A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems

[+] Author and Article Information
Feng Zhou

4901 Evergreen Rd Dearborn, MI 48128 Dearborn, MI 48128-2406 fzhou35@gatech.edu

Jackie Ayoub

4901 Evergreen Rd. Dearborn, MI 48128 jyayoub@umich.edu

Qianli Xu

1 Fusionopolis Way, #21-01 Connexis Singapore, 138632 Singapore qxu@i2r.a-star.edu.sg

X. Jessie Yang

1205 Beal Ave. Ann Arbor, MI 48109 xijyang@umich.edu

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received April 14, 2019; final manuscript received July 18, 2019; published online xx xx, xxxx. Assoc. Editor: Conrad Tucker.

ASME doi:10.1115/1.4044435 History: Received April 14, 2019; Accepted July 21, 2019


Creating product ecosystems has been one of the strategic ways to enhance user experience and business advantages. Among many, customer needs analysis for product ecosystems is one of the most challenging tasks in creating a successful product ecosystem from both the perspectives of marketing research and product development. In this paper, we propose a machine learning approach to customer needs analysis for product ecosystems by examining a large amount of online user-generated product reviews within a product ecosystem. First, we filtered out uninformative reviews from the informative reviews using a fastText technique. Then, we extract a variety of topics with regard to customer needs using a topic modeling technique named latent Dirichlet allocation. In addition, we applied a rule-based sentiment analysis method to predict not only the sentiment of the reviews but also their sentiment intensity values. Finally, we categorized customer needs related to different topics extracted using an analytic Kano model based on the dissatisfaction-satisfaction pair from the sentiment analysis. A case example of the Amazon product ecosystem was used to illustrate the potential and feasibility of the proposed method.

Copyright © 2019 by ASME
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