一种用于预测新产品线延伸消费者属性变化的深度学习模型 (A Deep Learning Model to Predicting Changes in Consumer Attributes for New Line-extended Products)

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.

翻译：产品线延伸是一种扩大公司影响范围的营销策略。由于过度的产品线延伸会破坏品牌形象，只有基于消费者需求的适当延伸才是可取的。在企业进入市场之前，营销人员应了解新产品线延伸产品主要目标客户的关键消费者属性。本文提出了一种利用新型深度学习模型预测新产品线延伸消费者属性变化的方法。所提出的模型——条件表格变分自编码器（CTVAE），能够从消费者与产品的大规模表格数据中生成合成数据。该模型可为营销人员提供关于有效产品线营销的多种启示。实验结果表明，CTVAE的预测性能优于现有模型。我们针对改变包装或口味的创新产品，提出了有效产品线营销的实践启示。该方法有望为规避市场蚕食效应、设计产品形象及制定营销策略提供支持。