Table of Contents

Abstract .......................................................................................................................................... 2

Introduction ................................................................................................................................... 3

Variable Brief and Graphical interpretation ............................................................................. 5

Variable Brief ............................................................................................................................ 5

Graphical Interpretation .......................................................................................................... 5

Literature View ............................................................................................................................. 7

Statistical Methods ........................................................................................................................ 9

Results .......................................................................................................................................... 11

Conclusion ................................................................................................................................... 12

Reference ..................................................................................................................................... 13

Appendix ...................................................................................................................................... 16

Abstract

The study tries to examine the sales and advertisement expenses trend for selected firm named GFC, from a period of 2000 to 2011. It evaluates the effectiveness of advertisement expenses on sales. The study is based on pool secondary data. In this study of pool data fixed effect approach with original variables having dummy values are applied to calculate the effect of advertisement effect on sales. Future more summary statistics are also calculated. We calculated linear line Y=a+bX, coefficient of determination r2, Standard error of regression line and T-test. We also used two statistical software’s (E-views and SPSS) for the purposes of comparing our calculated results. The results which we calculated and the results which we calculated using the statistical software’s shows the same results.

The questions which are raised and causes for the studies, are answered and that the sales and advertisement expenses have the direct relationship. The relationship of the sales and advertisement shows that the advertisement causes to increase in the productivity of the firm in cases of the profitability. The advertisement also has a value for the firm, it also causes to increase the future sales of the firm.

Introduction

Companies are investing millions of rupees in the marketing which includes advertisement expanses. Its impact is very important for the firms. In the market the rule is famous, practitioners (the persons who are professional for research) and researchers (the persons who are professional but do research as second priority) are giving importance to the marketing activities like advertisement expenses on firm valuation. The profitability of the firm is rising because of the raising in sales. This is because of raise in the marketing which includes advertisement expanses.

Today the impacts of the public/ customers/ consumers are very infusing in making the decisions of the firms. They are very rational, they are spending their money. If someone is spending his money so he/ she wants to buy those products which is useable for long time, better quality and good styles form the other firms. The firm must provides true and fair view like if they ask in the advertising adds that we are providing lifetime grantee of any product and later they disagree to accept the grantee, so it will decrease the trust of the consumers of the firm and this impacts the firm’s sales downwards.

The questions arise that advertisement expense has value for the firm. With our little research we focus our study towards the end. Followings are some questions

� Do advertisement expenses value the firm? � Do advertisement expanses help to increase the sales of the firm? � Do advertisement expenses impact the profitability of the firm? � Does current advertisement expense increase future Sales?

The data on which we are applying our research is based on the Sales Revenue and advertisement Expenses of the GFC fans which lie near S.I.E G.T.Road, Gujrat, Pakistan. The data is personally taken by one of our member from the GFC fans assistant accountant Mr. Nouman Farooq. As we mentioned before that the data is based on the Sales Revenue and advertisement Expenses of the GFC fans, the time period of the data is from 2000 to 2011 of the financial years of the firm defined accounting periods.

On this subject there many work is performed in the past by many researchers and the research students. The purposes of both are very different but the majority of these people’s results are almost same. (Kaldor 1950; Comanor and Wilson 1967 and 1974; Becker and Murphy 1993) sports that the current advertisement expenses increase our future sales. According to these researches if we make our advertisement expenses so the people who are willing to purchase those products which the firm is producing, so through the advertisements the firm name must came in their mind. This also causes to increase the demand of the producing products. Which has led to claims that advertising may serve an important anti-competitive role.

As some researchers reject this research (Stigler 1961; Kihlstrom and Riordan 1984; and Milgrom and Roberts 1986) who says that under the “informative” view, advertising does not change customer’s utility functions.

The methodology which we are using is statistical tools, which includes the regression, Coefficient of determination (r2), Standard Error (S.E) and T-test. Using these tools we take conclusion of the selected questions. In addition to compare our results we used two statistical software’s named as SPSS and Eviews.

Variable Brief and Graphical interpretation

Variable Brief We selected two variables of a same firm, first is our sales revenue and second is our advertisement expenses. We conclude that the sales revenue is the dependent variable and the advertisement expenses are the independent variable, because the sales can be increased through increase in the advertisement expenses. According to our knowledge the relationship lies between these two variables are direct/ positive relationship because the increase in the independent variable causes to increase in the dependent variable.

The relationship line between these two variables must be

Sales Revenue = Function (Advertisement Expenses)

The dependent variable (Sales Revenue) is denoted as Y in this function and the independent variable (Advertisement Expenses) is denoted as X. so the function would be

Y = Function (X)

Graphical Interpretation All three graphs data is written in the Table 1 in Appendix.

Figure 1 Sales Revenue of GFC Fans

0

20000000

40000000

60000000

80000000

1E+09

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Sales Revenue of GFC

Sales

Revenue

Figure 2 Advertisement Expenses of GFC Fans

Figure 3 Sales Revenue and Advertisement Expenses of GFC

In figure 1 the sales revenue of the firm is shown year by year. It is easily identifiable that the sales of the firm are increasing rapidly.

In figure 2 the advertisement expenses of the firm is shown year by year. It is also easily shown able that advertisement expenses is also increasing.

In figure 3 the comparison of the both sales revenue and the advertisement expenses are shown. The sales and advertisement have shown the direct relationship, which means that the increase in the advertisement expenses cases to increase in the sales.

0

5000000

10000000

15000000

20000000

25000000

30000000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Advertisement Expenses of GFC

Advertisement

Expenses

0

5000000

10000000

15000000

20000000

25000000

30000000

0

10000000

20000000

30000000

40000000

50000000

60000000

70000000

80000000

90000000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Sales Revenue and Advertisement Expanses of

GFC Company

Sales Revenue

Advertisement

Expenses

Literature View

The history of sales is started when the first time two persons made any transactions with each other. The history of man is very long, so it is hard to describe when first time the people made exchanges of the goods/ transaction. In early times humans are very simple they made transactions with anyone when they demanded something from someone. Like a person who wants wheat, he went someone who has the wheat. The transaction dependents on that person, who has the wheat . If he wants that good which the person takes with him, so he carry the transaction on otherwise he has the authority to stop the transaction any time.

This whole process shows that there is no need of the advertisement for the goods. The person who needs must have the complete knowledge that the demanded thing is available to that particular person. Same this think continuously circled between the peoples in this world.

The concept of advertisement is not very old as the concept of sales. The advertisements started when the after the invention of the money, so the people have the authority to make transaction which anyone any time. They both are not required to agree upon the transferred goods. Now the peoples only required to go to any shop or store and order their demands.

First time the advertisements are used in 1800’s, the advertisements based on papers and the ads are based on the cartoon that lives in the caves or on the writing text. In the early 1900’s the advertisements which are famous are for the wars these are also based on the papers. Later after the half century the ads moves to printed media like newspapers, magazines etc and the print media as well like television etc, and now a day the advertisement modes are changed, they move towards internet and television etc.

Many researches occurs on that purpose, to the relationship between the sales and advertisement, Bass, F. M. and Parsons, L, J. (1969); G. E. P. and Jenkins, G.M. (1976); Cains, P. E., Sethi, S.P. and Brotherton, T. W. (1977); Clarke, D.G. (1976); Clarke. D.G. and McCann J, . M. (1977); Granger, C.W.J. and Newbold, P. (1974); Granger. C,W.J . and Newbold, P(1977); Haugh, L.D. (1976), Haugh, L.D. and Box, G. E. P. (1977); Helmer, R. M. and Johansson, J.K. (1977); Houston, F. S. and Weiss, D.L (1975); Melrose, K.B. (1969); Nelson, C. R. (1972); Newbold, P. (1978); Pack, D. J. (1977); Palda, K. S. (1964); Pierce, D.A. (1977); Pierce, D.A. , Haugh L.D. (1977); Prothero ,D.L and Wallis, K.F. (1976); Quenouille M.H. (1957); and Schwert ,W.G. (1977) and many others researches have performed the research on the same topic.

The economic effect of advertisement expenses has been a much debated topic and studied widely at different point of time. Verdon etal (1968) while studying the relationship between advertisement and aggregate demand found that advertisement have positive relationship with aggregate demand. However, Ekelund and Gramm (1969) analyzed the relationship between the advertiseing expenditure and aggregate consumption, but could not establish any positive relationship between these two. Similarly, Taylor and Weiserbs (1972) studied the relationship between advertising expenditure and aggregate consumption on the basis of Houtakker-Taylor model and showed that advertising affects aggregate consumption and the relationship between advertising and consumption is not found to be unidirectional but simultaneous. Jagpal (1981) while applying the multiproduct advertising sales model to a commercial bank found that radio

advertising was relatively ineffective in stimulating sales of the joint outputs (number of savings and checking accounts). Sachdeva (1988) studying the trends in advertisement expenditure of India’s large corporate bodies stated that foreign controlled companies single-handly accounted for a dominant share in advertisement expenditure. Consumer goods producing organisations controlled by foreign companies have emerged as one of the most important contributors to advertisement budgets of the corporate world. Another study by Leong et al (1996) using co integration technique found a strong positive relationship between advertising expenditure and sales. Similarly, Lee et al (1996) found that the variables of advertising and sales are not only integrated of same order but also co integrated. The results explicated that causal relationship between advertising expenses and sales works in both directions. Leach and Reekie (1996) analyses the effect of advertising on the market share of a brand using variants of the Koyack Distributed Lag model. Further, the results of the Granger causality test showed that advertising expenses caused sales but sales do not simultaneously cause advertising. Elliot (2001) revealed that advertising has a significant positive effect on food industry sales and this relationship between advertising expenditure and sales appears to be stable. Pagan et al (2001) studied the effectiveness of advertising on sales using vicariate Vector Auto Regression model and showed that one time increase in advertising expenditure leads to increase in the sales of orange with a one month lag. It was also found that the impact of advertising expenditure on grape fruit sales is more immediate and relatively large. While analyzing the relationship between a company’s advertising expenditure and its sales during the recession, Kamber (2002) found a measurable relationship between advertising expenditure and sales, even after controlling other factors, such as, company size and past sales growth, etc. Guo (2003) examined the relationship between advertising and consumption at macro level using the US data on advertising expenditure, personal consumption and disposable income. The study with the use of unit root tests and co integration analysis substantiated the existence of co integration among variables, which reveals the presence of long-term equilibrium relationship among them. Sundarsan (2007) evaluated the effectiveness of advertising on sales of small and large firms, and for multinational corporations. The results showed that advertising has influenced sales, though its relative effectiveness was not the same for all the categories of firms.

The above review divulges that there is no consensus on the economic effects of advertising expenses on sales revenue. Different studies have shown diverse results. However, in general, majority of the studies have directed positive relationship between the two. Most of the studies have used time series data to capture the long-term effects of advertising on sales. However, it is important to know effects of advertising expenses on sales revenue for corporate sector. Moreover, the area that to what extent advertising’s persuasive character work to alter consumers wants and consequently sales have received scant attention. With this backdrop, the present study has been designed to find out the extent to which advertisement expenses cause impact on sales revenue. More specifically the objectives of the study are to examine the growth pattern and trend of sales revenue and advertisement expenses for the selected company operating in Pakistan. Further, the present contribution aimed to evaluate the effectiveness of advertisement expenses on sales revenue for selected company at aggregate as well as disaggregate level. The present study will also try to analyses the behavior of share of advertisement expenses in total sales revenue for the GFC fans.

Statistical Methods The statistical methods which we use to find or conclude our results are as follows,

• Linear Regression • Co-efficient of Determination • Standard Error • Hypothesis Testing

Regression analysis is any statistical method where the mean of one or more random variables is predicted based on other measured random variables. There are two types of regression analysis, chosen according to whether the data approximate a straight line, when linear regression is used, or not, when non-linear regression is used.

While the linear relationship between dependent and independent variables are called linear or simple regression.

Sample Regression Equation= Y= a+bX

a = Y- intercept

b = Slope of a line

Slope of a line or b =

Y-intercept or a =

Where X and Y are the variables. X is Sales Revenue and Y Advertisement Expenses of the GFC fans. b = The slope of the regression line a = The intercept point of the regression line and the y axis n = Number of values or elements X = First Score Y = Second Score ΣXY = Sum of the product of first and Second Scores ΣX = Sum of First Scores ΣY = Sum of Second Scores ΣX2 = Sum of square First Scores

The second thing which we use is co-efficient of determination. A measure used in statistical model analysis to assess how well a model explains and predicts future outcomes. It is indicative of the level of explained variability in the model. The coefficient, also commonly known as r2, is used as a guideline to measure the accuracy of the model.

Co-efficient of Determination or r2 =

Where x =

y =

Or

Co-efficient of Determination or r2 =

In statistics the co-efficient of determination is use to content of statistical model, its purpose is prediction about future outcome on the basis given or related information. It is the proportion of variability in a set of data that is accounted for by statistical model. R-square provides information about goodness of fit model.

The model is highly acceptable if the answer of the co-efficient of determination is near to 1. In more explanation, if the answer of the co-efficient of determination is more than .5 so we says that the model we produces is acceptable and if the answer is less than .5 so the mode is not acceptable or adoptable.

Third thing which we use is Standard error. It is a statistical term that measures the accuracy with which a sample represents a population. The observed values of (X and Y) do not all fall on the regression line but they scatter away from it. The degree of the scatter or observed values about the regression line is measured by what is called the standard error of estimates of Y on X. For the population data, the standard deviation that measures the variation of observations about the true regression line which is is denoted as . For sample data we estimate by .

Where

So

Results

From E-views

Dependent Variable: SALES_REVENUE_Y_ Method: Least Squares Date: 06/25/12 Time: 12:29 Sample: 2000 2011 Included observations: 12 Variable Coefficient Std. Error t-Statistic Prob. ADVERTISEMENT_EX 35.18012 7.094777 4.958594 0.0006 C -92976654 1.38E+08 -0.674939 0.5150 R-squared 0.710879 Mean dependent var 5.78E+08 Adjusted R-squared 0.681967 S.D. dependent var 1.61E+08 S.E. of regression 90660503 Akaike info criterion 39.63415 Sum squared resid 8.22E+16 Schwarz criterion 39.71497 Log likelihood -235.8049 F-statistic 24.58765 Durbin-Watson stat 1.697450 Prob(F-statistic) 0.000571 LS SALES_REVENUE_Y_ ADVERTISEMENT_EX C Estimation Equation: ===================== SALES_REVENUE_Y_ = C (1)*ADVERTISEMENT_EX + C (2) Substituted Coefficients: ===================== SALES_REVENUE_Y_ = 35.18011868*ADVERTISEMENT_EX - 92976653.61 From SPSS

Conclusion

The regression line / equation which we calculated from the data is Y =a+bX the value of a is - 92976653.61 and the b is 35.18011868.

A is 92976653.61 a negative intercept of simple linear regression line. It represents the position of the curve. We can get it by putting in simple regression line.

b is 35.18011868 is the positive slope of the simple regression line. It is derivative of Y with respect to X. then numerical value shows that if there is one unit change in X therefore it will be 35.18011868 unit changes in Y in the same direction.

The r2 is 0.710879. r2 shows that how good is the fit of the line to the sample observation. It is more than 0.5 so it shows that the line which we fit is good and our result is correct. It also shows that that % change in the variation of the dependent variable with respect to independent variable. So, it shows that the approximated 71% change in the dependent variable depends on independent variable.

So we conclude that the sales revenue of the GFC firm have relationship with the advertisement expense. The relationship which exists is direct relationship between these two variables and the advertisement expenses causes to increase in the sales of the same year.

Using the above conclusion, we can also answer the questions which cause and effect of the questions stated above. The advertisement has value for the firm because it causes to increase the sales. It the sales of the firm increases the profitability of the firm. So, advertisement expenses impact the profitability of the firm.

Does current advertisement expense increase future Sales? Using the above conclusion we only able that to answer that the advertisement has a little effect to increase the sales of the firm. For further results we have to conduct a separate research.

Reference

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Appendix

Let the dependent variable (Sales Revenue) is denoted as Y in this function and the independent variable (Advertisement Expenses) is denoted as X. so the function would be

Table 1 Years Sales Revenue Advertisement Expenses

2000 385072168 12412390

2001 397384479 12612390

2002 417384479 17612390

2003 467384479 18362390

2004 487384479 18612390

2005 527384479 18587390

2006 577384479 19087390

2007 597384479 19337390

2008 647384479 23837390

2009 805076181 20127810

2010 810076181 23957390

2011 812576181 24207390

.

Table 2 Years X Y XY X2 Y2 2000 12412390 385072168 4779665927361520 154067425512100 148280574568220000 2001 12612390 397384479 5011968029094810 159072381512100 157914424150101000 2002 17612390 417384479 7351138224094810 310196281512100 174209803310101000 2003 18362390 467384479 8582296083344810 337177366512100 218448251210101000 2004 18612390 487384479 9071390003094810 346421061512100 237543630370101000 2005 18587390 527384479 9802700991119810 345491067012100 278134388690101000 2006 19087390 577384479 11020762730619800 364328457012100 333372836590101000 2007 19337390 597384479 11551856650369800 373934652012100 356868215750101000 2008 23837390 647384479 15431956305869800 568221162012100 419106663650101000 2009 20127810 805076181 16204420406693600 405128735396100 648147657213545000 2010 23957390 810076181 19407310997927600 573956535612100 656223419023545000 2011 24207390 812576181 19670348518177600 585997730612100 660280049928545000 ∑ 228754100 6931876543 137885814867769000 4523992856229200 4288529914454670000