Yinxing Li

Li Yinxing, Tsukasa Ishigaki

Product line extension is a marketing strategy that enhances a company's sphere of influence. Because excessive line extensions disrupt brand image, only appropriate line extensions based on consumer needs are desirable. Marketers should know the key consumer attributes of the primary customers for new line-extended products before companies enter the market. This paper describes a method for predicting changes in consumer attributes for new line-extended products using a novel deep learning model. The proposed model, Conditional Tabular Variational Auto-Encoder (CTVAE), generates synthetic data from large-scale tabular data of consumers and products. It can provide various implications about effective product line marketing for marketers. The experimental results demonstrate that the CTVAE offers superior prediction performance than existing models. We indicate implications for new products that change containers or flavors for effective product line marketing. The proposed approach has the potential to contribute to avoiding cannibalization and to designing product images and marketing strategies.

Bingling Wang, Yinxing Li, Wolfgang Karl Härdle

$K$-means clustering is one of the most widely-used partitioning algorithm in cluster analysis due to its simplicity and computational efficiency. However, $K$-means does not provide an appropriate clustering result when applying to data with non-spherically shaped clusters. We propose a novel partitioning clustering algorithm based on expectiles. The cluster centers are defined as multivariate expectiles and clusters are searched via a greedy algorithm by minimizing the within cluster '$τ$ -variance'. We suggest two schemes: fixed $τ$ clustering, and adaptive $τ$ clustering. Validated by simulation results, this method beats both $K$-means and spectral clustering on data with asymmetric shaped clusters, or clusters with a complicated structure, including asymmetric normal, beta, skewed $t$ and $F$ distributed clusters. Applications of adaptive $τ$ clustering on crypto-currency (CC) market data are provided. One finds that the expectiles clusters of CC markets show the phenomena of an institutional investors dominated market. The second application is on image segmentation. compared to other center based clustering methods, the adaptive $τ$ cluster centers of pixel data can better capture and describe the features of an image. The fixed $τ$ clustering brings more flexibility on segmentation with a decent accuracy.