| http-equiv="Content-Type" content="text/html; | | | | increase in the companies advertisement budget by |
| charset=utf-8"> | | | | 0.8666 units, the correlation coefficient in this case is |
| The business strategy situation: | | | | equal to 0.8838 meaning that there is a strong |
| According to the simulation provided advertising plays | | | | relation between the two variables, the coefficient of |
| a major role in the organisation sales level, the | | | | determination is equal to 0.781 meaning that 78% of |
| company has a 6% market share in the 45 billion | | | | deviations in advertising budget are explained by the |
| industry. The first task is to determine the | | | | competitors budget. |
| advertising budget for the company, there are three | | | | Having stated the three models it then best to |
| models provided that are provided that determine | | | | consider the best model for forecasting, the sales |
| the advertising budget and they include the sales, | | | | model is the best due to the strong relationship that |
| retail coverage and competitors advertising budget. | | | | exist between the two variables. The correlation |
| According to the CEO competitors have increased | | | | matrix also shows that there is a strong correlation |
| their advertising budget and there is a high possibility | | | | between sales and other variables |
| that they may capture their market share if the | | | | The sales model: |
| company does not use proper advertising strategy. | | | | We choose the sales model to undertake forecasting |
| According to the previous data regarding | | | | of advertising budget, this strategy is in line with the |
| advertisement budget by competitors, the company | | | | advice from the Myra who is the vice president |
| has spent less than its competitors. However the | | | | production who states that the best option is to |
| advertisement budget has increased over the past | | | | check the variables with the strongest correlation. |
| years and as a result there has been an increase in | | | | The expected market in the industry is 40 billion |
| market share and also an increase in sales. Therefore | | | | which is 5 billion less, in year 11 the sales level was |
| an increase in advertising budget will increase sales | | | | 2,454 million and in year 12 sales level was 2,264 and |
| and at the same time increase market share. | | | | therefore we expect the sales level to increase and |
| There are three models and they include: | | | | if sales level increase then the advertising budget will |
| The sales model: | | | | also increase. If we expect the sales level to increase |
| This model states that an increase in one unit of | | | | to 2,500 then the advertising budget will increase to |
| sales will result into a 0.0676 in sales budget, this | | | | 169 million. Increase in advertising expenditure will |
| model has a 0.9131 coefficient of determination and | | | | increase promotional activity according to Tim. Also |
| this means that 91% of deviations in the advertising | | | | according to the CEO competitors have increased |
| budget are determined by sales holding other factors | | | | their advertising budget and they are likely to take |
| constant. The correlation coefficient for the data is | | | | over market share of the company and therefore |
| 0.9555 and this means that there is a strong positive | | | | the company should increase the budget. However |
| relationship between sales and advertisement | | | | the standard error of the model is 11.0 which is higher |
| spending. | | | | than for the other models and this shows you can |
| Retail coverage model: | | | | both get good or bad results. |
| The retail coverage model depict that if there is an | | | | Week two: |
| increase in retail coverage by one unit then the | | | | Determining fluctuations in sales: |
| advertisement budget will increase by 1.9 units holding | | | | Market size has steadily increased over the years and |
| all other factors constant, the coefficient of | | | | this means that there is a high possibility of increase |
| determination is 0.5934 depicting that 59% deviations | | | | in sales, in year 12 the sales level was 39,049 and this |
| in advertising budgets are explained by retail | | | | is expected to rise to over 40,000 in the next year, |
| coverage. The correlation coefficient is 0.7703 | | | | for this reason therefore we choose the weighted |
| meaning that there is a strong positive relationship | | | | moving average based on 2 periods where year 11 |
| between the two variables. | | | | has 0.1 weight and year 12 has 0.9 weight. From this |
| The competitor’s advertisement budget | | | | point we set the production level at 60 units with |
| model: | | | | seasonal variations of 12 units in the first quarter, 16 |
| This model depict that an increase in the level of | | | | units in the second quarter, 13 units in the third |
| competitors budget by one unit will result into an | | | | quarter and 20 units in the forth quarter. |