Interaction analytics takes unstructured data from customer interactions across multiple channels and harnesses it to let you understand the true voice of the customer. AI-powered interaction analytics gives you real-time insights to guide your agents and optimize the conversation as well as post-interaction analytics to inform customer experience decisions and strategies, automate and improve compliance and quality control, and improve agent performance.
The problem is that not all interaction analytics solutions deliver the deep insights contact centers need to transform the customer/agent experience and deliver strategic outcomes that drive business value. There is a wide gap between the many rudimentary speech analytics offerings on the market and advanced conversational AI-powered solutions built specifically for contact centers and deployed on an enterprise-class platform that optimizes and automates the end-to-end conversation.
While some legacy tools have added limited AI capabilities to the existing product, many are designed more for improving the accuracy of speech-to-text transcriptions than identifying trends and patterns in the data, analyzing agent performance or compliance, or understanding customer sentiment, emotion, and intent.
How can you distinguish between a product that offers a few basic speech analytics capabilities and a robust AI-powered interaction analytics solution? Here are a few things you should be looking for:
Domain-specific conversational AI technology
Understanding human-to-human conversations is the most difficult problem to solve in the AI world. However, domain-specific conversational AI has made huge leaps in sophistication by focusing on specific applications such as the contact center and certain industries such as financial services, telecommunications, healthcare, and others. That’s why, for optimal understanding of your agent and customer conversations, look for a conversational AI solution that is specific to the domain of the contact center as well as your industry.
Highly accurate speech and text recognition
An interaction analytics solution needs to understand your agents and customers with a high degree of accuracy. To do so, it must be able to listen for and detect the language, including specific dialects, automatically. This is not an easy feat. In English, for example there are more than 150 dialects around the world and more than two dozen in the U.S. alone. While English is one example, look for an interaction analytics solution that also supports other languages used by your agents and customers, both today as well as your future language needs.
Omnichannel conversation analysis
For the true voice of the customer, you need to understand interactions no matter where they are happening. In addition to seeking out a solution that can handle both voice and text analysis, be sure to choose integration analytics that give you an integrated view of customer conversations across multiple channels, including voice, email, and chat.
Intent recognition and analysis
An interaction analytics solution should be able to identify and understand customer intent, using machine learning to continuously tune and optimize algorithms to deliver the highest degree of accuracy possible in predicting true intent.
Automatic topic identification and business entity and keyword extraction
To go beyond word recognition to true understanding, the solution must also automatically identify and section key classifying elements in a conversation and match them to categories to add context and facts to an intent. For example, the interaction analytics solution should automatically identify the greeting, discover key issues and intents, and recognize and record the outcome/resolution with an accurate conversational flow analysis capability. Leading solutions will use machine learning to guide the technology in recognizing and classifying elements.
Customer sentiment and emotion recognition and analysis
Another important capability is sentiment recognition, which provides insight into the customer’s state of mind and further helps you uncover trends and patterns in the customer experience. At a minimum, the interaction analytics solution should recognize, extract, and score customer sentiment as positive, neutral, or negative.
However, to better understand your customers’ feelings within the context and intent of the call and take relevant actions based on your understanding, you should choose a solution that also identifies customer emotions such as sadness, frustration, anger, and happiness — as well as identifies the agent and customer behaviors that led to the emotions such as agent empathy, politeness, engagement, and positivity.
Support for your recording solutions
Choose a solution that supports the recording formats and tools on which your company currently relies so that you can harvest and analyze data from past conversations.
Learn more about the use cases, business value, and capabilities for interaction analytics in our buyer’s guide “Choosing an AI-Powered Interaction Analytics Solution.”