The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry
Abstract
:1. Introduction
1.1. Neuromarketing
1.2. Definition of Neuromarketing
1.3. The Birth of the Neuromarketing Concept
1.4. Importance of Neuromarketing
1.5. Background, Justification, and Purpose of the Study
2. Review of Literature
2.1. Neuromarketing and Human Decision-Making
2.2. Neuromarketing Main Tools
2.2.1. Positron Emission Tomography (PET)
2.2.2. Functional Magnetic Resonance Imaging (fMRI)
2.2.3. Electroencephalography (EEG)
2.2.4. Steady-State Probe Topography (SSPT)
2.2.5. Eye-Tracking
2.2.6. Magnetoencephalography (MEG)
2.2.7. Transcranial Magnetic Stimulation (TMS)
2.2.8. Facial Action Coding System (FACS)
2.2.9. Galvanic Skin Response (GSR)
2.2.10. Implicit Association Test (IAT)
2.3. Examples of Neuromarketing Applications
2.4. Artificial Neural Networks
2.4.1. Definition of Artificial Neural Networks
2.4.2. General Characteristics of Artificial Neural Networks
- Artificial neural networks perform machine learning.
- There is an information processing method utterly different from the methods in which traditional programming and artificial intelligence are applied [61].
- Fault-tolerant; the ability to work with incomplete information allows them to tolerate errors. If some network cells become corrupted and fail to work, the network will continue to run. Traditional computers usually require complete data [62].
- Can work with incomplete information; after being trained, artificial neural networks can produce results even with incomplete information in the new samples, while traditional systems cannot work with incomplete information [63].
- The artificial neural networks can organize and learn themselves, and artificial neural networks can adapt to novel situations to learn innovative events regularly [64].
- It has distributed memory; information in artificial neural networks is spread over the network. That is, the whole network characterizes the whole event [65].
- It can only work with numerical information; the information indicated by symbolic expressions must be translated into numerical values [66].
- It can detect events, shape and classify relationships, and pattern completion.
2.4.3. Advantages of Artificial Neural Networks
3. Methods
3.1. Fundamental Elements and Structure of the Artificial Neural Network
- Input Layer: There must be at least one predictor or element from the raw data set in the input layer. The input layer generates similar values without processing any estimation [70].
- Intermediate/Hidden Layer: This section or layer, also known as the hidden layer, is responsible for estimating and processing the raw data. The hidden or process layer has a specific function and structure, which could have variations as per the selected structure of networks. The middle or hidden layer could be comprised of one or more layers [78].
- Output Layer: The output may comprise at least one or more outputs. However, it solely depends on the neural network’s structure and function. The estimation operation is processed in this layer, and the estimated output will be directed to the outside world [69].
3.2. Research Structure and Aim of the Project
3.3. Input Criteria
3.4. Output Criteria
- Consumer buying behavior: It is a metric indicative of the measured image that can gather respondents’ concentration on their own. However, consumer buying behavior is the core function of the human brain, which generates unique brainwave activity patterns towards the change of behavior for buying some brands due to the stimuli of brains because of some essential characteristics of an advertisement of any brand [85].
- Predicted buying behavior: It is a neural network-generated predictive indicator that measures images that can influence respondents’ consumer buying behavior. It is a predictive function of the human brain, which generates unique brainwave activity patterns towards the change of behavior for buying the particular brand due to the stimuli of brains because of some essential characteristics of an advertisement of any brand [70].
3.5. Application
3.6. Creation of Artificial Neural Network Models
4. Results and Findings
4.1. First Application Using Model Training and Testing
4.2. Case Processing Summary of the First Application
4.3. Model Summary of the First Application
4.4. Model of the First Application
4.5. Parameter Estimates
4.6. Predictors’ Importance
4.7. Normalized Importance
4.8. Second Application Using Model Taring and Testing—Model Summary
4.9. Model of the First Application
4.10. Parameter Estimates
4.11. Predictors’ Importance
4.12. Normalized Importance
4.13. Forecasting of Neuromarketing Outputs
5. Discussion
6. Conclusions
6.1. Theoretical and Practical Implications
6.2. Limitations and Potential Areas of Future Studies
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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N | Percent | ||
---|---|---|---|
Sample | Training | 414 | 70.8% |
Testing | 171 | 29.2% | |
Valid | 585 | 100.0% | |
Excluded | 0 | ||
Total | 585 |
Training | Sum of Squares Error | 1.973 |
Relative Error | 0.010 | |
Stopping Rule Used | One consecutive step(s) with no decrease in error a | |
Training Time | 0:00:00.13 | |
Testing | Sum of Squares Error | 1.539 |
Relative Error | 0.019 |
Predictors | Predicted | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Hidden Layer 1 | Output | ||||||||||
H(1:1) | H(1:2) | H(1:3) | H(1:4) | H(1:5) | H(1:6) | H(1:7) | H(1:8) | H(1:9) | Consumer_Buying_Behavior | ||
Input Layer | (Bias) | 1.074 | −0.637 | −0.279 | 0.321 | −0.456 | −0.261 | 0.808 | 0.492 | −0.226 | |
Product_Features | 0.534 | −0.440 | −0.337 | 0.582 | 0.288 | 0.019 | −0.609 | 0.104 | −0.140 | ||
Adv_Content | 0.315 | 0.207 | −0.192 | −0.094 | −0.197 | 0.358 | 0.705 | −0.409 | −0.287 | ||
Target_Audience | −0.201 | −0.231 | −0.243 | 0.820 | −0.261 | 0.170 | 0.744 | −0.316 | 0.339 | ||
Celebirity_Endorsement | −0.731 | 0.364 | 0.743 | −0.246 | −0.528 | 0.031 | 0.028 | 0.034 | −0.494 | ||
Glamourization | 0.380 | −0.551 | −0.140 | 0.420 | 0.419 | −0.171 | 0.437 | −0.164 | −0.458 | ||
Creative_Value | −0.054 | 0.203 | 0.576 | 0.257 | 0.229 | 0.155 | 0.412 | −0.434 | −0.397 | ||
Product_Packaging | 0.369 | −0.294 | 0.138 | −0.383 | 0.091 | 0.386 | −0.115 | −0.129 | −0.450 | ||
Memorizing_Value | 0.152 | 0.143 | 0.010 | −0.130 | −0.005 | −0.290 | −0.176 | −0.492 | −0.184 | ||
Conviction_Value | 0.000 | −0.236 | −0.606 | 0.440 | −0.177 | −0.128 | 0.438 | 0.086 | 0.077 | ||
Time_Slot | −0.348 | 0.164 | 0.311 | 0.234 | −0.229 | 0.056 | 0.188 | 0.324 | −0.267 | ||
Hidden Layer 1 | (Bias) | −0.551 | |||||||||
H(1:1) | 0.883 | ||||||||||
H(1:2) | −0.688 | ||||||||||
H(1:3) | 0.925 | ||||||||||
H(1:4) | 0.544 | ||||||||||
H(1:5) | −0.331 | ||||||||||
H(1:6) | 0.467 | ||||||||||
H(1:7) | −0.797 | ||||||||||
H(1:8) | −0.035 | ||||||||||
H(1:9) | −0.273 |
Predictors | Importance | Normalized Importance |
---|---|---|
Product Features | 0.187 | 91.6% |
Advertisement Content | 0.071 | 34.8% |
Target Audience | 0.053 | 26.2% |
Celebrity Endorsement | 0.103 | 50.4% |
Glamourization | 0.106 | 52.2% |
Creative Value | 0.078 | 38.2% |
Product Packaging | 0.204 | 100.0% |
Memorizing Value | 0.026 | 12.6% |
Conviction Value | 0.105 | 51.6% |
Time Slot | 0.068 | 33.6% |
Training | Sum of Squares Error | 0.871 |
Relative Error | 0.004 | |
Stopping Rule Used | One consecutive step(s) with no decrease in error a | |
Training Time | 0:00:00.10 | |
Testing | Sum of Squares Error | 0.926 |
Relative Error | 0.010 |
Predictors | Predicted | ||||||||
---|---|---|---|---|---|---|---|---|---|
Hidden Layer 1 | Output Layer | ||||||||
H(1:1) | H(1:2) | H(1:3) | H(1:4) | H(1:5) | H(1:6) | H(1:7) | MLP Predicted Value | ||
Input Layer | (Bias) | 0.019 | 0.423 | 0.243 | −0.325 | −0.440 | −0.607 | 0.371 | |
Product_Features | 0.173 | −0.136 | 0.252 | 0.430 | −0.804 | −0.122 | 0.529 | ||
Adv_Content | −0.096 | −0.056 | 0.060 | −0.041 | 0.025 | −0.015 | −0.531 | ||
Target_Audience | 0.808 | −0.117 | −0.069 | 0.189 | −0.428 | −0.095 | 0.356 | ||
Celebirity_Endorsement | −0.566 | 0.229 | 1.028 | 0.094 | 0.051 | 0.162 | −0.776 | ||
Glamourization | 0.147 | 0.069 | 0.009 | 0.130 | −0.332 | −0.262 | 0.545 | ||
Creative_Value | −0.463 | −0.443 | −0.326 | −0.737 | −0.296 | 0.212 | −0.418 | ||
Product_Packaging | −0.526 | 0.521 | −0.367 | 0.067 | 0.353 | 0.356 | 0.689 | ||
Memorizing_Value | 0.059 | 0.618 | −0.212 | −0.123 | 0.431 | −0.050 | 0.321 | ||
Conviction_Value | −0.260 | 0.338 | 0.356 | 0.140 | 0.667 | 0.232 | −0.339 | ||
Time_Slot | 0.361 | 0.548 | 0.276 | −0.075 | −0.831 | 0.392 | −0.350 | ||
Hidden Layer 1 | (Bias) | −0.227 | |||||||
H(1:1) | −0.948 | ||||||||
H(1:2) | −0.614 | ||||||||
H(1:3) | 0.819 | ||||||||
H(1:4) | −0.364 | ||||||||
H(1:5) | −0.811 | ||||||||
H(1:6) | 0.638 | ||||||||
H(1:7) | 0.933 |
Predictors | Importance | Normalized Importance |
---|---|---|
Product Features | 0.200 | 100.0% |
Advertisement Content | 0.049 | 24.5% |
Target Audience | 0.073 | 36.3% |
Celebrity Endorsement | 0.128 | 64.3% |
Glamourization | 0.086 | 42.9% |
Creative Value | 0.134 | 67.2% |
Product Packaging | 0.109 | 54.6% |
Memorizing Value | 0.103 | 51.8% |
Conviction Value | 0.064 | 32.0% |
Time_Slot | 0.054 | 27.2% |
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Ahmed, R.R.; Streimikiene, D.; Channar, Z.A.; Soomro, H.A.; Streimikis, J.; Kyriakopoulos, G.L. The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry. Sustainability 2022, 14, 8546. https://doi.org/10.3390/su14148546
Ahmed RR, Streimikiene D, Channar ZA, Soomro HA, Streimikis J, Kyriakopoulos GL. The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry. Sustainability. 2022; 14(14):8546. https://doi.org/10.3390/su14148546
Chicago/Turabian StyleAhmed, Rizwan Raheem, Dalia Streimikiene, Zahid Ali Channar, Hassan Abbas Soomro, Justas Streimikis, and Grigorios L. Kyriakopoulos. 2022. "The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry" Sustainability 14, no. 14: 8546. https://doi.org/10.3390/su14148546