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Creating a ‘customer centricity graph’ from unstructured customer feedback
Certain industries, such as car insurance, do not have many customer touch points and do not offer a great deal of differentiation in the market. Marketers in such industries must therefore analyse vast amounts of customer-generated feedback in order to analyse customer preference in a quantitative manner. At present, this is done via market research or manual work, as an automated tool for summarising unstructured texts is as yet unavailable for certain European languages, including German. This paper discusses how Insaas and LMU Munich have used publicly available feedback on car insurance in Germany to develop a dedicated pipeline for the computation and visualisation of customer opinions. This paper provides an overview of the various steps of the procedure.
The full article is available to subscribers to the journal.
Elisabeth Lebmeier is a master’s student of statistics at LMU Munich, where she is currently working on her thesis about different approaches to aspect-based sentiment analysis. Her research interests include machine learning, deep learning and natural language processing.
Naiwen Hou is a master’s student of statistics at LMU Munich with an economic and social science background.
Korbinian Spann works for W.L. Gore & Associates (the GORE-TEX company) and is responsible for digital media buying and digital analytics for Europe, America and Asia. He focuses on data and communication concepts for different languages and regions, and is inspired by the continuous development of digital markets.
Matthias Aßenmacher researches natural language processing at LMU Munich, where he is currently studying for a PhD. His main research areas include inter alia the comparability as well as possible applications/use cases of large pre-trained language models.
CitationLebmeier, Elisabeth, Hou, Naiwen, Spann, Korbinian and Aßenmacher, Matthias (2021, January 1). Creating a ‘customer centricity graph’ from unstructured customer feedback. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 6, Issue 3.