Improved DNN-assisted Customer Behavior Analysis with Smart Visual Analytics
A customer behavior analysis examines each customer journey stage using qualitative and quantitative methodologies to understand what motivates consumer behavior. With visual analytics, marketers can decipher the complicated world of customer retargeting, allowing businesses to visualize data and as...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2024-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/455072 |
| _version_ | 1827281551200616448 |
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| author | Ming Hu Qinghua Li Hao Zhou |
| author_facet | Ming Hu Qinghua Li Hao Zhou |
| author_sort | Ming Hu |
| collection | DOAJ |
| description | A customer behavior analysis examines each customer journey stage using qualitative and quantitative methodologies to understand what motivates consumer behavior. With visual analytics, marketers can decipher the complicated world of customer retargeting, allowing businesses to visualize data and ask and answer infinite questions. Because of this, they are better able to comprehend who their consumers are and why they act in certain ways. This paper provides a significant solution named improved DNN-assisted Customer Behavior Analysis (iDNN-CBA) with smart visual analytics. This paper suggests an interactive section for collecting customer reviews and feedback. Their facial expressions have been collected and processed using the improved deep neural network (iDNN), and the visual analytics occurs with pattern analysis. The proposed iDNN-CBA has been trained and validated using the experimental analysis by public dataset KAGGLE and observed the highest accuracy of 96.55% compared to other existing behavior analysis schemes. |
| first_indexed | 2024-04-24T09:02:06Z |
| format | Article |
| id | doaj.art-aca087aef21f4eaebff6b5ab6d7fdfbd |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:02:06Z |
| publishDate | 2024-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-aca087aef21f4eaebff6b5ab6d7fdfbd2024-04-15T19:26:19ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392024-01-0131263764610.17559/TV-20231129001156Improved DNN-assisted Customer Behavior Analysis with Smart Visual AnalyticsMing Hu0Qinghua Li1Hao Zhou2Department of Art Management, Dongguk University, Seoul, Republic of KoreaCollege of Economics and Management, Yantai Nanshan University, Yantai Shandong, 265713, ChinaBusiness School, Hubei University, Wuhan Hubei, 430062, ChinaA customer behavior analysis examines each customer journey stage using qualitative and quantitative methodologies to understand what motivates consumer behavior. With visual analytics, marketers can decipher the complicated world of customer retargeting, allowing businesses to visualize data and ask and answer infinite questions. Because of this, they are better able to comprehend who their consumers are and why they act in certain ways. This paper provides a significant solution named improved DNN-assisted Customer Behavior Analysis (iDNN-CBA) with smart visual analytics. This paper suggests an interactive section for collecting customer reviews and feedback. Their facial expressions have been collected and processed using the improved deep neural network (iDNN), and the visual analytics occurs with pattern analysis. The proposed iDNN-CBA has been trained and validated using the experimental analysis by public dataset KAGGLE and observed the highest accuracy of 96.55% compared to other existing behavior analysis schemes.https://hrcak.srce.hr/file/455072customer behaviour analysisdeep neural networksmart visual analyticsvisualize data |
| spellingShingle | Ming Hu Qinghua Li Hao Zhou Improved DNN-assisted Customer Behavior Analysis with Smart Visual Analytics Tehnički Vjesnik customer behaviour analysis deep neural network smart visual analytics visualize data |
| title | Improved DNN-assisted Customer Behavior Analysis with Smart Visual Analytics |
| title_full | Improved DNN-assisted Customer Behavior Analysis with Smart Visual Analytics |
| title_fullStr | Improved DNN-assisted Customer Behavior Analysis with Smart Visual Analytics |
| title_full_unstemmed | Improved DNN-assisted Customer Behavior Analysis with Smart Visual Analytics |
| title_short | Improved DNN-assisted Customer Behavior Analysis with Smart Visual Analytics |
| title_sort | improved dnn assisted customer behavior analysis with smart visual analytics |
| topic | customer behaviour analysis deep neural network smart visual analytics visualize data |
| url | https://hrcak.srce.hr/file/455072 |
| work_keys_str_mv | AT minghu improveddnnassistedcustomerbehavioranalysiswithsmartvisualanalytics AT qinghuali improveddnnassistedcustomerbehavioranalysiswithsmartvisualanalytics AT haozhou improveddnnassistedcustomerbehavioranalysiswithsmartvisualanalytics |