From Data To Action: Leveraging Conversation Analytics To Enhance Customer Experience

Love them or hate them, chatbots, virtual assistants, and other forms of AI-powered customer interfaces are becoming increasingly widespread. The obvious reason for the enthusiastic adoption of AI deployments is cost – human representatives are much more expensive than their digital counterparts. However, another advantage of these interfaces is the tremendous amount of text data that is stored, indexed, and ready to be analyzed. Enter conversation analytics.

Conversation analytics (not to be confused with Conversation Analysis, which is a sub-discipline of sociology and linguistics), uses machine-powered analysis not just of chat data, but also text from emails, phone calls transcribed using speech recognition, and social media interactions, to create a deeper understanding of all crucial aspects of conversations including content, context, and attitude of contributors. 

The Power and Possibilities of Conversation Analytics

Conversation analytics is intimately tied with Natural language processing (NLP), a combination of computational linguistics, statistics, and machine learning models. The insights yielded by the application of NLP include sentiment analysis, which can determine how happy or unhappy the customer is with the organization, and also the ability to identify topics and issues and their relative prevalence. 

Conclusions drawn from conversation analytics often allow an organization to improve or enhance the customer experience by identifying areas that need improvement. These improvements can be applied across a wide range of business activities, including changes to products, services, and support processes.

Analyzing Data for Continuous Improvement

In addition to aggregating text data for analysis, software tools can gather and analyze other types of data such as time and duration of the conversation, as well as other customer data like location. These metrics can additionally be used to evaluate the performance of human customer service agents, and additional resources for training or support can be allocated as needed. 

Many industries are subject to strict regulatory requirements, where compliance with government regulations, internal policies, and specific industry standards may need to be continually monitored and assessed. For these companies, conversation analytics can act as an important tool to ensure that all company practices are in conformity with the expected norms.

A Powerful Forecasting Tool

The ability to analyze text for keywords and patterns gives a strong boost to a business’ ability to predict the future needs and preferences of customers. The simplest example would be data showing that many customers have inquired about a specific feature or product, allowing the company to act proactively to meet that need. A more complex scenario is one in which conversation analytics determines which interactions between sales representatives and customers are likely to result in sales, allowing for more accurate forecasts long before the actual purchasing event. 

Taken as a whole, this new field of big data textual analysis will provide a wide range of organizations with valuable insights into the temperament and decision-making processes of customers and potential customers. Companies, educational institutions, and even government agencies will need to invest in the necessary resources and talent to harness the full potential of conversation analytics, ensuring that insights translate into tangible results.