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

Yilin, Zhao and Fayoumi, Amjad and Shahgholian, Azar (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. ISBN 9798350327519

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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.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
193279
Deposited By:
Deposited On:
15 Jun 2023 15:15
Refereed?:
Yes
Published?:
Published
Last Modified:
17 Jun 2024 23:48