To boost customer experiences (CX), companies must use data and insights from various sources. Conversational analytics is one of the critical data sources that demand enterprise attention in 2022.
In addition to customer behavior, patterns, channel usage, agent performance, etc., the technology allows CX leaders to break down every conversation into component blocks. It then drills down and curates insights from conversations to help make smarter decisions and automate processes.
Conversational analytics can also reveal hidden insights into customer intent that are impossible to access through traditional business intelligence.
Read on to find out more, or get to grips with the basics of conversational analytics by checking out our introductory video below.
What Is Conversational Analytics? Definition and Types
Conversational analytics, also called conversational intelligence, refers to extracting data from text and voice conversations between customers and human agents or chatbots.
It relies heavily on artificial intelligence (AI) techniques to convert natural language conversations into a machine-readable format. It then applies statistical algorithms to correlate data across conversations, find patterns, identify trends, hunt for anomalies, and even detect the root cause behind trends to offer CX leaders an action point. The global conversational intelligence market was worth approximately $1.1 billion in 2021, and a February 2022 study by Research Reports World suggests that it could cross $6.6 billion by 2028.
Conversational analytics has the following subtypes and critical components:
- Text analytics - This type of conversational analytics applies to text interactions such as chat, email, and social media. Text in a natural language like English is converted to a machine-readable format through natural language processing (NLP). The algorithm then finds patterns and insights and displays them through a visual dashboard.
- Speech analytics - This type of analytics converts voice interactions into text through a process of transcription and then applies similar methods as text analytics. It allows machines to make sense of uttered speech, even though it is in the form of unstructured data.
- Voice analytics - This technology also applies to voice interactions, but it analyzes how something was said and not what was said. For instance, it can detect changes in voice modulation and the pace of speech to understand if the customer is agitated. The algorithms are entirely different from speech analytics, although this type of conversational intelligence also uses AI.
- Sentiment analytics - This is a standard algorithm that is helpful when analyzing both speech and text. It detects specific keywords and conversation patterns to understand the real-time pulse of the customer. For instance, a long pause may indicate a sense of disappointment or frustration; several negative words could signal a rise in temper, and so on.
Sometimes, companies may apply conversational analytics components to document reviews. For example, the technology can help contact centers go through large volumes of employee reviews to automatically discover performance trends from the text inputs that supervisors enter.
Conversational Analytics Use Cases
The critical use cases for this technology include:
1. Call center compliance
Most call centers process many voice interactions every day, and it is virtually impossible to enforce compliance manually. A company would need a team of supervisors to actively listen in on every call and ensure that every interaction meets quality and compliance benchmarks. Conversational analytics automates this process by checking voice calls against a preconfigured set of compliance rules.
