DATA ANALYTICS IN THE MARKET USA MARKETING COMPANY

INTRODUCTION

Market USA marketing company was founded in 2007 aimed at sales promotion. It has been the core provider of marketing means to different organizations. It has all through been growing its warehouse capability. The market USA personnel have been working with various local and international institutions to provide solutions based on products, concepts and customer service which enable their clients to major on the companies’ competence.

LITERATURE REVIEW

Marketing involves understanding the detailed cultural values of a particular region which impact the way of doing business. Thus for one to perform better in the environment he/she need to understand the characteristics which affect their relevant markets and customer actions. (Drews,R. & Lamson, 2016), in his book argues that in international business strategy is that sound decision making is the most important aspect a company needs to understand. Thus one needs to evaluate the probability of risk and success on entering the market.

Many European companies failed to penetrate into the USA market as a result of failing to put into consideration the cost of marketing and sales according to (padderatz, 2004). Further, he argues that in Europe these companies focus more on technical. In Europe, the emphasis is more on technical characteristics which does not apply in the USA since them they focus on presentation and glossy pictures. Thus making them non-competitive in the USA market hence there is a need to deeply understand the market. From the reviewed literature we realized that there many factors which affect the sales of products by a company which includes the cost of sales in terms training the employees and time spent selling and promoting the product.

PROBLEM STATEMENT

Market USA continues to emphasize on prospects for improvement in order to compete fairly with other companies in the market. The organization’s aim is to become the leading call center and have the highest number of sales. In an effort to achieve this, the company needed to check factors both influencing negatively and positively the numbers of sales they are making in a year thus or hindering the sales the company require an expert to analyze those factors affecting its sales.

HYPOTHESIS

To answer and solve the problem above the following hypotheses were tested;

1. Is there a positive relationship between sales, call made, type of training received, employee income, years’ of experience, employee’s earning, employee education level and age.
2. Does employees’ race affect sales made?
3. Is there a difference in the mean sales made by who received trained and those who did not

METHODS AND ANALYSIS

This study has employed secondary data source from the already collected data by Market USA. The dataset comprises 10 variables with 1000 respondents each. The variables include Gender, race, Sales made in the past one year, Calls which is the number of sales calls made in the past one year., Time average time spend by the employee making sales call, Years of experience of the employee and Type of training received by the employee prior joining the Market. The essence of using secondary data is because it is readily available and accurate since its recording occurs if an event takes place (Drews, 2016).The data is also free from heterosexuality effects since it is collected over a larger period of time.

Table 1: Summary statistics

Descriptive Statistics
Variable	N	Minimum	Maximum	Mean	Variance	Skewness		Kurtosis
	Statistic	Statistic	Statistic	Statistic	Statistic	Statistic	Std. Error	Statistic	Std. Error
Sales (Y)	100	20	67	42.04	58.847	-.001	.241	2.115	.478
Calls (X1)	100	120	210	161.81	369.004	.044	.241	-.469	.478
Time (X2)	100	8.0	20.0	15.253	5.967	-.129	.241	-.224	.478
Years (X3)	100	0	5	2.32	1.917	.313	.241	-.595	.478

From table 1, Market U.S.A marketing company has made an average sales of 42.04 in the past one year and these sales had a very large discrepancy which indicates that they recorded both low and high sales (58.847). they made on average 161.81 sale calls in the past one year and these sales call varied largely (369.004) showing that some of the calls made were too low while others were too high throughout this period. In addition, employees spend an average time of 15.253 minutes in making sales calls and there are low differences between the time taken to make this calls.

Figure 1: TYPE OF TRAINING

Half of the employees received online training (50%) prior joining the Market USA while almost a third of them received group type of training (29%) as shown in figure 1 above.

Table 2: T TEST

t-test for Equality of Means
t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
					Lower	Upper
-2.308	96	.023	-4.303	1.864	-8.003	-.603

Table 3: GROUP STATISTICS OF SALES

Group Statistics
	Type	N	Mean	Std. Deviation	Std. Error Mean
Sales (Y)	NONE	21	38.62	9.615	2.098
	ONLINE	77	42.92	6.935	.790

H0: the means sales of those who received training and those who did not are equal

H1: the means sales of those who received training and those who did not are not equal

Since p-value<α, 0.023<0.05 we reject the null hypothesis and conclude that the means sales of those who received training and those who did not are not equal that is are different which is shown by Table 3 above. Those who received training sold more than those who did not receive with 42.92 sales and 38.62 sales respectively. Therefore the training was necessary to be conducted to increase sales of the company.

Figure 2: HISTOGRAM OF SALES

From the above histogram, we can see that sales for the past one year are normally distributed hence it meet the assumption for fitting a linear regression model and thus we go ahead and fit a multiple linear regression model.

Table 4: ANOVA TABLE

Source of variation	Sum of Squares	df	Mean Square	F	Sig.
Regression	4824.913	3	1608.304	153.469	.000d
Residual	985.087	94	10.480
Total	5810.000	97
a. Dependent Variable: Sales (Y)
d. Predictors: (Constant), Type2, Time (X2), Years (X3) Table 5: Regression model Model Unstandardized Coefficients t Sig. B Std. Error (Constant) -6.962 3.381 -2.059 .042 Time (X2) 2.657 .310 8.581 .000 Years (X3) 1.567 .522 3.000 .003 Type2 .957 .564 1.695 .043

Fitting a regression model of the explanatory variables (years, time, age, gender, calls, type of training, race) versus the dependent variable sales and using backward elimination method in building the best model fit for the data. We found the best model having the above explanatory variables that are type of training, years of experience of the employee and average time spent by the employee making sales call as the only significant variables in our model. Thus indicating that the type of training an employee undergoes, the average time in minutes spent by an employee making sales call and years of experience of the employee affect the sales made in the past one year as shown in table 4.

Hence our regression model is Y=-6.962+2.657X2+1.567X₃+0.957X₄ where

Y represent sales made in the past one year

X₂ represents average time spent by the employee making sales call

X₃ represents years of experience of the employee

X₄ represents the type of training

From this we can see that for every unit increase in time spent by an employee results to 2.657 unit increase in sales, for a unit increase in number of years of experience there is a 1.567 increase in the number of sales and for any change in the type of training it results to 0.957 increase in the number of sales as shown in table 5.

CONCLUSION

From the analysis performed we found that sales have a positive relationship with the type of training, years of experience and average time spent in making calls by the employees. Thus the company should invest in training their employees and consider the experienced employees to make sales in order to realize more sales. In addition, the race of employees does not affect the number of sales he/she will make. Further, the company should consider training each of their employee on sales and marketing ethics for it to achieve the goal of being the leading marketing company in the USA.

References

Drews,R. & Lamson. (2016). Market entry into the USA. Springer International Publishing.

McClave, J.T. & Benson,P.G. (2014). the for Business and Economics.

padderatz, D. G. (2004). Mistakes european companies made in entering USA .