Technology to improve communication between business and customers is nothing new. In fact, these technologies have been evolving for more than 50 years. And although advancements have been made, many companies still rely on the first few technologies in the field: DTMF and IVRs. 

Decision-makers are always seeking the balance between cost savings, maintaining budget, and improving customer service. So it comes as no surprise that upgrading a legacy system can cause some hesitation. However, as customer experience has become the forefront of competitive differentiation in the past decade, there has been a noticeable shift toward improving customer service with contact center technology, namely with Conversational AI.

The good news is that contact center technologies have become more efficient, so better customer service doesn’t always come at a higher cost, making this balance easier to achieve than ever before.

Let’s explore the technologies and methods that are commonly used in leading enterprises today.

Agent Answering Phone Call

No Automation/Human Powered

Most small and steady-volume operations can utilize solely human powered customer service without much issue. Technologies, like live chat, provide platforms for human agents to converse with customers. But beyond that, this method relies exclusively on human agents to serve customers. Without any automation, this can become quite costly at higher volumes, which is why it is best used for small businesses who do not receive many queries.


Dual-tone multi-frequency (DTMF) signaling was created in the 1980’s to route calls without an operator. DTMF signaling occurs between the phone and the computer when callers use the keypad on their phone to select menu options.

This is a low-cost option and was frequently used during the first wave of tech adoption for contact centers. However, any cost savings that were realized from DTMF were quickly gone due to the drastic decrease in CSAT and lower revenue that resulted. DTMF was considered high-tech in the 1980s, but it’s now thought of as the most frustrating part of contacting a brand.

How does it work? It’s pretty simple, as the technology itself is quite simple. A customer calls the brand and is presented with menu options, also called menu trees. The customer uses their keypad to select the number associated with their reason for calling and the call is then routed to the correct department. 

While this technology does filter customers to (usually) their intended destination, it does little to reduce waiting times once the customer’s call has been routed.

Customer Calls the Brand

What is an IVR?

Interactive Voice Response (IVR) was created around the same time as DTMF in the 1980’s. While this system has a similar role to route calls, the way that it works is quite different. IVR calls begin with the system reading aloud menu options, similar to DTMF. However an IVR offers the option for customers to say their choice using speech recognition technology. A system may rely on both an IVR and DTMF to give callers an option to speak or dial their intended option.

IVR also offers customers the opportunity to solve very simple queries without the need to speak with a human agent. A customer could be directed to FAQ-style answers to hear the business’s operating hours, for example. It is important to note however that because IVRs do not have sophisticated integration with backend systems and cannot determine intent, self-service options are extremely limited and can be frustrating to use.

Customer Calls You

IVRs Require Specific Keywords

This frustration is due mostly to the rigid and precise wording that IVRs require, because they were programmed with specific keywords. Customers are forced to conform their speech to fit exact phrases, or they will not be understood. Mispronunciations, heavy accents, and too generic or too specific of phrases will trip up an IVR. If a customer says “Pay bill” instead of “Make a Payment” they will be asked to repeat the information. If a customer doesn’t find what they need in the menu or FAQs, they are often left without answers to their query.

Which brings us to the topic of call deflection. An objective of IVR systems is to deflect calls to free-up human agents. Call-deflection is the method of deferring customers to anything but human agents, such as self-service, website pages, or other digital channels. Because IVR systems do not offer a good customer experience through self-service, it becomes a very frustrating process for customers.

What is a Chatbot?

Chatbots were invented in the 1960s but recent focus on customer experience combined with more advanced website integration caused a boom in popularity in  the past few years. These pre-programmed automated interfaces communicate on online channels, such as a website, and social media platforms, such as Facebook Messenger, Whatsapp, Skype, Slack, WeChat, and more.  A chatbot works by being programmed to pull replies that match keywords or the most similar wording patterns based on what has been said  by the customer. This creates a quick response to a customer’s questions, although is not always accurate. They are most useful for simple and predictable tasks, such as answering FAQs like store hours.

Companies often rely on chatbots to be the first point of contact for customers. While they are prevalent, they often fall short of customer expectations. Why? Chatbots cannot determine underlying context, resulting in a lot of misunderstanding, repetition, and  dead ends for customers. When a user tries to find an answer to something that isn’t in the chatbot’s algorithm, there is no option but to switch to another channel, creating more effort for the customer, or the customer walks away without getting their request through.

Can I help you?

What is an IVA?

An Intelligent Virtual Assistant (IVA) powered by Conversational AI is the most advanced contact center technology available. With a deep backend integration into existing business systems, the highest-quality speech recognition and natural language processing engines, and human-assisted understanding, communication is automated while remaining effortless and productive for the customer, just like an interaction with a human agent. 

IVAs enable natural conversations by understanding underlying context and personalizing with data.  Machine learning ensures that the algorithms are constantly improving to better serve customers. Customers can therefore get more done through self-service, freeing up human agents to resolve queries with customers who need their expertise. This also allows businesses to better handle spikes in call volume without having to hire additional seasonal agents or increase customer wait times.

IVA Integration