- Email: [email protected]
Measuring advertisement effectiveness—a neural network approach
Expert Systems with Applications 31 (2006) 159–163 www.elsevier.com/locate/eswa
Measuring advertisement effectiveness—a neural network approach V. Ramalingam a,*, B. Palaniappan a, N. Panchanatham b, S. Palanivel a a
Department of Computer Science and Engineering, Annamalai University, Annamalainagar Chidambaram 608002, Tamil Nadu, India b Department of Business Administration, Annamalai University, Annamalainagar Chidambaram 608002, Tamil Nadu, India
Abstract This study aims to incorporate Artificial Neural Network (ANN) for measuring the effectiveness of the TV broadcast advertisements (toothpaste) by discovering important factors that influence the advertisement effectiveness. The information about the effects of each of these factors has been studied and it is used for measuring the advertisement effectiveness. Fifty attributes are examined to derive values from thirteen factors. These thirteen factors are used as input to ANN model. The data collected from 837 respondents are used for training and testing the ANN. The backpropagation algorithm is used for adjusting the weights in the ANN. Experimental results show that the ANN model achieves 99% accuracy for measuring the advertisement effectiveness. q 2005 Elsevier Ltd. All rights reserved. Keywords: Advertisement effectiveness; Artificial Neural Networks; Backpropagation algorithm
1. Introduction This study aims to incorporate ANN for measuring advertisement effectiveness. Specifically, its aim is to discover important factors that influence the advertisement effectiveness in Indian environment using ANN. Advertising is paid nonpersonal communication from an identified sponsor using mass media to persuade or influence an audience (Wells, Burnet, & Moriarty, 2003). In an ideal world every manufacturer would be able to talk one-to-one with every consumer about its product. But personal selling, a one-to-one approach, is very expensive. Today, advertisers can provide customization through print or TV, or interactive media such as the World Wide Web, but it is not the same as meeting with every customer individually to discuss a product or service. The cost for time for broadcast media, for space in print media, and for time and space in interactive and support media are spread over the tremendous number of people that these media reach. For example, one million dollar may sound like a lot of money for one product advertisement, but when you consider that the advertisers are reaching over 250 million people, the cost is not so extreme (Aaker & Myers, 1982). TV has three key advantages. First, its influence on consumers’ taste and perception is pervasive. Second, it can reach a large audience in * Corresponding author. Tel.: C91 4144228887; fax: C91 4144238275. E-mail address: [email protected] (V. Ramalingam).
0957-4174/$ - see front matter q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2005.09.014
a cost-efficient manner. Third, its sound and moving images create a strong impact. That is why TV advertisement is considered in this study. 2. Advertisement effectiveness Advertising will only survive and grow if it focuses on being effective. All advertisers are expecting specific results, based on their stated objectives. Clients expect proof, and, for the most part, that proof must lead to or actually produce sales (Richard Vaughan, 1986). Advertising must be effective. It must achieve its objectives. Each advertisement can be made effective only when its explicit objectives should drive the planning, creation, and execution. Advertisement should work with other forms of marketing communication to reach customers. Only the advertiser and the supporting advertisement agency know whether the advertisement campaign reached its objectives, and whether the advertisement truly was worth for the money invested. Effective advertisements are advertisements that help the advertiser to reach its goals (Doyle & Saunders, 1990). Effective advertisement’s characteristics work on two levels. First one is advertisers should satisfy consumer’s objectives by engaging them and delivering a relevant message. The other one is the advertisements must achieve the advertiser’s objectives. Initially, a consumer may be interested in watching an advertisement for its entertainment value or to satisfy his/her curiosity. If the advertisement is sufficiently entertaining, customer may remember it. However, customer may then learn that the advertisement relates to
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V. Ramalingam et al. / Expert Systems with Applications 31 (2006) 159–163
a personal need and provides relevant information for that need. Further, advertisements may reinforce consumer product decisions and remind how one’s needs have been satisfied. The advertiser’s objectives differ from the consumer’s. Ultimately, advertisers want consumers to buy and keep buying their goods and services. To move consumers to action, they must gain their attention. They must then hold their interest long enough to convince them to change their purchasing behavior, try their product, and stick with their product. The campaign’s humor means that it is likely to satisfy the viewer’s curiosity and need for entertainment. The consumer will probably remember the advertisement, and because it is such a likeable campaign it will reinforce the good feelings or satisfaction of consumers. Strategy, creativity, and execution are the three broad dimensions that characterize effective advertising. Therefore, effective advertisements must connect these three elements. Every effective advertisement implements a sound strategy. The advertiser develops the advertisement to meet specific objectives, carefully directs it to certain audience, creates its message to speak to that audience’s most important concerns, and runs it in media like print, TV, or the internet, that will reach its audience most effectively. The creative concept is the advertisement’s central idea that grabs consumer attention and sticks in memory of the consumer. Effective advertisements are well executed. That means the details, the photography, setting, printing, and the production values all have been fine-tuned. Many of these techniques are standard in the industry. So these three conditions must be met for an advertisement to be considered effective. 3. Neural networks ANNs are intelligent systems that are related in some way to a simplified biological model of the human brain. They are composed of many simple elements, called neurons, operating in parallel and connected to each other in the forward path by some multipliers called the connection weights. Neural networks are trained by adjusting values of these connection weights between the network elements. Neural networks have self learning capability, are fault tolerant and noise immune, and have applications in system identification, pattern recognition, classification, speech recognition, image processing, etc. (Ganesh K Venayagamoorthy,Viresh Moonasar & Kumbes Sandrasegaran, 1998). ANNs are potentially useful for studying the complex relationships between inputs and outputs of a system (Rumelhart, Hinton, & Williams, 1986a). The data analysis performed by ANN tolerates a considerable amount of imprecise and incomplete input data due to the distributed mode of information processing. There are many ANN models are available namely back propagation networks (Rumelhart, Hinton, E. & Williams, 1986b), counter propagation networks etc. Preprocessing, architecture, and post processing are three major steps in the ANN based research. (Weigend, Rumelhart, & Huberman, 1990). In preprocessing, information that could be used as the inputs and outputs of ANN are collected. These data must be first normalized or scaled in order to reduce
the fluctuation and noise (Simon Haykin, 2003). In architecture, a variety of ANN models that could be used to capture the relationships between the data of inputs and outputs that are built (Poh, 1991). Finally, the postprocessing can be achieved by reverse process of preprocessing. Backpropagation ANNs are used for the analysis and forecasting of advertising and promotion impact (Poh, Yao, & Jasic, 1998). Little, D. C. J, 1979 has developed the Marketing Decision Support System (MDSS), by discovering important variables that influence sales performance of color TV (CTV) sets in the Singapore market using ANN. Ramalingam, Kalyanaraman & Arulmozhi (2004), Ramalingam, Palaniappan, Panchanatham, & Rajendran (2002) and Ramalingam, Rajan, & Ganesan (2002a,b) have applied ANN model for prediction of ultrasonic velocities in binary liquid systems, natural language processing, classifying students, and tamil studies. In this paper, a four layer feedforward neural network with hyperbolic tangent (tanh) as activation function in hidden layers followed by a linear layer (output layer) is employed. The neural network is trained using backpropagation algorithm. A momentum term is used in the backpropagation algorithm to achieve a faster global convergence. A bias value is used to enable each neuron to fire hundred percent. Further the performance of the different neural network architectures was also compared. 4. Details of the methodology TV is considered to be the most glamorous and prestigious media. A pilot survey was conducted on TV advertising and viewers attitudes. That study was narrowed down to consider toothpaste advertisement, since toothpaste is used by majority of people. So, in this study, three brands (say A, B and C) of toothpaste product advertisements are considered. The objective of the study is to measure the effectiveness of the advertisement with the help of the reactions of the TV viewers for the toothpaste advertisements. TV advertisements in the form of film are most popular among the viewers. Viewers gave three major reasons for monotony while viewing TV advertisements:(i) large number of advertisements is being shown; (ii) repetitive advertisements; (iii) exaggerated claims made in the advertisements. Adequate information about the products is not included in all TV advertisements. Respondents were often induced to purchase them on the basis of advertisement. Viewers were satisfied with the products purchased by them, while some of the viewers had mentioned that they were mislead by TV advertisements. Advertisements play a major role in creating a strong opinion about the product in the minds of the consumers (Richard Vaughan, 1986). In this study, the following 13 factors are considered for measuring advertisement effectiveness: 1. Affectative—it includes attributes such as body language, brand name, and repetition of verses affects the viewer and instigates the viewer to purchase the product. 2. Attention—it includes attribute such as humor in the advertisement that will create attention among customers.
V. Ramalingam et al. / Expert Systems with Applications 31 (2006) 159–163
3. Attraction—it consists of attributes such as technical quality, graphics, logo etc. which attract the viewer and create a desire to buy the product. 4. Changes—it consists of attributes such as customer benefits which when changed frequently create a desire for the viewer to buy the product. 5. Desire—it includes the attributes such as trail offer, survey results etc. which create a desire for the viewer to purchase the product. 6. Economics—this includes the attributes such as demonstration, healthy comparison etc. which instigate the user to buy the product. 7. Emotions—this involves the various emotions that are experienced by the viewer such as irritation, confusion, satisfaction etc. on seeing the advertisement. 8. Exposure—this includes the attributes such as novelty, frequency of telecasting the advertisement, catchy caption etc., that make the viewer to buy the product. 9. Influence—this involves the attributes such as acceptability of the advertisement, which instigate the viewer to buy the product. 10. Persuasion—it includes attributes such as customer feedback, cultural norms etc. that creates an attempt to move, affect or determine a purchasing decision. 11. Psychological—this includes the psychological attributes which affect the viewer, say some advertisements may create a feeling of disinterest. 12. Senses—this includes the various senses such as smell, taste, and tone, color that may attract the viewer to buy the product. 13. Social—this includes the attributes such as advertisement being informative, creating awareness etc. The following 50 attributes are identified from the above thirteen factors: appealing theme, benefits, body language, calls for action, catchy caption, change of artists, change of customer benefits, change of personality, collaboration with foreign companies, color, confusion, cultural norms, customer feedback, demo, disinterest, evidence, famous personality appearance, feeling of trust, frequency of telecasting, frequent updation, graphics and animations, healthy comparison, humor, ideas, informative, irritation. Jarring sound, knowledge, logic and reasons, logo, novelty, problems and solutions, quality, repetition of versus, revealing of price, satisfaction, selling proportion, sequence of framing, short duration, smell, sound effects, specification of brand name, survey results, taste, technical quality, trail offer, truth, understandable, voice and tone, A questionnaire containing 50 questions was administered to the respondents. These 50 questions were prepared by considering the thirteen factors along with 50 attributes mentioned above. These 50 questions were ordered randomly to get unbiased response. First, the TV advertisements on toothpaste of three brands are stored in hard disk using the TV tuner card. The questions are stored in the Access database and linked to the Visual Basic application where the user is made to fill the questionnaire by viewing the advertisement of their
161
choice of the brand by selection, which is played with the help of window media player. Respondents were asked to fill their demographic data such as name, age, sex, religion, educational background, size of family, occupation of parents, source of annual income etc. After answering demographic data, the respondents are asked to answer 50 questions in the form effectiveness scale. The advertisement effectiveness scale responses are obtained on an 11-point response category, from the most effective to least effective. The user interactive screen contains a dropdown list box in which the (effectiveness scale) rating is given from 0 to 10. The 0 represents the least effectiveness and 10 the most effectiveness. The collected data are stored in MS Access database.
Fig. 1. (a) The ANN model used for advertisement effectiveness; (b) ANN training error versus number of epochs.
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A total of 837 respondents (571 male and 266 female students) at the Annamalai University, Tamil Nadu, India participated in this research study. The values for thirteen factors obtained from the respondents (x1, x2, . x13) are normalized to [K1, 1] using the Eq. (1), yi Z 2:0=ðxmax Kxmin Þðxi Kxmin ÞK1
(1)
where xmax and xmin are the maximum and minimum values of the un normalized data. These normalized data (y1, y2, . y13) are given as input to the ANN. The sum of x1,x2,.x13 (i.e. the overall effectiveness value) is taken as output to the ANN which is also normalized using the Eq. (1). In this paper, the backpropagation training algorithm is used to adjust the weights such that the neural network produces the required output for the given input data. The 69% of the data collected from 837 respondents are used for training and the remaining 31% of the data are used for testing the neural network. 5. Experimental results and discussion The four layer feedforward ANN model used for advertisement effectiveness is shown in Fig. 1(a). In this paper, the
Table 1 Performance of the various ANN models ANN model
Performance of the model (%)
13-13N-05N-1L 13-26N-10N-1L 13-10N-05N-1L
98.3 97.3 99.2
following three ANN structures are studied for comparison purpose. S1 (13L-13N-5N-1L) S2 (13L-26N-10N-1L) S3 (13L-10N-5N-1L) The integer number in 13L-13N-5N-1L indicates the number of units/neurons used in each layer (input layer, hidden layer1, hidden layer2, and output layer, respectively) and L, N denotes linear and nonlinear unit, respectively. The nonlinear units use tanh(s) as the activation function, where s is the activation value of the unit. In this study, all the ANN are trained with a learning rate of 0.01 and 5000 iterations/epochs. The results obtained from this study are given below. Fig. 1(b) shows the ANN training error versus number of epochs. From graphs at Fig. 1(b), one can infer that the ANN structure S3 (13L-10N-5N-1L) has fast convergence for the training data than that of S1 and S2. The Fig. 2(a) shows the error for the test data using the ANN trained for number of epochs. From graphs at Fig. 2(a), one can conclude that the ANN structure S3 (13L-10N-5N-1L) gives minimum error for the test data when compared with S1 and S2. Fig. 2(b) shows the performance for the test data using the ANN trained for number of epochs. From graphs at Fig. 2(b), one can infer that the ANN structure S3 (13L-10N-5N-1L) gives better performance than that of S1 and S2. The optimal performances for different ANN structures are shown in Table 1. From Table 1, one can observe that the ANN model S3 (13L-10N-5N-1L) gives better performance over the other two.
6. Conclusions The results from this study indicate that the ANN is able to capture the nonlinear relationships between thirteen input factors and output which are considered for measuring the advertisement effectiveness. From the above results one can conclude that the backpropagation algorithm is an efficient method for measuring the advertisement effectiveness. Three ANN models are used for measuring the advertisement effectiveness. Among these models S3 (13L-10N-5N-1L) gives better accuracy of 99% for measuring advertisement effectiveness. Acknowledgements
Fig. 2. (a) Error for the test data using the ANN trained for number of epochs; (b) Performance for the test data using the ANN trained for number of epochs.
This work is carried out in Neuro-Fuzzy Laboratory. This laboratory was funded by A.I.C.T.E. (All India Council for Technical Education), New Delhi, Government of India.
V. Ramalingam et al. / Expert Systems with Applications 31 (2006) 159–163
Appendix A Normalization procedure is given below. Suppose, an input variable say x having set of values with xmin as the minimum value of x and xmax is the maximum value of x to be normalized to a new variable say y with ymin as the minimum value of y and ymax is the maximum value of y. Use the following Eq. (A1) to get normalized value y for the given x. ðymax Kymin Þ ðxi Kxmin Þ yZ (A1) C ymin ðxmax K xmin Þ
References Aaker, D. A., & Myers, J. G. (1982). Advertising management. Englewood Cliffs, NJ: Prentice-Hall. Doyle, P., & Saunders, J. (1990). Multiproduct advertising budgeting. Marketing Science, 9(2), 97–113. Haykin, S. (2003). Neural networks: A comprehensive foundation. India: PHI. Little, D. C. J. (1979). Decision support systems for marketing managers. Journal of Marketing, 43(3), 26. Poh, H. L. (1991). A neural network approach for marketing strategies research and decision support. PhD thesis, Stanford University. Poh, H. L., Yao, J., & Jasic, T. (1998). ANN for the analysis and forecasting of advertising and promotion impact. International Journal of Intelligent Systems in Accounting, Finance and Management, 7(4), 253–268.
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Ramalingam, V., Kalyanaraman, S. B., & Arulmozhi, K. T. (2004). Artificial neural network modeling—Ultrasonic velocities in binary liquid systems. Chemical Engineering World , 75–77. Ramalingam, V., Palaniappan, B., Panchanatham, N., & Rajendran, K. (2002). Neural Network approach for classifying students. Proceedings of the national conference on applications of neural networks and fuzzy systems (pp. 140–146). Tamil Nadu, India: Department of CSE, Annamalai University. Ramalingam, V., Rajan, K., & Ganesan, M. (2002a). Applications of neural network for natural language processing. Proceedings of the national conference on applications of neural networks and fuzzy systems (pp. 40–43). Tamil Nadu, India: Department of CSE, Annamalai University. Ramalingam, V., Rajan, K., & Ganesan, M. (2002b). Application of neural networks for Tamil studies. Proceedings of the international seminar in Tamil computing. Chennai, India: University of Madras. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986a). Learning internal representations by error propagation. In D. E. Rumelhart, & J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the micro-structure of cognition (Vol. 1) (pp. 318–362). Cambridge, MA: MIT Press. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986b). Learning representations of back-propagation error. Nature (London), 323, 533–536. Vaughan, R. (1986). How advertising works: A planning model. Journal of advertising research, 26(1), 57–66. Venayagamoorthy, Ganesh K., Moonasar, Viresh, & Sandrasegaran, Kumbes (1998). Voice recognition using neural networks. IEEE , 29–32. Weigend, A. S., Rumelhart, D. E., & Huberman, B. A. (1990). Predicting the future: A connectionist approach. International Journal of Neural Systems, 1(3), 193–209. Wells, W., Burnet, J., & Moriarty, S. (2003). Advertising principles and practice. India: Pearson Education.
Measuring advertisement effectiveness—a neural network approach V. Ramalingam a,*, B. Palaniappan a, N. Panchanatham b, S. Palanivel a a
Department of Computer Science and Engineering, Annamalai University, Annamalainagar Chidambaram 608002, Tamil Nadu, India b Department of Business Administration, Annamalai University, Annamalainagar Chidambaram 608002, Tamil Nadu, India
Abstract This study aims to incorporate Artificial Neural Network (ANN) for measuring the effectiveness of the TV broadcast advertisements (toothpaste) by discovering important factors that influence the advertisement effectiveness. The information about the effects of each of these factors has been studied and it is used for measuring the advertisement effectiveness. Fifty attributes are examined to derive values from thirteen factors. These thirteen factors are used as input to ANN model. The data collected from 837 respondents are used for training and testing the ANN. The backpropagation algorithm is used for adjusting the weights in the ANN. Experimental results show that the ANN model achieves 99% accuracy for measuring the advertisement effectiveness. q 2005 Elsevier Ltd. All rights reserved. Keywords: Advertisement effectiveness; Artificial Neural Networks; Backpropagation algorithm
1. Introduction This study aims to incorporate ANN for measuring advertisement effectiveness. Specifically, its aim is to discover important factors that influence the advertisement effectiveness in Indian environment using ANN. Advertising is paid nonpersonal communication from an identified sponsor using mass media to persuade or influence an audience (Wells, Burnet, & Moriarty, 2003). In an ideal world every manufacturer would be able to talk one-to-one with every consumer about its product. But personal selling, a one-to-one approach, is very expensive. Today, advertisers can provide customization through print or TV, or interactive media such as the World Wide Web, but it is not the same as meeting with every customer individually to discuss a product or service. The cost for time for broadcast media, for space in print media, and for time and space in interactive and support media are spread over the tremendous number of people that these media reach. For example, one million dollar may sound like a lot of money for one product advertisement, but when you consider that the advertisers are reaching over 250 million people, the cost is not so extreme (Aaker & Myers, 1982). TV has three key advantages. First, its influence on consumers’ taste and perception is pervasive. Second, it can reach a large audience in * Corresponding author. Tel.: C91 4144228887; fax: C91 4144238275. E-mail address: [email protected] (V. Ramalingam).
0957-4174/$ - see front matter q 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2005.09.014
a cost-efficient manner. Third, its sound and moving images create a strong impact. That is why TV advertisement is considered in this study. 2. Advertisement effectiveness Advertising will only survive and grow if it focuses on being effective. All advertisers are expecting specific results, based on their stated objectives. Clients expect proof, and, for the most part, that proof must lead to or actually produce sales (Richard Vaughan, 1986). Advertising must be effective. It must achieve its objectives. Each advertisement can be made effective only when its explicit objectives should drive the planning, creation, and execution. Advertisement should work with other forms of marketing communication to reach customers. Only the advertiser and the supporting advertisement agency know whether the advertisement campaign reached its objectives, and whether the advertisement truly was worth for the money invested. Effective advertisements are advertisements that help the advertiser to reach its goals (Doyle & Saunders, 1990). Effective advertisement’s characteristics work on two levels. First one is advertisers should satisfy consumer’s objectives by engaging them and delivering a relevant message. The other one is the advertisements must achieve the advertiser’s objectives. Initially, a consumer may be interested in watching an advertisement for its entertainment value or to satisfy his/her curiosity. If the advertisement is sufficiently entertaining, customer may remember it. However, customer may then learn that the advertisement relates to
160
V. Ramalingam et al. / Expert Systems with Applications 31 (2006) 159–163
a personal need and provides relevant information for that need. Further, advertisements may reinforce consumer product decisions and remind how one’s needs have been satisfied. The advertiser’s objectives differ from the consumer’s. Ultimately, advertisers want consumers to buy and keep buying their goods and services. To move consumers to action, they must gain their attention. They must then hold their interest long enough to convince them to change their purchasing behavior, try their product, and stick with their product. The campaign’s humor means that it is likely to satisfy the viewer’s curiosity and need for entertainment. The consumer will probably remember the advertisement, and because it is such a likeable campaign it will reinforce the good feelings or satisfaction of consumers. Strategy, creativity, and execution are the three broad dimensions that characterize effective advertising. Therefore, effective advertisements must connect these three elements. Every effective advertisement implements a sound strategy. The advertiser develops the advertisement to meet specific objectives, carefully directs it to certain audience, creates its message to speak to that audience’s most important concerns, and runs it in media like print, TV, or the internet, that will reach its audience most effectively. The creative concept is the advertisement’s central idea that grabs consumer attention and sticks in memory of the consumer. Effective advertisements are well executed. That means the details, the photography, setting, printing, and the production values all have been fine-tuned. Many of these techniques are standard in the industry. So these three conditions must be met for an advertisement to be considered effective. 3. Neural networks ANNs are intelligent systems that are related in some way to a simplified biological model of the human brain. They are composed of many simple elements, called neurons, operating in parallel and connected to each other in the forward path by some multipliers called the connection weights. Neural networks are trained by adjusting values of these connection weights between the network elements. Neural networks have self learning capability, are fault tolerant and noise immune, and have applications in system identification, pattern recognition, classification, speech recognition, image processing, etc. (Ganesh K Venayagamoorthy,Viresh Moonasar & Kumbes Sandrasegaran, 1998). ANNs are potentially useful for studying the complex relationships between inputs and outputs of a system (Rumelhart, Hinton, & Williams, 1986a). The data analysis performed by ANN tolerates a considerable amount of imprecise and incomplete input data due to the distributed mode of information processing. There are many ANN models are available namely back propagation networks (Rumelhart, Hinton, E. & Williams, 1986b), counter propagation networks etc. Preprocessing, architecture, and post processing are three major steps in the ANN based research. (Weigend, Rumelhart, & Huberman, 1990). In preprocessing, information that could be used as the inputs and outputs of ANN are collected. These data must be first normalized or scaled in order to reduce
the fluctuation and noise (Simon Haykin, 2003). In architecture, a variety of ANN models that could be used to capture the relationships between the data of inputs and outputs that are built (Poh, 1991). Finally, the postprocessing can be achieved by reverse process of preprocessing. Backpropagation ANNs are used for the analysis and forecasting of advertising and promotion impact (Poh, Yao, & Jasic, 1998). Little, D. C. J, 1979 has developed the Marketing Decision Support System (MDSS), by discovering important variables that influence sales performance of color TV (CTV) sets in the Singapore market using ANN. Ramalingam, Kalyanaraman & Arulmozhi (2004), Ramalingam, Palaniappan, Panchanatham, & Rajendran (2002) and Ramalingam, Rajan, & Ganesan (2002a,b) have applied ANN model for prediction of ultrasonic velocities in binary liquid systems, natural language processing, classifying students, and tamil studies. In this paper, a four layer feedforward neural network with hyperbolic tangent (tanh) as activation function in hidden layers followed by a linear layer (output layer) is employed. The neural network is trained using backpropagation algorithm. A momentum term is used in the backpropagation algorithm to achieve a faster global convergence. A bias value is used to enable each neuron to fire hundred percent. Further the performance of the different neural network architectures was also compared. 4. Details of the methodology TV is considered to be the most glamorous and prestigious media. A pilot survey was conducted on TV advertising and viewers attitudes. That study was narrowed down to consider toothpaste advertisement, since toothpaste is used by majority of people. So, in this study, three brands (say A, B and C) of toothpaste product advertisements are considered. The objective of the study is to measure the effectiveness of the advertisement with the help of the reactions of the TV viewers for the toothpaste advertisements. TV advertisements in the form of film are most popular among the viewers. Viewers gave three major reasons for monotony while viewing TV advertisements:(i) large number of advertisements is being shown; (ii) repetitive advertisements; (iii) exaggerated claims made in the advertisements. Adequate information about the products is not included in all TV advertisements. Respondents were often induced to purchase them on the basis of advertisement. Viewers were satisfied with the products purchased by them, while some of the viewers had mentioned that they were mislead by TV advertisements. Advertisements play a major role in creating a strong opinion about the product in the minds of the consumers (Richard Vaughan, 1986). In this study, the following 13 factors are considered for measuring advertisement effectiveness: 1. Affectative—it includes attributes such as body language, brand name, and repetition of verses affects the viewer and instigates the viewer to purchase the product. 2. Attention—it includes attribute such as humor in the advertisement that will create attention among customers.
V. Ramalingam et al. / Expert Systems with Applications 31 (2006) 159–163
3. Attraction—it consists of attributes such as technical quality, graphics, logo etc. which attract the viewer and create a desire to buy the product. 4. Changes—it consists of attributes such as customer benefits which when changed frequently create a desire for the viewer to buy the product. 5. Desire—it includes the attributes such as trail offer, survey results etc. which create a desire for the viewer to purchase the product. 6. Economics—this includes the attributes such as demonstration, healthy comparison etc. which instigate the user to buy the product. 7. Emotions—this involves the various emotions that are experienced by the viewer such as irritation, confusion, satisfaction etc. on seeing the advertisement. 8. Exposure—this includes the attributes such as novelty, frequency of telecasting the advertisement, catchy caption etc., that make the viewer to buy the product. 9. Influence—this involves the attributes such as acceptability of the advertisement, which instigate the viewer to buy the product. 10. Persuasion—it includes attributes such as customer feedback, cultural norms etc. that creates an attempt to move, affect or determine a purchasing decision. 11. Psychological—this includes the psychological attributes which affect the viewer, say some advertisements may create a feeling of disinterest. 12. Senses—this includes the various senses such as smell, taste, and tone, color that may attract the viewer to buy the product. 13. Social—this includes the attributes such as advertisement being informative, creating awareness etc. The following 50 attributes are identified from the above thirteen factors: appealing theme, benefits, body language, calls for action, catchy caption, change of artists, change of customer benefits, change of personality, collaboration with foreign companies, color, confusion, cultural norms, customer feedback, demo, disinterest, evidence, famous personality appearance, feeling of trust, frequency of telecasting, frequent updation, graphics and animations, healthy comparison, humor, ideas, informative, irritation. Jarring sound, knowledge, logic and reasons, logo, novelty, problems and solutions, quality, repetition of versus, revealing of price, satisfaction, selling proportion, sequence of framing, short duration, smell, sound effects, specification of brand name, survey results, taste, technical quality, trail offer, truth, understandable, voice and tone, A questionnaire containing 50 questions was administered to the respondents. These 50 questions were prepared by considering the thirteen factors along with 50 attributes mentioned above. These 50 questions were ordered randomly to get unbiased response. First, the TV advertisements on toothpaste of three brands are stored in hard disk using the TV tuner card. The questions are stored in the Access database and linked to the Visual Basic application where the user is made to fill the questionnaire by viewing the advertisement of their
161
choice of the brand by selection, which is played with the help of window media player. Respondents were asked to fill their demographic data such as name, age, sex, religion, educational background, size of family, occupation of parents, source of annual income etc. After answering demographic data, the respondents are asked to answer 50 questions in the form effectiveness scale. The advertisement effectiveness scale responses are obtained on an 11-point response category, from the most effective to least effective. The user interactive screen contains a dropdown list box in which the (effectiveness scale) rating is given from 0 to 10. The 0 represents the least effectiveness and 10 the most effectiveness. The collected data are stored in MS Access database.
Fig. 1. (a) The ANN model used for advertisement effectiveness; (b) ANN training error versus number of epochs.
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V. Ramalingam et al. / Expert Systems with Applications 31 (2006) 159–163
A total of 837 respondents (571 male and 266 female students) at the Annamalai University, Tamil Nadu, India participated in this research study. The values for thirteen factors obtained from the respondents (x1, x2, . x13) are normalized to [K1, 1] using the Eq. (1), yi Z 2:0=ðxmax Kxmin Þðxi Kxmin ÞK1
(1)
where xmax and xmin are the maximum and minimum values of the un normalized data. These normalized data (y1, y2, . y13) are given as input to the ANN. The sum of x1,x2,.x13 (i.e. the overall effectiveness value) is taken as output to the ANN which is also normalized using the Eq. (1). In this paper, the backpropagation training algorithm is used to adjust the weights such that the neural network produces the required output for the given input data. The 69% of the data collected from 837 respondents are used for training and the remaining 31% of the data are used for testing the neural network. 5. Experimental results and discussion The four layer feedforward ANN model used for advertisement effectiveness is shown in Fig. 1(a). In this paper, the
Table 1 Performance of the various ANN models ANN model
Performance of the model (%)
13-13N-05N-1L 13-26N-10N-1L 13-10N-05N-1L
98.3 97.3 99.2
following three ANN structures are studied for comparison purpose. S1 (13L-13N-5N-1L) S2 (13L-26N-10N-1L) S3 (13L-10N-5N-1L) The integer number in 13L-13N-5N-1L indicates the number of units/neurons used in each layer (input layer, hidden layer1, hidden layer2, and output layer, respectively) and L, N denotes linear and nonlinear unit, respectively. The nonlinear units use tanh(s) as the activation function, where s is the activation value of the unit. In this study, all the ANN are trained with a learning rate of 0.01 and 5000 iterations/epochs. The results obtained from this study are given below. Fig. 1(b) shows the ANN training error versus number of epochs. From graphs at Fig. 1(b), one can infer that the ANN structure S3 (13L-10N-5N-1L) has fast convergence for the training data than that of S1 and S2. The Fig. 2(a) shows the error for the test data using the ANN trained for number of epochs. From graphs at Fig. 2(a), one can conclude that the ANN structure S3 (13L-10N-5N-1L) gives minimum error for the test data when compared with S1 and S2. Fig. 2(b) shows the performance for the test data using the ANN trained for number of epochs. From graphs at Fig. 2(b), one can infer that the ANN structure S3 (13L-10N-5N-1L) gives better performance than that of S1 and S2. The optimal performances for different ANN structures are shown in Table 1. From Table 1, one can observe that the ANN model S3 (13L-10N-5N-1L) gives better performance over the other two.
6. Conclusions The results from this study indicate that the ANN is able to capture the nonlinear relationships between thirteen input factors and output which are considered for measuring the advertisement effectiveness. From the above results one can conclude that the backpropagation algorithm is an efficient method for measuring the advertisement effectiveness. Three ANN models are used for measuring the advertisement effectiveness. Among these models S3 (13L-10N-5N-1L) gives better accuracy of 99% for measuring advertisement effectiveness. Acknowledgements
Fig. 2. (a) Error for the test data using the ANN trained for number of epochs; (b) Performance for the test data using the ANN trained for number of epochs.
This work is carried out in Neuro-Fuzzy Laboratory. This laboratory was funded by A.I.C.T.E. (All India Council for Technical Education), New Delhi, Government of India.
V. Ramalingam et al. / Expert Systems with Applications 31 (2006) 159–163
Appendix A Normalization procedure is given below. Suppose, an input variable say x having set of values with xmin as the minimum value of x and xmax is the maximum value of x to be normalized to a new variable say y with ymin as the minimum value of y and ymax is the maximum value of y. Use the following Eq. (A1) to get normalized value y for the given x. ðymax Kymin Þ ðxi Kxmin Þ yZ (A1) C ymin ðxmax K xmin Þ
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