Predicting iPhone Sales from iPhone Tweets

Abstract

Abstract-Recent research in the field of computational social science have shown how data resulting from the widespread adoption and use of social media channels such as twitter can be used to predict outcomes such as movie revenues, election winners, localized moods, and epidemic outbreaks. Underlying assumptions for this research stream on predictive analytics are that social media actions such as tweeting, liking, commenting and rating are proxies for user/consumer's attention to a particular object/product and that the shared digital artefact that is persistent can create social influence. In this paper, we demonstrate how social media data from twitter can be used to predict the sales of iPhones. Based on a conceptual model of social data consisting of social graph (actors, actions, activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate a linear regression model that transforms iPhone tweets into a prediction of the quarterly iPhone sales with an average error close to the established prediction models from investment banks. This strong correlation between iPhone tweets and iPhone sales becomes marginally stronger after incorporating sentiments of tweets. We discuss the findings and conclude with implications for predictive analytics with big social data.

Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced Twitter Data

“…For example, Twitter tweets have been used for the prediction of movie revenues (Asur and Huberman, 2010) and stock prices (Kharratzadeh and Coates, 2012;Bollen and Mao, 2011). Lassen et al (2014) predicted quarterly iPhone sales motivated by the AIDA model, but did not model AIDA directly as we do in this paper.…”

Buzz vs. Sales: Big Social Data Analytics of Style Icon Campaigns and Fashion Designer Collaborations on H&Ms Facebook Page

“…In their work, they used analysis of hourly rate of tweets about movies, their re-tweets and sentiment polarity to accurately forecast the box-office movies revenue. Lassen and colleagues predicted iPhone sales from iPhone tweets [10]. In terms of macro-societal relationships, a research study investigated whether the public mood as measured from large-scale collection of Twitter tweets can be correlated or even predictive of Dow Jones Industrial Average (DJIA) values has been explored by Bollen and Mao [4].…”

Forecasting Nike's sales using Facebook data

“…Similar to methodology of the research work on data analytics [2], [12], we followed these steps for the forecasting analysis as shown in Fig. 4.…”

“…In particular, the diverse and massive activity in social media can be seen as a proxy for collective opinions and attention of the population, previously hard to assess [1]. Using the AIDA-framework, it can be deducted that this attention may lead to purchases [2]. This is particularly interesting for companies selling to consumers, so-called B2C-companies.…”

“…Recent research in computational social science has shown how Big Social Data, defined as the large volumes of unstructured data that is generated from societal and organizational adoption and use of social media, can be used to accurately predict real-life outcomes such as box office sales for movies [1] and iPhone sales [2]. For corporations and their stakeholders, these new sources of information can be used to produce meaningful facts, actionable insights and ultimately valuable outcomes for decision-makers.…”