Conversational IVR uses natural language processing and automated speech recognition to understand what customers are saying, typically in the context of a customer support request.
While conversational IVR has been available for over two decades, recent advances in AI have expanded the possibilities for intuitive, natural, and useful phone-based dialogue. Unfortunately, new technology often comes with exaggerated claims about functionality.
We’ll walk you through the proven benefits of conversational IVR and debunk some of the common myths in this guide.
If you’ve ever called a customer support hotline, you’ve undoubtedly had the experience of navigating a maze of IVR voice prompts only to repeat the issue to an agent anyway. Fortunately, conversational IVR breaks this cycle with natural, dialogue-based interactions.
Here are some of the main benefits that come with leveraging conversational IVR to streamline customer support.
Traditional IVR systems rely on tedious menu prompts, asking callers to “Press one for sales, two for support…” to direct their needs.
It’s been shown that callers will typically get through no more than four menu levels before opting out to speak with an agent. This meandering navigation most often leads to misrouted requests, repeat calls, and abandoned support interactions.
Conversational IVR bypasses these overcomplicated paths by allowing callers to express requests conversationally using natural language.
Instead of navigating menus, customers simply ask their questions. Whether checking an order status or requesting a refund, the caller gets to the right resolution more quickly, with fewer transfers and less frustration.
This simplified navigation not only makes things easier for the caller but also optimizes resource allocation behind the scenes to keep your contact center running smoothly.
If wading through confusing menu trees wasn’t enough, most legacy IVRs come pre-built with automated support sequences that slowly guide users to the right support agents. While these default settings technically get the job done, they lack the personalization that can take the quality of your customer support from good to great.
Conversational IVR provides natural, humanized interfaces using automatic speech recognition and natural language processing.
Instead of pushing buttons to reach the appropriate support agent, callers can speak their requests just as if they were talking to a live person. The IVR converses with the customer until the support request has been resolved.
This human-mimicking dialogue keeps support conversations running smoothly by answering questions within the right context — not based on the press of a button.
By removing these mechanical menu barriers and replacing them with intelligent, human-like speech, your customers will feel heard and cared for.
With conversational IVR, you won’t have to worry about answering repetitive support questions or common queries ever again.
For questions like “What’s my account balance?” or “When will my order ship?” it will automatically interpret the intent and pull the necessary data to resolve the request without agent assistance.
This automation offloads simple calls away from human agents, drastically reducing mundane task handling and enabling your support reps to focus on higher-level, more complex interactions.
Better yet, natural language models improve over time. Over 50% of your inbound call volume can be fully automated once the IVR system learns how to answer your most common questions.
If the powerful automations weren’t enough to convince you that conversational IVR is leagues ahead of legacy IVR systems, then the integrations surely will.
By integrating an IVR system with your CRM, product inventory records, ERP platforms, and more, IVR can access the most current data to give callers accurate, personalized responses.
These kinds of back-end integrations allow your automated phone system to pull real-time order statuses, account details, shipping dates, and similar helpful information that customers often request over the phone.
This personalization shows callers that they’re not just another number — they actually matter to your business.
For example, when asking about order status, conversational IVR can reference the specific product purchased, shipping destination, and estimated delivery window based on integrated order records.
Traditional IVR systems fall short when callers ask complex questions, need clarification on answers, or expect personalized responses. These systems just weren’t built with this level of personalization in mind.
But by applying natural language processing, neural networks, and machine learning, conversational IVR can interpret intricate, nuanced inquiries across multiple topics. The difference is huge.
Whether a customer asks to change an existing order and check loyalty points or attempts to book travel while referencing previous trips, conversational IVR can handle these nuances easily.
These systems can also clarify answers through back-and-forth dialogue without losing context or getting frustrated.
By studying customer transcript data from all recorded customer calls, conversational IVR will get better at understanding multi-intent requests, analyzing customer sentiment, and resolving support requests through intuitive, smart dialogue.
While conversational IVR promises to revolutionize customer support experiences, that doesn’t mean it has endless capabilities with zero downsides. In this section, we’ll debunk some claims around conversational IVR and point out areas where it may create more work for your support teams instead of saving them time.
Some conversational IVR vendors boast quick, simplified deployments. But designing an intelligent IVR system personalized to your team’s needs involves more than just basic software installation.
Properly interpreting customer intent requires significant upfront development. Conversational IVR systems must analyze call transcripts, identify common queries and topics, label them as “intents,” and map IVR dialogue flows before anything goes live.
While you could push it live without all that, this will only result in a potentially worse customer support experience than customers would experience through a legacy IVR system.
Integration challenges add deployment delays, too. If you want to pull in real-time data to assist your customers, your conversational IVR system needs to integrate with contact center infrastructure like ACDs, CRMs for customer data, payment systems, inventory databases, and more.
Setting up these integrations takes time for end-to-end testing before they can be launched at scale.
And while the long-term gains of conversational IVR far outweigh the costs, organizations should expect several months of development, testing, and fine-tuning before these systems can deliver a positive ROI.
In other words, there are no shortcuts to improved customer support.
Some conversational IVR vendors boast seamless, human-like interactions. But even advanced natural language systems struggle with replicating and understanding everyday human speech.
Without any lived experience or cultural awareness, IVRs often misinterpret slang, sarcasm, niche references, and requests that require emotional intelligence.
While modern neural networks can identify intent and entities accurately in certain contexts, they often fall short in cases where complex personalization is required. For example, while humans can easily context switch between topics, IVRs rely on rigid dialogue trees.
AI augmentation is great for assisting support agents, but it’s not designed to replace them. Any promises of complete, scalable automation across every conversational scenario remain years away from reality.
Some conversational IVR vendors may imply that nearly all caller needs are addressable without human assistance, but this is hardly, if ever, the case.
Real-world containment rates using conversational IVR are supposedly higher than those found with legacy IVR systems. While this is impressive, it’s nowhere close to end-to-end customer support automation.
Instead, contact centers should be designed for a balance of automation paired with staff augmentation. This hybrid approach is ultimately the best way to balance managing costs, expectations, and customer experience.
The ROI argument for implementing conversational IVR systems seems pretty straightforward. Less involvement from support agents equals lower operating costs… right?
While it’s plausible, this is almost never true in practice. Developing a roll-out-ready system takes months before its automation can reduce operating costs. Intense upfront effort is required to analyze call drivers, define conversational flows, train natural language models, and then integrate these dialogues across backend systems.
As you may have already assumed, these build costs are substantial, from content licensing fees to development teams tuning speech recognition and call routing. Without proper design and testing before launch, your containment rates will suffer greatly.
And even after going live, conversational IVR still requires ongoing maintenance to address changes in call drivers over time. Only with continuous content enhancements do conversational IVR systems begin to show their real potential.
Conversational IVR systems can make it much easier for callers to get answers without talking to a live agent. However, the technology still has limitations when handling complex questions, and it takes a while before the system is ready to go live and begin routing customer calls.
As a general rule, taking a hybrid approach to customer support is best. But if you want to take your customer support from good to great, implementing a conversational IVR system is a solid first step.
