Case studies on advertising and sales promotion

Developing an Advertising Strategy Advertising Objectives Advertising Budgeting Creative Strategy Media Strategy Role of Advertising Agency Sales Promotion.
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That is, price has the greatest impact on sales followed by salesforce, advertising, sales promotion and distribution. F -value. The findings Table 3 of the tests of the hypothesis suggest that the coefficients of individual marketing efforts such as advertising, salesforce, sales promotion, distribution and price are significantly different from zero for the brand on one-tailed tests. Q 1 — Customer's emotional reaction towards advertising, Q 2 — Customer's emotional reaction towards product, Q 3 — Customer's emotional reaction towards distribution, Q 4 — Customer's emotional reaction towards price.

The interpretation of this equation is that all the independent variables have positive effects on log-odds, which is a function of overall customer satisfaction level. This indicates that the log-odds of overall customer satisfaction increase if the perceived customer satisfaction increases with respect to individual marketing efforts.

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Results also reveal that the estimated coefficients of all the marketing efforts are significant for this brand. The value of each estimated coefficient can be interpreted as the predicted change in the log-odds function of overall customer satisfaction level associated with one unit change in the corresponding variable, holding all other variables constant. Furthermore, the exponential constant raised to the power of the estimated coefficient represents the factor by which the predicted odds ratio changes from one when the corresponding variable changes by one unit, holding all other variables constant.

For example, the multiplicative factor is That is, the odds ratio will change from 1. Moreover, results indicate that advertising has the greatest impact on overall customer satisfaction, followed by salesforce, distribution and price for the brand. The estimated coefficient of overall customer satisfaction with respect to each marketing effort is zero. The findings Table 5 of the tests of the hypothesis suggest that the coefficients of individual marketing efforts such as advertising, salesforce, distribution and price are significantly different from zero for the brand on one-tailed tests.

The results of ROI in individual marketing efforts are also shown in Table 7. Results reveal that the ROI is highest in salesforce and lowest in distribution for the brand. That is, every rupee spent on each marketing effort does not yield the same amount of ROI over the study period. It means that the formulation and implementation strategies of each effort may impact its own effectiveness as well as others.

It has been concluded that the impact on sales is different from one effort to another, because each of them has to be performed to accomplish some specific goals.

But to fulfil an overall objective increase in sales in this case all the efforts have to be formulated and implemented collectively to get the maximum benefit of marketing expenditures. Moreover, the level of customer satisfaction varies from effort to effort in the marketing mix. That is, all the marketing efforts are not equally important to the customers.

The ROI helps the manager to identify which effort is earning and which is losing money for any brand. Moreover, it can be utilised to allocate limited resources to the different marketing efforts in question. In the case of this brand, the manager should invest as little as possible in distribution and advertising, and as high as possible in the salesforce and sales promotion efforts.

By doing so, the manager can reallocate the marketing budget to individual efforts for the brand. There are some limitations in this study. A relatively small number of efforts have been considered, since data were not available. Further, this work has considered only the short-term effect of each marketing effort on sales.

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Moreover, the ROI has been calculated with respect to sales, not with respect to contribution margin, since costs data were not available. It is desirable to assess the long-term effect of each marketing effort on sales in addition to the short-term. We gratefully acknowledge the constant support of the firm in utilising internal documents and permission to survey customers.

Skip to main content Skip to sections. Advertisement Hide. Download PDF. Effectiveness of marketing expenditures: A brand level case study. Paper First Online: 30 June The following three objectives have been identified: 1 To study the impact on sales of individual marketing efforts such as advertising, sales promotion, salesforce, distribution and price, as independent variables.

In this study, three models have been considered and are presented below: Model for objective 1 A double log function Equation 1 has been considered for Objective 1. This form has been chosen because there is some support for a convex—concave or S-shaped response function to each marketing effort. Moreover, many empirical studies support the diminishing marginal return to each marketing effort.

A logistic regression model Equation 2 with maximum likelihood estimation algorithm has been used for Objective 2. This form has been taken because the response variable is collected in binary form. At the same time, the present study is based on an assumption that marketing efforts may exhibit diminishing marginal returns in terms of probability of the customer being satisfied overall. Open image in new window. The model Equation 3 for Objective 3 is postulated below.

The idea of this model has been taken from Sheth and Sisodia's paper. Research instrument : The questionnaire has five parts. The first part has 13 statements, which measure the customer's emotional reactions towards advertisements. The second part contains eight statements, which measure the customer's emotional reactions towards the availability of the product.

The third part has 12 statements, which measure the customer's emotional reactions towards the product quality. The fourth part consists of 11 statements, which measure the customer's emotional reactions towards the price. Reliability and validity of this instrument have been tested and the results are shown in Tables 1 and 2.

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Table 1 Measures of reliability of each sub-scale of CSS. Sub-scale Alpha Advertising items 0. Table 2 Results of factor analysis. To estimate the relationship between sales and individual marketing efforts a regression analysis was performed and results are shown in Table 3. The estimated regression equation is presented below: Table 3 Response of sales to marketing efforts of Navratna.

Marketing Mix - Promotion Mix (Advertisement, Personal Selling & Sales Promotion), M.M. Part - 9

The results of the validation statistics of the regression equation are given in Table 4. The high value of R 2 indicates that the sales as a double-log function of individual marketing efforts provide a good fit to the observed data for the brand. Since the value of the JB-statistic is less than the table value, it can be concluded that the error terms are normally distributed in this case.

Further, the DW-statistic is not significant, which would lead to the conclusion that the error terms are not serially correlated in the sample data. Table 4 Validation statistics of regression equation for Navratna. To test whether the estimated elasticities of sales with respect to the individual marketing efforts are significantly different from zero, the following hypothesis is formulated in the context of Equation 1 : H 1 : The estimated elasticity of sales with respect to each marketing effort is zero.

The results of logistic regression analysis are presented in Table 5. The estimated equation is of the form: Table 5 Response of overall customer satisfaction to marketing efforts for Navratna. The validation statistics of the logistic regression equation are shown in Table 6. Results show that the G-statistic value is Since it is significant, this would lead one to conclude that at least one of the coefficients in the model is not zero.

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Further, the HL-statistic value is 9. This indicates that there is insufficient evidence to claim that the model does not fit the sample data adequately for the brand. Table 6 Validation statistics of logistic regression equation for Navratna. Brand G 4 p -value HL 8 p -value Navratna Figures in parentheses indicate degrees of freedom. To test where the estimated coefficients of overall customer satisfaction with respect to individual marketing efforts are significantly different from zero, the following hypothesis has been formulated in the context of Equation 2 : H 2 : The estimated coefficient of overall customer satisfaction with respect to each marketing effort is zero.

The CSI of individual marketing efforts are presented in Table 7.

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Results indicate that the CSI for advertising is 0. This means that customers are satisfied with the attributes of advertisement at a 74 per cent level. Furthermore, CSI is highest in the case of advertising and lowest in the case of price. Moreover, results reveal that the differences between the CSI of one effort and another are prominent in this case. This indicates that customers are not equally satisfied with the individual marketing efforts.