Modeling Internet Search Behavior of Cross-Laminated Timber

Abstract

The Internet is a powerful tool that can be leveraged to explore user search behavior. Google Trends is a compelling database that tracks the frequency with which all users search any given word. There is thus an opportunity to see if the search histories obtained from Google Trends can be merged with data analytics to tease out underlying relationships with similar searches for cross-laminated timber (CLT). In this study, multiple linear regression was used to predict the search strength of the term cross laminated timber from 60 possible variables that may be directly or indirectly associated with CLT. This study was able to model the search term CLT (R2 = 0.76) using a reduced model of 20 variables. However, while prediction strength was strong, our primary interest was to statistically classify and rank important variables that might be important to CLT. To achieve this, the Mallow's Cp statistic was used to build the most robust model possible. To confirm with the literature, we also compared our study with another Web-based study and found a significant linear relationship between the t statistic in our study and the frequency of the same or similar search term in their study (R2 = 0.76). This agreement between studies helps to support our hypothesis that multiple linear regression coupled with Google Trends is a new tool that may assist marketers to identify emerging trends important to CLT.