Most CX organizations operate on a reactive scale. A customer has an experience, encounters a problem, and contacts customer service. By the time they reach contact center support, the experience has already failed.

Conversational AI flips the script. With insights from your customers’ conversations across channels, you can identify intent, sentiment shifts, and problems long before they reach customer service. Then you can act on that intelligence to identify potential problems earlier, coach agents through live interactions, and resolve issues causing repeat contact.

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In this post, we’ll discuss how conversational AI works for customer service, how you can use it to establish proactive CX, and how it affects CX operations, including customer retention, call volume, compliance, and agent coaching.

In this article:

• What is conversational AI in customer service?
• Conversational AI vs. traditional automation
• How conversational AI enables proactive customer experience
• Business impacts of proactive CX
• Transform conversations into competitive insights with CallMiner
• Frequently asked questions

What is conversational AI in customer service?

Conversation AI is artificial intelligence that helps computers understand and respond to human text or speech in a way that mimics natural human conversation. In customer service, conversational AI powers chatbots or voice-based virtual agents that interact directly with customers and agents. They’re used to route calls, provide customers with answers, and assist contact center agents.

To do this, conversational AI systems are trained on massive amounts of speech and text. This helps the system learn how to process and understand human language. Systems use natural language processing, machine learning, and data analytics to understand the intent behind a person’s speech (or text) and respond with a message designed to mimic natural human conversation.

Conversational AI vs. traditional automation

Traditional automation, such as an interactive voice response (IVR) system, works based on predetermined rules: If X does something, then Y will happen. Since the system doesn’t understand human language, humans must tell the system information in a highly structured format. For example, they would press buttons on their phone’s keypad to select from a numbered menu of options.

IVR systems are trained to understand a specific set of words so customers can say a number or word to interact with the menu of options, rather than having to press numbers on their keypad. The customer must say the specific words that will trigger the action. The system processes the spoken words and tries to find a match in its list of predetermined responses.

Since traditional automation is programmed with scripts, these systems can only respond in certain ways. There is a strict set of flows and actions that can take place. If a situation falls outside of those scripts, there’s nothing the system can do.

Let’s say a customer needs to speak to a representative about a problem, but their question isn’t listed in the menu of options. It’s annoying for the customer to listen through the IVR’s automated menu options just to reach a menu asking them to listen through more options. If none of the options apply, they’ll have to navigate through all of these options just to reach a live agent.

Instead of understanding natural language, these systems follow explicit instructions given to them by developers. Conversational AI analyzes human language as it’s happening to generate a unique response every time.

There’s no listening through a menu of options for the customer. They can say or type whatever they want. Conversational AI systems can understand natural human language because it takes into context previous interactions and the conversation as a whole. Leveraging this data, it produces responses that are more relevant, specific, and helpful.

These responses are dynamic. Traditional automation responds only what it’s programmed to say based on a set of flows. Conversational AI responds based on what a person says to it.

Think of it like a conversation between two humans. One person says something, and the other responds based on what they said. Conversational AI can do this with customer data from any channel. And since it’s constantly learning from new data, these systems continuously get better over time.

How conversational AI enables proactive customer experience

As you can imagine, many things can happen during a customer’s journey with your company. They speak with friends, browse social media, search for your company online, read FAQs on your website, send emails to customer service, and much more. Conversational AI can apply intelligence to every single one of these customer interaction points.

Instead of waiting for something to happen, conversational AI lets you be proactive. Take customer churn for example. In a proactive CX model, Conversational AI examines data from all customer touchpoints as it happens. The system learns from this continually growing dataset every second of every day.

Say it detects that 80% of customers who call customer service more than three times in a seven day period tend to churn in the next quarter. It sends an alert indicating there are customers currently exhibiting this behavior.

You now have the opportunity to intervene before they churn. You can reach out to the customer and see if there’s anything you can do to improve their experience with your company.

Here are some examples of how conversational AI supports proactive customer experience.

Real-time intent & sentiment detection

Conversational AI doesn’t simply understand what is being said. It understands intent and sentiment on each interaction as it’s happening. Agents can respond to customers in real time with language and tone that’s perfectly matched to the customer’s needs and emotions.

Predictive issue identification

Since conversational AI analyses every interaction, it learns from a constantly expanding dataset. Whether looking at historical data from a single customer or hundreds of similar interactions with other customers, conversational AI can spot patterns and detect signals indicating that something is likely to go wrong. A customer that has contacted support multiple times for the same issue is likely frustrated and close to churn.

Automated next-best action guidance

Beyond predicting potential issues before they occur, conversational AI can also provide agents with guidance during live customer interactions. This includes triggering alerts when customers are likely to churn, calming down highly-emotional interactions, and identifying cross-sell and up-sell opportunities as they arise.

100% interaction visibility

Tools like CallMiner transcribe and analyze 100% of your customer conversations, no matter the channel. Whether it’s voice, email, text messaging, social media, or surveys. Companies gain full visibility into every interaction with customers, accurately measuring voice of the customer (VoC) and learning more about how customers behave, think, and feel than ever before.

Closed-loop automation

Tools like CallMiner Eureka don’t just analyze your customer conversations once and provide a static report. Conversational AI continuously learns from every interaction, applying that knowledge to improve how it perceives sentiment, keywords, intent, and other signals. This creates a closed loop system that constantly learns and gets better over time.

Business impacts of proactive CX

Customers don’t always wait until they’re angry to call your contact center. Sometimes, they call multiple times to solve the same problem. Other times, they’d already decided to churn well before they finally call you.

Proactive CX uses conversation data to identify potential problems before they arise. Businesses can address customer issues quickly and decisively, preventing customers from ever having to call back.

Decreased churn and improved retention

When you’re scaling your conversation analytics across thousands (or millions) of customer interactions you start to see common threads of frustration emerge.

Billing is too high or hard to understand. Products aren’t working like they should. Customers are frustrated with long wait times. Sounds we hear over and over in customer conversations point to much larger problems.

Instead of waiting for customers to churn, use conversation intelligence to detect these signals as they emerge. Someone calling about an issue they’ve called about three other times this month? Flag that account as high-risk for churn and have retention reach out.

Negative language is suddenly peppered throughout calls about a certain product? You might have a faulty product that needs to be addressed.

Helping customers before they ask for help keeps them your customers longer.

Contact volume drops with early resolution

Imagine you could identify problematic areas of your business before your customers call you about them. This is exactly what happens when you take a proactive approach to customer conversations. You’re not waiting for customers to call, email, or chat to point out areas of frustration.

Conversation analytics can find that information for you. Customers are saying your product manual is confusing? Improve the content around that topic on your website. Customers don’t understand a portion of the onboarding process? Update that section of the docs or change how that flow works within your product.

By addressing these issues before your customers have to call about them repeatedly, you’ll start to see your overall contact volume decrease. We’ve seen customer’s reduce contact volume by eliminating common roadblocks found in conversation analytics.

Higher CSAT and NPS scores

Proactive CX lets you fix friction points before your customers ever have to highlight them. When customers don’t run into problems, they don’t leave you negative CSAT scores or low NPS ratings.

Since conversational AI provides insight into the why behind negative scores, you can make targeted improvements to your products, services, and processes that customers actually care about. Want to improve CSAT scores on customer billing? Use conversation analytics to figure out why customers don’t like your billing process. Is it confusing? Too expensive? Taking too long?

Once you know what’s causing customers frustration you can make informed decisions to improve CX. Eliminate friction where your customers currently are (within your contact center), could be (public forums/social media), and will be (with AI-powered predictive analysis).

Stronger compliance posture

Did an agent miss a disclosure? Make an incorrect statement? Skip a step in the process that could result in legal exposure?

Conversational AI monitors customer conversations for compliance risks. Automatically scan every interaction for missed disclosures, risky phrases, and deviations from approved scripts instead of auditing a tiny subset of calls.

Compliance teams get alerts when issues happen so they can remediate faster. Agents can be coached by supervisors on how to handle similar situations in the future, and training can be updated to prevent future occurrences.

Better agent productivity and lower burnout

Your agents spend a portion of their day resolving repetitive problems that your company knows about but haven’t yet resolved. Something about your billing process is confusing to customers.

Some part of the onboarding process is unclear. Agents are searching for information during conversations instead of having it at their fingertips.

Conversation intelligence highlights these types of problems by aggregating feedback across thousands of customer conversations. Once you fix these systemic problems, agents spend less time dealing with repeated complaints and more time helping customers with more complex issues.

Listening tools also help agents during customer conversations. With an AI-powered conversation intelligence solution like CallMiner Eureka, your agents get next-best action guidance in real time. Agents can quickly find the information they need to resolve issues and spend less time digging through resources.

Transform conversations into competitive insights with CallMiner

Your customers tell your business what’s working, what’s broken and what they want next every time they pick up the phone or jump online to chat. But most companies are only listening to a fraction of those conversations. Call reviews, surveys and traditional QA programs analyze only a subset of interactions and often leave the majority behind.

CallMiner Eureka listens to 100% of your customer conversations whether they happen by voice or digital channels. We transform every call, chat, email and message into structured data your teams can act on. Learn what’s causing churn and which behavioral patterns lead to happy customers and closed deals.

Intent, changing sentiment, and compliance risks are revealed as they occur. Agents receive next best action recommendations to improve customer experience and compliance managers can intercede should a conversation pose a risk.

Learn how CallMiner gives you complete visibility and the insights your teams need to stop playing defense and start optimizing customer experience: request a demo today.

Frequently asked questions

What’s the difference between conversational AI and a chatbot?

Chatbots typically run on predefined rules or scripts. Scan for keywords. Navigate menus. Conversational AI uses natural language processing (NLP), artificial intelligence (AI) and machine learning technologies to understand context, intent and nuance to enable a more natural conversation. Conversational AI can also ingest new information it encounters to improve its conversational abilities.

Does conversational AI analysis include voice calls?

Yes. Conversational AI can analyze voice calls as well as digital conversations happening across chat, email, social media messages, messaging apps and more. Speech recognition technology can easily transcribe voice conversations into text that can then be analyzed for conversational AI.

How does conversational AI know when a customer is upset?

Conversational AI can utilize real-time sentiment analysis to identify when a customer is becoming frustrated, confused, or even amused during a conversation. Sentiment analysis uses language patterns, word choice, tone and other contextual clues to help AI-powered models detect a change in conversation sentiment. Conversational AI can immediately flag agents or supervisors if a conversation becomes negative.

How can I use conversational AI to reduce customer churn?

Because conversational AI can analyze millions of conversations, it can quickly identify trends across thousands of customers to help you pinpoint friction. Unhappy customers, product problems, and knowledge gaps can be remediated much sooner with more personalized service, leading to happier customers and a lower churn rate.

How do I use conversational AI for contact center quality?

AI can be used to automatically analyze 100% of your customer interactions for coaching and quality. Digital conversations can be evaluated for agent performance, scripting, and compliance. Leveraging those insights from your entire customer base will help contact center managers find coaching opportunities and improve service more effectively than humanly possible by sampling a few interactions.