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\title{Is Advertisement a Valid Tool to Increase Sales: A Study of Indian Manufacturing Companies}
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             \author[1]{Dr.  Sanchita}

             \affil[1]{  GGSIP University}

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\date{\small \em Received: 14 December 2015 Accepted: 1 January 2016 Published: 15 January 2016}

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\begin{abstract}
        


The paper studies the inter-relationship between advertisement expenditure, sales and profit. Taking ten-year data (2005-06 to 2014-15) of twenty manufacturing companies indexed in NSE?s NIFTY, the study applied various models including descriptive study, correlation and regression. The tools used (Regression and Correlation) clearly show that there is a significant relationship between advertisement expenditure, sales and profit. The study concludes that there is a one-sided relationship between advertisements, sales and profit wherein advertisement expenditure positively impacts the sales and profit of the business in case of Indian manufacturing companies.

\end{abstract}


\keywords{relationship between advertisement expenditure, sales and profit.}

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\let\tabcellsep& 	 	 		 
\section[{I. Introduction}]{I. Introduction}\par
he ongoing debate over the competitive effects of advertising is implicitly contesting the issue of economic durability of advertising expenditure (e.g. \hyperref[b3]{Ayanian 1975}; Comanor and Wilson 1974; Telser 1968, etc. Advertising plays multiple roles in that it is not only used by companies to create awareness among customers for their products and services, but also acts as a tool to build a strong brand image by dramatizing and presenting their products and services in such a way so as to attract customers' attention. The power of advertising in building strong brands has been proposed by both marketing practitioners (e.g. Martin 1989) and academics (e.g.  {\ref Aaker 1991} {\ref Aaker , 1996))}.\par
Though advertising is one of the most potent and effective marketing tools available to marketers for informing and persuading buyers, the efficiency and effectiveness of advertisement spending is of considerable interest both to academicians and practitioners  {\ref (Xueming and Donthu, 2002)}.\par
In general, sales or market response research has made it more difficult to answer a long-standing question: "Is advertising an investment or an expense?" (Mergy and Lade 2001). Many academic researchers have argued that advertising should be treated as an investment because of its role in improving the longterm market performance of a firm (Chauvin and Hirschey 1993; Dean 1966; Dekimpe and Hanssens 1995; Graham and Frankenberger 2000; Hirschey and Weygandt 1985; Hula 1988).\par
Further, the firms that allocate large amounts of their resources to value advertising expect their expenditures to contribute, ultimately, to the financial performance of the firm. Several studies have focused on the relationship between advertising expenditures and financial performance measures such as stock returns and ROI on advertising, while mainstream advertising effectiveness research in marketing has probed the relationship between advertising and market performance measures in relatively shorter time periods (Hanssens, Parsons, and Shultz, 1990).\par
There is a strong reason behind companies adopting advertising expenditures to escalate their sales and market share assuming a direct relationship between the two. Companies with a higher amount of sales revenue can afford to spend more on advertisements when compared to the ones with lower sales revenue. Therefore, it can be assumed that the businesses with higher sales in period 1 lead to higher advertising spending in period 2. While some of the researches reveal the presence of long-term equilibrium relationship between advertising and consumption  {\ref (Guo,2003and Phillip, 2007)}, some others view that advertising expenditure causes sales but sales do not simultaneously cause advertising \hyperref[b12]{(Leach and Reekie, 1996)}.\par
The present study attempts to establish the linkages between advertisement spending, sales and profit in the case of Indian manufacturing companies.\par
The study is organized as follows. The present section introduces the concept of the study and outlines the need for it; the second section presents the objectives of the study; the third section reviews the literature available; the fourth section describes the methodology for the research; the fifth section presents the results of the study and the sixth section concludes. 
\section[{II. Objectives of the Study}]{II. Objectives of the Study}\par
The study aims to achieve the following objectives: a. To understand the change patterns in the advertisement, sales and profit in Indian manufacturing companies; b. To study the inter-relationship between advertisement, sales and profit; c. To draw policy implications for marketers as to whether increase in advertisements leads to increase in sales. 
\section[{III. Review of Literature}]{III. Review of Literature}\par
In the past, researchers have attempted to explain some of the confusion regarding the impact and effectiveness of marketing communications, most often focusing on advertising and promotional expenditures. Farris and  {\ref Buzzell (1979)} explained in their study how and why differences in marketing communication intensity (as measured by advertising and promotion expenditures to sales) were related to some basic variables. Therefore, an attempt was made to identify the factors that empirically explain the variations in advertising and promotion to sales. Their study indicated that advertising and promotional expenditures expressed as a proportion of sales vary across industries, across firms within an industry and across time for a given firm.\par
Balasubramanian and Kumar (1990)also confirmed the same finding. Zinkhan and Cheng (1992) again used the ratio of advertising and promotional expenditures to sales as a proxy for marketing communication intensity. They investigated the variation of communication intensity due to the type of offering (productor service) and the type of market (consumer or manufacturing). They found that, both, the type of offering and the type of market affect the variation of communication intensity. Their results indicated that consumer product firms spend more on advertising than manufacturing product firms.\par
Simultaneously though, under pressure to produce immediate profits, managers still tend to view advertising as an expense and reduce advertising budgets in times of downturn, even though they recognize that advertising can be treated as an investment  {\ref (Dean 1966;} {\ref Hirschey and Weygandt 1985)}.\par
Even though this research stream has shed some light on how advertising works or should work, its contributions to our understanding of the role of advertising in a competitive, complicated, and everchanging market environment have been limited. For example, a group of marketing researchers in this area (Bass and Leone 1983; Clarke 1976; Srinivasan and Weir 1988) who employed market-level data to explore the long-termor carryover effects of advertising found that the duration of advertising effects depended on the data interval under study. Clarke (1976) and Assmus, Farley, and Lehmann (1984) suggested that 90 percent of advertising effects dissipate after three to fifteen months. Leone (1995) argued that the range of advertising effects should be2narrowed to six to nine months based on his study. However, Dekimpe and Hanssens (1995) concluded that the effects of advertising did not disperse within a year. These contradictory findings could be partially attributed to the different sources of data used in the studies (Vakratsas and Ambler 1999).\par
Empirical researches used different tools to analyze the data about relationship between advertising and sales. \hyperref[b9]{Guo(2003)} and \hyperref[b14]{Leong et al. (1996)} applied the cointegration to analyse and evaluate the data. \hyperref[b22]{Taylor and Weiserbs (1972)}put to use the Houtakker-Taylor model in their research for evaluation purpose. \hyperref[b12]{Leach and Reekie (1996)}   \hyperref[b9]{Guo (2003)} implemented the unit root test for evaluation. \hyperref[b15]{Metwally (1997)} implemented the correlation test for the evaluation of the data. \hyperref[b21]{Telser (1964)}, \hyperref[b18]{Rundfelt (1973)} utilized the correlation test to examine the data.\par
Leach and Reekie (1996) concluded that advertising expenditure causes sales but sales do not simultaneously cause advertising. Another point to note is that marketing is defined widely in the literature. As outlined by Webster (1992) there are four different aspects of marketing practice:\par
(1) transactional marketing involves managing the marketing mix to attract and satisfy customers; (2) database marketing uses technology to target and retain customers; (3) interaction marketing involves developing interpersonal relationships between buyers and sellers; and (4) network marketing develops interfirm relationships for mutual benefit. This thesis specially focuses on the relationship between MC (which comes under transactional marketing) and shareholder value. 
\section[{IV. Research Methodology}]{IV. Research Methodology}\par
In the present study the inter-relationship between advertisement, sales and profit has been studied. The study focuses on the manufacturing sector. The impact of advertisement on sales can be calculated for such companies because unlike the services sector, the sales in units are available for manufacturing companies. Hence, in order to establish the relationship between advertisement and sales, the study selects the sample from manufacturing companies. The paper draws its sample from the NSE's NIFTY index. Twenty manufacturing companies indexed in NIFTY are used as sample for the study. These include Tata Motors, Maruti Suzuki, Reliance, ONGC, Hindustan Uniliver, ITC, Cipla, Sunpharma, Mahindra \& Mahindra, Hero Motors, Dr. Reddy, Tata Steel, BHEL, NHPC, Coal India, Lupin, Gail, Bajaj, Asian paints and L\&T. These companies are among the most renowned in their respective industries.\par
The sample period for the study is ten years ranging from 2005-06 to 2014-15.To analyse the cause and effect relationship between sales and advertisement, the ten years data of profit, net sales and selling expenses are taken. The study uses descriptive statistics, correlation and regression for analysing the data.\par
Following tools are used for data analysis.\par
The mean is a particularly informative measure of the "central tendency" of the variable if it is reported along with its confidence intervals. 
\section[{Mean}]{Mean}i X n = ? (1.1)\par
Usually we are interested in statistics (such as the mean) from our sample only to the extent to which they can infer information about the population. The confidence intervals for the mean give us a range of values around the mean where we expect the "true" (population) mean is located (with a given level of certainty). s = ( )2 i x N µ ? ? (1.2)\par
where µ is the population mean and N is the population sizes = [S (x i -m) 2 /N] 1/2 (0.1)\par
The sample estimate of the population standard deviation is computed as: A line in a two-dimensional or two-variable space is defined by the equation Y=a+bX; in full text, the Y variable can be expressed in terms of a constant (a) and a slope (b) times the X variable. The constant is also referred to as the intercept, and the slope as the regression coefficient or B coefficient. Multiple regression procedures will estimate a linear equation of the form: Y=a+b 1 X 1 +b 2 X 2 +...+b p X p  {\ref (1.6)} The regression line expresses the best prediction of the dependent variable (Y), given the independent variables (X). However, nature is rarely (if ever) perfectly predictable, and usually there is substantial variation of the observed points around the fitted regression line.s = ( ) 2\textbf{( 1)} 
\section[{V. Findings and Discussion}]{V. Findings and Discussion}\par
The paper presents the analysis in three parts as discussed in the methodology section above. These include descriptive statistics, correlation and regression. The descriptive statistics present an insight into the variables of advertisement expenses, sales, and profits of the twenty companies under reference. Correlation presents the coefficient of correlation between sales-advertisement expenses, sales-profit and advertisement expenses-profit. The regression part is further divided into two sub-parts. One, sales are regressed by taking advertisement expenses as the independent variable. Two, profit is regressed by taking advertisement expenses and sales as two independent variables. \begin{figure}[htbp]
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\noindent\textbf{1} \par 
\begin{longtable}{P{0.04951456310679612\textwidth}P{0.13980582524271845\textwidth}P{0.14368932038834953\textwidth}P{0.14368932038834953\textwidth}P{0.19854368932038835\textwidth}P{0.17475728155339806\textwidth}}
Name of\tabcellsep \tabcellsep \tabcellsep \tabcellsep Standard\tabcellsep \\
Company\tabcellsep \tabcellsep Mean\tabcellsep Skewness\tabcellsep Deviation\tabcellsep Variance\\
\tabcellsep Sales\tabcellsep 30516.327\tabcellsep 0.040349\tabcellsep 12254.91892\tabcellsep 150183038\\
\tabcellsep Advertisement\tabcellsep 3780.125\tabcellsep 0.036389\tabcellsep 1917.558959\tabcellsep 3677032.36\\
Maruti\tabcellsep Profit\tabcellsep 2101.73\tabcellsep 0.802606\tabcellsep 787.6397806\tabcellsep 620376.424\\
\tabcellsep Sales\tabcellsep 233563.2\tabcellsep 0.118975\tabcellsep 112195.9084\tabcellsep 1.2588E+10\\
\tabcellsep Advertisement\tabcellsep 17525.4\tabcellsep 0.481937\tabcellsep 6449.962088\tabcellsep 41602010.9\\
Reliance\tabcellsep Profit\tabcellsep 17734\tabcellsep -0.96721\tabcellsep 4675.224487\tabcellsep 21857724\\
\tabcellsep Sales\tabcellsep 2920.756\tabcellsep 2.571337\tabcellsep 1795.802264\tabcellsep 3224905.77\\
\tabcellsep Advertisement\tabcellsep 1146.736\tabcellsep 1.969903\tabcellsep 938.3537108\tabcellsep 880507.687\\
Sun Pharma\tabcellsep Profit\tabcellsep 692.324\tabcellsep -1.44859\tabcellsep 907.6478216\tabcellsep 823824.568\\
\tabcellsep Sales\tabcellsep 36185.68\tabcellsep 0.640678\tabcellsep 9766.769921\tabcellsep 95389794.7\\
\tabcellsep Advertisement\tabcellsep 7053.497\tabcellsep -0.51568\tabcellsep 2331.922444\tabcellsep 5437862.29\\
Tata Motors\tabcellsep Profit\tabcellsep 766.403\tabcellsep -2.56461\tabcellsep 2047.994442\tabcellsep 4194281.23\\
\tabcellsep Sales\tabcellsep 28905.672\tabcellsep 0.239514\tabcellsep 9145.744174\tabcellsep 83644636.5\\
\tabcellsep Advertisement\tabcellsep 8575.945\tabcellsep 0.678051\tabcellsep 2833.699002\tabcellsep 8029850.03\\
Tata steel\tabcellsep Profit\tabcellsep 5414.049\tabcellsep -0.17665\tabcellsep 1139.706456\tabcellsep 1298930.81\\
\tabcellsep Sales\tabcellsep 338.489\tabcellsep 0.125146\tabcellsep 60.71547752\tabcellsep 3686.36921\\
\tabcellsep Advertisement\tabcellsep 212.934\tabcellsep 1.217516\tabcellsep 71.58499054\tabcellsep 5124.41087\\
Coal India\tabcellsep Profit\tabcellsep 9139.725\tabcellsep 1.629076\tabcellsep 7993.903869\tabcellsep 63902499.1\\
\tabcellsep Sales\tabcellsep 33146.544\tabcellsep 0.438787\tabcellsep 16336.15016\tabcellsep 266869802\\
\tabcellsep Advertisement\tabcellsep 8863.257\tabcellsep 0.438123\tabcellsep 5642.241033\tabcellsep 31834883.9\\
Gail\tabcellsep Profit\tabcellsep 3155.343\tabcellsep 0.157822\tabcellsep 748.3660618\tabcellsep 560051.762\\
\tabcellsep Sales\tabcellsep 68072.225\tabcellsep -0.04023\tabcellsep 12126.31876\tabcellsep 147047607\\
\tabcellsep Advertisement\tabcellsep 32979.851\tabcellsep 0.366099\tabcellsep 10616.018\tabcellsep 112699838\\
ONGC\tabcellsep Profit\tabcellsep 18417.509\tabcellsep 0.966152\tabcellsep 3345.946115\tabcellsep 11195355.4\\
\tabcellsep Sales\tabcellsep 54067.20556\tabcellsep -0.13621\tabcellsep 16302.6911\tabcellsep 265777737\\
\tabcellsep Advertisement\tabcellsep 2988.274444\tabcellsep 0.527008\tabcellsep 1166.507658\tabcellsep 1360740.12\\
NTPC\tabcellsep Profit\tabcellsep 9268.923333\tabcellsep 0.325871\tabcellsep 2015.608234\tabcellsep 4062676.55\\
\tabcellsep Sales\tabcellsep 6290.57\tabcellsep 0.366324\tabcellsep 3234.685093\tabcellsep 10463187.7\\
\tabcellsep Advertisement\tabcellsep 1329.772\tabcellsep 0.627092\tabcellsep 705.1993503\tabcellsep 497306.124\\
Asian Paints\tabcellsep Profit\tabcellsep 725.204\tabcellsep 0.028536\tabcellsep 404.5734782\tabcellsep 163679.699\\
\tabcellsep Sales\tabcellsep 32156.539\tabcellsep -0.07573\tabcellsep 11614.9505\tabcellsep 134907075\\
\tabcellsep Advertisement\tabcellsep 3907.817\tabcellsep -0.02245\tabcellsep 1865.624005\tabcellsep 3480552.93\\
Bhel\tabcellsep Profit\tabcellsep 3894.801\tabcellsep 0.497653\tabcellsep 2027.669929\tabcellsep 4111445.34\\
\tabcellsep Sales\tabcellsep 6156.864\tabcellsep 0.284038\tabcellsep 2232.297961\tabcellsep 4983154.18\\
\tabcellsep Advertisement\tabcellsep 1768.381\tabcellsep -0.47628\tabcellsep 625.6184556\tabcellsep 391398.452\\
Cipla\tabcellsep Profit\tabcellsep 998.588\tabcellsep 0.284401\tabcellsep 312.0602748\tabcellsep 97381.6151\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_2}Table 1}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.11632970451010885\textwidth}P{0.17185069984447898\textwidth}P{0.08063763608087092\textwidth}P{0.15202177293934682\textwidth}P{0.08195956454121306\textwidth}P{0.16524105754276827\textwidth}P{0.08195956454121306\textwidth}}
\tabcellsep \multicolumn{2}{l}{Sales-Advertisement}\tabcellsep Sales-Profit\tabcellsep \tabcellsep \multicolumn{2}{l}{Advertisement Expenses-}\\
\tabcellsep \multicolumn{2}{l}{Expenses}\tabcellsep \tabcellsep \tabcellsep Profit\tabcellsep \\
\tabcellsep Coefficient of\tabcellsep Sig\tabcellsep Coefficient of\tabcellsep Sig (2-\tabcellsep Coefficient of\tabcellsep Sig (2-\\
\tabcellsep Correlation\tabcellsep (2-tailed)\tabcellsep Correlation\tabcellsep tailed)\tabcellsep Correlation\tabcellsep tailed)\\
Maruti\tabcellsep .969 **\tabcellsep .000\tabcellsep .856 **\tabcellsep .002\tabcellsep .841 **\tabcellsep .002\\
Reliance\tabcellsep .884 **\tabcellsep .001\tabcellsep .844 **\tabcellsep .002\tabcellsep .877 **\tabcellsep .001\\
Sun Pharma\tabcellsep .965 **\tabcellsep .000\tabcellsep -.744 *\tabcellsep .014\tabcellsep -.653 *\tabcellsep .041\\
Tata Motors\tabcellsep .586\tabcellsep .075\tabcellsep -.042\tabcellsep .908\tabcellsep -.245\tabcellsep .494\\
Tata steel\tabcellsep .977 **\tabcellsep .000\tabcellsep .755 *\tabcellsep .012\tabcellsep .658 *\tabcellsep .039\\
Coal India\tabcellsep .241\tabcellsep .502\tabcellsep -.205\tabcellsep .569\tabcellsep .235\tabcellsep .513\\
GAIL\tabcellsep -.666 *\tabcellsep .036\tabcellsep .807 **\tabcellsep .005\tabcellsep -.622\tabcellsep .055\\
ONGC\tabcellsep .960 **\tabcellsep .000\tabcellsep .754 *\tabcellsep .012\tabcellsep .645 *\tabcellsep .044\\
NTPC\tabcellsep .891 **\tabcellsep .001\tabcellsep .900 **\tabcellsep .000\tabcellsep .821 **\tabcellsep .004\\
Asian Paints\tabcellsep .993 **\tabcellsep .000\tabcellsep .979 **\tabcellsep .000\tabcellsep .966 **\tabcellsep .000\\
Bhel\tabcellsep .850 **\tabcellsep .002\tabcellsep .878 **\tabcellsep .001\tabcellsep .563\tabcellsep .090\\
Cipla\tabcellsep .868 **\tabcellsep .001\tabcellsep .887 **\tabcellsep .001\tabcellsep .728 *\tabcellsep .017\end{longtable} \par
 
\caption{\label{tab_3}Table 2 :}\end{figure}
 			\footnote{© 2016 Global Journals Inc. (US)} 		 		\backmatter  			 \par
In table 2, the cases where correlation is significant are marked with **. The table shows that the correlation between sales and advertisement expenses is significant (at 95\% level of confidence) in the case of all companies except for Tata Motors and Coal India. Correlation between sales and advertisement expenses is positive in most of the cases with the exception of GAIL where coefficient of correlation is observed to be -0.666. Correlation between sales and profit is also significant in all the companies except for Tata Motors and Coal India. The correlation between sales and profit is positive in most of the cases with the exception of Sunpharma, Tata Motors, Coal India where coefficient of correlation is observed to be -.744, -.042, -.205. Further, correlation between advertisement expenses and profit is not significant in the case of Tata Motors, Coal India and BHEL, while it is significant in all other cases. The correlation is observed to be positive in most of the cases with the exception of Sunpharma, Tata Motors, GAIL where coefficient of correlation is observed to be -.653, -.245, -.622. Since the correlation between the variables under reference is observed to be significant as well as positive in most of the companies, it makes a case for building a regression model between the variables. The table exhibits that the coefficient of determination in case of all companies except Coal India and Tata Motors is close to 1. This implies that the model of regressing sales on advertisement expenses is a suitable one. This point is also justified by the significance value, which is observed to be less than 0.05 in all the companies except Tata Motors and Coal India. The table also presents the beta values on the basis of which regression equation can be built. The table exhibits that the coefficient of determination in case of all companies except Coal India and SunPharma is close to 1. This implies that the model of regressing Profit on advertisement expenses and sales is a suitable one. This point is also justified by the significance value, which is observed to be less than 0.05 in all the companies except Tata Motors and Coal India. The table also presents the beta values on the basis of which regression equation can be built. 
\subsection[{VI. Conclusion}]{VI. Conclusion}\par
The study uses various models including descriptive study, correlation and regression in order to find out the cause and effect relationship between advertisement expenditure, sales and profit. Taking tenyear data of twenty manufacturing companies of India, the study tested whether advertisement expenditure impacts the sales, the profits and vice-versa.\par
The tools used (Regression and Correlation) clearly show that there is a significant relationship between advertisement expenditure, sales and profit.\par
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