NLP Application in Digitally Empowered Contact Centers
The conventional trend to outsource non-critical business functions to offshore BPOs is shifting to Digitally Empowered Contact Centers. They utilize a platform of NLP based digital support tools, such as voice assistant, voice-based chatbot, voice-based sentiment analyzer, to provide a higher level of customer experience and retention. Benefits include increased efficiencies by offloading routine easy tasks to a digital assistant, almost eliminating frontline agents and a higher degree of data driven customer insights for the client.
The NLP engine listens for customer questions through a Visual IVR/Voice Assistant that recognizes speech and retrieves the most appropriate response from the database per its training (document classification) in the particular industry. If the identified response does not achieve a high probability of accuracy (per the tolerance levels set) the question is routed to a human.
A Voice-Enabled Sentiment Analyzer can be applied to determine the level of calmness or frustration of the caller and assist the agent with recommendations/suggestions to address the situation. The NLP engine relies on information extraction, sentiment analysis and contextual conversation capabilities (albeit not very advanced yet) to be successful.
This can be an effective way to reduce cost and differentiate your services as compared to the competition.
Benefits of Using Advanced NLP Algorithms within Medical Information Call Centers
In Medical Affairs functions, the Medical Information call center plays a particularly important function. Advanced NLP algorithms can be trained to predict the answer to questions being asked by the HCP or field medical resource as well as extract the relevant source documents. Addressing this need, will reduce call center tickets and resource costs.
For simple queries, the MSL can use a mobile app with natural language capabilities, to directly query information during conversations with the HCP. This will increase effectiveness and help differentiate the MSL in their field work (by reducing their reliance on the call center).
By parameterizing and classifying the source content within the document repository, the NLP engine can be trained to intelligently respond to related queries. As vast amounts of information are already in a digitized manner, an advanced NLP engine can predict the exact answer and select the appropriate source document to surface for reference.
The agent will require less time searching and analyzing content to fulfil the response. For previously asked questions, the engine can be adequately designed to predict the correct response based on past data sets. For complex question requiring human assessment, the agent needs to provide additional supervised and re-enforcement-based NLP training techniques to improve outcomes in the future. As the queries mostly contain regularity and are in the framework of the firm’s marketed drugs, it is possible for the advances in NLP algorithms to solve for these challenges.
These real-world applications demonstrate the power of NLP. If applied in the right context they can improve customer experience, reduce resource cost, differentiate the contact/call center, and accelerate insights generation.