As pointed out before, Joe Schmoe was content with his initial evaluation of the three products and as such, he did not make any effort to monitor the external environment in order to adjust the company’s marketing strategy accordingly. Instead of maintaining the project in an “autopilot” mode, I monitored the environment very closely and used the information gathered from both the internal and external environments to support my marketing strategies. I also liaised with other employees within the organization to ensure that they were cognizant of the strategic vision of the production and marketing departments. This not only facilitated efficiency and effectiveness within these two departments but also other parts of the organization.
Marketing and Production Strategies
X5 tablet is designed for consumers who are significantly price conscious as opposed to seeking functionality. In the last half a decade or so, this product has performed well particularly in the face of competing products by rival firms. In light of this, I reduced the research and development budget for this product to 10% and lowered the price slightly to $280 to cement the brands position in the market while at the same time maximizing the overall profitability of the company. On the other end of the spectrum, X6 tablet targets consumers who are have an inclination towards functionality and performance as opposed to price. As such, I increased the research and development budget for this tablet to 60% and increased its price to $525. Finally, despite the fact that X7 tablet is a relatively novel product to be introduced in the markets it is yet to perform well. The company can however not afford to discontinue the line because it is the most affordable brand for the company to produce. I set the research and development budget for X7 at an estimated US$ 7,200,000 (30%) and ensured that the product was sold to consumers at $80. Because consumers were sensitive to both price and performance, I decided to set the price for X7 lower than X5 to create the illusion that it was more economical despite the fact that it was cheaper to produce. This would however also create an impression that it was of less superior quality. Other than that the market saturation for X7 was only 2% which means that the company almost enjoyed monopoly power. Charging a low price would ensure that the company gained significant brand loyalty in the short run, which would prove to be invaluable in the long run.
In 2012, Revenue for the three brands increased significantly but the profitability of X7 declined slightly. Nevertheless, the overall profitability of the firm increased by 11% from US$ 81,571,138 (16%) to $325,720,907 (27%). The total profitability of X5 and X6 increased by 13% and 18% respectively while that of X7 declined slightly by 12%. On the other hand, despite its good performance, X6 still cost less than most other products in its category. Moreover, the fact that the tablet was still in the growth phase of its product lifecycle had a significant influence on its general performance. The performance of X5 also compared favorably with other tablets in its category. The slight decline in the profitability of X7 can be attributed to the fact that it was still new in the market. From the foregoing, I decided to raise the price for X6 from $525 to $550. Because X5 was performing very well in the market, I decided to increase the research and development budget to 20% and reduce the price slightly to $275 in an attempt boost sales and enhance brand loyalty. I also reduced the research and development budget for X7 to 20% and increased the price to $100. Having successfully introduced the brand to the market. This would enable the company to enhance its overall profitability.
Table 1: Pricing Strategy
|X5||$ 285.00||$ 250.00||$ 275.00||$ 250.00||$ 250.00|
|X6||$ 430.00||$ 525.00||$ 550.00||$ 525.00||$ 525.00|
|X7||–||$ 80.00||$ 100.00||$ 100.00||$ 100.00|
Table 2: Research and Development Activities
2013 was characterized by a 9% growth in the overall profitability form 3$25,720,907 (27%) to $596,324,580 (36%). This was due to an increase in the total profitability for the three tablets, from 29% to 33% for X5, from 34% to 42% for X6 and from -12% to 34% for X7. Nevertheless, due to an increase in market concentration in the market for X6, other competitors were offering lower prices for similar tablets. This prompted the company to reduce the price back to $525. X5 had reached a stakeout phase in its product lifecycle, which had resulted in a sales decline for the product. As such, I reduced the price for the tablet and despite the fact that customers were only sensitive to prices as opposed to functionality. I increased the research and development budget for the tablet not only to enhance its functionality but also to explore cheaper production techniques. Finally, because the performance of the X7 tablet was below that of other competitors in the market, I increased its research and development budget to 30% primarily to enhance its quality but maintained the price at $100.
In 2014 the company’s overall profitability only increased slightly from 36% to 39%. This was occasioned by the sharp decline in the total profitability of the X5 tablet. Nevertheless, the profitability of the X6 and X7 tablet increased to 43% and 41% respectively. I increased he research and development budget for X5 to 50% at the expense of the X6 tablet and maintained its price at 250 in a bid to boost its sales but apparently this was the wrong approach because the sales only worsened in 2015 resulting in a loss of -24%. I should have concentrated on lowering the price instead of trying to enhance it functionality. Additionally, efforts to minimize production cost through innovation, which would have been achieved through research and development were also futile. Despite reducing its research and development expenditure in 2015, the profitability of the X6 tablet remained at 43%. Unfortunately, the profitability of the X7 tablet also declined to 16% in 2015 but this was because I practically did nothing to promote its sales volume. I retained both the price and research and development expenditure for 2015.
Perhaps the worst mistake I made was to increase the research and development expenditure for the X5 tablet instead of focusing on reducing the price because the target market was more sensitive to prices compared to functionality. Notwithstanding, the company outperformed the best competitor in the market. While the company generated profits amounting to $2,361,970,075 over the five year period, the competitor only generated $2,100,000,000. The income statements for the company are illustrated below.
Table 3: Comprehensive Income Statement
Table 4: X5 Income Statement
|Total Profit||43,991,297||209,284,947||207,937,240||18,211,378||– 32,225,000|
Table 5: X6 Income Statement
Table 6: X7 Income Statement
|Total Profit||–||– 12,045,725||126,126,922||522,989,455||25,338,171|
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The main aim of this report is to evaluate the relationship between Credit Score and other factors that influence loan approval including years of credit history, revolving balance, revolving utilization and home ownership status. The report identifies the natural dependent variable to be predicted, which is the credit score and another variable, the credit approval status, which can also act as the dependent variable in another setting. The report further identifies the type of relationship and the type of model that best suits the data. Additionally, it evaluates the need for interaction terms and determines whether a categorical approach would be useful. Other than that, multicollinearity and residual analysis are carried to determine the reliability of the findings and finally a variable selection procedure is specified in order to produce the best model to analyse the data.
The data relates to credit approval decisions, particularly whether or not a client is approved for credit depending on their credit score, years of credit history, revolving balance, revolving utilization and home ownership status. The natural dependent variable to be predicted is the credit score, which is dependent on the years of credit, the revolving balance and the revolving utilization. Apart from the credit score, the approval decision can also be used as a categorical dependant variable. It should however be noted that the ordinary least squares method cannot suffice to produce a good linear unbiased estimator and as such, a linear probability model would have to be adopted. However, in this case, the regression line will not be a good fit for the data, which implies that usual measures such as the coefficient of determination () are more often than not unreliable. Moreover, LPM models are also characterised by heteroskedasticity and most likely produce estimates that are greater than 1, which makes them difficult to interpret because the estimates are probabilities, which should not be greater than one. The error term in such models is also likely to be non-normal, because they follow abnormal distributions. Finally, the relationship between the variables is also likely to be non-linear, which suggests that a different type of regression line would be required to fit the data more accurately, for instance an ‘S’ shaped curve.
The relationship between the credit score and the other variable is linear as illustrated using the excel output below. A first order model is appropriate for model, which implies that the independent variables are only included in the first power.
Table 1: SUMMARY OUTPUT
|Adjusted R Square||0.642769|
Table 2: ANOVA
Table 3: COEFFICIENTS
|Coefficients||Standard Error||t Stat||P-value||Lower 95%||Upper 95%|
|Years of Credit History||-2.09848||1.490347||-1.40805||0.165839||-5.09839||0.901431|
From the foregoing, the simple linear regression equation can be expressed as follows
It should however be noted that the coefficients for the years of credit history and the revolving balance are not statistically significant. This is because the P-Values for these variables are 0.165839 and 0.084266 respectively, which are both greater than the significance level (0.05). Consequently, these coefficients cannot be used to make inferences about the relationship between the credit score, the years of credit history and the revolving balance. This is reiterated by the line fit plots illustrated in Appendix 1. Notwithstanding, the coefficients for the intercept and the revolving utilization are statistically significant, which implies that it is only safe to conclude that there is an inverse relationship between the credit score and the revolving utilization. Moreover, the significance F is also greater than the significance level, which suggests that the results of the regression are also statistically significant at the 95% confidence level. The coefficient of determination suggests that 66.46% of the changes in the credit score are influenced by the three independent variables while the remaining 33.54% is explained by other factors.
Even though some coefficients cannot be used to forecast the credit score, interaction terms are not needed in this regression because it only involves continuous variables. Multicollinearity is not problematic because the highest correlation between the independent variables is -0.49, which exists between revolving utilization and years of credit history as illustrated in the table below
Table 4: Correlation Analysis
|Credit Score||Years of Credit History||Revolving Balance||Revolving Utilization|
|Years of Credit History||0.311459966||1|
The residual plots in appendix 1reveal that all the independent variables are normally distributed. Moreover, the graph below (Squared Residuals against Revolving Utilization) illustrates that there is no heteroskedasticity and finally, the Durbin-Watson statistic is 1.376, which is close to 2 and as such, autocorrelation is not a problem.
The most appropriate model for forecasting the credit score can be derived through a principal components analysis. In this analysis, all the variables with regression coefficients that are not statistically significant are omitted from the regression model. In light of this the most appropriate model will only include the revolving utilization and the intercept. Consequently, the simple regression equation should be obtained from a regression including only these two variables as illustrated below.
Table 5: SUMMARY OUTPUT
|Adjusted R Square||0.626666|
Table 6: ANOVA
Table 7: COEFFICIENTS
|Coefficients||Standard Error||t Stat||P-value||Lower 95%||Upper 95%|
Using the regression results illustrated above, the simple regression line that would serve as the most reliable predictor for credit score is:
The relationship between the credit score, years of credit and revolving balance is not statistically significant and as such the regression should only be conducted with the revolving balance. From the second regression, it is evident that there is an inverse relationship between credit score and revolving utilization. Specifically, a unit change in revolving utilization results in a decline in the credit score by 220.73.
Montgomery, D. C., Peck, E. A. & Vining, G. G., 2011. ntroduction to linear regression analysis. 5 ed. Oxford: Wiley-Blackwell.
Appendix 1: Residual Plots
Appendix 2: Line Fit Plots