IVR automation: Why it has failed you and how you can fix it

For decades, the unmistakably robotic voice of Interactive Voice Response (IVR) systems has been a staple of customer service, managing incoming calls in contact centers since the 1970s.

If you’ve ever yelled “Agent!” into your phone out of sheer frustration, you’re not alone.

Despite advancements in speech recognition and Natural Language Understanding (NLU), many IVR systems still fail to deliver. Misrouted calls, extended wait times, and rigid responses leave customers frustrated, as poorly structured call flows can leave users feeling stuck with no clear path to resolution.

In this post, we’ll explore why IVR automation continues to frustrate customers and how more adaptive, conversational AI solutions can bridge the gap—creating experiences that truly put customers first.

What is IVR automation?

Interactive voice response (IVR) automation refers to the use of automated systems by call centers to manage incoming and outgoing calls. These systems can process a variety of caller requests by providing pre-recorded responses or using text-to-speech technology, letting callers interact with systems through voice commands or keypad inputs.

How does automation in IVR systems work?

IVR inputs are typically recognized in two ways: through dual-tone multi-frequency (DTMF), touch-tone (keypad) inputs, or voice commands.

1. Touch-tone (keypad): callers press phone keys, and the system identifies the tones to understand a caller’s choices.
2. Voice commands: advanced IVR systems use automatic speech recognition (ASR) to interpret a caller’s words

The system then follows a pre-programmed menu tree to guide users. For example, when prompted to “Press 1 for sales” or “2 for support,” the user’s selection triggers specific actions.

These decisions are supported by integration with back-end systems like customer databases and CRM tools.

To deliver responses, IVR systems rely on either pre-recorded messages or Text-to-Speech (TTS) technology. Pre-recorded messages are used for static information, while TTS generates dynamic responses, such as reading out account details directly from a database.

Keyword-driven IVR solutions use natural language processing (NLP) to understand more complex phrases and provide conversational interactions. Rather than relying on specific commands, these processes are governed by decision trees and workflows, which means the system responds based on predefined rules.

The IVR roadblock: Common IVR issues

Contact center IVR systems have been a staple of customer support and customer service for decades, designed to automate call routing and reduce the burden on human agents. However, they often fall short of delivering a satisfactory customer experience.

Callers are greeted with a series of robotic prompts, forcing them to navigate through a maze of IVR menu options, often leading to dead ends or irrelevant departments. Frustration mounts as customers struggle to articulate their issues within the confines of pre-determined choices, leading to long wait times, higher abandonment rates, decreased satisfaction, and increased operational costs.

Why IVR automation is failing you

Traditional IVR systems often fail to meet customer expectations, and many businesses struggle with outdated call routing systems and call deflection processes that hinder efficient communication and resolution.

Without IVR best practices in place, automation might not be delivering the desired outcomes. However, more advanced solutions can bridge these gaps.

1. Callers are restricted to using keywords

How a person speaks differs from how they type or text. When we use a search engine or a chatbot, we are more concise with our queries, but when we talk, we tell long stories and ask clarifying questions at any point in the conversation.

Because of the varied nature of customer inquiries, some callers need to share more details than others, and there are multiple departments that direct callers to resolve their issues. That is why customers expect to be greeted by “Hi, how can I help?”, not “Please say in a few words the reason for your call…”.

Conversational IVRs rely on callers to say specific keywords to move the conversation along. Instead of encouraging free-flowing conversation, customers must select an option regardless of how closely it relates to their problem.

Customers aren’t always sure how to talk about their issues, especially if they’re related to something technical. Without the freedom to explain in detail, callers are often directed to the wrong department, causing more frequent routings, longer call handle time, and high call abandonment rates.

2. IVRs expect trust but don’t earn it

Because of poor user experiences in the past, most customers don’t trust voice technologies and the self-service options they offer. Voice assistants need to earn the customer’s trust to deliver genuinely helpful support. This trust comes from understanding callers, whatever they say, and however they say it.
When an IVR responds with, “Sorry, I didn’t quite get that” (we’ve all been there), the customer’s trust in the system is lost immediately, leading them to try and bypass the system by shouting “Agent!” repeatedly hitting “0”, or abandoning the call altogether.

Earning a caller’s trust means showing them that you understand their problem and are capable of solving it. Is a robotic voice that fails to understand callers really the first interaction you want customers to have with your brand?

3. Data is limited and unreliable

Gathering structured conversational data, such as when and why customers are calling, is fundamental to a contact center operating at optimal efficiency.

Insights into customer behavior enable companies to identify new revenue opportunities, remove friction from the customer journey and enhance products and services.

The restrictive nature of IVRs impacts the quality of customer data gathered. With predetermined menu options, customers can’t ask questions or provide feedback that would give more insight into their needs. A customer might select the wrong number from the IVR menu, or the system may misunderstand the customer’s response, leading to inaccurate data capture.

A lack of structured data limits the contact center’s ability to draw conclusions from customer insights, such as – 20% of calls are about billing, and 90% of questions on a Monday are about support — insights like this help to resource the right teams so the contact center can run more efficiently.

Is your IVR worth the effort?

Many contact centers continue to use touch-tone Interactive Voice Response (IVR) systems, where customers must press a key to choose which category their query falls under.

But often, customers are calling because they don’t know how to categorize their problem. They want to talk it through. Touch-tone IVRs require customers to think on your terms, guessing which category you think their query is most likely to fall under.

Steve Krug’s hugely influential book, Don’t Make Me Think, is based on the premise that good software or websites are highly intuitive, and don’t require users to think about what they need to do.

In Don’t Make Me Think, Krug proposed three laws of usability, two of which should be applied to the voice channel when designing effortless experiences:

1. Don’t make me think – Callers immediately understand what is required of them to resolve their problem without thinking about what they are doing.
2. Make every click (menu option) instinctive – Every action is instinctive and obvious, so callers feel sure of their choice.

The moment a caller has to think about something that isn’t the task at hand, it distracts from the action they want to take and increases the effort required to solve their problem.

Restricting callers to limited menus also leads to misrouted calls, where customers are passed between departments, further increasing the effort needed to resolve their problem.

Overcome poor IVR experiences with PolyAI’s customer-led voice assistants

Delivering genuinely helpful automated support requires best-in-class customer-led voice assistants focused on the unique challenges of spoken language over the phone.

Automated support that puts customers first

PolyAI’s voice assistants drive customer-led conversations by removing preset menus and allowing customers to speak naturally, regardless of the complexity of their issues. This eliminates the frustration of repeatedly shouting “Agent!” for assistance.

Advanced technology for better conversations

To provide freedom and confidence, PolyAI’s solutions incorporate machine learning, high-accuracy automatic speech recognition, and natural language understanding (NLU) optimized for spoken language. This technology enables voice assistants to listen, comprehend, and respond accurately to customer queries.

Clear insights from every conversation

As customers speak freely, PolyAI’s voice assistants transform conversational data into structured insights, revealing what callers want, when they call, and how they respond to various questions. These insights empower contact centers to continuously improve their operations, hit key metrics like first-contact resolutions, and deliver an end-to-end experience that enhances customer satisfaction.

Listen to a customer-led voice assistant engage in natural conversation with a customer.

Modern solutions to outdated IVR challenges

For over 50 years, traditional IVRs have limited automation capabilities, leading to poor customer experiences, long wait times, misrouted calls, and frequent misunderstandings. PolyAI’s advanced voice assistants provide a modern solution, overcoming these limitations and significantly enhancing the overall customer experience.

Discover how PolyAI can help you deliver effortless CX at scale and supercharge your contact center efficiency.

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