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.<\/p>\n\n\n\n
Conversational AI flips the script. With insights from your customers\u2019 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.<\/p>\n\n\n\n
In this post, we\u2019ll 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.<\/p>\n\n\n\n
In this article:<\/p>\n\n\n\n
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<\/a> that interact directly with customers and agents. They\u2019re used to route calls, provide customers with answers, and assist contact center agents<\/a>.<\/p>\n\n\n\n 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<\/a>, and data analytics<\/a> to understand the intent behind a person\u2019s speech (or text) and respond with a message designed to mimic natural human conversation.<\/p>\n\n\n\n Traditional automation, such as an interactive voice response (IVR) system<\/a>, works based on predetermined rules: If X does something, then Y will happen. Since the system doesn\u2019t understand human language, humans must tell the system information in a highly structured format. For example, they would press buttons on their phone\u2019s keypad to select from a numbered menu of options.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n 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\u2019s nothing the system can do.<\/p>\n\n\n\n Let\u2019s say a customer needs to speak to a representative about a problem, but their question isn\u2019t listed in the menu of options. It\u2019s annoying for the customer to listen through the IVR\u2019s automated menu options just to reach a menu asking them to listen through more options. If none of the options apply, they\u2019ll have to navigate through all of these options just to reach a live agent.<\/p>\n\n\n\n Instead of understanding natural language, these systems follow explicit instructions given to them by developers. Conversational AI analyzes human language as it\u2019s happening to generate a unique response every time.<\/p>\n\n\n\n There\u2019s 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.<\/p>\n\n\n\n These responses are dynamic. Traditional automation responds only what it\u2019s programmed to say based on a set of flows. Conversational AI responds based on what a person says to it.<\/p>\n\n\n\n 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<\/a>. And since it\u2019s constantly learning from new data, these systems continuously get better over time.<\/p>\n\n\n\n As you can imagine, many things can happen during a customer\u2019s 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.<\/p>\n\n\n\n Instead of waiting for something to happen<\/a>, conversational AI lets you be proactive. Take customer churn<\/a> 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.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n You now have the opportunity to intervene before they churn. You can reach out to the customer and see if there\u2019s anything you can do to improve their experience with your company.<\/p>\n\n\n\n Here are some examples of how conversational AI supports proactive customer experience.<\/p>\n\n\n\n Conversational AI doesn\u2019t simply understand what is being said. It understands intent and sentiment on each interaction as it\u2019s happening. Agents can respond to customers in real time with language and tone that\u2019s perfectly matched to the customer\u2019s needs and emotions.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n 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<\/a>, and identifying cross-sell and up-sell opportunities as they arise.<\/p>\n\n\n\n Tools like CallMiner<\/a> transcribe and analyze 100% of your customer conversations, no matter the channel. Whether it\u2019s voice, email, text messaging, social media, or surveys. Companies gain full visibility into every interaction with customers, accurately measuring voice of the customer (VoC)<\/a> and learning more about how customers behave, think, and feel than ever before.<\/p>\n\n\n\n Tools like CallMiner Eureka don\u2019t just analyze your customer conversations once and provide a static report. Conversational AI continuously learns from every interaction<\/a>, 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.<\/p>\n\n\n\n Customers don\u2019t always wait until they\u2019re angry to call your contact center. Sometimes, they call multiple times to solve the same problem. Other times, they\u2019d already decided to churn well before they finally call you.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n When you\u2019re scaling your conversation analytics across thousands (or millions) of customer interactions you start to see common threads of frustration emerge.<\/p>\n\n\n\n Billing is too high or hard to understand. Products aren\u2019t 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.<\/p>\n\n\n\n Instead of waiting for customers to churn, use conversation intelligence to detect these signals as they emerge. Someone calling about an issue they\u2019ve called about three other times this month? Flag that account as high-risk for churn and have retention reach out.<\/p>\n\n\n\n Negative language is suddenly peppered throughout calls about a certain product? You might have a faulty product that needs to be addressed.<\/p>\n\n\n\n Helping customers before they ask for help keeps them your customers longer.<\/p>\n\n\n\n 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\u2019re not waiting for customers to call, email, or chat to point out areas of frustration.<\/p>\n\n\n\n Conversation analytics<\/a> 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\u2019t understand a portion of the onboarding process? Update that section of the docs or change how that flow works within your product.<\/p>\n\n\n\n By addressing these issues before your customers have to call about them repeatedly, you\u2019ll start to see your overall contact volume decrease. We\u2019ve seen customer\u2019s reduce contact volume by eliminating common roadblocks found in conversation analytics.<\/p>\n\n\n\n Proactive CX lets you fix friction points before your customers ever have to highlight them. When customers don\u2019t run into problems, they don\u2019t leave you negative CSAT scores<\/a> or low NPS ratings<\/a>.<\/p>\n\n\n\n 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<\/a> on customer billing? Use conversation analytics to figure out why customers don\u2019t like your billing process. Is it confusing? Too expensive? Taking too long?<\/p>\n\n\n\n Once you know what\u2019s causing customers frustration you can make informed decisions to improve CX. Eliminate friction<\/a> where your customers currently are (within your contact center), could be (public forums\/social media), and will be (with AI-powered predictive analysis).<\/p>\n\n\n\n Did an agent miss a disclosure? Make an incorrect statement? Skip a step in the process that could result in legal exposure?<\/p>\n\n\n\n Conversational AI monitors customer conversations for compliance risks<\/a>. Automatically scan every interaction for missed disclosures, risky phrases, and deviations from approved scripts instead of auditing a tiny subset of calls.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n Your agents spend a portion of their day resolving repetitive problems that your company knows about but haven\u2019t yet resolved. Something about your billing process is confusing to customers.<\/p>\n\n\n\n Some part of the onboarding process is unclear. Agents are searching for information during conversations instead of having it at their fingertips.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n Listening tools also help agents during customer conversations. With an AI-powered conversation intelligence solution like CallMiner Eureka<\/a>, your agents get next-best action guidance<\/a> in real time. Agents can quickly find the information they need<\/a> to resolve issues and spend less time digging through resources.<\/p>\n\n\n\nConversational AI vs. traditional automation<\/h1>\n\n\n\n
How conversational AI enables proactive customer experience<\/h1>\n\n\n\n
Real-time intent & sentiment detection<\/h2>\n\n\n\n
Predictive issue identification<\/h2>\n\n\n\n
Automated next-best action guidance<\/h2>\n\n\n\n
100% interaction visibility<\/h2>\n\n\n\n
Closed-loop automation<\/h2>\n\n\n\n
Business impacts of proactive CX<\/h1>\n\n\n\n
Decreased churn and improved retention<\/h2>\n\n\n\n
Contact volume drops with early resolution<\/h2>\n\n\n\n
Higher CSAT and NPS scores<\/h2>\n\n\n\n
Stronger compliance posture<\/h2>\n\n\n\n
Better agent productivity and lower burnout<\/h2>\n\n\n\n
Transform conversations into competitive insights with CallMiner<\/h1>\n\n\n\n