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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.

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.

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.

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.

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.

The purpose of this paper is to explore the factors determining which communication mediums influence a given consumer deciding to purchase a specific product.

Using a consumer survey and a multivariate probit (MVP) model, the authors explore consumer information searches related to purchases in nine categories: milk, instant noodles, shampoo, mobile phones, televisions, cars, mobile communication services, credit card services, and life insurance.

The media channels that motivate a given consumer to make a given purchase vary depending on both socio-demographic variables and product categories.

As consumers can now obtain product information through different and multiple media channels according to their personal characteristics and the category of the product they seek to purchase, these findings will help companies develop media planning strategies that will effectively target specific market segments.

Unlike previous studies, the authors consider which media channels actually affect a consumer’s product purchase decisions, and the authors do so across product categories and media types to provide practical implications for media planning. Furthermore, this is the first application of the MVP model in this context.