Automated Analysis of Customer Contacts – a Fintech Based Case Study
The rapid development of information technologies has brought along abnormal amounts of data being generated on a daily basis and the need to automatically analyse it to gain a competitive advantage. Traditional data mining techniques have been efficiently applied in a variety of commercial applications, yet they are only applicable on structured data. However, an overwhelming amount of existing data is in an unstructured (e.g. textual) form, hence it is crucial for companies to build solutions to automatically extract useful information from it. Given master’s thesis is with a practical nature and its purpose was to implement an automated text analysis model using data from TransferWise Ltd. that can be used to efficiently prioritise and measure incoming customer contacts. To achieve this, the author conducted numerous experiments via employing classical as well as novel natural language processing techniques. Apropos, employing novel methods did not ensure a noticeably better outcome. The established model is important for both the company as well as its customers since it can be used to prioritise incoming contacts based on their complexity or urgency. This ensures a convenient customer experience and is likely to accelerate growth by making operational procedures more efficient. Besides its practical value, given thesis also provides an extensive comparison of numerous natural language processing techniques, their suitability and opportunities.
Graduation Thesis language
Graduation Thesis type
Master - Computer Science
Sven Laur, Taavi Tamkivi