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Product family lean improvement based on matching deep mining of customer group preference

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Abstract

Mining user preferences from online reviews to understand the representative preferences of different customer groups plays a critical role in product development and improvement, especially in personalized product design. Previous research on mining user preferences usually assumes that all consumers' preferences are homogenous and does not take differences in consumers’ personalities into account. Besides, traditional online review deep mining methods are too broad to focus on precise and detailed mining of customer preferences. To fill the gaps in existing research, our study develops a template matching deep mining method to segment customers and narrow the mining scope of customer group preference, and then proposes a product family lean improvement model. Firstly, K-means and structural change model are applied to cluster customers reliably based on the similarity of user preferences. Secondly, in order to decrease down mining scope of customer group preference, the Improved Deep Structured Semantic Model is designed to determine sentimental polarity sentimental polarity of different groups by matching the standard sentimental polarity review templates and online reviews. Finally, a KANO mapping model is developed to decide the user preferences for product attributes in each customer group according to their sentimental polarity and further summarize the common preferences and personalized preferences of various groups according to the Preference Commonality Measurement Function. Accordingly, product family lean improvement strategies are proposed to provide product developers with improvement directions. An empirical study is carried out on laptop data on JD.COM to verify the validity of the proposed model and product family lean improvement suggestions are put forward.

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Acknowledgements

This work was supported by the Chinese National Natural Science Foundation (no. 71871135).

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Authors and Affiliations

  1. School of Management, Shanghai University, Shanghai, 200444, People’s Republic of China

    Shugang Li, Fang Liu, Hanyu Lu, Yuqi Zhang & Yueming Li

  2. China United Network Communications Group Co., Ltd., Hefei, 230000, Anhui, People’s Republic of China

    Yueming Li

  3. Department of Automation, East China University of Science and Technology, Shanghai, 200237, People’s Republic of China

    Zhaoxu Yu

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  1. Shugang Li
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Correspondence to Yueming Li.

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Li, S., Liu, F., Lu, H. et al. Product family lean improvement based on matching deep mining of customer group preference. Res Eng Design 32, 469–488 (2021). https://doi.org/10.1007/s00163-021-00367-8

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