Analytics of customer feedback
Our goal is to analyze customer feedback that we receive by email.
1. First, a spam filter will assure that our dataset includes only messages sent by actual customers.
2. Afterwards, we prepare the text data for the upcoming NLP tasks by correcting potential spelling mistakes.
3. Next, we run a sentiment analysis on the texts to detect positive and negative tones and then cluster the data such that we can analyze similar documents together.
4. Now we can run a text summarization over each cluster of messages to see find the main keywords. One possible outcome would be clusters summarized by “design, phone, looks” and “phone, battery, charging”.
5. Now we can analyze how satisfied the customers are with aspects of our product such as the design or the battery by comparing the positive and negative sentiments of the messages contained in each cluster.
For example, 90% of the consumers might like the design, but 52% complain about the battery. Such analytics generate valuable insights and enable informed decisions, e.g. where to allocate R&D budget for the next model.