A leading insurance company realizes a need for an efficient way to mine customer data so that they cn understand customer sentiment in all areas of services.
Companies are sitting on a massive trove of customer data in the form of phone transcripts, emails, and surveys. The challenge lies in extracting, analyzing, and summarizing this type of data that is not easy to process.
Another consideration is keeping the customer’s personal data secure in order to remain compliant with company and government data protection policies.
Advanced forms of NLP are designed to handle the unique syntax, vocabulary, and other nuances of human language.
An NLP vendor can provide an on-premises solution to eliminate the complications of transferring data to a third party vendor and ensuring that vendor processes and handles the data according to the insurance company’s data protection policies. With on-premises, the client hosts the software on their own servers; the insurance company is responsible for processing and storing all data.
The insurance company supplies the NLP vendor with a taxonomy of topic areas that they are most concerned about, such as whether a service representative was rude or nice, or if a representative resolved an issue quickly or slowly. The NLP can be built to take into account various forms of language that relate to the various topics. It can also identify multiple sentiments and events within one call.
Example: "My first rep was great, but he transferred me to another rep, who then transferred me to another rep. It took two months for my claim to solved."
The NLP vendor can develop a model that quickly extracts, categorizes, and summarizes these various events, and also provides a sentiment score for those events.
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