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

Zhan, Yuanzhu; Tan, Kim Hua; Huo, Baofeng

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 realized 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.

Citation

Zhan, Y., Tan, K. H., & Huo, B. (2019). Bridging customer knowledge to innovative product development: a data mining approach. International Journal of Production Research, 57(20), 6335-6350. https://doi.org/10.1080/00207543.2019.1566662

• Journal Article Type: Article
• Acceptance Date: Dec 29, 2018
• Online Publication Date: Jan 16, 2019
• Publication Date: Jan 16, 2019
• Deposit Date: Feb 11, 2019
• Publicly Available Date: Jan 17, 2020
• Journal: International Journal of Production Research
• Print ISSN: 0020-7543
• Electronic ISSN: 1366-588X
• Publisher: Taylor & Francis Open
• Peer Reviewed: Peer Reviewed
• Volume: 57
• Issue: 20
• Pages: 6335-6350
• DOI: https://doi.org/10.1080/00207543.2019.1566662
• Keywords: product development; customer knowledge management; data mining; fast-cycle industry
• Public URL: https://nottingham-repository.worktribe.com/output/1535321
• Publisher URL: https://www.tandfonline.com/doi/pdf/10.1080/00207543.2019.1566662?needAccess=true
• Additional Information: Peer Review Statement: The publishing and review policy for this title is described in its Aims & Scope.; Aim & Scope: http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=tprs20; Received: 2018-03-01; Accepted: 2018-12-29; Published: 2019-01-16