Empowering Real-time Understanding and Action  

There are tried and true ways of understanding customer satisfaction – Net Promoter Scores (NPS), Customer Satisfaction (CSAT) surveys, and Customer Effort Score (CES) are some of the most common. Each of these metrics are in essence measures of the past. A good look in the rearview mirror can certainly help you tune actions for future similar interactions. But with advances in artificial intelligence (AI) and machine learning technologies, so much more is possible.  

After-the-fact (post-call) surveys and call reviews are an important part of managing customer interactions and agent performance. But today, analysis and actions can be taken in real time.

Imagine being able to:

• Get immediate help from a colleague or supervisor when a call is negatively escalating – all without the agent or caller even asking.
• Immediately present a “save” offer to a customer looking to cancel service based on specific phrases paired with the customer’s lifetime value – executing the most favorable win-win scenario for both you and your customer.
• Automatically open a case in your ticketing system based on your customer’s words and phrases in a phone call, text or email – reducing time to final resolution.

Whether you run a contact center, lead a team of agents, or take calls from customers, so much of your job depends on the data and information available to you. But data in-and-of-itself is hard to glean insights from unless it can be assessed, categorized, analyzed and then presented in a way that’s actionable. And when that data is combined with information about an interaction that’s happening real-time (such as sentiment analysis), that’s when the magic happens!

Sentiment analysis allows you to understand agent and customer sentiment, sentiment over time, sentiment by channel and sentiment around entities like brands, locations or custom products. You can combine positive, negative and neutral scores with other data to derive knowledge of what correlates to emotions within your conversations.

How does it work? Sentiment analysis listens for specific words and phrases, as well as understands customer sentiments during live conversations. That data is fed real-time into an AI-powered application that’s capable of taking any number of actions including:

• Offering virtual assistant to customer during self-service or an agent
• Texting or emailing supervisor or subject matter expert
• Flagging the interaction for QA and/or follow-up

So rather than only assessing your call center operations after-the-fact, use the power of AI to understand each interaction as it’s happening – and interject with the best course of action – no matter if a customer is talking with an agent or trying to self-serve. Understand what was said, what was meant, and what was felt throughout the conversation and across every channel. With these new tools, you can rethink your conversation strategies, improve agent effectiveness, and transform digital conversations with this deep contextual understanding across the contact center.

Equipped with the right toolset, you can identify how people feel about their interactions with your company and ways you can assist agents real-time.