How Voice AI is Helping Consumer Durables Brands Perfect the Art of CX

After enduring the pandemic lull, semiconductor shortage, and the rising cost of raw materials, India is again a hot market for consumer appliances and electronics. 

In 2021 the Indian consumer durables industry stood at $9.84 billion and is likely to reach $21.18 billion by 2025. This double-digit market growth is driven by the brands’ omnichannel reach and a massive shift in consumers’ thought process—from price consciousness to a preference for technologically advanced, premium products that promise higher quality, safety, and value.  While this is great news in terms of sales and profitability, it signifies the end of mass marketing and traditional customer engagement strategies.

Today’s consumer wants to feel special and expects a meaningful connection with the brands. Customer experience (CX) is important for long-term customer relationships and sustained value-creation.

The State of CX in the Consumer Durables Industry 

Unlike other industries, sales is the starting point for brand-customer relationship in the consumer durables business. Since today’s customers expect more, brands are bombarded with countless opportunities on the digital front to offer and improve their post-sales, product, and user-oriented services; understand audience demographics, product usage, and collect feedback. To navigate the challenge of delivering modern CX with the conviction to delight customers, the worldwide spending by companies on CX technologies is expected to touch $654 billion this year. 

To make investments worthwhile and master the art of customer centricity in the consumer durables industry, let’s first understand the common barriers to great CX:

  • Too Many Touch Points: Many brands these days have multiple touch points (online and offline forums) for customer interactions. This means too many, complex customer journeys where data remains fragmented and departments operating in silos are unable to collate customer insights and behaviors to analyze and personalize experiences. 
  • Customers’ Propensity towards Brands with Solid Digital Presence: Millennials and Gen Z consumers prefer brands with a strong online presence like social media, website UX, pricing, and product information and reviews before making purchases. These consumers relate brands’ digital savviness as an important factor for building trust and establishing personal connections. This expectation creates a myriad of variables for consumer durables brands to consider while providing consistent CX across the journey including post-sales support. 
  • Shifting Loyalties and Micro-moments: Consumer electronics and appliance companies suffer from poor customer loyalty due to countless competitors, promising similar products at better rates and features. McKinsey’s study found the average loyalty scores are below 20 percent in the consumer durables industry. 

Besides, brands are not evolved enough to leverage ‘micro moments’ or the few seconds when a customer online browses with the intent of buying a product or service. It takes cutting-edge expertise to encash on a limited window of opportunity to identify a potential customer, and provide them with the right information, at the right time, and right medium! 

  • The advent of Circular Economy and Sustainability: The consumer appliances and electronic industry have globally turned towards sustainability, driving their brands and manufacturers to practice circular economy models like recycling and reuse to avoid wastage. Customers these days are also more informed and favor brands that uphold their sustainability promises. 

This could be a Catch-22 situation for consumer durable companies as on one hand, their customer service will be flooded with queries and inbound calls regarding the maintenance, repairs, and responsible end-of-life actions for products that cannot be addressed by generic IVRs or FAQs. On the other hand, there is a lack of evidence-based data on mapping and driving customer experience which is crucial to the adoption of circular practices.

  • Delayed Product Servicing: Conventionally, the product repairs and servicing processes take up to days. Reaching contact centers for customer support, scheduling service and maintenance requests, follow-up and actual physical repairs involve a lot of waiting and frustrations. Sometimes, brands outsource repairs to third-party service providers which can further impact customers’ brand perceptions and experience. 

Explore how to Transform CX with Voice Automation 

The Rise of Voice AI in Customer Support

Top-performing brands can build long-lasting customer relationships by leveraging bespoke technologies like artificial intelligence (AI) and machine learning (ML) which have a demonstrable impact on areas like product design, marketing, sales, and customer service. When it comes to elevating CX, consumer durables companies must seize the moment by automating customer service and augmenting their contact centers with AI-powered, industry-specific platforms. Voice-first technology solutions like Voice AI help reshift the gears of customer service in the consumer durables industry.

Users of consumer durable products approach contact centers for a slew of reasons and prefer voice interactions with human agents over texts and IVRs. Besides, voice is the most instinctive and easy form of communication. Voice AI helps tap into customers’ voice conversations to improve contact center performance and guarantee personalization.

Customer support platforms built for typing and texts would be insufficient to articulate customers’ urgency, queries, complaints, and issues. Voice AI platform is built, designed, and optimized for voice conversations at scale for prompt query resolution and better personalization to callers. 

Explore how Voice AI can help you transform Travel and Tourism Companies 

The Digital Voice Agents automate multimodal interactions and take over cognitively repetitive tasks so that human agents can vest attention to addressing complex customer problems. 

9 Benefits of Voice AI for Contact Centers in the Consumer Durables Industry 

 

9 Benefits of Voice AI for Contact Center in Consumer Durable Industry

  1. Automation of Contact Center Operations: The Digital Voice Agent answers tier-1 calls, without the need for a human agent.  The tech stack in Voice AI can enable conversations that are modeled on human interaction. Every time when a Voice AI agent calls customers, it can be optimized to answer all basic questions and handle tier-1 queries. Besides, it can automate repetitive, zero-value tasks like call scheduling, reminders, post-service feedback, and more. 
  2. Round the Clock Support: Traditional 9-5 functioning contact centers don’t fit well with today’s customers’ lifestyles and schedules. Digital Agents are meant to provide 24/7 support and manage customer calls through the unavailability of human agents beyond business hours.  
  3. Call Containment:  Automate calls and improve your self-service function as well as answer tier-I questions without the need of a human agent. The higher the contained calls, the higher the cost savings. 
  4. Scale Up Sales Outreach and Inbound Calls: Voice AI helps take over high-volume tasks that are performed by human agents at less cost and in shorter timelines. The automation helps consumer durables brands cover millions of customers for sales outreach in a matter of few days using fewer agents. Additionally, the platform’s Speech Recognition algorithms and data help autonomously attend to inbound queries, understand customer pain points, and help them feel connected to the brand. 
  5. Personalized Empathetic Conversations: Voice AI’s tech stack allows contact centers to tailor conversations and responses in multiple languages. The semantic understanding of the spoken words, tone of voice, speed, and emotions help capture the intent of the customers to proactively respond with relevant options. Also, Voice AI’s intelligent and instant troubleshooting options for service requests reduce wait time when customers are on hold. 
  6. Reduced AHT: The agents’ tasks can be augmented by Digital Voice Agents that seamlessly plug into contact centers to solve queries with relevant insights and automated options like reminders, notifications, and call authentication, reducing average handling time (AHT) by 30 to 40 percent. This helps agents balance work and avoid burnout during inbound call surges.
  7. Cost Savings:  Contact centers of consumer durables brands can incur operational cost savings up to 35 percent with Voice AI by avoiding additional expenditure for infrastructure upgrades and maintenance and staff training and recruitment. The platform saves resources and time by executing outbound campaigns at scale and accuracy.
  8. Brand Consistency: Brands with a hyperlocal and global presence can streamline contact center operations and standardize their interactions based on their needs. They can customize the Digital Voice Agent to proffer consistent customer experience and service quality. 
  9. CSAT Levels: Brands can tap into the new era of self-service experience and guarantee constant engagement with reminders and notifications. By powering voice-centric interactions that customers cherish and largely resonate with, Voice AI helps consumer durables’ contact centers achieve customer loyalty and satisfaction scores of 4.0+. 

Digital Voice Agents – their Functioning and Benefits 

The Road Ahead 

Expert evidence points that we are in the ‘platinum era of CX’ and headed to a future of more emotionally and personally immersive CX. 

After braving a tumultuous ride of economic slowdown and digital acceleration, the global consumer durables industry is at an inflection point.  This is where technology and thought leadership come together to acknowledge the ‘voice’ of today’s customers through Voice AI.

For more information and free consultation, let’s connect over a quick call, use the chat tool below to schedule an appointment with one of our experts.

Are You Using Containment Rates to Measure Voicebot Performance? Think Twice!

Management guru Peter Druker’s most important quote resonates completely with voicebot performance: “If you can’t measure it you can’t improve it.”

Aren’t CXOs constantly debating the expenditure on technology and its RoI? While a razor-sharp focus on the end results is well warranted, the choice of metric is very important, too. Businesses can succeed only when technology goals are linked to the business goals, and they finally crystallize as positive outcomes.

Contact centers are one of the most dynamic types of organizations that have been on a relentless hunt for automation solutions. They measure outputs with awe-inspiring precision and optimize their process to be more effective and cost-efficient.

Often, and fallaciously so, contact centers use containment rate as the most important metric when measuring the voicebot performance. In this article, we will demystify the limitations and dangers of using containment rate as an absolute measure of voicebot performance.

What is Containment Rate?

The containment rate is the percentage of users who interact with an automated service and leave without speaking to a live human agent.

When a customer ends a customer service interaction without the need to speak to a human agent, the call is said to be contained. While that may be great news in terms of resource optimization and better usage of human agent bandwidth, what does it really reveal about the customer’s experience? The containment rate does not reveal whether the customer’s query was resolved or if the customer was satisfied. Nor does it reveal anything about the effectiveness of your voicebot or even the IVR.

Why Containment Rate Goes Against the Principle of CX

If your goal as a company is to prevent your customers from reaching a human agent for support, then the containment rate is the best metric. But is that strategy reflective of your vision?

Ideally, in a world with no resource constraints, there would be a human agent ready to answer every customer’s call. But the cost factor proves to be prohibitive, resulting in the need to find a cost-effective and scalable means to improve CX. The technology deployed may range from mundane IVR to state-of-the-art Voice AI. But if the focus is just on increasing the containment rate, it will end up damaging CX.

Every call is an opportunity to forge a long-lasting relationship that can help a company improve its top and bottom line, over time.

What are Voice AI Agents or voicebots deployed for? It is to serve the customers better, provide zero wait-time and 24/7 support, and not prevent them from reaching human agents. The general idea is to promote self-service, yes, but if a customer wants to interact with the company, closing that door is not an ideal way to achieve customer satisfaction.

Hence, the containment rate must be seen in the context of other metrics while deciding if the performance of a voicebot is improving or not. Here are the situations where containment rates can be a misguided yardstick:

  • Increasing Containment Rates: If seen in isolation, this can seem like an improvement. But customers may be ending the calls because the Automated Speech Recognition (ASR) engine is not recognizing their voice or words. It can also be that the conversation flows are not optimized, leading to customer frustration.
    There are several other situations where customer queries are not resolved and causing them to hang up. Here, the containment rate may rise, but at the cost of CX.
  • Decreasing Containment Rates – Scenario 1: Calls can be classified into two categories: Completely successful calls, or partially successful calls. Many times, a voicebot is able to answer customer queries, and collect information, but for further complex questions or disputes, customers may ask for a human agent. Containment rates may decrease in these cases, but CX will improve. This is because the voicebot eliminated any waiting time for customers, it answered basic questions. The collected data and conversation helped the human agent quickly resolve customer queries; all culminating in improved CX. If we look only at the containment rate, we might assume that the voicebot has performed poorly and can result in bad business decisions.

Decreasing Containment Rate – Scenario 2: Every Voice AI Agent is trained for certain use cases and that is what makes them more effective than any other horizontal AI solution. In a case where the Voice AI Agent is handling all the calls but is trained for limited use cases, the containment rates may vary depending upon the volume of in-scope and out-of-scope calls. Hence, the generic or overall containment rate would be a wrong measure of voicebot performance.

The 10 Most Ideal Voicebot Performance Metrics

All the discussion here surrounds inbound calls. Here are the metrics people must use to measure voicebot performance.

Yet again, it must be emphasized that no metric must be studied in a vacuum. Only when put together, the true picture will emerge. But here are some performance metrics that make the most sense:

Business-related metrics: KPIs that focus on business impact and Voice AI objectives.

  1. Service Level:

It is defined as the percentage of calls answered within a predefined amount of time. It can be measured over 30 minutes, 1 hour, 1 day, or 1 week. Also, it can be measured for each agent, team, department, or company as a whole.

A 90/30 Service Level objective means that the goal is to answer 90% of calls in 30 seconds or less.

Service Level is intimately tied to customer service quality and the overall performance of a call center. Thus, instead of containment rate, Service Level is a better measure of measuring performance and can facilitate key decisions better. Deployment of a voicebot must immediately jump up the service levels and thus create business benefits. 

  1. First Call Resolution Rate (FCRR)

A call is marked resolved when the voicebot grasps the users’ query and has done everything right to assist them, even if it means connecting them with a human agent and the issue getting resolved in the first call itself. FCRR is an important metric as it helps to understand whether the voicebot is performing correctly for the use cases it is designed for and how well it is escalating the call. 

Though a relatively marginal case for inbound calls, high FCRR will impact the cost of customer acquisition (CAC) and retention for obvious reasons. Instant call pickup, intelligent conversation, answering a customer query, and any follow-on questions can reduce the time lapse between customer query and purchase.

Also, higher FCRR goes a long way in increasing and maintaining customer retention. Higher FCRR is also necessary to navigate higher Costs per Call.

  1. In-Scope Call Success Rate 

Though contact centers can measure the overall success rate, a better metric would be the Inscope success rate. At any given moment, a voicebot may be trained for a limited set of use cases. For example, a Voice AI Agent might be equipped to handle PNR queries or schedule maintenance visits, but when a call goes beyond this scope, it should pass on the call to a human agent. Hence, true success can only be measured if only in-scope calls are considered to calculate the success rate.

  1. Average Handle Time (AHT) – In-scope Agent Transfer AHT and End-to-end Automation AHT

To understand better, let’s compare the AHT in the two scenarios where a Voicebot must create value.

  • AHT Comparison for End-to-end Automation – For a specific set of use cases the voicebot is designed to answer every query without the need for a human agent. The average call handling time AHT 1, as shown in the graph above, can be compared with a similar use case answered by a human agent. 

It must be noted here that typically the cost per call per minute of a voicebot is quite lower, 1/7th (though inherently subjective), of the same cost of engaging a human agent. Hence, even if the voicebot takes the same amount of time to resolve the query, business gains are 7 folds. 

  • AHT Comparison for Escalated Calls: Interestingly, AHT can be compared even when the call is forwarded to a human agent by the voicebot. This is because the voicebot captures essential data such as – it verifies the identity of the callers, captures their intent, and forwards the call to the human agent so that he/she can pick up the conversation from the last point. 

If the AHT of an escalated call is lower than the call answered by a human agent, then it means that even for out-of-scope calls, the voicebot is creating value. 

If the voicebot is escalating the calls for use cases it is trained for, it needs improvement. If it is escalating calls out-of-scope, then it is functioning perfectly well, and this information can still be used for broader decision-making.

Scenario: Agent Transfer After Resolution Due to Dispute or Second Query Many times atypical conditions arise when the customer just wants to speak with an agent, ex. when an insurance claim is rejected, the customer invariably wanted to speak with a human agent to vent out their agitation. Voicebot is not at all responsible when the call escalates to a live agent in such cases, and hence such situations must not be considered when assessing the performance of the voicebot, the situation warrants human agent intervention.

Such deep analysis is only possible when such metrics are considered to evaluate voicebot performance and business gains. 

User Experience Metrics: Companies must focus on CX that is useful, engaging, and enjoyable; creating a positive image that leads to product purchases, referrals, repeat purchases, and loyalty. 

5. CSAT

Finally, the moment of truth, the CSAT score. It is a result of the overall performance of the voicebot. It is a good measure because ultimately, everything is futile if the voicebot doesn’t move the needle on CSAT scores. You can have a high containment rate to boast about, but if your corresponding CSAT scores are falling, your business performance will suffer significantly.

6. Average Wait Time

A company has to take a decision, it can route every call via the Voice AI agent, and this will bring down the average wait time to zero. Wait times have a serious and direct bearing on CX. One single-shot way of engaging the customer without making them wait or having them get further frustrated with IVRs is by deploying the Digital voice agent at every call. 

7. Average Resolution Time

Once the customer is through and is speaking with the agent (human or voice AI) the time it takes to resolve the call matters a lot for consumers. This number must be looked at when CX is a priority. 

Technical Metrics: Ensure the conversational AI product works and adheres to the requirements for performance or latency.

8. Intent Recognition Rate – Most important voicebot performance metric, and refers to the accuracy with which the voicebot is able to capture the intent of the speaker. This is important because a voicebot can only troubleshoot when it is able to capture the intent accurately.

     9. Word Error Rate: The accuracy with which the ASR can recognize the words.        Lower does not mean the outcomes will be inferior if intent recognition is high. But, the higher the accuracy the better.

10. Latency: Latency is a delay in response, and unlike chatbots, voicebots need to be pretty quick and agile in their response else they risk losing the customer’s attention and being pigeonholed as ineffective. Typically a Chabot latency is the sum of latencies of = ASR + SLU + FSM + TTS

Typically the total latency of 1-2 seconds is good, though, the lower the better. 

Embrace Metrics that Truly Measure Intelligent Conversations  

Abandon call containment rate as an absolute reflection of voicebot performance. Yes, it holds value but it is not true to the purpose of creating a voicebot.

Measuring and monitoring the right metrics will help you capture precise voicebot performance and thus enable you to improve it. Only then will it result in cost and CSAT advantages that the voicebot has been deployed for.

To learn more about voice automation and how to measure and improve performance, you can book a demo using the chat tool below.

The Unique Advantages of Skit.ai, a Speech-first Voice AI Platform

Introduction

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
  • Chat-first 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:
    • Environmental noise
    • 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.

  1. 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. 
  2. 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. 
  3. 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.
  4. 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. 
  5. 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.

Voice AI: The Magic Pill for All Major Debt Collection Challenges

Let’s begin by addressing the elephant in the room—the collection rates have dramatically fallen in the last decade. The State of Debt Collection 2020 Report reveals that in 2010, U.S. businesses placed $150 billion in debt with collection agencies, of which they could collect just USD 40 billion. On delinquent debt, the collection rates have declined to 20% (industry average), a decrease from 30% as recorded a few decades ago. 

Anyone from the debt collection space would be cognizant that the industry has been under pressure from all fronts—inflationary pressures, agent attrition further fuelled by the great resignation and increasingly stringent regulations after Reg. F, and economic downturn.

Never before was the need for automation direr than it is in 2022!

What is Voice Automation for Debt Collection Companies?  

Before we go into the transformative role Voice AI can play in the debt collection industry, let’s understand voice automation. 

Voice Automation Refers to the automation of voice calls, decoupled from the assistance of a human agent. This means the capability to answer customer queries with the machine, striking intelligent, multi-turn conversations. 

Consider this scenario where an AI-enabled Digital Voice Agent interacts with a customer and facilitates an on-call payment. 

The demo is a perfect example of how an intelligent Voice Agent can help consumers willing to pay and facilitate a quick payment with remarkable ease.

  • Outbound Call Automation: A Digital Voice Agent can call consumers and establish the right party contact, remind them about the due date, capture their dispositions, raise dispute requests, accept and schedule a payment on call, or help them negotiate, and arrange a payment plan for a better recovery.
  • Automating Tier-1 Inbound Consumer Queries: The Voice AI agent can answer tier-1 calls, which are as much as 70% of total inbound calls, without the need for a human agent. Also, even when a Voice AI agent calls customers, it can answer all basic questions and handle tier-1 queries discussed in the outbound section above.

Hitherto, only IVRs have played a limited role in increasing the containment rate with the self-service option. IVR’s effectiveness can be debated, especially when reports have revealed that it plays a role in decreasing customer experience. The problem with IVR technology is that they have reached the culmination of what it can do for debt collection agencies. It is time to move beyond IVRs, especially when tech advancements have brought us to the sweet spot of cost-effective incorporation of AI-enabled solutions such as Voice AI.

Want more clarity; read this interesting piece – Voice AI Vs Robocallers 

Addressing the Elephant in the Room: Core Debt Collection Challenges

Debt collection companies face these core problems:

  • Dormant Files: Every debt collection agency sits on a pile of inactive accounts, as high as two-thirds of their portfolio, that they can not process because of its economic infeasibility. This is a sour point, and they are looking for tech solutions that can help them address this pain point. 
  • Non-Revenue Generating Calls:
    • Wrong Party: Proportions of wrong party contacts vary depending upon many factors, such as the age of debt, but it can be as high as 70-80%. All the calls made by human agents that turn out to be wrong contact numbers are pure costs with no return.  
    • Dispute: The next big chunk of the volume of calls is usually when a consumer fails to recognize the debt or disagrees with the outstanding amount. The regulations require debt collectors to raise the dispute request to investigate the debt further and provide relevant information to the consumers before any collection activities. Those calls where debt is disputed by the consumer or asked for more details of the debt eventually turn out to be a pure cost activity.
    • Cease-and-Desist: Be it inbound or outbound, there is always a set of consumers who ask agencies to stop all collection communication with or without any good reason. There is no real scope of value creation by a human agent in this case as well.
    • Attorney Representation: Often, the consumers ask to contact their attorney and not to approach them directly. All agents do in this case is update the system to not reach out to these sets of consumers as required by regulations.
    • Call Back Requests: More often than not, the consumers ask the agent to call some other time, in some cases beyond the working hours of the agency.

  • Right-Party Contact (RPC) Cycle: Traditionally, a human agent will call consumers to establish if the contact number is correct. Any debt collection agency has so many files to process that they can only call a fraction of them within a time frame, and take long to cover all consumers, if at all. The shorter the cycle, the larger would be the scope to improve recovery.
  • Propensity Based File Segmentation: Ideally, a debt collection agency would like to segment their portfolio into different buckets based on the consumer propensity to pay. But sadly, with a large number of files, it is challenging to do this within a limited time frame, if at all.
  • Agent Bandwidth Optimization: Agents are the most precious organizational resource and their time/bandwidth optimization is an utmost priority for them. But in absence of RPC and disposition capture, it is near impossible to optimize their time and effort.
  • Service Level Maximization: The number of calls a debt collector addresses per agent per hour is vital for enhancing operational performance. 
  • Compliance: The regulations have become increasingly stringent; this has two implications:
    • The penalties and fines are levied at instances of breach of regulations. They are typically bearable expenses, though they affect profitability.
    • Lawsuits filed by consumers: They do real damage as they consume time as well as cost, and are typically much higher than government fines and penalties.

Read more about how Voice AI can help debt collectors augment bottom lines

Leapfrogging Value Creation with Voice AI

With the coming of Reg. F, a new conversation has begun on the compliance of new-age technologies. Being AI-enabled and capable of striking an intelligent multi-turn conversation, Voice AI finds itself better placed to meet compliance. (read more about it in this Voice AI compliance white paper by Mike Frost and Skit.ai).

Voice AI is based on AI/ML, Automatic Speech Recognition (ASR), Spoken Language Understanding (SLU), Text-to-Speech (TTS) technologies, and more. A confluence of such great technologies enables Voice AI to understand the spoken word and respond to it most intelligently. Here is the gist of how a Voice AI Agent can create value for debt collection agencies:

  • Segregating Right and Wrong Party Contacts: 

With the great capability for executing thousands of concurrent calls, Voice AI can call and establish the right or wrong parties in a matter of minutes, for a significant portion of files. No technology has been able to accomplish this except Voice AI. 

Value Creation: At a fraction of the cost, a debt collection company can identify if the contact is right or wrong without involving their human agents. Time and cost advantages can help them improve performance in a big way.

  • Enabling File Segmentation by Capturing Disposition: 

Classifying customers into 4-5 different segments solves a lot of problems for the collection agency. A Voice AI agent can call thousands of customers and, based on dispositions, can segment millions of accounts into various buckets such as consumers who disputed the debt, consumers with cease-and-desist requests, consumers with attorney representation, consumers who agreed to a payment plan, etc.

Based on this segmentation, accounts can be allocated to respective specialists and departments for further processing.

Value Creation: Only after capturing the disposition for the entire portfolio, the company will be able to draft an optimal strategy and optimize the time spent by their agents.

  • Compliance Adherence: 

Since the coming of Reg. F, the compliance has become difficult to keep and the corresponding implication of its breach is getting higher. With large portfolios, it is difficult for agents to execute their follow-ups with perfection. 

Mandatory rules such as the 7/7/7 rule, along with Mini Miranda, use of decorous language, and more, make it difficult for the human agent to always stick to the script especially when a majority of calls are repetitive and low-value. 

Value Creation: Voice AI agent, once trained for compliance will always stick to the script, and use the right language. It will also stick to the schedules of follow-ups increasing the probability of conversion as well as saving the company thousands of dollars in fines and lawsuits. This also increases the speed of the company processing its portfolios.

  • Value Out of Non-Revenue Generating Calls:

Voice AI, at a fraction of the cost – 1/6th, can process these calls (mentioned in the above section) and help human agents avoid these and focus on value-creating calls.

Voice AI: Helping Debt Companies Strategize Better

Voice AI, with its consummate coverage of debt portfolio, can help debt collection companies have a more precise understanding of their consumers and devise better strategies. Below is a graph that depicts VaR and the ideal file segmentation and corresponding strategy.

Strategizing with Voice AI

Age of Debt and Voice AI: A debt collector will typically have a mixed portfolio with debt lying in various age brackets. Typically the older the debt, the lower the probability of recovery, and hence Voice AI is more suited to engage with these accounts.

It must be noted here that the segmentation is only possible after the Voice AI Agent covers the entire portfolio to uncover consumers’ propensity to pay.

  • Capturing Propensity to Pay: Once the Voice AI Agent has captured the disposition of the consumer, a debt collector can then segment or classify it and assign it according to the disposition.
  •  Strategizing for Value at Risk: Since Voice AI costs one-sixth of a human agent, and is as effective for simpler conversations, it is ideal for Voice AI agents to address these accounts and follow up meticulously.

High Willingness to Pay (WTP) – High Value: When the willingness to pay is high, the voice AI agent can call promptly and facilitate on-call payments or remind them to pay. Debt collectors have been able to achieve a high degree of success in this category. 

High and Low Willingness to Pay (WTP) – Low Value: This segment of the portfolio is prohibitively costly for human agents to process because of its low value, making it ideal for the voice AI agent to process it and help prop up recovery rates.

Low Willingness to Pay (WTP) – High Value: High value and low willingness to pay makes this segment of consumers ideal for human expertise. Human agents can deploy their cognitive skills to convince and help them pay.

  • Optimizing Campaigns: Armed with new insights on consumer behavior, debt collectors can refine and optimize their campaign strategy. An ideal mix of Voice AI, human agents, and SMS/emails, can make a difference.

Impressive Contact Center Outcomes with Voice AI

No, the capability of Voice AI is not just based on conviction and hope, there are solid stats to second every value proposition. 

It must be noted that the higher volume that Voice AI Agent handles, the greater the scope of value creation. This is necessary if a debt collector wants to strategize based on consumer disposition. 

Here are a few outcomes that the debt collectors as well as other contact centers have achieved with Voice AI: 

  • Up to 38% improvement in service levels
  • Nearly 50% decrease in operational costs
  • Up to 70% automation of your consumer support efforts
  • Reduction of 40% in average handle time

Conclusion 

There are multiple challenges from diverse fronts plaguing the debt collection companies. They can break the status quo and make the necessary changes on many fronts such as cost, performance, recovery rates, compliance, and speed.  

Voice AI technology has been successful in value creation for debt collection companies. However, it takes an expert vendor and meticulous execution to achieve desired results.

To further understand the nuance of Voice AI and the scope of transformative value it can create for your business please – Book a Quick Appointment.

Transforming Debt Collections with Voice AI: 9 Reasons Why NBFCs Should Watch Out!

The world and Indian economic growth find themselves precariously placed in 2022, with bleak economic data raising concerns. People are reeling under the increasing cost of living, and rising defaults on loans are beginning to be a worrying trend. The rise in default rates was seen in every segment, without exception, with Loan Against Property (LAP) having the highest default rate at 4.14% in November 2021; loans due past 90 days in the two-wheeler segment are also high at 3.64%, up 140 basis points, while consumer durable loan delinquencies are the third highest. Delinquencies in the credit card segment have eased sharply by 77 basis points to 2.22% (data from credit monitoring agency Transunion Cibil). Also, the spike in fund disbursement will increase the asset size of NBFCs, while the GNPA rate will also increase significantly to 6-8%.

On account of inflationary pressures, and a slowdown in the growth of fresh credit, the situation is becoming challenging for companies in the debt collection space. To maintain profitability, NBFCs are faced with the dual challenge of improving their performance, while improving cost-efficiency at the same time. This calls for a different approach, leveraging technology such as Voice AI to automate a significant portion of their outbound customer reach outs.

In this blog, we shall explore the role of Voice AI as the ‘agent of change’ in the growing debt collection space, and why the shift to Voice AI is one of the most profitable moves NBFCs and debt collection agencies can make in 2022.

Voice AI Is Redefining the Future of Debt Collection

Customer experiences are critical to the brand and business performance agree 73 percent of the business leaders, suggests a study by the Harvard Business Review. The global proliferation of voice-led technologies and voice-assisted interfaces built on AI-based NLP across industries have set massive expectations in the way customers prefer digital interactions and engagements. IVRs have proven to decrease CX, and bulk robocalls have proved ineffective. This is of significance in debt recovery, because as companies lose time, the probability of recovery dwindles.

On the other hand, AI-enabled voice agents are capable of engaging in meaningful conversation that runs beyond generic reminders by gathering insights and feedback that may facilitate on-call payment, rescheduling, and dispute resolution.

Money talks are uncomfortable, Voice AI exactly helps debt collection agencies achieve that, allowing human/machine partnership, the future of intelligent work. 

Human-Machine Partnership: Voice AI platform built specifically for the debt collections industry also helps automate voice conversations while enabling context transfer capabilities from across modalities (text, chat, email, and speech), empowering the agents to operate without burnouts whenever call volumes peak. Automation of cognitively routine work also allows more time for contact center agents to prioritize their bandwidth and use it for solving complex challenges without the need to upscale the team.

Learn more: Explore 7 Reasons Why NBFCs Must Not Miss Out on Voice AI

All in all, integrating Voice AI can help create three ideal scenarios—NBFCs can improve their Collection Efficiency Ratio while reducing the cost and even improving customer experience!

Skit.ai’s Augmented Voice Intelligence in Action

Challenges Facing Indian Debt Collections Companies (NBFCs)

These are challenging times for NBFCs trying to improve their recovery rate. As CXOs look forward to improving the performance of the debt collection agencies here are the core problems they are trying to solve: 

  • Intensifying competition is putting pressure on profitability
  • Low Collection Efficiency Ratio
  • High Cost of Collections 
  • Slower campaigns and limited customer coverage

Before we deep dive into how Voice AI can solve all the major challenges, let’s first look at the definition of  Collection Efficiency Ratio.

Understanding the Performance of a Debt Collection Agency or an NBFC

The entire performance of a collection agency can be expressed to a large extent by these metrics:

  • Collection Efficiency Ratio

The collection efficiency ratio is a measure of how well the collection department collects debt. It’s essentially a way to determine the effectiveness of the collections team.

The collection efficiency ratio is calculated as a percentage that depicts the proportion of debt collection achieved out of the total portfolio. The higher the percentage, the better the debt collection performance of the company.

How to Calculate the Collection Efficiency Ratio

The total collectible amount for month X – This includes the overdue at the start of the month as well as all the due dates throughout the remainder of the month.

Remaining recovery amount for month X – This is the amount remaining on a specific day that the team failed to collect.

Formula: (Total Collectible Amount – Remaining Recovery Amount) / Total Collectible Amount

A higher percentage depicts greater success in the collection of debt, ie., better debt recovery. 

How Important is Collections Efficiency Ratio for NBFCs?

The collection efficiency ratio gives an overall monetary recovery status. Irrespective of the number of accounts recovered, at the end of the day, the Rupee value counts. 

It is also a good measure because it gives a clear picture of value at risk (VaR) which is the most important thing to monitor for a debt collection agency.

  • Age at List (AAL)

Time is important when it comes to debt collection. If your account is 7 months old, its recovery rate drops to 50%. After 12 months, it drops to only 25%. 

Therefore, Age-at-List is one of the most important KPIs in the collection. This is the average number of days your account has been in expired status. AAL provides a general overview of the collection cycle. This is an excellent comparison indicator between collection agencies and the industry. 

Successful agencies are aiming for a low AAL. A high AAL indicates that the agency needs to be more effective in debt collection. However, AAL tends to fluctuate. Therefore, you need to review the data for about a year to gain valuable insights.

The Digital Voice Agent can be trained to pursue B0 and B1 buckets and will be very effective with its precise regime to convert the default accounts. This will lower the age of debt and improve the performance of the company.

  • RPC rate (Right Party Contacts)

The RPC rate is the first of the more specific metrics in this list.

 This KPI measures the ratio of all outgoing calls to a valid phone number for the person (or “right person”) for whom the collection was requested. For collectors, the higher the score, the better the success rate of finding the debtor.

 Of course, the first step in collecting claims is to find and contact the right person. If your company has a lower RPC rate than its competitors in other industries, you need to think carefully about what’s wrong and how to improve them.

  • Percentage of Outbound Calls Resulting in Promise to Pay (PTP)

The PTP rate is just as important as the RPC rate when measuring efficiency and is the next logical step to a successful collection. It measures the percentage of all calls that end with the debtor’s promise of payment.  That is if the RPC rate measures the success rate of dialing the appropriate person, this metric measures the success rate of those RPC calls. This is another percentage that you want to get as close to 100 as possible.

Voice AI agents can help companies with lower PTP figures buy prompt calls for which the best agents can train the voicebot whose perfect timing and schedules will help improve the PTP stats. 

  • Profit per Account (PPA) 

Finally, PPA measures how much profit each account in your collection makes on average. In short, this KPI measures the impact each account has on revenue. 

This metric is calculated by dividing the company’s gross profit (calculated by subtracting total operating expenses from total revenue) for a particular period by the total number of overdue accounts managed for that period. 

A Voice AI Agent can help reduce the operational cost to the tune of 50%, along with a faster sales cycle, improving this performance metric.

Improving these numbers is a big challenge and we will now go into detail about how Voice AI can help debt collection agencies and NBFCs transform their performance. 

Transform Your Collections Efficiency Ratio with Conversational Voice AI

The effort of a collection agency is to collect as much as possible and as fast as possible from every account, i.e. a higher recovery amount with a shorter collection cycle will improve the majority of performance indicators mentioned above. 

Voice AI Core Benefits for Debt Collection Agencies and NBFCs: 

  • Voice Automation: Voice AI will help your company by automating 70% of the calls, primarily tier-I calls, and by automating payment reminders and collection calls at B0 & B1 buckets. This would cut down the time & effort of human agents and they can focus on RTP cases. This would reduce the burden on human agents and improve the use of their time on meaningful and complex problems. 
  • Augmenting Agent Productivity: Since the human agents focus just on RTP cases that require more empathy and intelligence, their efficiency and productivity take a big leap. This has an impact on CX as well as recovery rates as agents are not wasting their time on trivial tasks.
  • Cost-Efficiency: Be it collection calls or running outbound campaigns for reminders or notifications, etc., the Voice AI Agent can do everything at a fraction of the cost. This has long-term and big benefits for the company. 

Quality and Compliance: The Voice AI Agent never fails to follow proper protocols and thus, avoids any potential legal challenges. Also, its delivery is always the same and hence its service quality does not deviate with mood, as with human agents.

9 reasons why NBFCs and Debt Collections Agencies Should Not Miss Out on Voice AI

  1. Allowing human agents to focus on RTP (Refuse to pay) accounts. Thus increasing the profitability by converting high-risk accounts. 
  2. Automate simpler calls like reminders & FAQ – Proactively reminding debtors at the right time and helping them with FAQ related to their loan/upcoming payment. plays a far bigger role in collections and Voice AI does it perfectly. 
  3. Improve contact-ability & customer coverage – Voice AI is capable of contacting millions of customers in a matter of weeks. Thus the company can reach out to every customer for payment reminders, follow-up on DPD cases, and assist with queries related to the loan.
  4. Shorten collection cycle – Faster reach outs and quicker conversion with Voice AI Agent helps shorten the collection process. 
  5. Persistency in follow-ups and call-back requests – Human agent may err in follow-ups but the Voice AI agent follows up as well as calls back at the requested time without fail. This is a big help and improves collections. 
  6. Capability to handle spikes in volume – Traditionally the call center teams are unscalable over short periods, but with Voice AI, this is not the case as it can handle any spike in call volumes.
Here are some other unique capabilities of conversation Voice AI for debt recovery:
  1. Feeds data to the CRM tool and provides analytics for further action
  2. Persuades customers to pay at the earliest, offering payment plans and options
  3. Helps agent plan post-call follow-up campaigns with Voice AI Agent

These unique capabilities prove indispensable and give the debt collection agency or the NBFCs a big edge over their competitors. 

How a Digital Voice Agent Adds Value to Collections Efforts 

The Digital voice agents are highly effective at B0 & B1 collection buckets with no human assistance. The later bucket requires a bit more contextual conversation and dunning from the collection agent.

The digital voice agent gets a custom design flow to interact with customers at Bucket Zero and Bucket 1 for debt collection. At bucket Zero, the Voice AI gives out a “The last day of payment” reminder, requesting the debtor to maintain the payment amount balance in their account for auto-debit. The Voice AI also assists in converting manual payment to auto-debit. 

At bucket one (1-30 days), the debtors are expected to pay off the due within the grace period so that their CIBIL is not impacted and does not affect their eligibility for future loans. The Voice AI communicates the same to the customer and assists them with on-call payment. The Voice AI can make calls, follow-ups, and take call-back requests concurrently at any time during the week and to which the human agent has limited capacity.

For situations when the customer is refusing to pay or is unable to maintain sufficient balance, the digital voice agent dispositions them accurately and notifies them of a call back from the human agent.

Voice AI Outcomes

It will be interesting to note that the Voice AI agent helps in improving every performance metric.

  • Collection Efficiency Ratio – Higher, overall collections help improve the collection efficiency
  • Age at List (AAL) – faster collections help in keeping the debt age towards the lower side
  • RPC rate (Right Party Contacts) – Voice AI calls and identifies the Right Party Contact so that human agents do not have to waste time. This is a radical improvement, without spending much money and time
  • Percentage of Outbound Calls Resulting in Promise to Pay (PTP) – Voice AI agent calls at the right time and to the right person when they prefer to interact, thus increasing the probability of recovery
  • Profit per account (PPA) – since with Voice AI agent, the costs as much lower, it has a direct impact on this metric

Here is what a debt collection agency or an NBFC can expect from Voice AI agents such as Skit’s Digital Voice Agent. 

  • 49% of the total collection value recovery per campaign 
  • Debt recovery from 78% of delinquent accounts without any human assistance 
  • Lower the cost of collection by 40% by addressing the collection challenges like unreachability, unresponsiveness, callback request, and collection after business hours
  • Achieve 90% of collections in the first 3 days
  • Increase customer coverage by 30% 
  • Speed up the collection campaign TAT by 70%
  • 80% contact-ability rate
  • 84% engagement rate with customers
  • 68% disposition capture rate on CRM allowing your agents to focus on accounts in the B2 bucket onwards for personalized interaction for recovery

The data mentioned above has been taken from project implementations, and will certainly vary for each company. Thus, it is indicative at best, of the results that can be achieved and the potential of the Voice AI agent. 

For any questions on the application, operations, outcomes, and pricing of a voice AI agent in the debt collection space, feel free to contact us – Book A Demo.

For more information on debt collection space and the role of voice AI, please visit the page. 

Transforming Customer Experience with Optichannel Support and Augmented Voice Intelligence

We have all been in dire straits and dealt with frozen bank accounts and medical or travel emergencies. We can vividly recall the palpitation and frustration felt during those moments, waiting for customer support, navigating IVRs, or a chatbot.

Companies have long wanted to change it, but challenges such as high attrition rates, unscalable teams, inconsistent CX, and cost pressures have curtailed their capability to serve customers.

Consequently, customer frustration is on the rise. A 2019 report said that customers are annoyed by the irrelevant options presented by the IVR. In fact, two out of three Americans (66 percent) say they would choose AI-powered voice-over chat if it were effective at answering their questions.

Riding the wave of recent advancements in NLP and AI, we are graduating from machines automating crude, low-value tasks to a new era where AI-enabled voice customer support would help companies create enormous value, conversing in their preferred language with semantic understanding to resolve their problems.

Explore How to Improve Customer Experience With Voice AI

For Exceptional CX – Technology and Channel Strategy Matters

A Harvard Business Review survey revealed that 73% of business leaders view reliable customer experience as critical to the overall business performance of their company.

Companies now realize that multi-channel or omnichannel strategy has failed to live up to the expectations of improving CX, primarily because different customer segments prefer specific channels to connect. Thus, an optichannel or optimal channel strategy is more prudent as it focuses on the capability to support a customer journey via a channel/modality optimal for that problem.

Even today, after years of decline in customers’ preference for voice support to troubleshoot, voice is still over 50% in contact volume. Though companies may pursue an omnichannel strategy, if they are not good at voice support, they must be cognizant of its impact on CX. Optichannel is thus a more prudent strategy as being good at different modalities such as emails and chat will not compensate for the damage done by poor voice support. Companies have to choose wisely, there is no one-size-fits-all solution.

Text and Voice: Don’t Mix and Serve

Acing CX means that the company must be good at serving customers with their preferred channels. Voice is complex, subtle, and requires semantic understanding. Nuances of a voice conversation such as a change in the rate of speech, voice modulations, and more that convey a customer’s feelings are lost if your solution is not built from scratch for voice. Bundling a chatbot with a readily available Automated Speech Recognition (ASR) to add voice capability just kills the beauty of spoken conversations because it can transcribe but not converse.

Shortcuts like these fulfill notionally the goal of being present in every channel but defeat the goal of being good at the relevant channel. 

Moving Beyond the Complexity of Digital Transformation with Voice AI

In the last few decades, the world moved from voice to text to chatbots. But as customers still prefer voice over other communication channels, even brands are taking notice. WhatsApp, a chat messaging platform, is building voice-led solutions for businesses. A Deloitte study reveals that by 2030 there will be a proliferation of voice-led technology across the globe and that 30% of sales will be via voice by then.

As companies hustle to achieve digital transformation, the low success rates, and disturbingly lower rates of sustainable DX success are proof of the precarious journey. 

Fortunately, there is one way to not only automate contact centers with the most cutting-edge technology but also ensure that it succeeds without a big resource commitment from the organization. Yes, Voice AI is one such solution with stand-alone deployment and stunning success rates. 

Companies must consider deploying Augmented Voice Intelligence for contact center automation as a good starting point toward digital transformation. But before that, brands must ponder over the most significant question – What does the shift towards voice entail as we cross the voice automation rubicon? What is its impact on the market and competitive landscape? 

Look before you leap!

Only if you feel that your human agents are doing zero-value repetitive tasks, and there could be enhancement of their productivity. Your company is continuously facing resource, cost, and compliance challenges. Perhaps it’s time to contemplate and change.

For more information and free consultation, let’s connect over a quick call; Book Now!

Also, for more information: How We Can Transform Customer Experience

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The Wait is Over! Transform Customer Experience with Augmented Voice Intelligence

Just over a decade ago, with our first tryst with Siri, little did we imagine its significance and how Voice AI will change customer support forever. Several generations, from baby boomers to millennials to gen Z, are using voice searches on popular platforms such as Google Assistant, Alexa, Siri, and others is a testimony of its potential. Today, we stand at the cusp of Voice-tech revolutionizing customer service.

Since speech is an integral part of being human, we covet meaningful conversation to connect and express. But today, even the thought of being stuck with Interactive Voice Response (IVRs) and chatbots in an emergency/urgent situation gives us the heebie-jeebies, right? And the long wait for a customer service agent to pick up, if at all, forges a lasting negative emotion towards the brand.

Companies have been trying hard to deliver a delightful customer experience; but with existing legacy systems, it is just not possible. The emerging answer to CX woes is Augmented Voice Intelligence that not only understands and responds but is semantically capable of comprehending the context of customer queries or problems.

For brands, customer experience can make or break their reputation. A Harvard Business Review survey revealed that 73% of business leaders view reliable customer experience as being critical to their company’s overall business performance.

The Conversational AI space has been experiencing explosive growth, and within it Augmented Voice Intelligence sits at the frontier, with the incredible potential of disrupting the way companies interact with their customers.

Why Voice-first Solutions Will Take it All!

Written language differs significantly from spoken one. Spoken content has more information, hidden in the form of pauses and pitch modulations, Chatbots bundled with ASR can transcribe, but not converse. As organizations pour millions into automated voice support, they would want their virtual agents to understand the semantics, such as sarcasm, and not take “oh, you did a good job’ at face value. Those subtle nuances of human conversations get annihilated when we strap a readily available Automated Speech Recognition (ASR) over a chatbot.

Though chat has distinct use cases it excels at, a simple conversion of text to voice and vice-versa does not meet even the table stakes of a voice conversation.

Watch Skit’s Intelligent Voice Agent in Action

Augmented Voice Intelligence for Contact Centers | Skit.ai

Nearly 9 in 10 people preferred speaking to someone over the phone rather than navigating a pre-set menu, showed research from Clutch. That is why the future belongs to Augmented Voice Intelligence!

Companies cannot simply use generic voice engines, as they are built for contextless conversations. Simply because a customer interacts with a company with a very specific context, and expects it to troubleshoot as skilled human support would do.

Voice-tech solutions that are built for voice, from the ground up, will be the ones delivering successful conversations. This boils down to conversing with customers within a specific context enables automated voice support to solve their problems, even complex ones, in a frictionless manner. It requires training in domain-specific knowledge at the speech recognition layer and creating different design guidelines for every vertical and for specific use cases within those verticals.

It is an uphill task, but what’s the prize for all the effort? The biggest prize indeed: Conversations that your customers will love!

The Conversational AI space has been experiencing explosive growth, and within it Augmented Voice Intelligence sits at the frontier, with the incredible potential of disrupting the way companies interact with their customers.

Augmented Voice Intelligence focuses on empowering an enterprise’s workforce by combining the power of human voice and AI. A Digital Voice Agent can easily resolve tier 1 customer service issues and automate cognitively routine work while human agents can focus on more complex customer problems. 

Human/machine collaboration is the future of intelligent work. The intent is not to replace the human workforce, but to enhance their productivity by taking away the mundane workload. 

The opportunities for the Voice AI ecosystem are only getting started. Data from research platform Allied Market Research showed that the conversational AI space has the potential to touch $32.62 billion by 2030, registering 20% YoY growth between 2021-30.

For brands, customer experience can make or break their reputation. A Harvard Business Review survey revealed that 73% of business leaders view reliable customer experience as being critical to their company’s overall business performance.

End-to-End Customer Support 

The main applications of Voice AI or AI-enabled Intelligent Voice Agents can be subsumed into four categories:

  • Resolving Tier-1 Issues: All calls can be routed through the Voice AI agent, and it will be able to answer a large chunk of calls completely, without any human assistance.
  • Pre-Call Assistance: As the call gets forwarded to the human agent, Voice AI can fetch all the relevant information for the agent to engage in the most meaningful way.
  • On-Call Assistance: The Digital Voice Agent listens to the conversation and provides instant data and help to the human agent, augmenting the agent’s capability to serve manifolds.
  • Post-Call Assistance: Call summary is essential for feedback, and the agent needs to fill it out. An AI-enabled Digital Voice Agent can perform such post-call activities with semantic understanding; the agent simply has to look and approve.

Read More: Voice AI – The Biggest Automation Trend of 2022

Voice AI is the Voice of the Future

Think of a contact center with a seamlessly scalable team available 24*7, with agents who have customer data at their fingertips, where the quality of calls never drops and no one gets frustrated. Where personalization, relevant up-, and cross-selling are table stakes and call data analytics and feedback are available on the fly. 

Sounds like utopia, right?! But this is well within the grasp of businesses, with the right voice technology solution. Now imagine the competitive edge your company can derive from the successful adoption of augmented voice intelligence.

This is the defining moment for custom-centric companies, as their voice strategy will have ripples far into their future.

For more information and free consultation, let’s connect over a quick call; Book Now!

Also, for more information: How We Can Transform Customer Experience

Why CFOs Must Consider ‘Voice AI’ for Better ROI and Customer Acquisition Cost (CAC)

CFOs see numbers such as ROI and behold the beauty hidden within them. Today, Voice AI is churning out such convincing stats that every CFO must consider investments in Voice AI in an amicable light.

Business-customer interaction is a two-way street. Interestingly Voice AI solutions are ideal for both Outbound and Inbound calls. Companies are spending millions to reach out to potential customers. Engaging human agents has proved expensive and a significant managerial challenge. Deploying Voice AI helps companies achieve their most coveted goal – cost-efficient scale.

Voice remains the most-preferred channel for customer service. However, around 70% of all customer service requests are non-critical and repetitive, making it challenging for human agents to remain engaged, motivated, and empowered to solve everyday challenges. By taking away the bulk of the calls, Voice AI helps agents create value by solving complex customer problems and enjoying their job. Also, every company covets 24/7 intelligent customer support that is not entirely human agent dependent, and Voice AI is the perfect solution.

Core Challenges Contact Center Face

Contact centers for any organization, small or big, are complex institutions and face some key challenges:

  • Human Dependent Processes 
  • Cost Reduction
  • Optimizing Resource Utilization
  • Agent-time Utilization
  • Updating Legacy Systems
  • Delivering Consistent Customer Experience

Sadly, with IVRs, most contact centers have reached a point of saturation, where they have automated, measured, and monitored the operations with no further scope of improvement. Augmented voice intelligence is a technology that opens up new opportunities for creating value and growth.

Automate Non-revenue Generating Transactions with Voice AI

Shockingly, agents spend over 30% of their time on zero-value, non-revenue generating tasks that Voice AI could easily automate. Here are a few examples:

  • Providing account balances
  • User/Caller verification
  • Removing wrong numbers
  • Updating phone numbers and addresses 
  • Do not call handling
  • Bankruptcy data capture
  • Frequently asked questions

These functions are essential to proper functioning but do not create revenue for the company. They prove costly as they consume expensive agent time and loss of opportunity cost as the same effort could have gone into revenue-generating transactions.

These are just the lowest hanging fruits of Voice AI, and the technology is capable of creating enormous value.

IVRs have reached their zenith and are now causing customer dissatisfaction. Advanced solutions are the need of the hour. Chatbots are advanced and capable, but they suffer from one serious drawback—‘voice’ is the most preferred mode of customer support, not text. Voice AI can be a disruptor, accelerating digital transformation and creating a world of difference in the customer experience.

But before we deep dive into the transformation of a contact center, if you are curious about use cases of Voice AI in debt collection space you can explore: Meeting Debt Collection Compliance With AI-Powered Digital Voice Agents. Also, here everything you want to know more about Digital Voice Agents.

Transforming Contact Centers: Outbound Efforts

How do Voicebots help achieve operational excellence and reduce customer acquisition costs (CAC)?

Banks and financial institutions looking for growth and expansion reach out to hundreds of thousands of potential customers. An Intelligent Voice Agent can help a company reduce its customer acquisition cost by executing, with perfection, the various steps of the process such as:

  • Lead Qualification
  • Lead Generation 
  • Onboarding, and Documentation
  • Debt Collection
  • Subscription Reminders 
  • Feedback Collection

Instead of a human agent calling, following up, and coordinating, which is time-consuming and costly, a voice agent can finish the tasks at a fraction of the cost and expedite the sales cycle. It reduces the customer acquisition cost as a result. 

Perfect execution of such efforts at a large scale can make a radical difference for companies. Not only does CAC go down, but the results are also better. A win-win for companies.

Voice AI will always come as a powerful tool when a company wants to run various campaigns at scale. According to the Deloitte report, the global conversational AI market that includes both chatbots and intelligent voice assistants can grow at a 22% CAGR growth from 2020–to-25, reaching a US$14 billion market size. By partnering with the right augmented voice intelligence platform, businesses can optimize contact center OPEX.

Transforming Contact Centers: Inbound Call Handling

How does automation of voice conversations help organizations enhance cost efficiency?

Hitherto, IVRs provided a source of rudimentary automation. But their cognitive inabilities are resulting in customer frustration as no one wants to wait in lines for a human agent and start all over.

Voice automation is helping businesses free their operational bandwidth by answering simple calls, saving human-agent time, and reducing operations costs by 40-60%. Voice AI is thus empowering businesses to address significant challenges by automating repetitive queries, reducing wait time, and providing a delightful customer experience through human-like conversations.

Optimizing and automating processes is key to enhancing cost-efficiency. Here is how Voice AI helps in achieving this goal:

  • Self-service Optimization: On average, around 70% of calls fall in the non-urgent category. The intelligent voice agent can take most of these calls without engaging the human agent, enhancing a company’s ability to serve customers 24×7 without a human agent.
  • Scalability: The most neuralgic point of contact centers is team scalability. With the waning and waxing of call volumes, there is an urgent need to scale the support team. It is a nightmare for managers and has significant cost underpinning. By deploying a Voice AI solution, the intelligent voice agent will handle the bulk of the calls, passing only a fraction to the human agents.
  • Call Routing and Distribution: The primary focus of augmented voice intelligence solutions is to enhance customer experience. Tier 1 customer issues are resolved automatically with a voice AI agent. Voice AI solutions can prioritize requests and route them to the right human agent where needed. Such intelligent call distribution results in better customer satisfaction.
  • Meeting Compliance: More significant for collections space and banking, but every industry has a set of protocols and regulations to honor. Human agents handling large portfolios are prone to err. Calling a customer on the DND list or calling outside of time limits often results in lawsuits and penalties. A voice agent can easily be trained for any protocols, saving companies time and money.

Voice AI for Sustainable Business Benefits 

Augmented Voice Intelligence has displayed tangible improvements in all of the core metrics targeted by support centers, such as First Call Resolution (FCR), Average Handle Time (AHT), Customer Satisfaction (CSAT), Average Speed of Answer (ASA), Queue Length, Abandonment rate, and other Service level metrics.

The other significant advantage of using an AI-enabled voice product is that it gets better with time, and new use cases emerge. Voice will continue to play the cardinal role in customer support, and early adopters will create lasting competitive advantages.

Meeting Debt Collection Compliance With AI-Powered Digital Voice Agents

Owing to far-reaching repercussions, compliance management has become an issue of gravitas. It’s a challenge of change. Often, frequent regulatory changes create ambiguity for collection agencies. For instance, Regulation F of the Consumer Financial Protection Bureau (CFPB) came into effect on November 30, 2021, and is the most significant debt collection rulemaking. Any creditor–either the original issuer or a debt buyer–faces challenges in responding to it. And even more tedious is training and retraining agents, reiterative setting up processes and tools to meet regulatory requirements.

When it comes to compliance, the devil is in the details. A human agent under varying stress and performance pressure is prone to make mistakes. But even an innocuous breach of compliance results in hefty fines and penalties. Even without state or local mandates around debt collection practices, federal regulations must be followed to avoid penalties or lawsuits from consumers or enforcers. CFPB levied $1.7 billion in civil penalties and over $14.4 billion in relief for American consumers in the last ten years. Compliance has thus evolved as a significant pain point for debt collections agencies.



We have reached a point where compliance is not just an expense item but also a source of differentiation for collection agencies. Unsurprisingly, most debt collection agencies are looking for tech solutions that can help them be more agile and efficient. Voice AI is one emerging solution with the most disruptive potential and growing use cases.

Too Many Calls, Too Little Communication

One of the prime objectives of compliance is to protect the customer from unfair practices and harassment. CFPB bases much of its enforcement authority on the concept of UDAAP (unfair, deceptive, and abusive acts or practices).

A call at the right time, to the right person, and with the right message can achieve the 3 Cs of debt collection: Cost, Compliance, and Customer Experience. A human agent may struggle to accomplish the triad, making too many or too few calls, but it’s a cakewalk for an intelligent voice agent.

Explore how Voice AI solutions are Transforming Debt Collection

Current Compliance Challenges

The formal, statutory fees and levies, which are increasingly hefty, represent just the tip of the compliance cost iceberg (around 10%) of total regulatory costs. The broader cost of compliance is much bigger, making it a formidable force. 

Here are the common challenges faced by debt collection agencies today:

  • Ever-Expanding List of Laws: Fair Debt Collection Practices Act (FDCPA), Telephone Consumer Protection Act (TCPA), Federal Fair Credit Reporting Act (FCRA), Payment Card Industry compliance (PCI), and Health Insurance Portability and Accountability Act (HIPAA) are a part of a growing list of regulations, adherence to which is a core driver to the success of debt collection agencies and similar financial institutions.
  • High Cost of Continual Training and Vigilance Process: A survey of sector firms by the Credit Services Association (CSA) reveals that in staffing terms, the proportion of resources involved (in compliance) seems to trend generally between 15% and 25% of total resources. That is a significant percentage and an opportunity to cut down the cost.
  • Client Expectation and Audit Requirements: Clients of collections agencies are deeply wary of meeting compliance and exert pressure, even more than regulators, to comply. As per a report by CFPB, collection agencies with large clients face 17 audits in a year. That’s an average of 3 audits every 2 months. The lack of transparency between debt collectors and consumers makes it difficult for agencies to facilitate these audits effectively. It is a formidable challenge to meet such high expectations cost-effectively.
  • Insufficient Time to Design and Implement Compliance Effectively: A rapid and frequent change in regulation leads to collection agencies running from pillar to post to update their processes. Deploying AI-enabled voice agents can minimize the training and guidance cost.
  • High Cost of Not Meeting the Compliance Requirements: Failing to meet the compliance requirement has, in the past, led to grave heavy consequences. Encore and Portfolio Recovery Associates, two giants in bad debt collections, were fined $18 million in 2015. They were forced to refund or halt collection of over $160 million in consumer debts. Violating the Do Not Call registry can cost agencies anywhere between $500-$1500 per case, as per TCPA. Moreover, razor-thin margins make the total cost of attorney fees, settlement costs, and the opportunity cost of time too much for agencies to bear.

Voice AI and its Ability to Empower Collection Companies Manage Compliance

More often than not, compliance is a matter of adhering to protocols and procedures. AI-enabled digital voice agents that can religiously follow a given set of instructions prove far superior in adherence to the regulatory framework.

There are numerous instances where small mistakes land collection agencies in trouble. Here are some simple yet powerful examples of how Voice AI can help with compliances:

  • Honoring Do Not Call Registry and Data Scrubbing: The telephone Consumer Protection Act (TCPA) maintains a register of subscribers who do not want to be called for telemarketing calls and automated dialer calls unless you have consent to do so otherwise. It’s essential to scrub the data before dialing these contacts and check for permission. Solution is to scrub the data against certain database such as Do-not-call registries (external and internal), consumers represented by attorneys and debt settlement companies, deceased consumers, serial litigators, bankrupt consumers, cease-and-desist order consumers. Unlike human agents, who can fumble, digital voice agents perform this with the help of APIs in a fraction of a second.
  • Calling Within Permissible Hours: FDCPA does not allow collection agencies to contact customers outside of 8:00 a.m. to 9:00 p.m. local time unless the consumer has given explicit consent. Additionally, customers with night jobs may not wish to be contacted during the day. Such personalization in large portfolios prove to be a daunting task for a human agent but an effortless one for a digital voice agent.
  • Calling Frequency: Regulation F of CFPB limits the frequency of calls under the 7/7/7 rule, restricting the agencies from attempting to establish communication with their consumers for more than 7 times in 7 days. The 7/7/7 rule includes voicemail, unanswered calls, and messages left on the consumer’s phone, and excludes email and text messaging. Furthermore, agencies cannot try to establish contact in the next 7 days after a successful communication. It’s taxing for human agents to consistently follow these rules for the entire customer base while optimizing time and cost at the same time. On the other hand, configuring machines to follow all these rules is possible with a click. 
  • Mini-Miranda is mandatory as per FDCPA in the first communication in any channel. Digital voice agents never fail to comply with such regulatory requirements.
  • Failure to Discontinue Communication Upon Request: Communicating with consumers in any way (other than litigation) after receiving notice with certain exceptions can lead to lawsuits. Machines follow strict protocols and comply with the request submitted by the consumers.
  • Communicating with Consumers at Their Place of Employment: It’s illegal to contact the consumer after being advised that this is unacceptable or prohibited by the employer. Human agents under dier conditions fail to honor guidelines. On the other hand, since machines reachout at the right time and frequency have high conversion rate while meeting compliance.
  • Contacting a consumer represented by an attorney: Agents must not contact the consumers who have chosen not to be contacted by agencies and have signed up attorneys for communication with certain exceptions.
  • Communicating with a Consumer During Validation Period: Human agents can make a mistake and try to establish communication with the consumer or pursue collection efforts after receiving a request for verification of a debt made within the 30-day validation period. On the other hand, Digital Voice Agents are configured to not engage in any such activities and trigger the automatic collection calls once validation period is over.
  • Misrepresentation & Threatening Arrest or Legal Action: With variable incentive as a major wage component, it’s quite common for debt collectors to misrepresent as attorney or law enforcement officer. FDCPA prevents such kind of misrepresentation and has punitive enforcement directives. Digital voice agents follow strict protocol and never succumb to such malpractices.
  • The abusive or Profane Language used during communication related to the debt is prohibited. Digital voice agents never fall back to such practices in order to achieve the results.
  • Communication with Third Parties: revealing or discussing the nature of debts with third parties (other than the spouse or attorney) is prohibited except to know the location of the debtor without mentioning debt related information. Intelligent Voice Agents can confirm the right party before giving out any information.
  • Raise a Dispute: Voicebot can also help consumers raise a dispute over a call and tag it in the CRM so that the relevant team can pick it up.
  • Validation: Upon asking for validation information, the voice bot can immediately send the electronic copy of the validation notice and mark the contact with a relevant tag so that human agents can see the status, and neither the voicebot nor human agents try to communicate to the consumer for the next 30 days.
  • Raise Tickets: Voicebot can even raise tickets to send the physical copies of the validation notice if explicitly requested by the consumer.

With Distinct Advantages, Voice AI Will Play a Bigger Role in Compliance Management 

Apart from numerous other use cases, the utility of Intelligent voice agents in improving the compliance of debt collections agencies is fast emerging and very promising. 

Apart from the direct costs of compliance, indirect costs such as fines and penalties take a heavy toll on companies. Today, compliance has become more than an expense but a source of differentiation. Many companies have already begun adopting Voice AI, and its ever-expanding use cases will help them create a distinct competitive advantage.

For more information and free consultation, let’s connect over a quick call; Book Now!

Also, for more information visit our Collections Page.

Outbound IVR Robocaller vs. AI-Powered Digital Voice Agents 

One in four Americans (28%) have at least one debt. This underscores the significance of debt collection services. As more consumers depend on credit for multiple purchases from homes to vehicles, household appliances, and sometimes everyday living expenses, debt collection services are playing an even more significant role in the availability and recovery of credit.

Though the use of IVR outbound Robocaller or outbound IVR is largely demotivated for debt collection through TCPA and FDCPA, we would discuss a little bit about the use of IVR outbound Robocaller for debt collection in this blog. 

Over the last couple of decades, it was perhaps wise to deploy Outbound IVRs, Voice Blasters, or Robocallers. The technology helped companies send pre-recorded phone messages to hundreds of consumers at once. 



In the last couple of decades, they have helped companies reduce calling errors, call costs, and improve productivity. But with rapid advancements in technology, especially Voice AI, the competitive landscape has changed rapidly in favor of intelligent voice conversations. 

In this blog, we delve into the core of the issue to explain why Intelligent Voice Agents are the way to deliver superior business performance and customer experience.

Explore how Voice AI solutions are Transforming Debt Collections

Understanding IVRs and why they fail to deliver real value

Typically, an Outbound IVR (Interactive Voice Response) is used to proactively reach out to a large number of customers in a personalized manner using different interaction channels, such as voice messages. The most common use cases are feedback, promotions, announcements, reminders, etc. 

Robocaller or outbound IVR has essentially two components in it; a dialer capability and a text-to-speech engine (Advanced Outbound IVRs) or a recorded voice message (Robocaller). Businesses can upload thousands of contacts in the dialer and configure certain parameters such as number and time of retry attempts, time of call, etc. Dialer calls up these contacts and plays a voice message that consumers can listen to. At the end of the call, the consumer can provide keypad-based number input to listen to the message again and certain other things.

In the 1990s this technology was a game-changer and led to a huge improvement in efficiency, however, today it is ineffective and unnecessary, to say the least. 

Even the best outbound IVRs ail from persistent challenges as enumerated below:

  1. Unidirectional Communication: IVRs are capable of only unidirectional communication with a limited DTMF (Keypad-based) feedback mechanism.
  2. Low Engagement: IVRs have extremely low engagement rates owing to their non-conversational unidirectional communication.
  3. Right party contact: Inability to capture conversational inputs and run verification to check for right-party communication. Today, you cannot pass on debt-related information to the wrong contact even inadvertently.
  4. Lack of ability to capture important dispositions: Robocallers or outbound IVR can’t capture meaningful dispositions that can be used downstream, such as:
    • Willingness to pay, and expected date and mode of payment
    • Refusal to pay and associated reasons
    • Debt dispute and reasons
    • Willingness to pay partially and offer payment arrangements.
    • Ability to capture call-back dates and times for busy customers.
  5. Lack of insights for segmentation: inability to segment the pool of consumers based on disposition to help debt collection companies make meaningful strategic decisions.
  6. Inability to reach out to consumers at their preferred time: Since Robocaller cannot capture the disposition of busy consumers, it cannot intelligently call back or arrange a call back from human agents.
  7. Payment assistance and goal completion: can not help or guide the willing consumer to make the payment during the call.
  8. Human-Agent Dependence: for a large chunk of calls, the agent is needed to reach a meaningful end result.
  9. Compliance adherence: Since every call campaign is triggered manually, compliance is left with the operator who is running the campaigns.
  10. Customer Experience: being extremely impersonal, they miserably fail at contributing to CX.

IVRs, even at their best, do not contribute to CX or major productivity gains, whereas a bad IVR experience can prove very costly. The State of IVR in 2018 noted that 83% of customers would avoid a company after a poor experience with an IVR. 

The more pressing problem still remains:

“How to automate the mundane, repetitive and non-value additive tasks human agents are doing”

For a long time, we did not have an answer, or we did not have a commercially viable technology solution, but today we have, and it is an Intelligent Voice AI Agent.

Explore how AI-enabled Voice AI Agents are the Perfect Solution to Meet Compliance Requirements

Understanding Digital Voice Agents

Digital Voice agents are AI-powered virtual agents that allow customers to converse intelligently, without having to punch 1,2,3,4 on their screen to hold a meaningful contextual conversation. It is able to converse with your consumers just like your human agents. It is capable of understanding, interpreting, and then analyzing conversational voice input expressed by an individual and responding to them in an everyday language.

A Virtual Voice Agent goes beyond understanding words and determines what the consumer is saying based on underlying semantics, without relying on specific keywords. Using machine learning, a Virtual Voice Agent is continuously improving itself and the customer experience. Read more about Digital Voice AI agent here.

Unlike Siri and Alexa, which are designed to handle everyday context-less tasks such as setting up an alarm or playing songs, AI-powered digital voice agents are trained specifically to handle complex problems, and understand what a customer may want in all probable scenarios, making them highly effective in solving customer problems and requests. 

A Comparative Look: Digital Voice Agent Vs Outbound IVR

4 Core Benefits: Why Top Collection Agencies are Deploying Digital Voice Agents 

For any company, AI-enabled Digital Voice Agents are a quantum leap from aging outbound IVRs. There is no comparison. Digital Voice Agents are AI-enabled, making them improve exponentially with time. One can surmise the amount of competitive leg-up companies can create as they start early. Here are the core business benefits of deploying Digital Voice Agents over IVRs:

  1. Reducing Cost and Improving Speed of Collections: The Digital Voice Agents can make or handle hundreds of concurrent calls at scale, economically, and in just an hour. Not only that, voice agents, being a machine, are very punctual and reach out to debtors that request a callback or make reattempts right on time when the probability of connecting to contact is highest. All this is done within the prescribed compliance framework.
  2. Superior Recovery and Collection Efforts: Better collection and recovery demand persistent efforts. When nudged at the right time, a debtor who is willing but unable to pay now might pay a few months down the line. Thus, what matters is how persistently collection agencies can reach out to a certain segment of debtors, ideally disposed to pay. It’s a piece of cake for Digital Voice Agent to schedule follow-up calls, honoring the regulatory guidelines, spread over weeks/months, and ensure better recovery rates. With timely and adequate calls going out to customers, and 24*7 support, the right voice-tech solution checks all the boxes to improve collections and recovery.
  3. Minimize Errors, ensure Compliance and Security: With a myriad of ever-changing regulations, disparate for each state, it is challenging for agents to keep abreast and be flawless. Training and development are costly, but Digital Voice Agents are easy to update and ensure perfect compliance. IVRs play a limited role, as unidirectional communications have a low impact.
  4. Human-Agent Bandwidth Prioritization: The beauty of deploying an Augmented Voice Intelligence is that it can call all the customers and filter the cases of complex cases that need human agent interference. In the present system, agents call the entire list, be it a simple case or a complex one, not creating desired value in the process. For the dispositions where human intervention is required, Voice Agent can segment the portfolio so that relevant human agents can be assigned the downstream tasks. This prioritization of bandwidth unlocks massive value for the collection companies.

For more information and free consultation, let’s connect over a quick call; Book Now!

Also, for more information visit our Collections Page.