Understanding the Nitty-Gritties of Customer Interaction Analytics
With customers interacting through more and more touchpoints, including phone, email, chat, sms/text messaging, social media, etc., contact centers are flooded with customer-related data. Stored into different contact center and enterprise repositories, this customer-related data enwraps valuable business insights.
The need to get a single view of customers and derive optimal business insights from both speech and text data, are driving the adoption of contact center interaction analytics solutions by organizations. Besides focusing on keywords and phrases, interaction analytics provides the context and relevancy, which are essential to understand what customers are saying and why they are saying so.
Data flowing to organizations from different communication channels has widened the scope to understand customers’ behaviors and sentiments in a better way. But, this has also increased the need to efficiently extract and analyze huge and diverse data sets.
Customer Interaction Analytics enables extracting and analyzing data from all customer interaction channels. Evaluating all forms of data, including speech and text, it provides a unified view of customers, emerging trends, and prevailing issues for effective customer experience management and business growth.
customer-table
Using the valuable insights gleaned from customer interactions analysis, organizations can optimize their day to day operations for improved operational efficacy, customer acquisition, retention, and satisfaction levels. These insights can equally be helpful in improving sales, compliance, collections and operational KPIs.
How Customer Interaction Analytics Works
Performing as a single application, customer interaction analytics leverages speech analytics and text analytics to analyze both speech and text-based interactions across all communication channels. The application features combined dashboards and reports to provide findings in a clear and concise manner, while allowing end-users to drill down and further filter the results.
Speech Analytics carries out the first phase of analysis primarily through two types of speech engines, which are as follows:
customer-table
1. Phonetic Engine: Breaks down speech into phonemes, the smallest part of spoken language. After that the segments within the huge file of phonemes are identified matching with the phonetic index file comprising target words and phrases.
2. Large Vocabulary Continuous Speech Recognition (LVCSR) Engine: Leverages a language model containing a vocabulary/ language dictionary for converting speech of audio files into text. The text files are then searched for targeted words and phrases.
Though LVCSR-based speech analytics have demonstrated to be more precise in finding out the reasons of customer calls, phonetic-based speech analytics are more preferred because of their ability to quickly and cost-effectively process large volumes of speech data.
Besides these two engines, there is one more type of method emerging fast to analyze speech data, which is as follows:
3. Direct Phrase Recognition: This method evaluates speech based on specific phrases predefined as significant to the business. Instead of first transforming speech into phonemes or text, it directly analyzes speech. Since no data is lost in the conversion process, this approach provides a high-level data reliability.
Customer Interaction Analytics usually harnesses the same Text Analytics engine, used to evaluate transcribed calls, to analyze text-based interactions. Thus, Customer Interaction Analytics can effectively evaluate 100% of all customer interactions happening across all channels to unearth meaningful insights for informed decision making.
When combined with other workforce automation tools like Quality Management System (QMS), Customer Interaction Analytics can help identify the key drivers of AHT, Call Transfers, FCR, Quality Scores, Customer Churn, and Process Strengths and Weaknesses. Therefore, help optimize agents’ performance and contact center metrics as well.
About Ashish Bhardwaj
Ashish is a B.Tech and an MBA with 10+ years of outsourcing experience in technology consulting, investigation, research, and call center operations management. He is currently working in R Systems as a Senior Business Development Manager and is responsible for optimizing performance, driving revenue growth and strengthening the company’s competitive market position.

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