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Can search engine data improve accuracy of demand forecasting for new products? Evidence from automotive market

机译:搜索引擎数据可以提高新产品需求预测的准确性吗?来自汽车市场的证据

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Purpose-The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such data can be used in forecasting new product diffusion.Design/methodology/approach-This research proposes a new product diffusion model based on the Bass diffusion model by incorporating consumer internet search behavior. Actual data from search engine queries and new vehicle sales for each vehicle class and region are used to estimate the proposed model. Statistical analyses are used to interpret the estimated results, and the prediction performance of the proposed method is compared with other methods to validate the usefulness of data for internet search engine queries in forecasting new product diffusion.Findings-The estimated coefficients of the proposed model provide a clear interpretation of the relationship between new product diffusion and internet search volume. In 83.62 percent of 218 cases, analyzing the internet search pattern data are significant to explain new product diffusion and that internet search volume helps to predict new product diffusion. Therefore, marketing that seeks to increase internet search volume could positively affect vehicle sales. In addition, the demand forecasting performance of the proposed diffusion model is superior to those of other models for both long-term and short-term predictions.Research limitations/implications-As search queries have only been available since 2004, comparisons with data from earlier years are not possible. The proposed model can be extended using other big data from additional sources.Originality/value-This research directly demonstrates the relationship between new product diffusion and consumer internet search pattern and investigates whether internet search queries can be used to forecast new product diffusion by product type and region. Based on the estimated results, increasing internet search volume could positively affect vehicle sales across product types and regions. Because the proposed model had the best prediction power compared with the other considered models for all cases with large margins, it can be successfully utilized in forecasting demand for new products.
机译:目的 - 本文的目的是使用大数据分析新产品扩散和消费者互联网搜索模式之间的关系,并调查这些数据是否可以用于预测新产品扩散.Design/Methodology/ApproChis-该研究提出了新的通过结合消费者互联网搜索行为,基于低音扩散模型的产品扩散模型。来自搜索引擎查询和每个车辆类和区域的新车辆销售的实际数据用于估计所提出的模型。统计分析用于解释估计结果,并且将所提出的方法的预测性能与其他方法进行比较,以验证预测新产品扩散中的互联网搜索引擎查询的数据的有用性。 - 所提出的模型的估计系数提供清晰地解释新产品扩散与互联网搜索卷之间的关系。在83.62%的218例中,分析互联网搜索模式数据对于解释新产品扩散并且互联网搜索量有助于预测新产品扩散。因此,寻求增加互联网搜索卷的营销可能会对车辆销售产生积极影响。此外,所提出的扩散模型的需求预测性能优于其他模型,用于长期和短期预测。搜索限制/含义 - 因为自2004年以来只有搜索查询,与早期数据的比较多年不可能。可以使用来自其他来源的其他大数据来扩展所提出的模型。 - 这项研究直接演示了新产品扩散和消费者互联网搜索模式之间的关系,并调查了互联网搜索查询是否可用于预测产品类型的新产品扩散和地区。基于估计结果,增加互联网搜索卷可以积极影响产品类型和地区的车辆销售。由于所提出的模型具有最佳的预测功率,而另一种具有大幅边距的所有案例相比,因此可以成功地用于预测对新产品的需求。

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