Forecasting new product diffusion using both patent citation and web search traffic

Accurate demand forecasting for new technology products is a key factor in the success of a business. We propose a way to forecasting a new product’s diffusion through technology diffusion and interest diffusion. Technology diffusion and interest diffusion are measured by the volume of patent citations and web search traffic, respectively. We apply the proposed method to forecast the sales of hybrid cars and industrial robots in the US market. The results show that that technology diffusion, as represented by patent citations, can explain long-term sales for hybrid cars and industrial robots. On the other hand, interest diffusion, as represented by web search traffic, can help to improve the predictability of market sales of hybrid cars in the short-term. However, interest diffusion is difficult to explain the sales of industrial robots due to the different market characteristics. Finding indicates our proposed model can relatively well explain the diffusion of consumer goods.精准的新技术产品需求预测，是企业经营成功的关键一环。本研究提出一种融合技术扩散与兴趣扩散的新产品扩散预测方法，其中技术扩散与兴趣扩散分别通过专利引用量与网络搜索流量进行量化表征。本研究将所提方法应用于美国市场混动汽车与工业机器人的销量预测任务。实验结果表明，以专利引用量表征的技术扩散，能够有效解释混动汽车与工业机器人的长期销量走势。另一方面，以网络搜索流量表征的兴趣扩散，则可在短期维度提升混动汽车市场销量的预测精度。但由于市场特性存在差异，兴趣扩散难以解释工业机器人的销量变化。研究结果显示，本研究所提模型能够较好地阐释消费品的扩散规律。