University of Twente Student Theses


Predicting the sales figures of TVs using data from Google Trends

Lehmann, L.P. (2016) Predicting the sales figures of TVs using data from Google Trends.

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Abstract:Predicting real-world events by using either social media analytics or search engine activity is a widely discussed topic. This paper shows how to predict the sales of TVs with data from Google Trends. The TVs were categorised into three groups, namely small, medium-sized and large TVs. In order to test the correlation between Google Trends data and sales numbers, linear regression was used. It was found there is a time difference between an increase in search activity and an increase in sales. This can be explained by the AIDA model, the consumer buying process, and the customer journey model. This research proposes that Google Trends can predict an increase or decrease in sales for specific models, and thus can provide help in inventory planning. In future research it can be tested whether the model can be adjusted and thus also be applied to other consumer goods. This research paper contributes to the Google Trends literature by predicting the sales numbers of different sizes of TVs for the first time and by delivering an explanation to the time lag with the use of different models.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:85 business administration, organizational science
Programme:Business Administration MSc (60644)
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