Corner Store Chain Owners - Example

Heading:  Corner Store Chain Owners Report Your name: Course name: Professors’ name: Date Executive Summary This paper has presented statistical findings of corner store by analysing the data present using excel scatter (X-Y) tool. The analysis reveals all the important determinants of gross monthly sales of corner store with population density the leading determinant of the gross sales followed by the average income and competition respectively. The major limitation of the statistical analysis done is that it ignores other marketing essentials like advertising that helps in enhancing sales turn-over. A compounded approach which considers all the three determinants coupled with marketing essentials like advertising will increase sales and establishment of new niches by corner store within the area identified. Summary of data and variables The corner store has six variables that are analysed in order to find the major determinants of the gross sales. These determinants are as follows; Gross monthly sales ($), The number of competitors within 10km, The population (in 1000’s) within 10km, The average income of the population within 10km,The average number of cars owned by households within 10km and The median age of dwellings within 10km. One variable which is the gross monthly sales is to be used as the main variable and compared with the other five variables in order to determine the extent of the relationship between the variables. Any positive relationship will be important in the decision making process, since corner store will rely on the findings in order to make decision on whether to open a new corner store in that area or not. Analysis of data and variables The relationship of the five variables against gross monthly sales was analysed closely using excel relationship scatter (X-Y) axis tool. The rationale for the use of the scatter (X-Y) axis tool in this analysis is because it accurately compares the relationship of the variables and plots the best line of fit using the spread variables giving a clear picture of the relationship that exist between the two variables. Two variables namely the average number of cars owned by households within 10km and the median age of dwellings within 10km were not included in this paper as a result of the uneven or poor relationship between these variables and the gross monthly sales ($). In essence, these two variables do not make any market sense in terms of their consideration while establishing a new corner store premises within the 10km area. As shown in Figure 1 a graph representing number of competitors within 10 km (Y-axis) vs. Amount of gross monthly sales ($) was plotted using the scatter (X-Y) axis of excel. It is evident that there is a relationship between the gross sales and the number of competitors within 10km. This implies that the more the competitors the lesser the gross monthly sales. This is thus a factor that corner store should consider while setting up a new store in the area. It is advisable for corner store to set up a new store where there are fewer competitors (Mort 2003). In Figure 2, a graph representing Population within 10km (1000’) vs. Amount of Gross monthly sales ($) is plotted using scatter (X-Y) axis of excel. As noted, there is a strong relationship between the gross sales and the population density within 10km. As shown in the figure, the higher the population density the higher the monthly gross sales ($). This is a very important aspect to corner stone since it reveals that as long as there is a large population density, there is a higher chance for the gross sales to increase (Wang 2003). Corner store should thus consider establishing their stores in areas with larger population density. Figure 3 shows a graph of average income of residents within 10 km vs. Gross monthly sales in ($) plotted using scatter (X-Y) axis of excel. The figure indicates that there is a relatively strong relationship between the average income of residents within 10 km and gross monthly sales in ($). This means that the higher the income of the residents, the higher the gross monthly sales of corner store (Mazzocchi 2008). This is thus an important consideration when looking for a better place to establish another store in the area (of about 10 km). Based on this analysis of data, corner store should consider the population density as an important parameter because there is a strong relationship between the population density and the monthly sales turn-over. As shown in figure 2, the graph is almost linearly upwards and hence an important factor to note, it is in essence the most important factor since despite the number of competitors in an area the population density is a determinant factor. This may be because of the high value and customer perception of corner store products or a shared market where everyone gets adequate sales (Sweeney 2010). Based on this analysis, the first parameter that corner store should consider while setting up a new corner store is the populations density. According to Härdle & Simar (2007, p 45) a compounded approach will however yield more results to corner store. It is advisable for corner store to consider the population density, the average income and the number of competitors in an area before setting up a new store. In essence the lesser the number of competitors in an area and the more the average income and populations density, the gross monthly sales will be optimised because of the combined consideration of the determinant sales factors. The limitation of this analysis is the lack of other essential marketing consideration such as advertising, improving of brand image and customer perception analysis. These factors can change the statistical findings and cause increased sales in niches experiencing low sales (Wilson 2004). This research therefore overlooks these important factors and thus not comprehensive in terms of the market condition that exist in the area. I will advise corner store to consider the implementation of these factors together with the statistical findings established in this analysis for optimum increase of sales in the new and current corner stores. Read More