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Know your customer: Detection of Customer Experience (CX) in Social Platforms using Text Categorization

Abstract:

Customers nowadays are one online post away from their stores, specially when it comes to post-shopping experiences. This translates to large amounts of text messages to evaluate and process for big brands that aim to maintain a good quality of service as well as a digital channel of communication for their customers. Automating the understanding of this text data poses questions such as how large the corpus should be and which are the best algorithms to discriminate whether a social media post is related or not to customer experience (CX). In order to help answering these questions, first, we get hold of posts from three different platforms: Foursquare (77K) , Twitter (153K) and Facebook (2.2M). Such posts are directed to brands ranked in the ForeSee CX Index and the Forrester CX Index rankings. Second, we build a binary classifier using different algorithms to identify customer experience posts on a social platform. The accuracy of the best performing setting is 86.4% for Facebook and 91.2% for Twitter. Third, we explore the effect of increasing the number of training samples, and how a plateau is reached after 5K posts. Finally, we conduct experiments using different combinations of n-grams as features for the text mining process. As a result we observe that uni-grams and bi-grams are the best combination when we need to choose features for a classifier discriminating customer experience social media posts on Twitter and a combination of up to four-grams on Facebook.