The Growing Role of Text Analytics in Voice of the Customer Strategies
October 14, 2014 Leave a comment
- As customer care organizations recognize the importance of including ‘voice of the customer’ (VoC) tools in their contact center analytics toolboxes, the capture and analysis of unstructured data will grow in importance.
- Because text analytics provides the ability to include large streams of input from a broad collection of unstructured data sources, it is a very complementary solution to other analysis tools such as speech analytics and post-call customer surveys.
In previous blogs I have commented on the growing importance of collecting, managing and using “big data” effectively to drive proactive efforts designed to improve overall customer service. Today many companies base their customer feedback analysis, or so-called VoC solutions, on a single data collection tool such as post-call surveys or speech analytics. While these tools can provide excellent insights into the customer’s thought process, emotions and purchase intentions, they are often limited by their focus on a single source of information or the fact that customer inputs are confined to a set of multiple choice questions posed to a customer. I am finding that as VoC campaigns mature, companies are beginning to realize that capturing the benefits of big data analytics requires broadening the collection of data to all the data that is available to them. This should include analysis of voice calls, web chats, responses to open-ended questions of customers and notes recorded by contact center agents and other front-line employees.
In order to do this effectively, when developing a VoC program, businesses should consider which data sources are available to them today and will be in the future. This would include information from post-call surveys, agent transcripts, sales notes, emotion detection systems and data from customer relationship management (CRM) systems, etc. Additionally, forward-thinking organizations should include the growing information flow of mobile customer service access endpoints such as smartphones and tablets. In addition to traditional customer feedback mobile endpoints can provide a wealth of new information including customer location information, photos and videos. Those mobile endpoints can also act as a distribution channel for proactively reaching out to these customers while in transit after information is analyzed and actionable solutions developed.
Given the diversity of data input channels and the varied format of the data available to feed VoC systems, it is natural for business analysts and customer care managers to seek the least common denominator among the choice of analysis tools that could enable the collection of these multiple sources of unstructured data, analysis of the information and integration with the tools analyzing more structured data sources. This leads me to believe that text analytics will be a growing choice of maturing VoC solutions going forward. Text analytics enables the analysis of open-ended survey questions, ongoing agent/customer chat interactions, agent call notes, transcribed voice customer conversations and front-line employee notes, and it can add a great deal of data to the more structured information currently being collected. In addition, as the information from mobile customer interactions grows to exceed current, more traditional interaction channels, large quantities of useable data can be added via text analytics.