of heavy users. In this case, the R square value is 0. 86 which is very close to the ideal R square value of 1. This suggests a strong relation between all users and percentage of heavy coffee users. Figure1 Regression Analysis: All users versus heavy users

The regression equation is All users = 18. 8 + 1. 87 heavy users Figure2 shows the relation between age and percentage of medium users. The graph of linear regression suggests inverse relation between x-axis and y-axis. The value of R square is 0. 85 which is close to 1 gives a strong possibility that percentage of medium coffee users are on a downward trend as there is a linear relationship between the region and percentage of medium coffee users. Figure2 Regression Analysis: All users versus medium users The regression equation is All users = 5. 6 + 2. 83 medium users Figure3 below shows the relation between age and percentage of light users. The value of R square is 0. 45 and suggests a weak relation between the values on x-axis and y-axis. Thus, the percentage of light coffee consumption goes downwards with the regional trend. Figure3 Regression Analysis: All users versus light users The regression equation is All users = 7. 8 + 1. 79 light users Figure 4 shows the linear regression graph to explore the relation between region wise consumption of coffee and percentage of non coffee drinkers.

The value of R square is 100 which suggest that percentage of non users goes downwards in a linear decreasing trend. . Figure 4 Regression Analysis: All users versus nonusers The regression equation is All users = 100 – 1. 00 nonusers Conclusion As a marketer in the coffee industry this approach has a huge impact in terms of market inroads as the brand can position itself in the high intensity regions where it would have connoisseurs for this kind of product besides it would save a huge chunk of their marketing and promotion budget by targeting niche markets

The linear regression tool has clearly defined trends among category of coffee drinkers across regions in UK. This also paves way for channels of marketing the product to the core consumer. The Indian restaurants markets across niche markets in key geographical areas are another avenue that the company could look at targeting for their product. Key retail chains need to be tapped in target areas as consumers prefer to try coffee in the food court of these chains and that result in the buying pattern . As the consumer prefers to get experience of the product before purchasing it.

The scope for niche brands is very limited and they need to play around with their strengths rather than being in direct competition larger brands which already have an existing market share and the objective to enhance it by downsizing the smaller players. REFERENCE LIST: Mik Wisniewski, Richard Stead(1) Mik Wisniewski, R. S. (1996). Foundations Quantitative methods for business. Wisniewski, Mik(1) Wisniewski, M. (2002). “Quantitative Methods For Decision Makers. ” www. academic. mintel. com(1) www. academic. mintel. com.

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