An explainable machine learning-based approach for analyzing customers'
  online data to identify the importance of product attributes
Aigin Karimzadeh Amir Zakery Mohammadreza Mohammadi Ali Yavari
Online customer data provides valuable information for product design and
marketing research, as it can reveal the preferences of customers. However,
analyzing these data using artificial intelligence (AI) for data-driven design
is a challenging task due to potential concealed patterns. Moreover, in these
research areas, most studies are only limited to finding customers' needs. In
this study, we propose a game theory machine learning (ML) method that extracts
comprehensive design implications for product development. The method first
uses a genetic algorithm to select, rank, and combine product features that can
maximize customer satisfaction based on online ratings. Then, we use SHAP
(SHapley Additive exPlanations), a game theory method that assigns a value to
each feature based on its contribution to the prediction, to provide a
guideline for assessing the importance of each feature for the total
satisfaction. We apply our method to a real-world dataset of laptops from
Kaggle, and derive design implications based on the results. Our approach
tackles a major challenge in the field of multi-criteria decision making and
can help product designers and marketers, to understand customer preferences
better with less data and effort. The proposed method outperforms benchmark
methods in terms of relevant performance metrics.