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Online Data Improves TV Forecasting
Consumers reveal their offline preferences through social posting activity.
Using data from a variety of online sources, Xiao Liu, Param Vir Singh, and Kannan Srinivasan show how easily accessible online information can be useful for marketers to accurately predict consumer demand for TV shows. They combine methods from cloud computing, machine learning, and text mining to illustrate how online platform content, such as Twitter, can be effectively used for forecasting.
Based on analysis of nearly two billion Tweets, Liu, Singh, and Srinivasan find that their information content and timeliness significantly improve forecasting accuracy. In contrast, other online data (e.g., Google Trends, Wikipedia views, IMDB reviews, and Huffington Post news) are very weak predictors of TV show demand. They note: “Users tweet about TV shows before, during, and after a TV show, whereas Google searches, Wikipedia views, IMDB reviews, and news posts typically lag behind the show.”
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A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing by Xiao Liu, Param Vir Singh, and Kannan Srinivasan, Marketing Science (May-June 2016)
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