Conversational Voice AI technology is redefining the way people shop, communicate, and perform day-to-day tasks. As per the Capgemini Consumer Survey Report 2019, close to 74% of consumers say they use conversational assistants to research or buy products and services and other functions. And 71% say they are satisfied with their smartphone voice assistants, like Siri. These consumers are accustomed to getting fast and relevant responses anytime, anywhere. Recognizing the growing focus of customers on voice for on-demand service, implementing autonomous voice bots across customer journeys is the way forward for businesses to deliver exceptional customer experience.

Voice bots are designed to be ultimate intelligent assistants that help businesses engage customers at scale and provide phone support instantly and autonomously. Customers don’t have to wait and listen to extended menu options and press corresponding numbers on their keypads. The voice interface helps in empowering a more intuitive and engaging experience.

How do voice bots work?

Voice bots are AI-powered solutions that understand natural language and use it to converse with users. Users communicate with these bots via voice, just like they would with another person. Voice bots listen, understand, and respond to customer requests in real-time without human intervention.

The building blocks required to build a robust, AI-powered voice bot include:

  • Automatic Speech Recognition — Listen
  • Natural Language Understanding — Understand
  • Conversations Module — Determine
  • Text-to-Speech System — Speak
Automatic Speech Recognition Technology

Speech recognition models use advanced algorithms to capture data from a caller’s voice, filter out background noise, and learn voice patterns in real-time. 

Natural Language Understanding

A layer of NLU is included while building a voice bot to achieve a higher level of understanding. An NLU engine enables a voice bot to understand the intent behind the customer’s speech and identify and extract entities from it that drive the conversation turns. For example, a travel bot identifies the below intent:

When a caller says, “Please book me a flight for next week,” the NLU engine helps the bot to identify that the intent behind the caller’s request is BookFlight. However, humans do not always express themselves in a specific way. The NLU layer enables the bot to understand and connect various requests to a particular intent. Like when the caller says, “I need to fly to London,” the NLU accurately comprehends the user intent here as BookFlight too.

Filght Booking NLU (Voice Bot - Natural language Understanding)
Conversations Module

Contextual conversation makes interaction with a bot a humanized experience. It lets users interact naturally and does not force them through a specific flow. The conversational approach focuses on the user request context, identifies the intent, and gathers all the related information through fully autonomous natural, unstructured dialogues. For example, when the caller says, “I need to fly to London,” the voice bot understands the context, identifies the intent as BookFlight, and requests the caller for the missing information.

Text-to-Speech

Text-to-Speech turns text into natural-sounding speech. 

What are the key voice bot implementation considerations I should know about?

Without a robust bot strategy and awareness of some critical privacy and security considerations, businesses can’t make the most of their voice bot implementation. Before implementing and deploying a voice bot, consider the following points to achieve maximum business value from your voice bot.

    1. Bot purpose and expectation. It is essential that as a business you identify the use case for which you would like to implement a voice bot and the problem you are trying to solve for the end-user.
    2. Accuracy in speech recognition. Most voice bots are not yet robust and are influenced by several factors – poor articulation, a high degree of acoustic variability caused by accents, noise, interruptions, sloppy pronunciation, hesitation, repetition, and much more. Hence, the key to a successful voice bot is its accuracy in interpreting and responding to customers.
    3. Monitoring bot performance. Meaningful analytics and reporting on voice bot performance are important for managing your customer service based on relevant KPIs.
    4. Security and Privacy. Voice bots require access to data from both internal and external systems. Therefore, ensure that the bots are compliant with  GDPR, data privacy and protection, and other regulatory concerns.

Do I need to build a voice bot?

Interestingly, NO. Why should you spend time, effort, and money to painstakingly build a voice bot, when you could automate your voice conversations with Agara. Agara’s real-time conversational voice AI engine can listen to, understand, and respond to phone calls from customers autonomously and at scale. It offers highly scalable, enterprise-grade deployment options and unmatched security to meet virtually every possible business requirement. Agara offers pre-built bots for various industry-based use-cases, and businesses can easily customize it from the UI, without writing a single line of code.

How can I implement Agara’s voice AI technology into my business?

Agara is a configurable agent and can be deployed in minutes to any geography for different use cases in your business. This is as simple and effortless as onboarding an experienced customer support agent to your team. 

Agara’s implementation for a business starts with identifying the use case and bot type to meet the business requirement. The subsequent steps are modifying the call workflow, configuring pre-built conversational blocks, customizing call scripts, integration with CRM and telephony, testing, and launch.

Discover

The first step to implementing the voice bot is to identify the bot or the agent’s purpose. 

    1. Understand how the contact center’s calls are currently being handled. Discover the requirements from the end user’s viewpoint to identify the problem they are trying to get solved.
    2. Select the use-case and bot type relevant to your business from the UI and identify the actions that are to be carried out by the voice bot. Suppose yours is an airline industry, and you choose the “Flight_Cancel_or_Reschedule” bot.
    3. Establish baseline metrics by analyzing the existing customer care calls. These metrics are the numbers that the business wants to achieve to determine its customer service effectiveness. Some of these are call handling time, NPS, cost per call, first-call resolution rate, abandonment rate, and speed of answer.
Configure call workflow and customize conversational blocks

To craft conversations, Agara uses Conversation Blocks and Conversation Manager. A call workflow consists of multiple pre-built conversational blocks meant to identify callers’ intent (e.g., Cancel_Flight or Reschedule_Flight) and extract relevant entities based on the user request (e.g., Name, PNR, Dates for rescheduling a flight). Blocks are independent pieces of conversation like understanding the reason you are calling, getting your ticket details, executing a change to your order, or initiating a transaction. Agara has built scores of these. Each block already includes possible questions that can be asked. For instance, when canceling a ticket, questions about the refund policy, refund timelines, re-issuance, dispute handling, and more are already included in the relevant block. Your IT team no longer needs to spend days identifying every possible question.

You can easily customize the sequence of the conversational blocks from the drag-and-drop feature in the UI; for example, do you want to ask the name first or get the PNR. There are elements within the conversational blocks that are also configurable to an extent.

  • The format of certain entities could be different for businesses in the same industry. For example, one insurance provider has an 8-digit policy number, while another has an alphanumeric policy number. This is configurable by the client.
  • The client could set the script or the wording of the dialog the bot says.
  • If the bot fails to capture the intent, the number of retries to be done before the call transfers to an agent could be decided by the client.

The Conversation Manager knows how to use the conversation blocks in the best possible way. The business clients need to provide Agara the best-case scenario flow, and Agara would automatically add all the variations possible when a customer does not follow the path. For example, the client just needs to select the Capture_PNR conversational block. However, for scenarios like what happens when the caller does not provide a PNR or when one letter/number has not been captured correctly, these complexities are abstracted away from the client and are handled by Agara.

Integrate with Telephony services and CRM

When it comes to integrations, Agara works as a conversational/intelligence layer on top of the customer’s existing enterprise systems. These enterprise systems typically fall into three buckets:

  • Telephony: Agara works in 2 methods with telephony systems. 
    1. It can be integrated with the client’s existing telephony system (like Genesys) through a SIP Trunk. 
    2. On the contrary, the client can also be provided with a standalone number managed by Agara.
  • CRM: Agara integrates with major CRMs like Salesforce, Zendesk for case management.  It carries out requisite fulfillment actions like sending payment links, processing refunds, updating order status, etc. on a case-to-case basis. 
  • Industry Specific and transactional backend systems:  ERP, Airline GDS systems, Order management systems in retail, underwriting systems in insurance as required by the use cases being dealt with by it.

To support the integrations, Agara requests data via APIs. When implementation is done in the Standalone Mode, Agara provisions a new phone number for the client and assigns it to specific call flows. When customers call the new phone number, the call lands directly on Agara. Agara manages call handling, network capacity, and uptime.

Validate

After development, the system is tested for performance, conversation accuracy, data integrity, compliance, feeding the data into the CRM to complete the process, and other mission-critical tasks associated with accomplishing the goals. 

Launch

The business could launch the voice bot in a live production environment in a phased manner.

 

Agara’s ability to handle autonomous conversations and provide highly personalized behavior makes it one of the most advanced Real-time Voice AI products anywhere. The flexibility of deployment and speed of execution sets Agara apart. Learn how Agara’s Real-time Voice AI can help you deliver the best experience for your customers.

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