Master's Thesis Michael Legenc
Using Natural Language Processing and Machine Learning to Assist First-Level Customer Support for Contract Management
Check24 is a comparison portal, whose customer support is crucial to its business model. At peak times 1.800 incoming emails are recorded per day, which entails a corresponding supply of manpower to process them.
These emails have recurring topics of which one can probably draw a profit from by automation. Especially, questions regarding bonus payments, duration and terms and conditions of available or concluded contracts repeat themselves in the domain of check24´s energy department. A bot with artificial intelligence using text mining approaches could automatically answer a part of these mails by enriching boilerplates for the answers with personal data and information from the email´s context.
Thus, the purpose of this research is to find an appropriate approach to cluster emails by their semantics, to estimate the correctness of their assignment to a cluster and then to collect enough data for the answer to be generated. These clusters and their emails´ estimations are required for selecting emails, that are suitable for automatic processing without failing the well-known Turing test. This test checks whether a human is capable of telling if an answer has been written by a computer or a human.
All of this will be applied in the context of check24´s energy department to gather knowledge about applicability and in how far both, customers and economic viability, can benefit from it.
| Attribute | Value |
|---|---|
| Title (de) | Nutzung von Natural Language Processing und Machine Learning zur Unterstützung des First-Level Customer Supports im Vertrags-Management |
| Title (en) | Using Natural Language Processing and Machine Learning to Assist First-Level Customer Support for Contract Management |
| Project | |
| Type | Master's Thesis |
| Status | completed |
| Student | Michael Legenc |
| Advisor | Dr. Daniel Braun |
| Supervisor | Prof. Dr. Florian Matthes |
| Start Date | 15.07.2017 |
| Sebis Contributor Agreement signed on | 20.06.2017 |
| Checklist filled | Yes |
| Submission date | 15.01.2018 |