What is Conversational Intelligence?

The road ahead to a post-pandemic new normal may be uncharted territory, but its impact on digital interactions has sped up the way businesses communicate with customers. Digital customer service interactions will increase by 40 percent in 2021 alone, according to Forrester Predictions. This increase in digital interactions will only make the voice channel that much more critical for businesses because all the complex, emotionally charged conversations will be routed to the voice channel.

Business communication and customer service are now reliant on empathetic conversations that are bolstered by insights that humans may miss. Conversational intelligence has emerged as the means for humans to meet that need and improve digital customer interactions in ways that help people, businesses, and communities thrive. To understand how public and private entities can harness this power requires a basic understanding of what is conversational intelligence?

What is Conversational Intelligence?

Conversational intelligence combines forms of artificial intelligence (AI), including machine learning (ML) and natural language processing (NLP) technology. They are used to create and train algorithms that can deduce intent and emotional sentiment from customer speech or text. This analysis can then provide support to human agents in customer support or sales to improve interactions and customer experiences to:

• Quickly and efficiently resolve customer needs and issues
• Streamline Customer Support
• Improve customer satisfaction
• Simplify Coaching and Onboarding

Businesses can focus conversational intelligence on customer text conversations across social media and other media channels, email, and other forms of correspondence as part of the sales and marketing channels. It is most often and increasingly focused on conversational human-to-human and human-to-machine speech, such as via customer support channels, call centers, and chatbots. The dominant forms of conversational intelligence include:

• Transactional with the intent to sell
• Communication intending to inform
• Educative for learning
• Social to connect
• Diagnostic to identify problems

The power of any given conversational intelligence platform to reveal sentiment and intent behind customer interactions can work across channels and encounters. That power is often shallow in terms of conversational intelligence gathering by chatbots, for example. Despite their ubiquitous growth, 75 percent of consumers prefer to interact with an actual person, according to PwC. This is one reason augmented intelligence is the driving force behind conversational intelligence growth and innovation.

How Augmented Intelligence Advances Conversational Intelligence

AI, customer sentiment analysis, and conversation intelligence usually conjure images of chatbots. This is not inaccurate as chatbots are still limited to using these tools for highly repetitive tasks in well-defined, closed interactions. Augmented Intelligence has far more possibilities by focusing on human-aware technologies for machine collaboration with human control.

This takes advantage of the algorithm-derived customer sentiment analysis to build brands and improve customer experience. Augmented intelligence for sentiment analysis and conversational intelligence still uses advanced technologies, including AI and ML for NLP to gather the appropriate conversational data.

The analysis of that data yields real-time, actionable insights to marketing and customer support teams to better serve customers. Sentiment analysis (and conversational intelligence) require large amounts of data from real customer interactions. Automation is used to gather, clean, and analyze the data. When done correctly, this can yield many benefits to businesses and customers.

While conversational intelligence software uses a form of sentiment analysis for analyzing customer and prospect conversations, the latter still has limitations in accomplishing this goal. Today’s businesses and brands require conversational intelligence technology that goes beyond simply looking at the polarity of words being spoken after a conversation and scoring a call based on that polarity.

Conversational Intelligence and Sentiment Analysis

The most advanced conversational intelligence platform uses next generation sentiment analysis algorithms for instant, real-time measure of customer sentiment. These types of platforms analyze the emotion in a customer’s voice based on the way they say speak.

This augmented intelligence approach provides an actionable coach for support agents in human-to-human interactions so they can act on that sentiment and positively change the course of a conversation and achieve first call resolution. One third of consumers say this first call resolution is the most important aspect of customer service, according to Statista.

This changes the dynamic reality of the question, what is conversational intelligence? It shifts from something only humans can do to an augmented intelligence platform that helps humans do it better. But like sentiment analysis technology, they do not create every conversational intelligence platform with equal capabilities.

Evaluating Conversation Intelligence Technology

Conversation intelligence software is the natural progression of the concept of developing a conversational intelligence coach for humans in business to improve all interactions. But not all software solutions have the advanced capabilities to provide emotional intelligence insights into speech. This requires evaluating the quality of chatbots and intelligent conversational agents through the augmented intelligence lens.

The most advanced chatbots are still only trusted to handle the most routine customer interactions. This is limited to providing product info, small purchases, and simple requests. Most can only recognize when customer requests and emotions are beyond their ability to respond. They must then hand them off to a human customer service agent.

Chatbots are mostly limited to customer interactions that are deemed to have minimal circumstantial weight in customer finance, health, or significant needs. This often comes down to chatbots lacking an ability to engender:

• Trust
• Customer control
• Genuine responsive empathy

Most chatbots and forms of customer sentiment analysis that take humans out of the loop cannot parse the nuances of emotion in speech and text to respond empathetically and change the course of negative interactions. The latest forms of sentiment analysis software algorithms driven by augmented intelligence excel in this area. The ideal conversational intelligence platform should be judged on:

• Ease of use
• Broad functionality across a single-source-of-truth conversational intelligence dashboard
• Real-time analytics feedback that can act as a conversational intelligence coach to customer service agents

The best of these conversational intelligence software solutions leverage data to continuously improve with each new conversation across many agents. These are the attributes that businesses and brands will require today and in the future to improve customer service.

Present and Future of Conversational Intelligence

Next-generation conversational intelligence software is improving and transforming the call center and customer support agent, sales, and marketing experience, in addition to the customer experience. Future use possibilities will only expand in ways that drive revenue across marketing, sales, and even R&D practices that drive:

• Product/service upselling
• Innovative marketing campaigns
• New and improved products and services

Leaders in the field now make third-party integrations with CRM, BI, and marketing automation a simple prospect. The best of these solutions are cloud-based, software as a service (SaaS). They are equipped with a centralized conversational intelligence dashboard within their own platform where users can find call data, analytics, and user insights.

This ongoing innovation will continue to increase the understanding of the true potential of conversational intelligence. As business adoption grows, ongoing use of sophisticated self-learning algorithms will drive revenue, business growth, and innovation in ways not yet imagined.