Amalgameted Food Products wishes to determine the influence of promotion and advertising on the sales of their meatloaf mix. This will show if the invested money on adverting and promotion wasted or they should be used different to achieve best results. The problem is that based on a quick look of the historical date of the past quarters, there seem to be little to no connection between the sales and the money spent on promotion. Two quarters with relatively the same promotion expenditure, have sales, which differ drastically.
An Excel multiple regression model will help to correctly identify the influence of promotion and advertising of the product at hand. The data used for the regression model will be the amounts spent on promotion and advertising, as well as the sales and economic index for 24 quarters. There are some dependable variables in the model. They are the advertising and promotion. They can influence the sales. We made the assumption that the sales are not directly and very much influenced nor by the season, nor by the economic situation of the country. Based on this, the quarter and economic index are the independent variables.
The data use for generating the model included 24 months of sales of the mix and expenditures for promotion and advertising for those months in 1,000 dollars. The economic index was also included in the provided data to show the general economic conditions in the meat loaf market area. The higher the economic index, the better the economic time.
These graphs indicate the tendency and dependence. The dependency of promotion over sales is 5.05 and of advertising over sales - 3.21 based on the graphs above. Consequently, promotion has a bigger effect on sales than advertising. As the results are only approximations, it must be mentioned that the accuracy of the promotional date is higher, which makes us more confident in the aforementioned conclusion.
Based on the table above, where the last variable (variable 3) is the economic index, we can draw a conclusion that the economic index actually does influence the sales of the mix, as the coefficient is -5. Based on the multiregression analysis we can see the dependence of the variables.
Based on the data collected and the analysis done, the testing consists of taking another close look at the data. I had initially made a mistake when filling in the numbers and had to redo the charts. However, the results above are proper and they do make sense, as it is only logical that advertising and promotion increase sales. Moreover, the implementation of the solutions does not require radical changes, so there should not be many issues
Consequently, the recommendations are to increase the budget for promotion instead of advertising, however, not to exclude advertising completely. In the 25th quarter, $30,000 should be given to promotion and $10,000 to advertising in order to increase sales.
