There exists a vast trove of Customer Experience data in the form of product reviews, forum posts, customer service/customer satisfaction surveys and suchlike. This data is often in unstructured form. Companies that own this data would like to summarize these (often vast) data-sets.
One of the most common methods of text mining (for lack of a better word), is Topic Modeling. Given a large corpus of text, a topic model can assign a probabilistic score for each document-topic pair.
At BAICONF'15, I presented a paper and presentation on the effectiveness of using Topic Modeling for summarizing Customer Experience data. This paper was the result of our experiences (working with Extrack at Bridgei2i) of applying multiple methods such as Unsupervised Topic Models, Semi-Supervised Topic Models and others, on multiple types of Customer Experience data. The paper, as well as the presentation, are presented below. (Note, due to scheduling issues, the paper/presentation is not listed in the BAICONF schedule).
Here's the link to the paper along with the accompanying presentation.