Using Twitter to Predict Sales: A Case Study

TL;DR

This study explores how classifying Twitter activity, especially positive Tweets by individuals, can improve sales prediction for less-publicized products, demonstrating the importance of advanced statistical analysis.

Contribution

It introduces a method for classifying Tweets to reveal relations between Twitter activity and sales for products with low social media attention.

Findings

Positive Tweets by persons forecast sales increases

Peaks in positive Tweets correlate with sales spikes

Classification of Tweets enhances sales prediction accuracy

Abstract

This paper studies the relation between activity on Twitter and sales. While research exists into the relation between Tweets and movie and book sales, this paper shows that the same relations do not hold for products that receive less attention on social media. For such products, classification of Tweets is far more important to determine a relation. Also, for such products advanced statistical relations, in addition to correlation, are required to relate Twitter activity and sales. In a case study that involves Tweets and sales from a company in four countries, the paper shows how, by classifying Tweets, such relations can be identified. In particular, the paper shows evidence that positive Tweets by persons (as opposed to companies) can be used to forecast sales and that peaks in positive Tweets by persons are strongly related to an increase in sales. These results can be used to improve sales forecasts and to increase sales in marketing campaigns.