Skit.ai‘s Augmented Voice AI Platform is now powered by Generative AI. With the incorporation of Generative AI, we are taking a giant step forward in capabilities of our Conversational Voice AI solution. The conversations are about to become more natural, and the capability to handle complex conversations significantly better leading to better CX and more collection for collection agencies.
At Skit.ai, we embrace the future and prepare to go beyond it.
The Giant Leap in Capabilities of Skit.ai’s Augmented Voice Intelligence Platform with Generative AI
With the application of LLMs, we are seeing a big jump in the conversational capabilities of our solution:
Higher Conversational Accuracy: Significantly high conversational accuracy as LLMs are capable of understating conversational complexities with a profoundly superior understanding of context, sentence parsing, and response accuracy.
Better Handling Complex Conversations: Superior understanding of complex conversations that were earlier escalated to human agents. This would cut down the percentage of calls passed over to live agents drastically.
Out-of-scope Calls: As LLM-powered voice AI solutions can handle a wide array of questions and issues, its ability to handle out-of-scope calls is thus much more.
Better and more natural bot utterances.
Faster Voicebot Creation: Incorporating Generative AI give a big boost to the speed at which new voicebots can be created as the inherent complexity and effort involved in the design, and creation is a fraction of earlier effort.
Massive Performance Gains with Generative AI Springboard
Not resting with massive gains we are achieving with basic LLMs, we intent to take it a notch further up and outperform human collectors.
1. Going Beyond Human Agent Performance
An agents performance rests on two things: ability to communicate and technical skills. At Skit.ai, we see that with current LLMs, we can achieve superlative communication skills, and by training extensively with end-user data, we can achieve high degree of technical skills. Hence our solution can excel on both the fronts.
To share a rough estimate: the best performing agent finds success on 5% call (out of connected calls), while low performers convert ~2% of calls.
With generative AI, we take a big jump. From the current voicebot conversion capability of around 1-2%, we expect the performance to jump 3-4 folds. Beyond this, our Reinforcement Learning platform learns from outcomes to personalize the conversation to figure out the ideal strategies, learning from thousands of daily conversations.
2. Better, and More Natural Spoken Conversations
Generative AI with its unparalleled conversational capabilities needs to be complemented with equally capable speech synthesis and understanding systems that produces the right speech given the output from LLMs. And that is one of the major areas from the many below:
A more natural-sounding TTS voice
Conversational context handling prosody of generated audio
Full duplex and backchannels in speech conversations
Ultimately, we will be able to deliver the most engaging conversations that delight consumers by solving their problems better than human agents.
Business Outcomes of Incorporating Generative AI
Below are the major impact areas we will more the needle on:
Higher Collection Rate, ~5%: It is a difficult number to quantify, but as the incorporation of Generative AI matures, we expect its collection capability to move beyond 5%, surpassing even the best of human agents.
Lower Agent Dependency, reduction by 50-80%: As the voicebot will be able to handle more complex queries we expect 50-80% reduction in agent touch points.
Higher Resolution Rate, ~100%: Better accuracy and better conversation with higher engagement will help us achieve a conversational resolution rate of close to 100%.
Creating New Voicebots: The effort involved in creating a new voicebot will see a significant dip, as the complexity will be remarkably lower.
Entering New Markets with Ease, 15X faster: Entering into new markets, training for new use cases and applications needing less effort and resources. We are estimating the process to be 15X faster.
The Way Ahead
Though the improvements in our Augmented Voice Intelligent Platform are visible and clear, we will further our efforts to achieve greater performance gains and stay ahead of the curve.
To learn more about how Voice AI can help support your human resources and scale their collection efforts with call automation, schedule a call with one of our experts or use the chat tool below.
Artificial intelligence is experiencing exponential innovation. Generative AI, ChatGPT, DALL-E, Stable Diffusion, and other AI models have captured popular attention, but they have also raised serious questions about the issue of ethics in machine learning (ML).
AI can make several micro-decisions that impact such real-world macro-decisions as authorization for a bank loan or be accepted as a potential rental applicant. Because the consequences of AI can be far-reaching, its implementers must ensure that it works responsibly. While algorithmic models do not think like humans, humans can easily and even unintentionally introduce preferences (biases) into AI during development and updates.
Ethics and Bias in Voice AI
Voice AI shares the same core ethical concerns as AI in general, but because voice closely mimics human speech and experience, there is a higher potential for manipulation and misrepresentation. Also, people tend to trust things with a voice, including friendly interfaces like Alexa and Siri.
Call automation for call centers and businesses is not a new concept. Unlike computerized auto dealers (pre-recorded voice messages) like Robocall, Skit.ai’s Voice AI solution is capable of intelligentconversations with a real consumer in real-time. In other words, Voice AIs are your company representatives. And just like your human representatives, you want to ensure your AI is trained in and acts in line with company values and displays a professional code of conduct.
Human agents and AI systems at any given point should not treat consumers differently for reasons unrelated to their service. But depending on the dataset, the system might not provide a consistent experience. For example, more males calling a call center might result in a gender classifier biased against female speakers. And what happens when biases, including those against regional speech and slang, sneak into voice AI interactions?
In contrast to human agents, who might sometimes unintentionally display biases, Voice AI follows a predetermined, inclusive script while strictly adhering to guidelines that prioritize consumer satisfaction and compliance. This level of professionalism eliminates the potential for misbehavior and creates a positive consumer experience.
Our team is always potentially looking out for any potential bias that accidentally seeps in, as ‘biases’ as constantly evolving. One thing can be acceptable today, but may bee seen as a bias tomorrow. At Skit.ai our skilled team of dedicated designers meticulously construct the dialogue patterns to guarantee balanced responses. Following these predefined scripts allows our Voice AI solution to offer consistent, unbiased interactions, thus establishing an inclusive user experience. This emphasis on conversation design aids us in overcoming potential biases that may surface in human interactions, thus securing a more balanced and impartial user experience.
Consumer Convenience and the Growing Preference for Voice AI
Consumers increasingly prefer interacting with Voice AI rather than human agents due to the convenience it offers. Voice AI allows users to communicate naturally through voice commands, eliminating the need to type or navigate complex menus. This convenience aligns with the preferences of many individuals who find it easier and more natural to speak rather than type. Furthermore, Voice AI is available 24/7, providing round-the-clock support without the need to wait for human agents.
This instant access to information and assistance enhances consumer satisfaction and can lead to faster issue resolution. Additionally, voice interactions can be personalized and tailored to individual preferences, creating a more personalized and engaging consumer experience. The convenience and preference for voice-based interactions make Voice AI a valuable tool for meeting consumer expectations.
Building Ethical Voice AI
Empathetic conversational design eliminates bias. At Skit.ai, we’re dedicated to developing leading-edge Voice AI technology. Our mission is to facilitate communication that is equitable and devoid of bias. Through conversational design, biases are eliminated, ensuring fair and inclusive interactions. A significant part of our strategy involves refining the conversational capabilities of our systems, striving for a natural, seamless exchange of speech that ensures equal treatment for all and eradicates discriminatory tendencies. As we navigate the future of work, Voice AI stands as a valuable tool, empowering enhanced communication, fostering seamless consumer conversations, and further elevating customer satisfaction.
To learn more about how Voice AI can help support your human resources and scale their collection efforts with call automation, schedule a call with one of our experts or use the chat tool below.
When building any product, solution, or interface, you want the end result to be as user-friendly as possible. To achieve this, companies typically conduct a thorough background research of the product’s prospective users. That’s where User Experience Research comes into play.
User Experience Research (UXR) is the study of target users, their behavior, and their needs; this multi-step process enhances the design process with a user-centric approach.
A Conversational Voice AI solution — i.e. a voicebot — is no different. Skit.ai relies on a team of CUX (Conversational User Experience) designers and UX researchers to build its Digital Voice Agents for new clients and use cases. In this article, I’ll walk you through the research process required to build a Digital Voice Agent.
How Does the CUX Process Work?
Building the Conversational User Experience for a Digital Voice Agent typically involves five main steps: planning, design, testing, deployment, and maintenance. In the table below, you can see the different steps and the sub-steps they involve:
How does the research process work? First of all, the CUX researcher meets with the client, the Solutions Product Manager, and the CUX designer. Together, they identify the company and brand’s values for a preliminary persona ideation of the voicebot. More on that later.
Secondary Research: Industry, Competitors, Use Cases
The researcher conducts in-depth research on the client’s industry, the use cases (or functions) that the Digital Voice Agent will need to address and help customers with, and the target audience the voice bot will cater to.
Additionally, the researcher conducts a competitor analysis to assess the existing landscape, the competitors’ offerings, and their target audiences. For example, the researcher might look into which FAQs are addressed by the competitors’ offerings. Through the competitive analysis, the researcher might identify windows of opportunity and help the client gain a competitive edge.
Primary Research: User and Customer Service Agent Interviews
At this stage of the process, the researcher conducts interviews with both internal and external stakeholders. Internal stakeholders are team members currently working for our company, while external users are usually customer service agents who operate in a specific industry or company; these often include the client’s live agents.
The researcher usually interviews the client’s top-performing agents to get insights into their approach and techniques. The agents are asked to solve some example scenarios, provide a process view of their call flow, and share any insights they have gathered from their experience.
Through this round of interviews, the researcher seeks to learn more about the client’s product or solution, the frequently asked questions (FAQs), and what makes a call successful. Any call data analyses that the client can provide are helpful, too.
At the end of this step, the researcher usually gathers all of the findings in a comprehensive, data-based analysis.
Voicebot Persona Research
Every company has its own voice, and therefore every company deserves a custom-made voicebot. The Digital Voice Agent can also be tailored to the company’s voice and brand, as it will inevitably become another expression of the brand itself.
The persona design is not always performed, but it’s often an essential part of the design process. It involves shaping the bot persona around the company’s values and brand identity, which are expressed through the way the voicebot communicates and interacts with the consumers.
User Research: User Flow, User Journey, and User Behavior
Another important aspect of the research process is the study of the users that will ultimately be interacting with the Digital Voice Agent. This step is essential for the CUX Designer to be able to create useful and meaningful conversation flows for the voice bot.
User flowsare diagrams used by designers to understand the patterns users may take when interacting with the voicebot. User flows will change significantly depending on the use case and the customer’s needs. User flows are usually granular and detailed.
The user journey is a more macro view of the user experience during the interaction with the voicebot.
User behavior depends on the audience that the company commissioning the Voice AI solution is targeting. With thorough user research, the CUX researcher aims to understand the users’ behavior, needs, and the approach they typically prefer. Studying user behavior helps researchers and writers make the solution more user-friendly.
The team creates user personasand dialogued interactions in order to see how each user is likely to interact with the Digital Voice Agent. For example, one user persona could be Jane, a 33-year-old entrepreneur and micro-influencer who lives in Green Point, Brooklyn. Three years ago, Jane took a loan to launch he custom embroidery t-shirt brand. Today, Jane receives a call from the Digital Voice Agent on behalf of a collections agency about her overdue loan. The designer will draft a sample conversation between Jane and the voicebot.
User Experience Research is just the beginning of the process. These research insights are then converted to meaningful design actionables. Design and testing follow, with deployment completing the process.
Are you curious to learn more about Voice AI and its applications across various industries and use cases? Check out our blog!
We are at the initial stages of Voice AI’s evolution, in an epoch where well-functioning vertical Voice AI solutions will be instrumental in helping companies transform customer support and gain customer loyalty. But to a significant faction of CXOs, the understanding of Voice AI technology, its capabilities, and nuances remain obscure. Our earlier articles have tried to elucidate voice technology and how it can prove instrumental in transforming contact centers. In this article, we further that conversation and move on from discussing the Voice AI ‘product’ to the ‘platform’ and why companies looking to automate their contact centers must consider platform capabilities as a factor that will impact their long-term success.
The platform question holds greater gravitas when the top priorities are ROI, time-to-live, control over performance, and market leadership. In this blog, we deep dive into the core questions: what does a Voice AI platform look like, why does having a capable platform matter, and what are its far-reaching implications?
Today, voice technology has advanced sufficiently to deliver intelligent voice conversations. The wait is finally over, and companies can transform their CX with voice-first Augmented Voice Intelligence platforms.
Even coming to the correct conclusion about a Voice AI vendor capabilities is not easy. But let’s assume the product is good, but before signing up, look into the vendor’s platform capability. It is the next big and most important task because, in the long run, the performance will depend mainly on the platform’s capabilities.
Before we go deep into the topic, let us, distinguish a product from a platform.
A product is essentially an application that solves a specific use case.
The Platform is the underlying structure that provides the core building blocks and the infrastructure for the functioning of one or many products.
In other words, a platform is an enabling environment over which many products run. The architecture of a chat-first voice-capable platform will be very different from that of a voice-first platform because the latter is built and optimized for voice, giving it a distinct performance edge. Here is a glimpse of a purpose-built Augmented Voice Intelligence Platform:
The Platform View of a Vertical Voice AI Company
From the above diagram one thing comes out clearly: that for smooth functioning of a Voice AI solution, its various constituent parts must work in perfect synchronicity. Hence, beyond the product, i.e., the voicebot, various other platform features are needed for an ideal Voice AI solution.
Let’s deep dive to answer the questions: why should companies look for platform capabilities in their potential Voice AI vendor?
At the core of this issue is the increasing realization that voice as a medium of customer support will see an irreversible rise in the coming years, led by Voice AI technology. In the long run, any company that wants a firm hold on its market share or leadership must look into the Platform capability of its Voice AI vendor to enhance the probability of sustainable success and competitive advantage. Here are the five core advantages of a robust Voice AI platform:
Long-Term Success: The performance, strength, and sophistication of the Platform, not the product, determines the success of the company in the long run. Choosing the right Platform will help contact centers mitigate the risk of changing the vendor and starting from scratch mid-course.
Replicating Platform Technology is Challenging: Platforms can not be built overnight. Creating a state-of-the-art platform technology takes vision, resources, capability, and time. Over time the benefits multiply due to network effect and learning curve advantages associated with AI models. This initial advantage creates a remarkable difference as years add on.
Leveraging Modularity: A robust platform always aces modularity as it provides diverse and latest technology options for contact centers to create their solution the way they want. It allows for ease and diversity of integrations. This gives the company flexibility in cherrypicking integrations.
Multiplier Effect: In the extended run, contact centers, Voice AI providers, and other application providers benefit from a robust platform as it harnesses the multiplier effect by leveraging the presence of dozens, hundreds, or even thousands of third-party vendors. So, any company using the platform to deploy a voicebot will have not only a multitude of choices, but they will also benefit from the innovation they bring in, as it can be easily incorporated into their voicebot.
Faster and Agile: A strong Voice AI platform will make it easy for companies to create and upgrade their voicebots. Reduction in time-to-go-live and ease of creating, maintaining, and enhancing the voicebot makes it easy to change and maximize its effectiveness.
Here are some of the capabilities of an evolving Voice AI platform:
A Unified View: It should give a unified view of the entire voicebot, from stats on conversational design to integration to ASR.
Voicebot Creation: It must allow companies to create conversational flows and test and deploy them with minimal help from the Voice AI vendor.
Collaboration: It must allow the users to collaborate and comment at any point of voicebot creation.
Enhancements and Testing: Changes in policy, customer preferences, or offers must reflect changes in conversational design. The users must be able to easily do these upgrades and modifications and test them before deployment.
Campaign Management: The effectiveness of the voicebot depends on the capability of the user to run campaigns with complete control. It must allow them to upload data, run campaigns, and modify them real-time.
A Wide Range of Tools and Integrations: Creating a voicebot with autonomy requires giving a choice of a wide range of tools. A robust platform would provide that to its users along with a great variety of integrations.
A Voice AI vendor can have a great product and a short time to market. But if it is missing a great platform, then, in the long run, its clients will lose their competitive advantages. A CXO can indirectly identify the signs of a weak platform. Here are a few major red flags of a weak platform:
Opaque: The creation of the voicebot will be opaque to the contact center.
No Clear Visibility: The elementary constitution of the voicebot and its functioning will have no visibility.
Lack of Agility: For every minor tweak, the user must catch hold of the engineering team to code and execute the change. This is a waste of time, resources, and money.
Operational Friction: Constant and copious communication between the user and the Voice AI vendor will decelerate the pace of implementation of changes.
Slower and Patchy Delivery/Updates: Delays in deployment, updates, and upgrades
Absence of a Marketplace Advantage: A robust platform grows rapidly, and with its growth comes the network effect, i.e. the presence of third-party solutions that can augment performance in many dimensions.
Lack of Control on Quality: Giving absolute control over the creation and deployment of the voicebot helps the users engage more deeply with their voicebot and mold it with their vision. The outcomes are much better and are sustained for a longer period.
Some great ways to identify these telltale signs is to engage in a free-of-cost pilot or to ask relevant questions during detailed demos.
The essential thing is, a Voice AI vendor must possess a great product that can converse intelligently with consumers or callers. Additionally, this product must be facilitated by a robust underlying platform that enhances its capabilities, adding to the overall experience of creating, deploying, and improving the voicebot.
To learn more about Voice AI solution and what it can do for a contact center, book a consultation now: www.skit.ai
You are ready to adopt a Voice AI solution for your contact center, or you are in the process of adopting one — congratulations! Now is the time to think about integrations. In this article, we’ll discuss the benefits of integrating your Voice AI platform with various tools and applications, and we’ll offer some guidance on where to get started.
What are Voice AI integrations? They are the APIs that connect your Voice AI platform with other tools and applications you may already be using, allowing you to view and control data from multiple sources in one place. Integration augments the system’s capabilities, as it ensures a more unified view, allows you to personalize your automated calls, and helps you automate a lot of work that you would otherwise have to do manually.
Integrations are critical — but they vary significantly depending on your industry, your use case, and your specific needs. For example, voicebot integrations for a bank’s customer service will be very different from those for a debt collection agency. Additionally, integrations can be tricky from a technical standpoint sometimes, so you want to make sure that your provider has the necessary experience and tools.
Integration with internal systems is the top criterion considered when selecting a conversational AI platform provider, according to research by Gartner.
The most common types of integrations for Voice AI are with Customer Relationship Management (CRM) systems and ticketing platforms, payment gateways, speech analytics tools, and messaging tools. In this article, we’ll explain the role and importance of integrations and go over the most common types for various use cases.
What Are the Benefits of Integrating a Voicebot Platform with Other Tools and Applications?
For a seamless collaboration between human agents and voicebots, an Augmented Voice Intelligence solution requires various tools that perform different functions while working well together. Through integration between tools, the entire process can be as smooth and efficient as possible.
The main benefits of integrating your Voice AI platform with other tools and applications are:
Ensuring a better customer experience, as the Digital Voice Agent will be able to perform multiple tasks and better serve the customer
Maximize call personalization, as the Digital Voice Agent will be able to address customers by name, easily access their records, and base its interactions on context
Automating several tasks, freeing the contact center’s staff of the administrative burden
Generating automated metrics to track the performance of calls and maintaining records of all customer interactions
3 Things To Consider When Thinking about Voice AI Integrations
Stay lean at first. The number-one tip for companies adopting a Voice AI solution is to avoid focusing too much on integrations at the beginning of the adoption process. This is because when you adopt a new technology, it’s important you focus on gaining experience with it and fully understanding how it can benefit your business before you invest a lot of time and money in integrating it with several other tools and platforms. First implement the solution with the most basic and necessary integrations, and then you can start investing in the heavier ones.
Your Voice AI solution might be hybrid at first. If your contact center already has an automated response system in place, like an interactive voice response (IVR) system to take inbound calls, you might choose to have the Voice AI solution work hand-in-hand with the existing system at first. That would result in a hybrid approach—in which the first node of the call is handled by IVR, and then, depending on which option the caller selects, you may transfer them to the new Digital Voice Agent (voicebot). If this is the case, you’ll need to integrate the two systems so that they can work with each other. Once the Voice AI has been fully tested, you are likely to fully remove the IVR and let the Digital Voice Agent handle all inbound calls.
Data privacy. Data privacy and data protection are elements that you should always keep in mind when integrating different systems. You want to secure the data against unauthorized access, adopting processes like encryption, secure communications protocols, and relevant security policies.
The Most Important Integrations for a Voice AI Platform
Voice AI Integration with Customer Relationship Management (CRM) Systems
Companies use a CRM software to gather, organize, and manage customer information. The primary benefit of integrating your Voice AI solution with your CRM system is to easily personalize all calls, whether they are outbound or inbound, and automate the calls end-to-end.
For outbound calls, for example, the Digital Voice Agent can gather the customer’s information from the CRM and address them on the call by first name: “Hi John, this is a Digital Voice Agent calling from…” The CRM also feeds the DVA more detailed information and context on the customer’s existing orders or accounts depending on the use case.
For a debt collection agency, for example, the Digital Voice Agent can gather not only the name of the customer it’s calling, but also the balance of their account.
For an ecommerce company, the Digital Voice Agent can quickly gather the information on existing orders, shipping, etc.
This integration also allows customers to open new tickets with the company’s customer service. At the end of the call, thanks to the integration in place, the Voice AI solution will feed the new information based on the interaction with the customer to the CRM system. Therefore, the new data will be stored and will be on file.
Examples of CRM systems are HubSpot, Salesforce, Zoho, Freshdesk, and Zendesk.
Voice AI Integration with Payment Gateways
Integrating the voicebot platform with payment gateways or payment applications can make the customer experience significantly smoother and ensure the completion of various transactions during the call without the need to involve human agents. Examples of payment gateways are PayPal, Stripe, Amazon Pay, 2Checkout, Apple Pay, and Square.
Customers can easily pay a bill — for example, a telephone bill — during the call without the need to complete the transaction by opening a link or logging into an online portal.
For debt collection agencies, this integration can be very useful, as customers can make a payment during the phone call with the Digital Voice Agent, making the collection process fully automated, cheaper, and smoother.
Without this integration, in order to complete a payment, a customer needs to change the communication channel, moving to text message, email, or having to access the company’s website.
Voice AI Integration with Messaging Channels
For an omnichannel experience, it’s best to integrate the Voice AI platform with various messaging channels, at least those that your company uses the most to interact with its customers. Examples of messaging channels are email, text messaging (SMS), WhatsApp, Viber, Signal, Facebook Messenger, and Instagram.
Messaging integrations can be used both for inbound and outbound messages.
Confirmations and receipts. After a customer has made a payment during a call with the Digital Voice Agent, the company can send a payment confirmation and receipt to the customer. Confirmations can also be sent for any other type of transaction or request, such as a travel reservation change.
Payment link. The company can send a link to an online payment portal via text message (SMS) or email during an automated call with the Digital Voice Agent.
User authentication. While a user can be easily authenticated on-call by the Digital Voice Agent, authentication in other instances can also take place in a chat tool before or during the call.
Collection of images or other information from the customer. During a customer service call, the Digital Voice Agent might ask the customer to send an image or the photo of a receipt via SMS. This integration can be used to allow customers to send any type of information to the company during a call with a Digital Voice Agent.
Voice AI Integration with Telephony Platforms
Many companies might already have a telephony system in place when they decide to adopt a Voice AI solution. Examples of telephony and call center platforms are Genesys, RingCentral, 8×8, Five9.
Integrating the Voice AI platform with your company’s existing telephony platform will certainly make the adoption of Voice AI smoother, especially if you already have some level of call automation or IVR in place.
If the adoption of the Digital Voice Agent is gradual, and the system is hybrid at first, this integration allows your company to align both IVR and Voice AI solutions side-by-side.
Voice AI Integration with Speech Analytics Tools
Many businesses also use speech analytics solutions to analyze the phone conversations they have with their customers. These tools transcribe the text of the phone call and then analyze the voice of the customer, discern their feelings, identify emerging issues, and further your understanding of the customer experience (CX).
Examples of speech analytics solutions are CallMiner Eureka, Salesken, and Genesys.
If you have further questions on Voice AI integrations or you’re ready to start exploring how a Voice AI platform can take your contact center operations to the next level, contact our experts using the chat tool below!
For contact centers, Interactive Voice Response (IVR) systems were a turning point a few decades ago, but now have become a customer experience turn-off. IVR systems have helped companies manage call volumes as well as create value with self-service options, information gathering, and call routing. But a recent study found that, on average, IVRs cost businesses $256 per customer each year! Additionally, a whopping 61% of these customers are unhappy with IVR systems and believe they contribute to a poor customer experience.
“About 83% of the customers abandon the call and company after their IVRs encounters.”- Vonage report.
Why IVR Systems a Customer Service Turn off
Historically, IVRs have failed to delight callers due to the poorly designed phone menu and the inability to dispense an answer or connect an agent on the go. Companies and businesses receive a lot of flak due to the general notion of associating IVRs as cost-effective replacements for contact center agents. It is paradoxical that customers warmly accept other forms of automated, self-service options for an instant response like ATMs and a variety of mobile applications but not IVRs!
The reasons for it are pretty simple. Since their introduction into the contact center market, IVR systems have undergone few iterations, and their main features haven’t changed much. The hold time, lengthy pre-recorded menus, and the need to repeat query information, especially during an emergency, continue to be a liability for businesses. More importantly, customers have a strong affinity for resolving queries with a human representative than with restrictive, pre-recorded systems that only leave them with unsavory emotions towards the brand.
Nearly 47% of callers reportedly experience frustrations with IVRs. A significant number of them admitted feeling angered and stressed, according to the Vonage report.
The same report also revealed that instead of IVRs, if the customers were able to get a hold of a live agent, they experienced relief (27%), less frustration (26%), and less anger (24%). However, call center agents often end up at the receiving end of customer frustrations from navigating a labyrinth of IVR menus. Therefore the onus is on brands to elevate customer experiences without negatively impacting agents’ morale and productivity.
In this article, we share our insights on overcoming common contact center and customer experience challenges associated with traditional IVRs by diving into the capabilities of Voice AI. We will explore how brands can elevate their customer support with intelligent voice automation of nearly 70% of calls and human-like conversations.
Imagine a scenario—a customer calls a banking company’s contact center to block their stolen debit card. In lieu of pre-recorded messages and caller authentication protocols, the call is handled by a voice agent that is capable of contextually comprehending the caller’s urgency and making appropriate suggestions. The overall call experience is different! Why?
Zero waiting time
The instant response instead of punching numbers, a refreshing change from the lengthy IVRs menu options, annoying IVR theme music, and even from the exasperating experience of going down the rabbit hole of the menu by accidentally pressing a wrong button.
For simple queries, no need for human agents
That’s our Digital Voice Agent (DVA) at work. Skit.ai’s DVA, for instance, is an AI-enabled virtual agent built from the ground up to understand human conversations. It can be plugged into contact centers to resolve tier 1 customer problems and automate cognitively routine work.
Digital Voice Agents vs. IVRs
Built for Voice: Unlike conventional IVRs and chatbots that are capable of understanding only transcriptions, Digital Voice Agents are crafted specifically for voice conversations. Whenever a customer calls the contact center, they can interact with the voice agents in the same way as they converse with human agents.
Built for Personalization: With DVAs, there wouldn’t be any psychological barriers that callers experience when they are forced to interact with IVRs or chatbots. Besides, an intelligent voice agent that can sound like a human, picks up on the immediacy of the issue, giving callers a sense of relief and comfort in their critical moments, adding a more personal touch to customer service. Besides, they can even interact in the caller’s preferred choice of language.
Built for Accuracy: Another issue when dealing with IVRs is that they work well only when there are no external disturbances like background noise or music. They can sometimes not recognize text inputs and end up redirecting the caller to the undesired part of the IVR menu. But DVAs can take in both voice and text inputs, and even filter out the ambient noise to capture the accurate voice response by the customer.
Built for Capturing Intent: Voice agents are based on powerful spoken language understanding (SLU) algorithms and can identify the semantics of the conversation. They can accurately capture the caller’s sentiment, tone of voice, and speed of the conversation to identify intent.
Built for Resolution: In emergency situations that require a quick response from customer support, a call hold would reflect poorly on the company’s services. It can even make them lose customers to their competitors. Most IVRs cannot pick up on non-linguistic cues like pauses, gasps, and utterances in between sentences. It is purely designed for text inputs. DVAs are capable of having contextually accurate interactions without relying on a limited stack of keywords, enabling quick query resolution.
Built for Intelligent Human and Machine Collaboration: IVRs are automated and function independent of human agents. DVAs are capable of end-to-end automation of simplistic calls and pass on complex ones to human agents, involving them only in complex use cases.
Now, let’s look into 7 specific angles where Skit.ai’s purpose-built, industry-specific voice-first technology, Voice AI, makes a tremendous difference to contact centers. Skit.ai’s voice agents are a better fit than traditional IVRs in enhancing the quality of customer service.
Speed and Simplicity: Simple and easy-to-understand customer support is a formula for delighting a captive audience. There’s a good chance that the majority of callers may not get past the common obstacles in IVR menus, complex navigation, and confusing terminologies. IVRs can best offer five top-level and three sub-level menu options whereas DVAs immediately attend to calls, keeping it short and simple.
Quick Resolution with Cost Efficiency: Apart from resolving customers’ problems, customer service organizations look at cost and call time spent as success metrics. Instead of wasting time, waiting for the right menu option on IVRs, customers’ queries with DVAs are addressed instantly and at a fraction of the cost while also engaging with the callers over voice conversations at scale.
For Intelligent Customer Service: Today’s customer service is expected to be built intuitively to absolve current issues and anticipate the next course of action. DVAs help make the most of the voice conversations with customers by mimicking human-like conversations and leveraging customer data to make appropriate recommendations, suggest steps or make intelligent call transfers to human agents.
Quick Agent Reach during Emergency: Even the most loyal customers lose patience and abandon calls midway when forced to repeatedly go over the IVR system. For critical use cases that require timely resolution, DVAs work best. They not only hold an immediate voice interaction with the callers but also identify short, conversational utterances, pick up on callers’ intent, and capture customer details for quick call transfers to human agents.
Making Query Resolution Interactive: Speaking to a live agent immediately is not the magic bullet for customer support success. Augmenting IVR systems or replacing them with Voice AI-driven automation for call back features at customers’ preferred time helps personalize and enhance the call experience making the conversations more empathetic. The rapid scalability and robust integrations of the DVAs help include options to reach customers with interactive emails and voicemails along with call-back options.
Easy Integration with Customer Experience Systems: Customer service calls can be more proactive and intuitive when integrated with customer relationship management (CRM) platforms and automated call distribution (ACD) systems. Voice agents have access to caller history, previous purchases, and other customer data based on the caller ID number. It provides enough pre-context to authenticate calls before call handovers to human representatives.
Timely, Useful Insights for Enhanced CX: DVAs help brands adopt advanced analytics-driven approaches to unlock a treasure trove of insights on call performance as well as define relevant KPIs and areas for improvements in the customer’s journey for cost savings and better CX. IVRs need optimizations to deliver this capability. While DVAs work as productivity enhancers with timely insights that help add incremental value to the brand or business’ customer experience.
Despite several detractors that customers unanimously agree on, IVR systems remain a staple in customer support. The worldwide growth rate of the IVR market is expected to reach $6.7 billion by 2026. This growth trajectory can be a blessing to CTOs who chose IVRs for long-term customer service investments, but certainly a nightmare for CMOs against the backdrop of increasing customer calls. Technological innovation and AI-driven upgrades are needed to drive the progression of IVR systems. Until then, Voice AI helps empower businesses to elevate inbound and outbound initiatives for better CX in ways that IVR systems fail to live up to.
Are you interested in contact center automation with our Digital Voice Agent to elevate customer experience? Book a demo with one of our experts: www.skit.ai
Nowadays, there are many companies offering voice assistants and other voice intelligence solutions, and it can be challenging to navigate this newly-crowded market. The goal of this article is to guide you through the various voice-based tech solutions available and their inherent differences so that you can pick the most suitable option for your organization’s needs.
In this guide, we’ll go over the following items:
The technology behind a Voice AI solution and all of its components, such as ASR, SLU, and TTS.
The factors that make voice conversations challenging for voicebots, such as urgency and latency, spoken language imperfections, and environmental challenges.
What makes a Voice AI vendor truly “voice-first.”
In the last section of this guide, we’ve outlined the main categories of vendors offering Voice AI solutions and the challenges you might encounter when engaging with them:
Telephony and ARM companies
Conversational analytics companies
Voice-first companies, whose primary focus is to develop and offer Voice AI solutions (e.g. Skit.ai)
The Technology and Mechanisms of a Typical Voicebot
A Digital Voice Agent (Skit.ai’s core product) is a Voice AI-powered machine capable of conversing with consumers within a specific context in place. The graphical illustration below is a simplistic view of the various parts that work together, in synchronicity, for the smooth functioning of the voicebot, in this instance Skit.ai’s Digital Voice Agent.
If you need a more exhaustive explanation of the functioning of a voicebot, please read this article for further understanding.
Telephony: This is the primary carrier of the Digital Voice Agent. Whenever a customer calls up a business, it is through telephony that the call reaches the Voice Agent (either deployed over the cloud or on-premise). There are various types of telephony providers; Skit.ai also provides an advanced cloud-telephony service, enabling even faster deployment times and flawless integration.
Typically a conversation with a voicebot involves the seamless flow of information, and here is how it happens:
The spoken word is transmitted through the telephony and reaches the first part of a voicebot, i.e. the Dialogue Manager, which orchestrates the flow of information in a voicebot. It also captures and maintains a lot of other information for example – it keeps a track of state, user signals (gender, etc.), environmental cues (like noise), and more.
The Dialogue Manager directs the voice to the Automatic Speech Recognition (ASR) or Text to Speech (TTS) engine where the speech is converted into text or the voicebot will speak to the request information if needed.
SLU: The text transcripts are then forwarded from ASR to the Spoken Language Understanding (SLU) engine, the brain of the voicebot, where:
It cleans and pre-processes the data to get the underlying meaning,
And then extracts the important information and data points from the ASR transcripts.
A good voicebot utilizes all the best ASR hypotheses (about the actual intent/meaning of the spoken sentence) to improve the performance of downstream SLU.
TTS: The Dialogue Manager comes into play again and fetches the right response for the customer based on the ongoing conversation. Text-to-speech (TTS) takes command from the Dialogue manager to convert the text into the audio file that will eventually be played for the caller to listen to.
Integration Proxy: Voice Agents talk with external systems such as CRM, Payment Gateways, Ticketing systems, etc., for personalization, validation, data fetching, etc. These are integration sockets that connect with external systems in order for voice agents to be effective and efficient in end-to-end automation.
What Makes Voice Conversations Difficult for Voicebots
We now have an understanding of how a state-of-the-art voicebot works. But coming back to the questions on the significance of selecting the right vendor, we have to understand the nuances of voice — what makes it so challenging and more complex than chat or any other conversational or contact center solution?
Environmental & Network Challenges:
Unlike a chatbot, a voicebot has to face interference from environmental activities and has to overcome them to deliver quality conversations.
Background Noise: Inherent to voice conversations is the problem of background noise; it can be of different types:
Multiple speakers in the background
And extraneous speech signals such as the speaker’s biological activities
In order for the SLU to identify intent and entities precisely, ASR should be able to differentiate the speaker’s voice from background noise and transcribe accurately. On the other hand, chatbots get clean textual data to work on and do not face this issue.
Low-quality Audio Data from Telephony: Typically, a telephony transmission involves low-quality audio data, and there is a limit to how much one can pre-process the data.
Spoken Language Imperfections:
User Correction: Often in real-life conversations we speak first and then correct in case of mistakes, for instance: the answer to the question – for how many people do we need to book the table? – “I need a table for 4… no 5 people” This can be very confusing for the voicebot. Or even the answer – 4-5 people can be construed as 45, hence SLU needs to be good to decipher the real intent.
Small Talks: Many times during actual conversations, the consumers ask the voicebot to ‘hold on for a sec’, delaying their response due to an urgent issue. Such, and similar situations add to the complexity of conversations.
Barge-in: Voicebots work perfectly when both parties wait for their turn to speak, and do not barge in while the other is speaking. But in the real world, customers speak while the voicebot is completing its message. This creates complexity and errors in communication.
Language Mixing and Switching: The speaker may decide to switch between languages or even mix them. For the voicebot, it creates difficulty in comprehending the message and in language selection while replying. Chatbot, on the other hand, gets clean text data so it does not deal with the vagaries of spoken communication, as people are more thoughtful while writing.
Lack of Interface & Fallback: Typically in a chat window, when the chatbot does not understand an answer, it gives other options to the person. In a voicebot, there is no option to fall back, hence it makes the voice difficult to perfect.
Unique Paralanguage:The message encoded in speech can be truly understood by analyzing both linguistic and paralinguistic elements. More than the words, the unique combination of prosody, pitch, volume, and intonation of a person helps in decoding the real message.
Urgency and Latency:
Calling is usually either the last resort or the preferred modality for urgent matters, so expectations are sky high. Hence for preserving or augmenting the brand equity, customer support must work like a charm. Else it will have a lasting negative impression on the brand. On the contrary, if you reply to a chat after 30 seconds, it won’t hamper the conversational experience whereas the voice conversation is in real-time. Skit.ai’s Digital Voice Agent responds within a second, but, unlike chat, it can not wait for the customer for half an hour.
Too Many Moving Parts: A system is as good as its weakest link. Dependence on external party solutions makes management more challenging and limits the control a vendor has over voicebot performance. For instance, ASR, TTS, SLU, etc., which are advanced technologies in themselves, require a dedicated team responsible for the proper functioning.
Continuous Learning and Training: Conversational AI is not a magic pill that you take once, and you are done. Over time, changes in your customer behavior would necessitate optimization of your product mix and thus you need a dedicated team and bandwidth to keep it improving with time. Constant efforts have two consequences – one is the focus on upgrades and the other is the learning curve advantages that come with time.
Types of Vendors in the Voice AI Space
Coming back to our original discussion of the different types of vendors in the space, there are mainly four types of vendors that provide AI-powered Digital Voice Agents. We’ve outlined them below with their respective limitations.
Telephony and CRM Vendors Trying to Enter the Voice Space
Telephony and CRM vendors usually have IVR as one of their offerings. This enables synergy in their sales operations and utilizes their existing customer base to cross-sell the voice AI solution. To make this possible they collaborate with small vendors or white-label the solution along with utilizing the existing open-source tech (e.g. Google, Azure, Amazon, etc.) designed for simplistic horizontal problems in single-turn conversations, rather than complex ones.
Problems and challenges while engaging with such vendors:
Low Ownership and Responsibility: Since it is not their primary revenue-earning business they are not seriously invested.
High Reliance on Third-party Services: When a vendor relies heavily on third-party solutions, the control it has over the entire process gets compromised, unless it has its own tech stack working in sync. For example, Google’s ASR API has very low accuracy for short-utterances such as yes, no, right, wrong, etc. And if your use-case requires handling such conversations, one needs to have its ASR to notch up the performance.
Constant Effort and Training: Any AI application requires constant effort in terms of maintenance and upgrades. A company that is not AI or voice-first will never have the resources to do this in the long term, a major disadvantage.
Chat-first Companies Trying to Get into Voice AI
The chatbot does not require ASR and TTS blocks as chatbots get the input in textual format and responses are also in text format. So they just need the NLU block.
These chat-first companies try to utilize their existing chat-first platform’s NLU by utilizing the third-party ASR and TTS engines.
Chat-first Voicebot = ASR + TTS (third party) + NLU
Here a chat-first voicebot will use a third-party ASR and TTS, that will give its chatbot the ability to speak and understand the spoken word. But since it is based on NLU, it will not be able to capture the essence and nuances of the speech we discussed earlier.
SLU vs. NLU: Without SLU, NLU might treat the ASR transcriptions without considering the speech imperfections we discussed earlier. For example, in the case of debt collection, if someone says, “I can pay only six-to-seven hundred this month, not more”. We need to understand the context and underlying meaning that the user wants to pay anywhere between $600 and $700 and not $62700. Such nuances can only be addressed by SLU, and hence its indispensable significance.
Oftentimes transcripts from ASR are corrupted due to noise, differences in accents, etc. NLU systems are trained on the perfect text and often cannot deal with the imperfections present in ASR transcripts. In a voice-first stack, ASR imperfections are taken into account while designing the SLU.
Challenges while engaging with such vendors:
Expect more failures with chat-first voicebots, as it is at best a patchwork, a ragtag coalition of most easy, and cheap technologies.
Low ownership as the voice-tech solution is not their primary revenue-earning business.
High reliance on external third-party services (as explained in the above section).
Not Being Voice-first: an AI application needs constant effort to remain accurate and updated. A company that is not voice-first will struggle to catch up as it can not dedicate a team and the solutions will perpetually be an underperformer.
How to spot such vendors: It is difficult for companies to decide which is a voice-first company and which is chat-first, so here are a few tips to separate the wheat from the chaff:
Look at the Revenue Split: If the vendor claims to be a voice-first company, but has a majority of revenues coming from chat, text services, or other products then it is not a voice-first company.
Proprietary Tech Stack: Look into the scope of their proprietary technologies, it gives a clear view of the seriousness of being voice-first. If for everything they are using third-party applications such as Google, Amazon, and Siri, they are not serious voice vendors and are just experimenting to get additional revenue sources.
Voice Team Size: Another valuable insight can come out of analyzing their voice team size. A chat-first company will not typically devote a significant part of its team to voice.
Voice Road Map: A company of the ilk of Skit.ai will always have a tech roadmap of the features they are going to release, the impact that will have and how is their R&D going to innovate for being future-proof.
Additionally, we are now starting to see also an additional type of vendor — conversational analytics companies entering the Voice AI space.
Why Choose Voice-first Companies or Vertical AI companies?
One important thing that is evidently clear at this point is that voice conversations are more challenging than they seem, there is so much more than meets the eye.
High Ownership: The entire organization of a voice-first company is streamlined to deliver and own the outcomes of their voicebot. There are no distractions, only a razor-sharp area of focus. This makes their projects most likely to succeed and deliver transformative outcomes.
Deep Domain Knowledge: A voicebot is a symphony, an orchestra of technologies working in tandem with each other to deliver the intelligent, fluid, and human-like conversations that every consumer covets. Only voice-first companies that labor hard to make every part function smoothly, and efficiently will be the ones delivering outcomes with maximum CX and RoI.
Proprietary Tech Stack: Not that voice-first companies don’t utilize the third-party stack, they leverage them to further performance and control. They tune third-party tech stack and use it along with their existing proprietary tech to maximize the impact. For example, a company such as Skit.ai uses Google, Amazon, or Azure’s ASR along with its own domain-specific ASR parallelly to get the highest accuracy and optimal performance. The results are tangible and impressive. As Skit.ai’s ASR is significantly better at short-utterance, at instances where the conversational experts expect them, Skit.ai’s ASR kicks in for higher accuracy and performance.
Dedicated Team: Running an AI-first product comes with its own challenges. But for a company like Skit.ai, which has a dedicated team of 400-500 people laboring to solve just the voice conundrum, you can expect an outstanding product that is always further along on the learning curve and stands true to its promises.
Long-term Engagement: Voice is the future of customer support. No other modality will come close, especially with the blazing advancements in Voice AI. So, a voice solution must not be implemented with a very narrow view of time and cost. Deeply committed Voice AI vendors will be the ones to seek as they will deliver superior results that not only help companies save costs but also aid them in carving out an exceptional voice strategy for brand differentiation.
For further information on Voice AI solutions and implementations, feel free to book a call with one of our experts using the chat tool below.