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Bridging customer knowledge to innovative product development: a data mining approach

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  • Yuanzhu Zhan
  • Kim Hua Tan
  • Baofeng Huo

Abstract

In the big data era, firms are inundated with customer data, which are valuable in improving services, developing new products, and identifying new markets. However, it is not clear how companies apply data-driven methods to facilitate customer knowledge management when developing innovative new products. Studies have investigated the specific benefits of applying data-driven methods in customer knowledge management, but failed to systematically investigate the specific mechanics of how firms realised these benefits. Accordingly, this study proposes a systematic approach to link customer knowledge with innovative product development in a data-driven environment. To mine customer needs, this study adopts the Apriori algorithm and C5.0 in addition to the association rule and decision tree methodologies for data mining. It provides a systematic and effective method for managers to extract knowledge ‘from’ and ‘about’ customers to identify their preferences, enabling firms to develop the right products and gain competitive advantages. The findings indicate that the knowledge-based approach is effective, and the knowledge extracted is shown as a set of rules that can be used to identify useful patterns for both innovative product development and marketing strategies.

Suggested Citation

  • Yuanzhu Zhan & Kim Hua Tan & Baofeng Huo, 2019. "Bridging customer knowledge to innovative product development: a data mining approach," International Journal of Production Research, Taylor & Francis Journals, vol. 57(20), pages 6335-6350, October.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:20:p:6335-6350
    DOI: 10.1080/00207543.2019.1566662
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    Cited by:

    1. Keeheon Lee, 2021. "A Systematic Review on Social Sustainability of Artificial Intelligence in Product Design," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    2. Yang, Jie & Xie, Hongming & Yu, Guangsheng & Liu, Mingyu, 2021. "Achieving a just–in–time supply chain: The role of supply chain intelligence," International Journal of Production Economics, Elsevier, vol. 231(C).
    3. Mu-Jung Huang & Kuo-Chih Cheng & Ching-Ju Huang & Kun-Meng Lin & Huo-Ming Wang & Cheng-Kuo Chuang & Ming-Cheng Wu, 2021. "Establishing a Dynamic Capital Structure Model for Company Sustainability Performance Using Data Mining Techniques," Sustainability, MDPI, vol. 13(11), pages 1-15, May.
    4. Juan Hao & Xinqin Gao & Yong Liu & Zhoupeng Han, 2023. "Acquisition Method of User Requirements for Complex Products Based on Data Mining," Sustainability, MDPI, vol. 15(9), pages 1-19, May.
    5. Denitsa ZHECHEVA & Nayden NENKOV, 2022. "Business demands for processing unstructured textual data – text mining techniques for companies to implement," Access Journal, Access Press Publishing House, vol. 3(2), pages 107-120, April.
    6. Rakshit, Sandip & Mondal, Sandeep & Islam, Nazrul & Jasimuddin, Sajjad & Zhang, Zuopeng, 2021. "Social media and the new product development during COVID-19: An integrated model for SMEs," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    7. Francesco Castagna & Piera Centobelli & Roberto Cerchione & Emilio Esposito & Eugenio Oropallo & Renato Passaro, 2020. "Customer Knowledge Management in SMEs Facing Digital Transformation," Sustainability, MDPI, vol. 12(9), pages 1-16, May.

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