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CXM Guide

Voice Bots

Learn everything about voice bots — what a voice bot is, how it works, why you should incorporate voice bots into your customer service strategy, and more.

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What is a voice bot?

A voice bot is an artificial intelligence (AI) powered software used in contact centers that is capable of interacting with inbound callers. It captures, interprets, and analyzes a person’s vocal input and responds to them via voice using natural language processing (NLP) and machine learning, allowing a caller to navigate an IVR menu, explore self-service options, and be easily transferred to live agents if necessary. 

These conversational AI bots can understand human language and intentions — without requiring customers to use specific programmed commands. When users have questions while browsing through your products and services, they can interact with the voice bot and receive real-time, contextualized, and relevant responses. 

How do voice bots work? 

Voice bots can differ based on their functionalities and interaction quality. But in general, voice recognition technology works on the principle of understanding human language by encoding and decoding a spoken message. Through machine learning, it automatically improves its data and algorithms to continuously deliver more accurate responses. A fully functional AI-powered voice bot follows this step-by-step process before finally answering a customer’s query: 

1. Capture input 

Automated speech recognition (ASR) technology helps the bot filter out insignificant sounds so it can focus on understanding the customer’s spoken language, intent, and accent. The technology works as a pre-speech recognition system. It breaks down the users’ vocal input into groups of information that are more easy to process than complex human speech. 

2. Remove background noise 

Ambient sounds such as people chattering, car horns, and other disturbances are unavoidable when speaking into a microphone. These sounds can distort the relevant information in a message. However, using a neural network that functions similarly to the human brain, AI can differentiate between the actual message and the background noise. 

3. Process information to find a logical response

AI-driven voice bots categorize the simplified data to comprehend every element one more time. They use trained natural language processing (NLP) and natural language understanding (NLU) models to identify the actual meaning behind the message — based on user intent, sentiment, and industry-specific use cases. This way, bots can narrow down logical and effective responses. 

Learn more: How Conversational AI helps brands be human@scale

4. Process data using syntactic and semantic techniques

A semantic analysis system helps the bot recognize hidden context behind human sentences and words. Simultaneously, the syntactic method scrutinizes and backs up the messages using grammatical rules.  

5. Narrow down the final reply

After assessing the customer’s voice input, the voice bot reaches a specific range of potential answers. The voice bot then evaluates and filters those response options — finding the one that most accurately and objectively provides a solution to the customer’s inquiry. 

Learn more: The Top 7 Messenger Bots By Brands (And Why They’re Awesome)

6. Send out the response

An AI voice bot converts the most fitting answer into an audio format using a text-to-speech system. You can train this system to have a voice of its own so that it’s more relevant to industry-specific use cases. These pre-built, custom-trained intents help the voice bot pick up future conversations where the customer left off earlier — ensuring a seamless, personalized interaction

Automate Customer Care with Conversational AI bots

How are voice bots different from chatbots and callbots? 

Although voice bots, chatbots, and callbots all operate using the same technology (conversational AI), they are different in practice. 

Voice bots vs. chatbots

Chatbots interact with customers over text-based channels, while voice bots interact with them over voice-based channels. Because of this difference in the type of input, two features differentiate voice bots from chatbots: voice recognition and speech synthesis. Voice recognition helps transform the customer’s spoken language into a series of text-based words that an engine, similar to the type a chatbot uses, can analyze. Speech synthesis then enables the bot to convert the responses back into spoken language. 

Learn more: Why 1:1 Automated Chat is Poised to Dominate 2022

Voice bots vs. callbots

Conversing with a user over a phone call requires a specialized voice bot called a callbot. A voice bot can understand a user’s problem and solve it autonomously (think of a voice-powered assistant on a phone or computer). Customer service teams, on the other hand, use callbots — or voice bots deployed on a phone channel — to handle entire customer interactions from end to end. 

How to Resolve Issues Faster with the Right AI-Powered Routing Strategy

Why should you include voice bots in your customer service strategy? 

Since voice is the fastest, most intuitive and basic form of communication, voice bots can elevate the level of customer service you provide — and help to speed up response times. Here are some reasons businesses should consider implementing AI-powered voice bots: 

Provide instant customer support

Voice bots improve the customer experience by providing them with immediate responses. They are easy to use and available at all times — promising quick and customized interactions without a long wait. And their high comprehension rate makes the customer experience smoother. 

Learn more: How Does AI Affect the Customer Experience?

Reduce service costs

As a self-service AI communication tool, voice bots automate human-like inbound and outbound interactions at scale — without human intervention. This can significantly reduce customer support costs.

Maximize your agents’ productivity

Voice bots are not a replacement for human agents. Rather, they are a complement. Bots can handle simple and repetitive tasks, freeing up your live agents’ time, allowing them to focus on more complex issues that need a human touch.

Learn more: Turn high-performing service agents into a guide for others with smart AI and automation

Scale your contact center on demand

When your business experiences increase in call volume, it’s possible to scale your contact center up by adding voice bots in just a few clicks. You can also turn these voice bots off during lean periods. 

The Essential Guide to AI for Customer Service

Sprinklr’s best-of-breed Voice Bots improves the customer experience

Built on the only unified customer experience management (Unified-CXM) platform, Sprinklr Voice Bots help customer service organizations scale and handle fluctuating caseloads — without the expense of hiring more agents.  Provide 24/7 human-like support to your customers, with premium features including: 

  • No-code, AI-powered bot models to get started building bot journeys quickly, using an intuitive UI and drag-and-drop builder

  • Intent and ASR models that retrain themselves using AI-sourced customer data, resulting in accuracy of over 90% 

  • Multilingual capability to build voice bots across global languages case by case, deploying new languages in just 4-6 weeks with 85% accuracy 

  • Text-to-speech feature to add a voice of your choice to your bot — and train the voice to resemble any preferred human voice

  • Custom intents trained with over 300+ hours of customer recordings to capture important voice-specific elements  

  • Custom reports to track bot performance, including the number of conversions, latency, drop-outs, so you can identify potential areas of improvement

  • Dual-tone multi-frequency signaling (DTMF)-based responses to gather and secure sensitive customer data the right way

  • IVR deflection to route customers to self-service support, reducing large call volumes into experiences where customers can resolve their issues themselves — on the channels they prefer

  • AI-led intuition moderation to automatically classify the conversations that need a human agent intervention

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