Measuring advertisement effectiveness—a neural network approach

Ramalingam, V.; Palaniappan, B.; Panchanatham, N.; Palanivel, S.

doi:10.1016/j.eswa.2005.09.014

Cited by 41 publications

(26 citation statements)

References 9 publications

(8 reference statements)

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“…Second, advertisements must be consistent with the objectives of the advertiser. Effective advertising has three broad dimensions, namely, strategy, creativity, and execution (Ramalingam et al, 2006). In order to have an effective advertisements, firms should connect these three elements (Ramalingam et al, 2006).…”

Section: Advertisement Effectivenessmentioning

confidence: 99%

“…An advertisement is considered effective if it attracts the customer's attention, has a memorable impact on the customer, stimulate the customer's purchase intentions, and affect the customer's emotions (Adibi, 2012). According to Ramalingam et al (2006), an effective advertisement has two main characteristics. First, advertiser should satisfy consumer's objectives by engaging the customer in the product/service experience and by delivering a relevant advertising message to the customer.…”

Section: Advertisement Effectivenessmentioning

confidence: 99%

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An Investigation of Factors Affecting Brand Advertising Success and Effectiveness

Ansari¹,

Riasi²

2016

IBR

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The purpose of this study is to identify the factors that affect advertising effectiveness and to investigate the effects of these factors on advertising success. Using a sample of 252 customers the study identified seven factors that affect brand advertising success and effectiveness, namely, advertising message and creativity, advertising media selection, market research, competitiveness, market share, uniqueness, and customer relationship. Path analysis and structural equation modelling (SEM) were used in order to test the proposed conceptual model of the study. The results revealed that advertising media selection has the strongest relationship with brand advertising success and effectiveness and can be considered as the most important factor affecting advertising effectiveness. The second and the third most important factors affecting advertising success were found to be advertising message/creativity and customer relationship. The results also indicated that although competitiveness impacts brand advertising success and effectiveness it is the least important factor among the seven factors identified in this study. Establishing appropriate relationships with customers and other stakeholders in order to understand their needs and demands is an important step toward improving the chances of brand advertising success and effectiveness. These relationships will also help the organizations to design their promotional campaigns according to the characteristics of their target customers which will contribute to the cumulative effectiveness of their brand advertisements.

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Section: Advertisement Effectivenessmentioning

confidence: 99%

Section: Advertisement Effectivenessmentioning

confidence: 99%

An Investigation of Factors Affecting Brand Advertising Success and Effectiveness

Ansari¹,

Riasi²

2016

IBR

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“…Thus, soft computing can be viewed as the key ingredient of RWC systems. Several authors (Abraham et al 2001;Zhang and Scofield 1994;Zhang and Knoll 2001;Chaudhuri and Chattopadhyay 2005a, b;Ramalingam et al 2006) have discussed the potentials of the soft computing techniques in solving realworld problems. The important soft computing components are, namely, artificial neural network and fuzzy logic.…”

Section: Introductionmentioning

confidence: 98%

Comparison of FFNN and ANFIS models for estimating groundwater level

et al. 2010

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Prediction of water level is an important task for groundwater planning and management when the water balance consistently tends toward negative values. In Maheshwaram watershed situated in the Ranga Reddy District of Andhra Pradesh, groundwater is overexploited, and groundwater resources management requires complete understanding of the dynamic nature of groundwater flow. Yet, the dynamic nature of groundwater flow is continually changing in response to human and climatic stresses, and the groundwater system is too intricate, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models are introduced into groundwater science as a powerful, flexible, statistical modeling technique to address complex pattern recognition problems. This study presents the comparison of two methods, i.e., feed-forward neural network (FFNN) trained with Levenberg-Marquardt (LM) algorithm compared with a fuzzy logic adaptive network-based fuzzy inference system (ANFIS) model for better accuracy of the estimation of the groundwater levels of the Maheshwaram watershed. The statistical indices used in the analysis were the root mean square error (RMSE), regression coefficient (R 2 ) and error variation (EV).The results show that FFNN-LM and ANFIS models provide better accuracy (RMSE = 4.45 and 4.94, respectively, R 2 is 93% for both models) for estimating groundwater levels well in advance for the above location.

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“…It has been found by research that artificial neural network can give many different answers in many different problem areas (Salim, 2004). The reason is that, the field of neural networks is immensely diverse, drawing interest from and applications for, medicine, mathematics, computer science, chemistry, economics, business management and many other fields (Ramalingam, Palaniappan, Panchanatham, & Palanivel, 2006;Geetha, Ramalingam, Palanivel, & Palaniappan, in press).…”

Section: Introductionmentioning

confidence: 98%