An explainable machine learning-based approach for analyzing customers'
online data to identify the importance of product attributes
Abstract
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, which is more connect to customer needs and preferences.
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, providing a guideline for assessing the
importance of each feature and its positive or negative influence on
overall 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 cost and
effort. The proposed method outperforms benchmark methods in terms of
relevant performance metrics.