Analysis of Brand Image Effect on Advertising Awareness Using A Neuro-Fuzzy and A Neural Network Prediction Models
Almost all the worldwide and nationwide companies utilize advertising to increase their sales volume and profit. These companies pay millions of dollars to reach consumers and announce their products or services. This forces companies to evaluate advertising effects and check whether ads meet compan...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2017-01-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25870785/view |
| _version_ | 1817983006749491200 |
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| author | Ali Fahmi Kemal Burc Ulengin Cengiz Kahraman |
| author_facet | Ali Fahmi Kemal Burc Ulengin Cengiz Kahraman |
| author_sort | Ali Fahmi |
| collection | DOAJ |
| description | Almost all the worldwide and nationwide companies utilize advertising to increase their sales volume and profit. These companies pay millions of dollars to reach consumers and announce their products or services. This forces companies to evaluate advertising effects and check whether ads meet companys strategies. They need to evaluate the ads not only after announcement, but also before advertising, i.e. they can be one step ahead by predicting the future advertising awareness through artificial intelligence tools such as fuzzy systems and neural networks. In this study, we propose to use adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) to analyze advertising decision making. ANFIS creates fuzzy rules and trains the neural network using given input data. This training ability of ANFIS and ANN leads to predicting the advertising awareness outputs. Here, we investigate three advertising awareness outputs, namely, top of mind, share of voice, and spontaneous awareness. In order to achieve the valid predictions, data are randomly divided into training data with 70 percent, validation data with 15 percent, and testing data with remained 15 percent of data. The correlation between actual data and predictions are calculated to check the accuracy of the predicted outputs. |
| first_indexed | 2024-04-13T23:28:18Z |
| format | Article |
| id | doaj.art-cbebcfce9e9d4a08bba7b4247b95e318 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-13T23:28:18Z |
| publishDate | 2017-01-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-cbebcfce9e9d4a08bba7b4247b95e3182022-12-22T02:25:00ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832017-01-0110110.2991/ijcis.2017.10.1.46Analysis of Brand Image Effect on Advertising Awareness Using A Neuro-Fuzzy and A Neural Network Prediction ModelsAli FahmiKemal Burc UlenginCengiz KahramanAlmost all the worldwide and nationwide companies utilize advertising to increase their sales volume and profit. These companies pay millions of dollars to reach consumers and announce their products or services. This forces companies to evaluate advertising effects and check whether ads meet companys strategies. They need to evaluate the ads not only after announcement, but also before advertising, i.e. they can be one step ahead by predicting the future advertising awareness through artificial intelligence tools such as fuzzy systems and neural networks. In this study, we propose to use adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) to analyze advertising decision making. ANFIS creates fuzzy rules and trains the neural network using given input data. This training ability of ANFIS and ANN leads to predicting the advertising awareness outputs. Here, we investigate three advertising awareness outputs, namely, top of mind, share of voice, and spontaneous awareness. In order to achieve the valid predictions, data are randomly divided into training data with 70 percent, validation data with 15 percent, and testing data with remained 15 percent of data. The correlation between actual data and predictions are calculated to check the accuracy of the predicted outputs.https://www.atlantis-press.com/article/25870785/viewTop of mind (TOM)share of voice (SOV)spontaneous awareness (SA)adaptive neuro-fuzzy inference system (ANFIS)artificial neural network (ANN) |
| spellingShingle | Ali Fahmi Kemal Burc Ulengin Cengiz Kahraman Analysis of Brand Image Effect on Advertising Awareness Using A Neuro-Fuzzy and A Neural Network Prediction Models International Journal of Computational Intelligence Systems Top of mind (TOM) share of voice (SOV) spontaneous awareness (SA) adaptive neuro-fuzzy inference system (ANFIS) artificial neural network (ANN) |
| title | Analysis of Brand Image Effect on Advertising Awareness Using A Neuro-Fuzzy and A Neural Network Prediction Models |
| title_full | Analysis of Brand Image Effect on Advertising Awareness Using A Neuro-Fuzzy and A Neural Network Prediction Models |
| title_fullStr | Analysis of Brand Image Effect on Advertising Awareness Using A Neuro-Fuzzy and A Neural Network Prediction Models |
| title_full_unstemmed | Analysis of Brand Image Effect on Advertising Awareness Using A Neuro-Fuzzy and A Neural Network Prediction Models |
| title_short | Analysis of Brand Image Effect on Advertising Awareness Using A Neuro-Fuzzy and A Neural Network Prediction Models |
| title_sort | analysis of brand image effect on advertising awareness using a neuro fuzzy and a neural network prediction models |
| topic | Top of mind (TOM) share of voice (SOV) spontaneous awareness (SA) adaptive neuro-fuzzy inference system (ANFIS) artificial neural network (ANN) |
| url | https://www.atlantis-press.com/article/25870785/view |
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