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Why IVR automation has failed you

May 22, 2023


The unmistakably robotic voice of Interactive Voice Response (IVR) systems has greeted customers calling the contact center since the 1970s.

There’s a high chance you’ve interacted with an IVR, and if you’ve ever shouted ‘Agent!’ into your phone during this interaction, you’re not alone.

Despite improvements in speech recognition and Natural Language Understanding (NLU), IVR automation continues to fail contact centers and customers, resulting in misrouted calls, increased wait times, and frustrating customer experiences.

In this post, we’ll look at why IVR automation fails and how you can overcome these issues by focusing on delivering customer-led experiences.

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 query, 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 detail than others, and there are multiple departments to 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 increased misroutes, increased call handle time, and high abandon rates.

Customer story: How a multinational automotive manufacturer contained 38% of calls with a customer-led voice assistant

The company’s IVR system misrouted hundreds of calls ranging from simple to complex queries.

The complexity of car-related issues meant callers used unpredictable phrasing, such as “my car is making a clunking noise it shouldn’t,” or telling drawn-out stories that were difficult for the IVR to understand. As a result, customers were directed to the wrong department.

Unreliable data and limited insight into why customers were calling meant the company needed a better understanding of caller intents that would enable them to save costs and automate processes more effectively.

PolyAI deployed a voice assistant capable of guiding callers to a resolution, including sending customers further information and useful links via SMS. This proactive approach reduced the need for the customer to speak to an agent and bridged the gap between the phone channel and the company’s online resources, resulting in the containment of 38% of calls.

The voice assistant automatically turns unstructured data, such as call transcripts, into structured data. With these insights, the company better understands why customers are calling and identify where there is friction in the customer journey.

2. IVRs expect trust, but don’t earn it

Because of poor experiences in the past, most customers don’t trust voice technologies. 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 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.

Overcoming poor IVR experiences with 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.

Driving customer-led conversations requires a voice assistant that removes preset menus and gives customers the freedom to speak however they want, no matter how long or complicated their issue, and the confidence they can resolve their problems without needing to shout “Agent!”.

Freedom and confidence require high-accuracy automatic speech recognition and NLU optimized for spoken language, which enables voice assistants to listen, understand and respond to what the customer is saying.

While customers speak freely, customer-led voice assistants automatically turn conversational data into a structured format that gives clear insights into what a caller wants, when they are calling, and how they respond to specific questions, enabling contact centers to drive continuous improvement.


With over 50 years since the inception of IVRs, the limited automation provided by these systems continues to create poor customer experiences that negatively impact a brand’s image due to restrictive experiences, long wait times, misrouted calls, and misunderstanding customers.

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