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.
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.
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.
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.
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.
Companies all over the world spend billions of dollars on contact centers. In 2020, the global contact center market size was estimated to be over $339 billion, and that number is projected to grow significantly over the next decade.
Whenever a company invests in their contact center — whether it’s in-house or outsourced — they end up spending most of the budget on staffing, and significantly less goes to fund the technology. Even then, contact centers are dealing with major staff shortages that have caused customer service wait times to skyrocket.
In this article, we’ll discuss the pros and cons of contact center outsourcing, and we’ll compare them with the benefits of contact center automation with innovative conversational voice AI solutions.
What Is Contact Center Outsourcing?
Over the last decade, many companies have begun outsourcing their contact center operations. Contact center outsourcing is the use of contracted labor from a third-party source or organization to manage a company’s contact center and customer service operations. These third-party sources are commonly known as business process outsourcing (BPO) and they are often located offshore.
As a result of outsourcing, staffers who do not work directly for your company will handle all of your communications with your customers; they will be the ones to pick up the phone when you receive an inbound call from a disgruntled customer or a technical question related to your products or services. Additionally, customer interactions that may be handled by the BPO may include social media (e.g. Twitter), live chat, and email.
It’s common for contact center outsourcing to take place offshore, as it’s typically cheaper to manage these operations abroad.
The Benefits of Outsourcing Your Contact or Call Center
Here are some of the most relevant pros of outsourcing your contact center:
It’s cheaper. This is not to say that it’s cheap—just that it’s cheaper than running your in-house contact center. Hiring, training and managing staffers is expensive, in addition to the expenses related to the facilities and equipment required to run the center. Unloading all of these challenges to an external organization that is familiar with this type of operation is likely to save you a significant amount of money.
It’s one less headache. Money aside, managing a contact center can be a major headache, especially when dealing with the current attrition rates, which are very high, and short-staffing issues. If you are not ready to invest the time that goes into this type of operation, outsourcing may be worth looking into.
Customer service around the clock. Offering 24/7 customer service has become the key to a successful customer experience. However, maintaining an in-house team of customer care specialists that can deliver 24/7 service is a challenge most businesses cannot handle.
It allows for some scaling flexibility. As your business grows, customer queries will grow, too, and you’ll need to seamlessly scale up your service operations. Additionally, temporary crises and unexpected events can cause query and call volume surges, which are difficult to manage for a smaller, in-house contact center.
The Disadvantages of Contact Center Outsourcing
Yes, contact center outsourcing may have its perks, but it’s not the perfect solution to all of your contact center needs. Here are some of the disadvantages:
Limited control over your contact center. Outsourcing means trusting a third-party organization to manage your interactions with customers. While as a business you often have to rely on other companies and solutions, when you outsource your contact center you accept that you will likely have limited control over its operations.
Limited knowledge of your company. BPOs typically service many companies, which means that your contact center agents may be assigned to work with other companies, as well. Additionally, because they don’t work directly for you, the agents are likely to have a limited knowledge and expertise of your company and the products and solutions you offer; by definition, the outsourced agents will be less exposed to your company and will not be able to regularly communicate with other departments within your organization.
It’s still expensive. Yes, outsourcing is relatively cheaper than creating and managing an in-house contact center, but it’s still a very large investment. Additionally, if your customer experience deteriorates because of the poor customer support, then you will end up losing customers, which will directly affect your profit.
What Is Contact Center Automation?
Contact center automation is the process of adopting technological solutions that process and respond to customer service queries automatically, boosting the center’s efficiency, bringing down costs, and offloading human agents of repetitive, tedious tasks. In this article, we are focusing specifically on voice automation, which consists in the adoption of a voicebot.
As maintaining a contact center has become an increasingly expensive and complex operation, more businesses have been looking into automation as an ideal solution to the customer service crisis. Especially for the more simple customer queries, automation can be an easy-to-implement solution that leaves customers satisfied by easily solving their most common issues.
Skit’s Digital Voice Agent is a prime example of contact center automation, as it’s an purpose-built and industry-specific AI-powered agent that can converse with customers, answer their questions, and process their requests as needed.
Does automation mean that human agents are no longer needed? Absolutely not. At Skit.ai, we are firm believers in the power of Augmented Intelligence, which sees human agents partner with artificial intelligence to provide a seamless customer experience.
Benefits of Contact Center Automation as Opposed to Outsourcing
Significantly cheaper. You can adopt an AI-powered Digital Voice Agent at a fraction of the cost of a team of human agents. Some estimate that businesses that adopt voice automation save approximately 50% of what they were previously spending on their call center.
Direct control over your use cases. As opposed to an outsourced human agent, the Digital Voice Agent is built with your company’s specific needs in mind. Before it’s built, you get to express all of your needs; even afterwards, you can tweak it and improve its performance or add new use cases as needed by easily using Skit’s Studio platform.
Empowering human agents. For more complex queries or issues, the Digital Voice Agent escalates the call to a human agent—while providing the human agent with the appropriate context. You can read more about how Voice AI empowers human agents in this article.
Customer verification is much faster. Human agents typically spend 4-5 minutes just to verify the identity of the customer or caller at the beginning of the call. A Digital Voice Agent can do it in just a couple of minutes, costing you less resources.
24/7 support. We’ve already pointed out the importance of 24/7 customer support for seamless CX. The Digital Voice Agent never sleeps, and it doesn’t take a single day off.
Are you curious to see how Skit’s Augmented Voice Intelligence solution works? Would you like to see a demo of our product and speak with an expert? Schedule a call with one of our experts using the chat tool below!
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.
High Ownership: The entire organization of a voice-first company is streamlined to deliver and own the outcomes of their voicebot. There are no distractions, only a razor-sharp area of focus. This makes their projects most likely to succeed and deliver transformative outcomes.
Deep Domain Knowledge: A voicebot is a symphony, an orchestra of technologies working in tandem with each other to deliver the intelligent, fluid, and human-like conversations that every consumer covets. Only voice-first companies that labor hard to make every part function smoothly, and efficiently will be the ones delivering outcomes with maximum CX and RoI.
Proprietary Tech Stack: Not that voice-first companies don’t utilize the third-party stack, they leverage them to further performance and control. They tune third-party tech stack and use it along with their existing proprietary tech to maximize the impact. For example, a company such as Skit.ai uses Google, Amazon, or Azure’s ASR along with its own domain-specific ASR parallelly to get the highest accuracy and optimal performance. The results are tangible and impressive. As Skit.ai’s ASR is significantly better at short-utterance, at instances where the conversational experts expect them, Skit.ai’s ASR kicks in for higher accuracy and performance.
Dedicated Team: Running an AI-first product comes with its own challenges. But for a company like Skit.ai, which has a dedicated team of 400-500 people laboring to solve just the voice conundrum, you can expect an outstanding product that is always further along on the learning curve and stands true to its promises.
Long-term Engagement: Voice is the future of customer support. No other modality will come close, especially with the blazing advancements in Voice AI. So, a voice solution must not be implemented with a very narrow view of time and cost. Deeply committed Voice AI vendors will be the ones to seek as they will deliver superior results that not only help companies save costs but also aid them in carving out an exceptional voice strategy for brand differentiation.
For further information on Voice AI solutions and implementations, feel free to book a call with one of our experts using the chat tool below.
The U.S. economy has been shrinking, with many experts pointing out that technically we have already entered a recession, as the economy has now contracted for two consecutive quarters. Fears of a recession have dominated most sectors of the economy over the last few months.
The economy is slowing, inflation is high, and the Federal Reserve has been increasing its interest rates, and yet the data suggest that we find ourselves in a more complex and nuanced situation. Unemployment is still very low and the economy has been adding hundreds of thousands of new jobs each month, suggesting that it’s not all doom and gloom.
The latest reports, however, predict there will be a “mild recession” between 2022 and early 2023, with inflation being a major indicator of the direction the economy is headed towards, according to authoritative institutions such as Bank of America and Wells Fargo.
How can your contact center prepare for a recession? How can you rely on technology to future-proof your contact center operations?
How Does a Recession Threaten Contact Center Operations and Customer Service at Large?
It’s impossible to predict what a possible recession will look like. Each past recession has been different, and the best businesses can do is prepare and future-proof their organization across all departments, optimizing their operations and cost-correcting wherever possible.
The possible threats that a recession may pose to your contact center may be:
Budget cuts: The overall business may have reduced profits and management might decide to cut costs across various departments. This will leave you to manage the same workload as before, but with less resources, and you might be forced to let go of some of your agents.
Increased call volumes: While your funds might decrease, inbound calls and customer queries might actually increase.
Chain reaction: As your resources become more limited and you’re unable to offer the same level of customer service as before, the overall customer experience will be affected, causing the loss of customers. This chain-reaction can spark a vicious cycle in which the contact center management may be held responsible for the loss of customers.
5 Steps Contact Centers Can Take To Prepare for a Recession
Optimize All Processes
To make your business recession-proof, the first step is to optimize all of your internal processes and workflows. Analyze the existing processes and the customer journey:
Can you identify any pain points?
Where are resources missing and where are they abounding?
Are there any workflows that can be shortened or reshuffled?
Are there any tech tools to add to your stack that can help with any of the issues you’ve identified?
Invest in Customer Self-Service
The existing data on customer service and customer experience indicates that customers expect companies to offer self-service customer care options. 39% of U.S. consumers find it very important to have access to a fully self-serve customer care option available to resolve their issues, according to a report by Emplify.
Self-service customer service allows customers to view, change and cancel their orders, make payments, request information, make a reservation, request technical support, and solve common issues on their own without the need to involve a human agent; this can be done through the company’s website, a mobile application, or an AI-powered Digital Voice Agent.
Invest in Agent Retention
Agent attrition in contact centers tends to be high. The current data suggests that contact centers have at least a 35-40% attrition rate. This trend creates additional expenses, as the business needs to cover recruiting, hiring, and training costs each time an agent leaves their job.
Investing in agent retention is a must for businesses preparing for a recession. You want to keep your agents happy and make sure they don’t feel overly stressed or overwhelmed with calls. If you have to let go of some of your agents, you should adopt a strategy to hold on to the ones you plan to keep. Consider adopting tech solutions that could automate some of the most repetitive and tedious agent workload.
Prepare for Call Fluctuations
As query volume becomes more volatile, contact centers may experience more fluctuations in volume of inbound calls, needing more or less resources depending on the time. Agencies should develop a strong plan to address these scenarios; plan ahead even if you might not be experiencing this issue yet.
Offer an Omnichannel Experience
Customers expect to get assistance and care seamlessly and across an integrated network of touchpoints, devices, and apps. The COVID-19 pandemic has certainly made the importance of digital customer service and omnichannel experiences more prevalent, accelerating a phenomenon that was already taking place.
Recently, the customer service industry has begun focusing on an alternative strategy, focusing on an optichannel or optimal channel CX strategy. Each company must focus on the best channels and apps for their specific customers, products and services. No matter what strategy you adopt, customer experience should be front and center.
How Contact Centers Can Rely on Voice AI To Prepare for a Recession
Adopting technological solutions that can help you automate processes and improve your customer and employee experiences is one of the best ways to future-proof your business, especially as we prepare for a possible recession. For customer service, online chatbots and Voice AI are excellent solutions to consider.
Voice AI consists in the adoption of AI-powered Digital Voice Agents to receive all inbound calls and perform some of your outbound calls; it is becoming more and more popular among businesses and consumers.
The Digital Voice Agent is able to help customers with the most common queries and can reroute the more complex queries to your human agents. When you enable the perfect synergy between human and digital agents, you de-facto adopt an Augmented Voice Intelligence strategy.
In a recession, you not only want to save money, but you also want to ensure you maintain a competitive edge over your competitors. Looking into the adoption of artificial intelligence technologies that can automate your operations is key to securing a competitive advantage.
To learn more about how to modernize your contact center, schedule a call with one of our experts using the chat tool below!
The debt collection industry is a heavily regulated space; the number of laws and regulations in place can be quite overwhelming. Whenever a new technology or solution emerges, therefore, it is natural to wonder whether it is compatible with the existing laws and whether the provider is fully compliant.
As more collection agencies look into adopting a Conversational Voice AI solution to automate their collection calls, it can be confusing to go through the regulations and determine which ones apply and which ones don’t.
In this article, we’ll unpack one important law — the Telephone Consumer Protection Act — and analyze its key provisions from the perspective of a Voice AI provider.
An Overview of the TCPA
The Telephone Consumer Protection Act (TCPA), first passed by the U.S. Congress back in 1991, is one of the most important laws that regulate telemarketing and the use of automated telephone equipment.
The TCPA is a law that governs and regulates all telemarketing calls, auto-dialed calls, pre-recorded calls, and unsolicited faxes. Though this law primarily focuses on protecting consumers from unwanted communications, it also governs and prescribes restrictions in the context of debt collection calls.
The TCPA authorizes the Federal Communications Committee (FCC) to exempt certain types of calls from its restrictions, including “calls made to residential lines that are not made for a commercial purpose, calls made for a commercial purpose that do not contain an unsolicited advertisement, calls from tax-exempt nonprofit organizations, and healthcare-related calls.”
Does the TCPA apply to debt collection calls? And how does it affect the use of Voice AI?
The Key Provisions of the TCPA and Collections
Calling Curfew
Solicitors can’t call customers at night time (indicatively, between 9:00 p.m. and 8:00 a.m.). However, the specific hours are determined by each state. For example, certain states do not allow calls on Sundays (e.g. Alabama, Louisiana, and Mississippi, among others). During the week, the starting time when calls are allowed varies by state—between 8:00 and 10:00 a.m. Calls need to be interrupted between 6:00 and 9:00 p.m. depending on the state.
National Do Not Call List
The National Do Not Call Registry was created to stop unwanted sales calls; anyone can register their phone number. Good news! This provision only applies to telemarketing calls. Luckily, debt collections do not qualify as telemarketing. However, if the customer explicitly asks not to be called, the collection agency needs to honor the request.
Self-Identification via Voicemail
If the collection agency wants to leave a voice message to the customer, then the collector must identify themselves and the agency and provide their telephone number.
Calling Mobile Phones
Nowadays, fewer people have landlines at home, and virtually everyone owns a cellphone. Still, the TCPA rules that callers can’t call a mobile phone without prior consent when using an automatic dialer, artificial voice, or a pre-recorded message. Therefore, one must obtain direct consumer consent prior to the use of these technologies to contact cellphones.
In the next section, we’ll see why Voice AI is not considered an automatic telephone dialing system (ATDS).
How the TCPA Impacts the Use of Voice AI
Over the years, there has been much confusion about what exactly qualifies as an automatic telephone dialing system (ATDS) under the TCPA. In 2021, the Supreme Court released its decision on Facebook v. Duguid, settling this long-standing uncertainty.
In a unanimous decision written by Justice Sonia Sotomayor, the Supreme Court established that an automatic telephone dialing system (ATDS) is a system or device that either:
Stores a telephone number using a random or sequential number generator;
Produces a telephone number using a random or sequential number generator.
Here’s why our Voice AI technology does not come under the purview of the ATDS definition under TCPA:
Skit.ai’s solution does not randomly or sequentially store or produce telephone numbers.
Our solution is designed to call the phone numbers provided by our clients, which are based on the accurate and complete databases of the clients, which enables Skit.ai to perform the services throughout the contract period.
Since our clients have prior express consent to send communications to the identified consumers, Skit.ai can communicate with the consumers on behalf of its clients through its Voice AI technology.
Please note that the information in this article is not intended to be legal advice and may not be used as legal advice. For more information and to request a free demo, you can use the chat tool below to schedule a call with one of our collections experts.
You’re not sure about Voice AI, you have some doubts, and you need some guidance? Are you wondering what a Voice AI solution can do for your company or agency; which risks are involved; and will this technology help you get ahead of your competition?
This guide seeks to answer all of your questions about Voice AI.
This is a unique ebook designed to enable informed and quick decision-making for debt collection CXOs — a comprehensive step-by-step guide for CXOs in the debt collection space to explore Voice AI technology and understand its core capabilities and the qualities of an ideal vendor. Additionally, we’ve included a section detailing the entire implementation process, from ideation to execution and beyond.
The ebook is divided into three sections:
Section 1: Fundamentals. In order to be able to take the informed decision, one needs to know about the product or services. This section contains the fundamentals of Digital Voice Agents, the tech behind it, and why it is important for the debt collection space.
Section 2: Selection Criteria. This section details the capabilities that a debt collection company must look into when considering Voice AI vendors. Several capabilities and complexities should be considered before making a decision.
Section 3: Implementation Guide. This section is a deep dive into the process of implementing a Voice AI solution, from ideation to execution, every step, in granular detail. This will prove vital in not only ensuring final success but also in time and ease of execution.
Section 1: Fundamentals of Voice AI
From its peak in 2009, consumer debt grew by $2.3 trillion to almost $14 trillion in 2019. In 2010, U.S. businesses placed $150 billion in debt with collection agencies but recovered a fraction, i.e., just $40 billion. The industry averages a 20% collection rate on delinquent debts, decreasing from 30% a few decades ago. Overall, the performance of debt collection companies seems to be facing major challenges.
Rapid changes in regulatory and customer experience expectations are taking place in the collection space and are posing serious challenges to collections agencies.
Typical Challenges
High number of untouched files: One of the third-party debt collectors has over 1 million files across portfolios, but because of the lack of human resource bandwidth, they are not able to reach out to all of them. Though they might send automated text messages to all of these, they know it’s not enough. They could actively pursue and call only 30-40K prioritized files with an outstanding balance of more than $1,000. The agency is not able to get any collection out of 960,000 files that are completely untouched.
High wrong party contacts: The menace of having a wrong contact number and associated problems is prevalent in the industry and is eating away the margin. Every call placed to a wrong party causes a financial loss for your business. These calls are simply non-value adding for any human agent.
High number of non-revenue-adding calls: Other than wrong-party connect, there are many other calls which do not add much value. For example, requests to dispute a debt, through an inbound call or outbound; another example is second-party contact or speaking with a customer who is busy and wants a call-back later. For an agency, any call that does not resolve in payment in the immediate future does not add much value.
Lack of persistent efforts and follow up: One of the most important things in collections is persistency. One industry expert argued that it requires 16 calls to reach an average consumer.Another industry expert, a large debt buyer, stated that, when searching for a consumer, it places between 50-75 calls per debt before achieving RPC.When trying to establish contact, consumers sometimes ask to get a call-back at a later time. After agreeing to pay, collectors have to remind the consumers on a regular basis. If your agents are not able to follow up persistently, collection rates are bound to go for a toss. And it’s humanly impossible to be able to follow such a strict schedule.
Compliance and script breach: Compliance requirements have become stricter. It’s essential for a collection agent to follow a strict script, be it Mini-Miranda, communication protocols such as 7-7-7, or keeping their cool after a bad day.
High attrition: Attrition in our industry is at an all-time high. One of our customers jokingly said that a McDonald’s worker earns more than a debt collector. Average attrition in some of the cases we’ve seen is around 200%, meaning that the average employee stays at the company for no longer than 6 months. With such high attrition rates, hiring, training, and employee-related costs have become extremely high.
Scaling up/down: At times, when you have a new portfolio or file, the workload increases. However, it’s not wise to hire agents only for such surge periods, so operations leaders end up deciding to work only with the available resources. This approach significantly reduces the sped of collections.
These issues ultimately result in lower collection rates and high collection costs.
Before we dive into how Voice AI solutions can help debt collectors, let’s understand the fundamentals of what a Digital Voice Agent is and how it works.
The Tech Behind a Digital Voice Agent
What is a Digital Voice Agent?
A Digital Voice Agent is an AI-powered conversational robot (commonly known as a voicebot) that has the ability to interact with a user and take a certain sets of actions to meet an end goal. It is very similar, but not the same, as voice assistants like Apple’s Siri, Google Assistant, and Amazon’s Alexa.
How is it different from voice assistants?
Voice assistants are designed to handle one or two turns of conversation to meet generic day-to-day tasks and are not designed to retain context longer than that.
Example of a single-turn conversation
Intelligent Voice Agents, on the other hand, are designed to solve specific problems which require much more than two turns of conversation, just the way humans solve queries by first asking multiple questions to understand the context and all the required information to solve a given problem.
For example, a lost credit card can be blocked by asking a series of standard questions. The first couple of questions are to verify the caller, and the next set of questions are to confirm which credit card should be blocked, and then followed by an action where the customer is issued and sent a new credit card. Typically, this is a 6-7 turn conversation that generic voice assistants are not designed to handle. Specialized voice AI agents are required to be built and trained to handle such tasks.
Digital Voice Agents sit on top of telephony and dialer systems. So apart from these two, fundamentally, there are at least five components (engines) to any voice bot:
Components of a Digital Voice Agent
ASR (Automatic Speech Recognition): This converts the voice into text transcription. This is alternatively called speech-to-text or STT Engine.
SLU (Spoken Language Understanding): This is the brain of the voice bot. It extracts intents and entities (data points) from the text sentence produced by ASR and then comes up with the best possible action. That action can be performed in terms of voice reply or sending a document or a text message, or transferring the call or raising a ticket etc.
TTS (Text to Speech): The block that translates the text into voice to generate a reply.
Dialogue Manager (Orchestrator): The block that manages the flow of data among the above three blocks and the flow of the conversation.
Integration Proxy: These are integration sockets that connects with CRMs, Payment gateways, Ticketing systems, etc in order for voice agent to be effective and efficient in end-to-end automation.
These processes happen in real time and within milliseconds. This is only one turn of the conversation and the process is repeated for subsequent turns.
All of these processes are performed in the cloud after the voice packets are received from a user. So it doesn’t really matter which device the caller is using—whether it’s a smartphone or a feature phone or a wired telephone. Skit’s Digital Voice Agents leverage all of these layers to seamlessly plug into contact centers and augment the work of human agents.
How are Digital Voice Agents different from chatbots?
Technically, an AI-powered voice bot has two extra engines that a chatbot doesn’t need. Since chatbots do not deal with voice, the two engines related to voice (ASR and TTS) are not required. The text input is fed directly to NLU and the intents and entities are extracted and the response is synthesized in text format and relayed back to the user.
Furthermore, voice queries on call bring with it certain challenges like noisy backgrounds, different accents and dialects of speaking the same language, language disfluencies and unique way of adding filler words and pauses, barge-in by a person while the other one is speaking; all of which directly impact accuracy.
And for the same reason, voice bots are much more difficult to build. Everything has to be real-time within milliseconds and there is little to no room for error, else communication experience is hurt.
What sets voice bots apart is that they’re faster. Voice is the quickest and most natural form of human communication—faster than typing or navigating drop-down menus with a mouse. It continues to be one of the most sought-after by end customers seeking support.
How Is Augmented Voice Intelligence Different from IVR?
What is an IVR?
Interactive Voice Response (IVR) is an automated phone routing system that interacts with callers and gathers information by giving them multiple choices via a menu. The system then performs actions based on the answers of the caller through the telephone keypad, which is also called DTMF (Dual Tone Multi Frequency).
IVRs are used by companies or contact centers to route calls based on the choices made by the caller in order to organize call queues of call centers. Through the caller’s selection, the system can determine if the caller wants to contact the billing department, the technical support team, or simply wants to talk to a human operator.
IVR in its backend is a top-down tree structure in which input from user determines which downstream node the call will flow to. End of the node can be either human agent transfer node or self-serve node. In case of self-serve node, a pre-recorded message is fetched from the database and played, for example, in account balance enquiry node, a pre-recorded message with be played along with a variable value, in this case fund balance.
IVR is also used to provide information like promos, updates, or other important information or instructions. One example is to inform callers that the system will record the call.
Lately, IVR providers have come up with voice response instead of DTMF. For example, to reach the billing department, the caller has to say “billing” instead of pressing a key on the the phone. This works on keyword matching. However, if caller utters a long sentence and doesn’t include the relevant keyword, IVR would not be able to recognize the input.
Typically, an Outbound IVR (Interactive Voice Response) is also used to 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: (1) a dialer capability and (2) a text-to-speech engine (Advanced Outbound IVRs) or a recorded voice message (Robocaller). Businesses can upload thousands of contacts to the dialer and configure certain parameters such as number and time of retry attempts, time of call etc. The dialer calls these contacts and plays a voice message which consumers can listen to. At the end of call, the consumer can provide keypad based number input to listen to the message again and perform other tasks.
Limitations of IVR
In the 1990s this technology was a game-changer and led to a significant improvement in efficiency. However, today this system is ineffective and unnecessary, to say the least.
Even the most sophisticated outbound IVRs ail from persistent challenges as enumerated below:
Unidirectional Communication: IVRs are capable of only unidirectional communication with a limited DTMF (keypad-based) feedback mechanism.
Low Engagement: IVRs have extremely low engagement rates owing to their non-conversational unidirectional communication.
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.
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 date and time for busy customers.
Lack of insights for segmentation: Inability to segment the pool of consumers based on disposition to help debt collection companies make meaningful strategic decisions.
Inability to reach out to consumers on their preferred time: Since Robocaller cannot capture disposition for busy consumers, it cannot intelligently call back or arrange call back from human agents.
Payment assistance and goal completion: Cannot help or guide the willing consumer to make the payment during the call.
Human-Agent Dependence: For a large number of calls, human agents are needed to reach to a meaningful end result.
Compliance adherence: Since every call campaign is triggered manually, compliance is in the hands of the operator who is running the campaigns.
Customer Experience: Because this system is extremely impersonal, it miserably fails 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 Intelligent Voice AI Agent.
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 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.
A Comparative Look: Digital Voice Agent vs Outbound IVR
Section 2: Selection Criteria
Debt collection is not a simple industry. It is heavily regulated and involves a whole gamut of laws, which keep on changing. Additionally, it’s affected by the pressure to cut down on costs for the collection agencies.
For the first time, there is a technology that answers most of the challenges faced by debt collections agencies. Still, incorporating this tech presents its own set of risks.
Being experts and experienced in the debt collection space, we at Skit.ai have outlined a guide that helps CXOs understand what capabilities to look for when selecting and evaluating a Voice AI vendor.
Look for these core capabilities as you decide how to transform your debt collection business with Voice AI.
Deep Understanding of Business Operations and Processes
A voice technology company can have an impressive tech stack but may still not be suitable for you if they lack domain or industry expertise. They need to understand the nuance of the business and the consequences of conversations, reach out, and promises.
Why is it important?
A deep knowledge and understanding of business operations and processes in the collections space is essential, because debt collection is a complex, heavily regulated industry. Lack of knowledge is not only risky from a compliance standpoint; it can also hinder the creation of intelligent and intuitive conversation designs.
Designing a DVA is as much an art as it is a scientific and technical process.
The conversation with a consumer will be drastically different for a debt which is 30 days old compared to the one that is 5 years old, consumers might not remember the debt or card after some time. Conversation design will drastically change on various factor such as:
Nature of Debt: Knowledge of intricacies of different types of debt – credit card, healthcare, mortgage, telecom, etc.
Age of Debt: Knowledge of nuances involved with debt with different ages. A 30-day DPD debt is remarkably distinct from 180 DPD debt.
Conversation Design Capabilities: Is the vendor capable of managing the subtle differences and incorporating those in conversation designs.
If these factors are not considered, the end product would be suboptimal and end consumer will drop out of the conversations.
Consequences of lack of expertise in the area
Here are some of the issues you are likely going to run into if your Voice AI provider does not meet the aforementioned standards:
Higher involvement at every step: If they are not familiar with the business challenges and operations, they are going to reach out to you for every issue they encounter and seek help in designing flows.
Poor quality of voice agents: A voice assistant or agent can only be as good as its conversation designs. It takes humongous effort and time to create natural and intuitive flows that already understand the most probable customer queries and follow-up questions. Only an experienced voice solution provider can help you succeed in having a voice agent with a stellar performance.
Longer implementation time: There will be multiple to-and-fros as your vendor will come back to you asking for input every step of the process.
Internal resource time and effort: You expect your Voice AI vendor to do most of the work on its own, but that may not happen if there is a lack of expertise. You will end up dedicating a big team to help them design a functioning voice agent. This will disrupt your organizational functioning on an ongoing basis.
Higher cost: Longer implementation time, higher internal resource involvement, and higher need for testing will ultimately culminate in a higher cost for you, both directly and indirectly.
Ability to Handle End-to-End Automation
You should expect your Digital Voice Agant to have the capability to deliver end-to-end automation. In other words, they must have the capability to handle calls from start to finish without the help or intervention of a human agent.
Why is it important?
These days, AI-powered Digital Voice Agents should be capable of handling conversations end-to-end. It would be limiting to use DVAs only for call routing and to identify right-party contacts and transfer calls to human agents.
On average, 70% of customer requests fall into the tier-I bucket; this means that a Voice AI agent must be able to automate, End-to-End, a majority of calls.
This is the most vital capability of a Voice AI solution as entire value creation, productivity enhancement, and business performance rest on it.
Imagine the kind of value that can be created by taking away more than 70% of frustrating calls your human agents are handling.
Here is a list of a few capabilities that augment End-to-End Automation:
Capability to collect payment on call
Debt dispute handling (end-to-end)
Sending digital validation
Identifying RPC and WPC
and more.
Consequences of lack of expertise in the area
What happens if the vendor you are speaking with does not have a high-end-to-end automation capability?
Impact on scalability: We know that maintaining a large human agent team is a painful task. The highest attrition rates, not only make it an operational hassle but also escalate the costs to retain them, and keep them engaged and satisfied. With End-to-End Automation capability, Voice AI technology is minimizing your reliance on human agents. You do not need to recruit more when call volumes surge, nor do you need to have a larger team if you want to deal with a bigger portfolio of delinquent accounts. Let’s compare to make the point crystal clear:
Vendor 1: End-to-End Automation capability of 70%: You need human agents for just 30% of complex calls. This means 24/7 majority of your customers will be able to solve their problems instantly, without IVRs and then to human agents. You need to keep a minimal team, a happy team that will work even better as they are now not dealing with interesting and value-creating calls. This has a lasting positive impact on cost structure, HR costs, and other indirect costs.
Vendor 2: No End-to-End Automation: Though the Voice AI agent will be able to identify the right-party, you will always need human agent for every call as call is transferred from DVA to a human agent. This means you will always need human agents for DVA to realize the value since there is no end-to-end automation.
Platform Approach for Rapid Roll outs and Time-to-Market
A platform approach has its typical advantages. Cloud-based modularity makes enhancements and tweaks very easy.
Why is it important?
A platform gives visibility into the system, and for many elements, the adopting company can have the option to tweak things such as conversation flows to better voice agent performance. Additionally, it is easier to deliver upgrades and enhancements collaboratively and transparently.
For instance, Skit.ai offers access to the Skit studio platform, which gives its clients a comprehensive view into how things are moving along. This makes the entire BTDME — build, test, deploy, monitor, and enhance — journey significantly smoother.
Having a user-friendly platform also helps with the integration of third-party applications such as payment gateways, CRM, and other business applications. In the long run, these capabilities can be the difference between winning and losing.
Consequences of lack of expertise in the area
The lack of a platform converts the Voice AI solution into a black box. You have no idea about its functioning, and you will depend on your vendor for everything. This will not only elongate the enhancement process but will also make it costly.
More often than not, time is everything. Consider the damage a wrong information-based conversational flow can do if not updated in time. The compromise on agility is severely debilitating for any company sensitive to CX and changes in consumer behavior.
Compliance Expertise and Experience
Everyone in the debt collection space is aware of Reg F. and the challenges it posed to debt collection agencies as they work to understand the implications and ensure proper compliance. If your vendor does not have the required knowledge and expertise on compliance and regulations, the consequences can be problematic for your agency.
Why is it important?
Leaving alone the increasing fines and penalties imposed by the regulators way more significant are getting involved in lawsuits and court battles.
Companies must seek a vendor who knows the law in and out. Considering the direction of regulations going stringent by the year, the significance of expertise in this area can not be hyperbolized.
Various tasks such as data scrubbing are difficult for a human agent but a breeze for Voice AI and can prevent a potential lawsuit. Furnishing statutory information such as Mini Miranda or relating to other laws is easy for voice AI agents, but your vendor must have the in-depth expertise to train the voicebot for it.
Consequences of lack of expertise in the area
There are two significant disadvantages if your vendor lacks in this area:
Lost Advantage: One indisputable fact is that Voice AI Agent is better at ensuring compliance. Human agents are prone to err and engage in false promises and indecorous use of language. A state-of-the-art voice AI agent makes compliance adherence bulletproof. But if your vendor is conversant with regulations you not only run the risk of breach of compliance but also you miss out on one of the biggest advantages associated with voice AI agents.
Cost Implications: Running into lawsuits costs companies dearly that are already dealing with thin margins.
Business Performance: Faltering at one regulation, or one lawsuit puts the entire company on a backfoot and triggers introspection which slows down the entire business.
Looking into MLops, capabilities are essential as they have a lasting impact on the performance and competitive edge.
Why is it important?
At the core of Voice AI lies the capability of the algorithms to learn and improve as more and more conversations are fed into it.
The more extensive this capability, the more robust will be the learning gains, and the ability of the system to improve the conversations.
Consequences of lack of expertise in the area
The absence of AI/ML or only feeble attempts at it has severe consequences because as companies who are updating their AI/ML models, regularly feeding more and more data will create superior conversations, and will augment their capability to handle conversations.
Technology Ownership
This means having a proprietary technology stack and not relying on open source technologies.
Why is it important?
A score of reasons are there for you to look for proprietary technology.
Process Efficiency: If a Voice AI company is using its own tech, they have labored hard to optimize it, as well as the integration they are using. This enhances the overall performance to a great extent and makes a world of a difference.
Constant Improvement: Having ownership of the tech stack helps in rapid improvements and releases.
Safety and Security: For a sensitive industry such as debt collection, safety and security are of grave importance. Having tech ownership enables companies to have greater control over the flow of data.
Control: It is as simple – we can not control what we don’t own.
Consequences of lack of expertise in the area
Lack of tech ownership has many negative consequences. It slows down the entire process. Also, your vendor will not have control over the process because it is using many third-party integrations, and failure at one will cause the failure of the entire process.
In essence, the entire experience is compromised because of inferior performance if the vendor does not have ownership of the core tech stack. Every company uses integrations, they are the best ways to scale capabilities, but it should not be the case for the core tech stack.
Actionable Analytics and Dashboard
A unified view of the entire process and the ability to analyze and have actionable insights.
Why is it important?
Every conversation is a potential treasure trove of value. Companies must not waste such valuable resources and an ideal vendor must possess the capabilities to draw insights from data such as dispositions.
Look for capabilities such as bucketing dispositions into meaningful buckets, forwarding disputes to select departments, and more.
A dashboard to monitor the effectiveness of conversations is an essential feature. Also, analysis of AHT trends and more are a must.
Consequences of lack of expertise in the area
We can not improve that which we can not measure. Not having the capability to run analytics will impact business performance improvements and will lead to competitive losses.
Section 3: Implementation Guide
In this section of our guide, we’ve compiled a list of essentials to help your company properly onboard your chosen vendor and implement their Voice AI solution for debt collection.
Have the vendor sign an NDA (non-disclosure agreement)
In order for the DVA to be effective, you will have to share a lot of information for your vendor to be able to understand the consumer persona. Always sign an NDA before sending any documents or sensitive information.
Form a steering committee and assign Single Point of Contact (SPoC)
Ensure to have a focused approach to incorporate the Voice AI from the very beginning of the process. A steering committee can have a mix of expertise from technology to business, operations, and HR.
Always pilot and follow a lean approach in pilot
This is of serious importance. Lean means that your pilot should be undertaken in such a way that your organization gets disturbed in a minimal manner. Avoid unnecessary integrations that will increase the load and complexity of the pilot and can affect the results in a complex way. Also keeping it lean will minimize your and your team’s involvement so that your sunk cost in terms of time investment is low if the project goes south and doesn’t bear the fruits.
Pilot the biggest segments you handle
Going all out is not the best strategy here. Segment the portfolio you are handling in terms of volume and value. Prioritize 2-3 different segments for the pilot and provide representative call recordings for your vendor to understand the consumer persona. Also help your vendor with call dispositions i.e. different kind of flows your typical calls end up in, for example, percentage of calls that are wrong party, debt dispute, cease communication requests etc. This will help your vendor plan the development strategy.
The Voice AI agent will be as good as the information you feed it. It is essential that you provide to the vendor all the essential information, e.g. if you have 12 types of customers, then provide the audio recording of each type of customer. Failing that will result in poor conversation flows that are designed for only a few types of customers.
Additionally, the number of files shared is also important to help in the training of the voice agent. It is best if you share actual conversations in large volume so that it makes ML models better.
Review the call-flows
After reviewing the call recordings, your vendor should be able to come up with the conversational design, call flows, and scripts. Once your vendor is ready with conversation designs and flows, it is crucial that specialists from your organization review and help them refine the those. This step will have a lasting impact on DVA performance.
Stress-test the DVA before rolling out
A lot of people delegates the UAT (User Acceptance Test) tasks to junior resource or ignore all together. It’s the worst mistake to make especially in the debt collection space where one small mistake can be costly. It’s important to stress-test the DVA built by the vendor before deploying and rolling out for customers.
Pilot on as many consumers as you can
You can pilot on 100 calls per day for a week and decide to go for the full-scale implementation. However, for an AI solution, 100 calls are not a representative enough sample, especially for debt collection applications. In case of outbound, 80% of the call might go unanswered, so you will be left with 20% of the calls to test the bot. If you pilot on 20 calls per day for 5 days, you have piloted only on 100 calls, which might not be a bog enough datapoints to base your decisions on.
At Skit.ai we recommend at least 10,000 calls/day for about 4 weeks.
Calculate ROI for Go/No-Go decisions
You must run an ROI exercise, to understand what quantum of value the Voice AI solution will create for your company before moving any further.
This exercise must be done for one year period, ideally for 2-5 years. The variables involved are simple – call volume, cost of the human agent, cost of deploying voice agent, number of integrations, inbound/outbound, call complexity, and deployment type. Your vendor should be able to provide you with notional value creation/cost savings.
Value creation is not as simple:
Higher levels of voice automation will lead to higher augmentation of human agents – productivity, efficiency, and engagement
More top line as the same set of agents will now handle a larger number of accounts
Better recovery rates as the voice AI agent will be more persistent in collections
Better disposition capture for precise campaigns
Time-bound campaigns and 100% coverage on all accounts
You may choose to factor in direct and indirect benefits out of voice AI deployment.
A lot can go wrong here, so it’s better to be aware of the risks of lack of proper technology architecture planning.
Be clear about the call volumes you expect over the years because you need to assess the supporting tech infrastructure around it. Relevant integration, legacy telephony assessment, CRMs, gateways, and more must be assessed and optimized for minimum human interventions and sufficient to last the planned phase.
It must be duly noted that running a Voice AI solution is a process, a continuous journey filled with improvements and upgrades. In order to sustain and be further along the learning curve, training the Voice AI solution on new data is vital.
Upgrades and Training for Sustainable Competitive Advantage
New use cases, business verticals, customer regulations, and more — we live in a dynamic world, and constant effort to innovate the voice solution are essential for being at the top of the game and beating the competition.
Conclusion
It is essential to assess a voice solution in granular detail before moving forward with it. We hope this guide will help you in your buying journey.
For more information and a free demo, you can schedule a call with one of our collections experts. We’ll be happy to help!
It doesn’t matter what industry you’re in: whether you work in retail, hospitality, banking, or any other industry selling products or services, you know how important it is to gather your customers’ feedback.
Bad customer experience costs companies a lot of money—a study estimated that U.S. companies typically lose $75 billion a year due to poor customer experiences. Therefore, feedback plays an important role.
According to a Microsoft report, almost 4 in 5 consumers (77%) have a more favorable view of brands that ask for and accept customer feedback. In other words, not only customer feedback allows your company to be aware of the Voice of the Customer and make the necessary changes, but it also fosters a positive brand reputation among consumers.
In this article, we’ll go over the most common ways to collect customer feedback and we’ll explore Voice AI as the most innovative, efficient and cost-effective solution for feedback collection.
As you can see in the table above, we’re considering three key factors when analyzing feedback collection methods: response rate, qualitative results (through open-ended questions), scalable and cost-effective.
The Traditional Feedback Collection Methods and Their Drawbacks
Depending on the scope of the survey, there are many different types of feedback a company can seek out, from a very basic 0-10 customer satisfaction (CSAT) survey to a more in-depth, qualitative questionnaire.
Here are the most common feedback collection methods:
Digital Feedback: Email and Text (SMS)
It’s frequent for companies to collect feedback by sending an email or a text message, which usually includes a link to a customer satisfaction survey.
Depending on the type of purchase or service, the timing of these requests may vary. For smaller services, like a food delivery, the request should come right after the service takes place. Instead, for a larger purchase—like a piece of furniture or a kitchen appliance—the company may request the feedback a couple of months after the transaction.
This type of survey is not only quantitative, as it often allows customers to leave their comments and express their feedback in their own words.
The drawbacks: Email and text surveys are notably unpopular among consumers, with low click-through rates. If many customers don’t even open the email, fewer will click on the link, and even fewer will complete the survey. According to MailChimp, the average click-through rate in emails across most industries is only 2.62%.
The purpose of feedback collection is to listen to the voice of the customer and make the appropriate changes to the service and product when needed; but if the feedback you collect is so small in volume, you are likely to base your business decisions on an irrelevant data sample.
Mobile Applications
For mobility services like Uber and food delivery services, it’s easy to request feedback directly from the same mobile application through which users have requested the service itself. As soon as the service is complete, the app can notify the user asking to submit their feedback, which is usually as simple as a 1-5 star rating.
This method, when applicable, ensures a very high response rate. It’s easy and user-friendly, and most customers will be excited to provide their feedback through the app.
The drawbacks: In order to make the feedback collection user-friendly and avoid consuming the user’s time, the feedback collected through mobile apps is usually very simple. A star rating may be a good indicator of the overall customer satisfaction, but it won’t inform the company of the quality of the customer experience on a deeper level.
IVR Phone Survey (Interactive Voice Response)
Many companies use the traditional IVR system to request feedback from customers, either through an outbound call or at the end of an interaction with an agent. A common use of IVR is for a company to automate outbound calls after performing a service, like repairing your car.
IVR is not expensive and easily scalable, but due to its limited technological capabilities, the type of feedback it’s able to collect is very narrow.
For example, an IVR system might ask: “Was your issue or concern resolved? For Yes, press 1, for No, press 2.” Or: “How likely are you to recommend our service to others, from a scale to 1 to 5, 1 being not likely at all, and 5 being very likely?”
The drawbacks:IVR only registers responses as digits, giving you quantitative feedback and not enabling you to get a more complete and complex picture of the customer experience.
Let’s say 34% of your respondents are unhappy with your service and express it in their satisfaction survey; if you don’t know why they are unhappy, there is very little you can do to improve your score.
Sometimes, a company will employ a number of agents to reach out to customers on the phone and gather their feedback on products and services. This is one of the most in-depth methods for feedback collection, as customers are more likely to engage with the agent and provide detailed feedback about their customer experience.
This type of feedback is more qualitative than quantitative, allowing for more nuance and personalization. Whenever you let your consumers express themselves freely, you get richer insights. Open-ended questions are therefore very helpful.
The drawbacks: While phone interviews may lead to excellent results, they are expensive, time consuming, and not scalable. You can set up an IVR to call as many customers as you need, but you can’t employ an infinite number of agents to manually call every customer and request in-depth feedback. Therefore, this method is the least practical to implement.
Adopting a Voice AI Solution for Your Feedback Collection Needs
Even Stanford University says it: Speaking is three times faster than typing. Therefore, implementing an intelligent Digital Voice Agent powered by AI to connect with customers over the phone and gather their feedback in a short and friendly conversation is a winning strategy for product and service companies.
Let’s say you want to set up feedback collection outbound calls with Voice AI for a food delivery service. You can easily automate the Voice AI platform to initiate outbound calls to customers about one hour and half after the food has been delivered. The Digital Voice Agent will proceed to ask a couple of questions to the customer; for example, “How was your food?”, “Are you satisfied with our service?”
Another example would be feedback collection after a prospective customer does a test drive at a car dealership. After the customer leaves the dealership, the Digital Voice Agent will reach out and ask: “How was your test ride?”, “What was missing?”
One more example. Many companies selling consumer durables like kitchen appliances, whenever repairs are needed, send technicians from third-party companies. With Voice AI, they can collect feedback on the third-party company to ensure that it has met the needs of the customer.
Thanks to the qualitative and versatile nature of the questions Voice AI can ask, the feedback the company will get is likely to be different for each customer, focusing on different topics and issues. This will allow the company to get a much wider picture of the customer experience.
Voice AI Feedback Analytics
Let’s take it one step further. Not only Voice AI is an excellent solution to gather the feedback, but it’s also an ideal tool to analyze the feedback.
Voice AI solutions like Skit’s Augmented Voice Intelligence Platform can easily aggregate the data and help you identify insights and takeaways that will ultimately lead to data-driven decision-making.
This is the direction the customer experience industry at large is moving towards:
Attentively listening to the Voice of the Customer
If you want to learn more about Skit.ai’s Augmented Voice Intelligence platform and speak to one of our experts, you can book a demo using the chat tool below.
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.
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.
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, 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.
