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Understanding Online Customer Touchpoints: A Deep Learning Approach to Enhancing Customer Experience in Digital Retail

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Understanding Online Customer Touchpoints: A Deep Learning Approach to Enhancing Customer Experience in Digital Retail. / Yilin, Zhao; Fayoumi, Amjad; Shahgholian, Azar.
The 9th International Conference on Information Technology Trends (ITT 2023) . IEEE, 2023.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Yilin, Z, Fayoumi, A & Shahgholian, A 2023, Understanding Online Customer Touchpoints: A Deep Learning Approach to Enhancing Customer Experience in Digital Retail. in The 9th International Conference on Information Technology Trends (ITT 2023) . IEEE. https://doi.org/10.1109/ITT59889.2023.10184269

APA

Yilin, Z., Fayoumi, A., & Shahgholian, A. (2023). Understanding Online Customer Touchpoints: A Deep Learning Approach to Enhancing Customer Experience in Digital Retail. In The 9th International Conference on Information Technology Trends (ITT 2023) IEEE. https://doi.org/10.1109/ITT59889.2023.10184269

Vancouver

Yilin Z, Fayoumi A, Shahgholian A. Understanding Online Customer Touchpoints: A Deep Learning Approach to Enhancing Customer Experience in Digital Retail. In The 9th International Conference on Information Technology Trends (ITT 2023) . IEEE. 2023 Epub 2023 May 24. doi: 10.1109/ITT59889.2023.10184269

Author

Yilin, Zhao ; Fayoumi, Amjad ; Shahgholian, Azar. / Understanding Online Customer Touchpoints : A Deep Learning Approach to Enhancing Customer Experience in Digital Retail. The 9th International Conference on Information Technology Trends (ITT 2023) . IEEE, 2023.

Bibtex

@inproceedings{3b66a65b1ccf41139db26b92f4e5a581,
title = "Understanding Online Customer Touchpoints: A Deep Learning Approach to Enhancing Customer Experience in Digital Retail",
abstract = "This study investigates the main touchpoints that customers value most when shopping online and their attitudes towards them, using Ocado's customer reviews as a case study. Employing machine learning and deep learning methods, such as word2vec, CNN-based sentiment models, and embedding-based topic models, the analysis identified seven critical touchpoints across pre-purchase and post-purchase stages. Recommendations were provided regarding promotional opportunities, technology utilization, and customer experience creation, highlighting the need for different strategies based on customer stages in their journey. The findings offer valuable insights for retail companies transitioning to digital platforms, emphasizing the importance of understanding customer needs and prioritizing touchpoints. Future research could explore additional retail companies with various channels and incorporate different types of customer views to provide a broader perspective on touchpoints.",
author = "Zhao Yilin and Amjad Fayoumi and Azar Shahgholian",
year = "2023",
month = jul,
day = "24",
doi = "10.1109/ITT59889.2023.10184269",
language = "English",
isbn = "9798350327519",
booktitle = "The 9th International Conference on Information Technology Trends (ITT 2023)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Understanding Online Customer Touchpoints

T2 - A Deep Learning Approach to Enhancing Customer Experience in Digital Retail

AU - Yilin, Zhao

AU - Fayoumi, Amjad

AU - Shahgholian, Azar

PY - 2023/7/24

Y1 - 2023/7/24

N2 - This study investigates the main touchpoints that customers value most when shopping online and their attitudes towards them, using Ocado's customer reviews as a case study. Employing machine learning and deep learning methods, such as word2vec, CNN-based sentiment models, and embedding-based topic models, the analysis identified seven critical touchpoints across pre-purchase and post-purchase stages. Recommendations were provided regarding promotional opportunities, technology utilization, and customer experience creation, highlighting the need for different strategies based on customer stages in their journey. The findings offer valuable insights for retail companies transitioning to digital platforms, emphasizing the importance of understanding customer needs and prioritizing touchpoints. Future research could explore additional retail companies with various channels and incorporate different types of customer views to provide a broader perspective on touchpoints.

AB - This study investigates the main touchpoints that customers value most when shopping online and their attitudes towards them, using Ocado's customer reviews as a case study. Employing machine learning and deep learning methods, such as word2vec, CNN-based sentiment models, and embedding-based topic models, the analysis identified seven critical touchpoints across pre-purchase and post-purchase stages. Recommendations were provided regarding promotional opportunities, technology utilization, and customer experience creation, highlighting the need for different strategies based on customer stages in their journey. The findings offer valuable insights for retail companies transitioning to digital platforms, emphasizing the importance of understanding customer needs and prioritizing touchpoints. Future research could explore additional retail companies with various channels and incorporate different types of customer views to provide a broader perspective on touchpoints.

U2 - 10.1109/ITT59889.2023.10184269

DO - 10.1109/ITT59889.2023.10184269

M3 - Conference contribution/Paper

SN - 9798350327519

BT - The 9th International Conference on Information Technology Trends (ITT 2023)

PB - IEEE

ER -