The advent of Web 2.0 had a strong effect on consumer behavior. One of these effect is the appearence of eWOM (electronic Word Of Mouth): “any positive or negative statement made by potential, actual or former customers about a product or company, which is made available to a multitude of people and institutions via the internet” (Henning-Thurau, Qwinner, Walsh and Gremler,2004). The contents generated during these information exchanges can take various forms and contain a huge amount of valuable insights about consumers’ preferences and struggles. All this information is vital for companies, which can find hints about how their products are percieved in the market, which are the most beloved features of the offered products or services and which are the main criticalities that need to be solved. The problem is that individuating and structuring this information present online is a complex task and for this reason, ad hoc procedures are needed in order to tackle the problem. Text analytics is the way in which meaning can be extracted from text data available online, such as online reviews.
This thesis is structered as a single case study on Trustpilot, an online platform that works as an online reviews aggregator. The aim is that of finding ways through wich Text Analytics techniques can be applied in the context of online reviews. The result of the investigation is a list of suggestions about possible products that Trustpilot could implement to widen the range of products offered to its business customers.
|Educations||MSc in Management of Innovation and Business Development, (Graduate Programme) Final Thesis|
|Number of pages||82|