From Calls to Insights: Using NLP and LLMs to Understand Customer Needs at Scale

This was a project for a big german mobile provide with over 6 mio. customers. The goal was to better understand customer needs and satisfaction based on what they were reporting, when they were calling the companies' service hotline. To this end, transformers and LLMs, speech-to-text models and natural language processing techniques were used. Finally an end-to-end ML-Pipeline was deployed, delivering new and valuable insights into their customer's need on a daily basis.

In this project I was responsible for establishing an end-to-end pipeline for data preparation, processing, and training related to incoming customer service calls. This involved leveraging methods from natural language processing (NLP) and generative artificial intelligence (AI) to transform the underlying data into text and subsequently into results that could be analyzed by the business unit. To achieve this, machine learning models (Transformers; LLMs) were trained to predict relevant parameters concerning customer concerns and satisfaction, and these models were implemented into the processing pipeline within the AWS cloud. Through this MLOps process and the daily influx of new resulting data, valuable and previously unavailable insights into customer call behavior were provided to the business unit and customers alike.

Due to confidality obligations further details about this project cannot be disclosed. For a reference letter detailing my contributions to this company, please feel free to reach out.

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From Calls to Insights: Using NLP and LLMs to Understand Customer Needs at Scale

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