By Dan Miller | Lead Analyst and Founder, Opus Research
Just one year ago in response to an executive survey, Opus Research found businesses were significantly underutilizing (80%) “voice data” that was captured and analyzed. Well, that didn’t take long to remedy. When asked a similar question in a survey this year, we witnessed a reversal in the fortune of speech technologies. Roughly two-thirds of respondents told us that they were making good use of more than half their conversational intelligence.
Based on their responses, the data definitively show that the proverbial lightbulb has come on to illuminate a direct connection between highly accurate automated speech processing and a trifecta of measurable business outcomes surrounding customer experience, employee productivity and competitive advantage. Close to two-thirds (64%) see Voice Services as “important” or “very important” to the future of their company’s strategy. And 92% said that they expect such solutions to be “widespread” within the next five years.
Those “widespread” implementations are driven by investment in capabilities regarded to be highly impactful. When asked what areas are “most impactful” three-quarters of respondents mentioned “customer experience analytics” and 54% mentioned “Conversational AI and Voicebots.” This is vivid evidence of a move from (or augmentation) of old-guard implementations surrounding employee coaching and development, as well as pragmatic applications like “call summarization.”
High Expectations for a New Voice Platform
Survey responses reflect high expectations for platforms that combine Automated Speech Processing with Analytics, Natural Language Processing and Business Intelligence to improve customer experience and stay ahead of their competition. Across the board, decision makers showed that they now perceive a direct connection between highly accurate speech recognition to support transcription of utterances and a direct connection to their bottom line.
These findings confirm, and even reinforce a number of observations that Joe Hagan, Chief Product Officer at Lumenvox and I discussed in a recent Webcast entitled “The Age of Voice Innovation, Part II: What Keeps You Up at Night.” We noted that a “Voice Renaissance” of sorts is transpiring as companies simultaneously make a “move to the cloud” in support of an overall “Digital Transformation.”
As IT departments opt to move a number of “internal processes” into the server farms that comprise the public clouds operated by Amazon, Google, Microsoft or Salesforce, it has become easier and more affordable to implement speech-enabled applications.
Having posed the rhetorical question, “what keeps you up at night?” the conclusion was, “precious little.” The move to the cloud is accompanied by usage-based pricing and consumption models for the whole alphabet of resources like ASR, TTS, NLU/NLP and ML, as well as vital capabilities like analytics and business intelligence. We posited that the new architectures and pricing models were successfully “democratizing speech” and these survey findings validate those observations.
Voice has found new life as we enter the new year. Enterprises and their customers have high expectations for solutions that closely link accurate speech recognition, human-like text-to-speech renderings, natural language understanding (NLU), and voice biometrics to serve specific, or even multiple, use cases. ASR has grown up to become the conversational intelligence opportunity that’s bigger than the sum of its parts.
Once upon a time, automatic speech recognition (ASR) resided most comfortably in the enterprise contact center. It didn’t get out much. Once Alexa arrived in 2013, ASR found a massive new audience in the home but living in the cloud. A rush of new use cases quickly gathered around ASR. The big three smart home assistants, voice authentication, and contact centers are largely where ASR remained and thrived. But as we enter 2022, we’re in the early days of the great ASR acceleration, or as Opus Research’s own Dan Miller calls it, the ASR Renaissance — born of strategic acquisitions, product innovation, tech stack modularization, and partially in response to a worldwide pandemic.
As part of a recent two-part webinar series, Opus Research joined LumenVox Chief Product Officer, Joe Hagan, to discuss how ASR is being redefined, and the opportunities it presents for self-service and enterprise communications.
“Traditional call center IVR type applications continue to be the bellwether for growth and utility for speech applications,” Hagan explained, “but we’re seeing an awful lot of new applications that need to be served, things in consumer and retail, and things like audio mining and virtual assistants. These are important applications that are a course of growth for ourselves and our customers.”
Consider the rich information available for a retail call center wherein they’re fielding and recording calls about garment fit, price, inventory, shipping, and returns or refunds. ASR tech is capable of differentiating between caller and agent voice, tracking interest in certain brands and sizes, evaluating and quantifying reported issues of fit or quality inconsistencies, voice biometrics can determine caller demographics enquiring about certain brands and product types, etc. This is conversational intelligence in action, informing business decisions with a direct impact on inventory and sales.
Yet, despite the easy sell for that integrated vision, a recent Opus Research survey found that of 80% of companies leveraging ASR for transcription, less than one-third of those same companies are applying their findings to improve outcomes and drive new business decisions.
There’s also the matter of how entrenched a brand is in its current technology stack. Many of them might feel locked into a legacy ecosystem of custom integrations and services. When considering their next move, cost and time are huge considerations. Historically, the road to leveling up an organization’s conversational intelligence has been a precarious and potentially costly one. That’s not necessarily the case anymore as LumenVox and others begin to modularize their services in a way to become more accessible.
As the technology improves and becomes more modularized and open (i.e. through APIs, containers, etc), the playing field levels for companies providing solutions in the ASR space. This will no doubt drive new competition and more attractive price points for enterprise customers looking to realize this holistic model for conversational intelligence.
The current pace of innovation means that companies no longer need to take a giant and expensive leap of faith with a vendor who promises to deliver it all. Speed and accuracy no longer come at a steep premium, and the modularization of services also means that each component can be judged on its own merits and weaved together for a solution that specifically addresses a company’s most critical needs.
There’s no disputing that insurance companies manage a mountain of recorded audio files. From adjusters recording claimants and interviews with eyewitnesses, to conversations with medical, legal and insurance professionals, the sheer number of these captured verbal records can be overwhelming, especially when your claims team processes hundreds of claims a month.
Imagine how much time adjusters and back-office personnel expend in manually transcribing interviewees’ responses and statements into words. For complicated incidents, such as three or more cars in an accident, an arson case, or personal injury claims, these transcriptions can grow insurmountable for mere mortals quickly. Alternatively, the majority of insurance companies don’t even attempt to transcribe recorded audio files due to the high cost, time investment and privacy concerns.
There’s an innovative and readily available solution – the Automated Speech Recognition (ASR) transcription engine. It quickly and accurately converts verbal audio files into secure digital documents at significantly faster rates and lower costs than manual transcription. Some insurance companies have begun to see the light, and here’s why.
How Can a Transcription Engine Help Insurance Companies?
An ASR-based transcription engine can take audio and video files and turn them into written documents. They produce a clear written record of verbal interactions that adjustors, underwriters, legal teams and operations teams can later reference and analyze, removing a major burden from the shoulders of your claims team.
Additionally, ASR transcription can reduce the risk of fraud and afford a greater level of security for firms employing transcription with their own in-house, dedicated transcription tool. For those enterprises that choose not to use it – due to cost, time or for perceived privacy issues – those stagnant, unutilized audio files aren’t able to focus and deliver the full picture of the info and data within.
What Can be Transcribed With an ASR Engine?
In-person or phone interviews with claimants, eyewitnesses, suspects and other victims involved in an insurance claim
Interviews and conversations with medical or legal professionals who are assisting a claim
Field notes, recorded statements, and summary reports, especially those from an adjuster who is observing first-hand damage to property or possible fraudulent activities
Meetings and conversations with insurance professionals who are helping with a claim.
The Value of Your Own ASR Transcription Engine
It doesn’t matter if you handle property and casualty, life, health or even business insurance, an ASR transcription engine can make your specialty teams and claims process run more efficiently, with fast turnarounds and improved accuracy rates, and economically, with an ROI achieved usually within months.
Let’s dive a little deeper into scenarios that can benefit from an ASR transcription engine:
For arbitration files: Keeping transcripts of recorded statements for arbitration files is beneficial, as transcripts of recorded statements can come in handy and can even help you win a case.
To recognize fraud: Statement transcriptions can easily aid in the detection of any indications of fraud. It can assist adjusters in proving whether or not any wrongdoing occurred. The claim handler can compare each statement and discover any unknown differences using the transcribed papers. If a case goes to court, the transcripts can be used to prove evidence, reduce losses, and prevent fraud from occurring again.
Useful for adjusters: Adjusters find that having transcribed statements saves them significant time. The adjusters can use the transcripts to refer back to the case and understand even the most minute details.
Enables easy file transfer between departments: Insurance claims must be distributed to multiple departments and adjusters. The files can be simply moved between departments by transcribing the recorded statements into accurate digital transcripts.
Having a complete file: In the insurance industry, keeping a detailed file is crucial. All recorded statements can be transcribed verbatim, which aids adjusters in compiling a complete claim file. Maintaining a full file allows adjusters to access insurance documentation and records faster in the future.
Innovative improvements in automatic speech recognition (ASR) and voice technologies at LumenVox have transformed the role of transcription. Our award-winning ASR engine with transcription offers an extremely high degree of accuracy over other automated transcription tools and manual (human) transcription. LumenVox’s ASR operates on a foundation of artificial intelligence (AI) and deep machine learning (ML) to deliver the highest performing, future-proof voice and speech technology. Our rich speech and voice technology history enables customers to build voice-enabled solutions that not only understand what is being said, but also identify who is saying it.
Deploying a LumenVox customized in-house ASR transcription solution will deliver significantly more privacy, more security, more control and deeper analytics than contracting with third-party transcription service. Best of all, keeping it in-house is easy, your data and information is protected and safe from fraud, and it is supremely affordable. It’s an innovative solution that has the potential to become a vital component in the insurance field.
Enterprises across every industry vertical are investing in conversational AI. Contact centers have the unique distinction of being at the intersection of people, processes, and technologies in every enterprise. As such, contact centers need conversational AI now, more than ever. These conversational AI applications and solutions can be catered to various needs depending on what purpose your contact center serves.
For example, it could be used to help with lead generation, and overall marketing efforts or it could be to cater to your customer service needs. You can also deploy these AI-based technology tools to help your sales teams upsell and cross-sell. Here are some of the top five reasons why call centers around the world have to invest in conversational AI.
Contact centers need conversational AI to deliver powerful customer experiences
1. Deliver personalized conversations at scale
In today’s world, customers expect more personalized customer conversations, and conversational AI can help deliver that at scale. Regardless of a customer moving from a messenger app to a live chat to social channels, the conversational AI-based tool can help personalize the experience, follow the customer across channels and even understand context and history to offer a truly seamless customer service experience.
2. Support call volume spikes
Customer service departments are under tremendous pressure to deliver results with a finite number of resources. This is where conversational AI plays a pivotal role. It is possible to deploy AI chatbots and voice assistants to help take care of call spikes and resolve customer queries.
With conversational AI, you can categorize calls based on a customer’s voice, past interactions, and context. Thus, a good volume of calls can be routed to an intelligent virtual assistant and help reduce contact center agents’ workload. This way, the contact center agents can concentrate on what they do best – provide great customer experiences.
3. Provide 24/7 customer support
Your customers want service when and how it is convenient for them, therefore no customer support is complete without the inclusion of conversational AI and tools. Self-service options are no longer nice-to-have but critical to your customer service success. Chatbots and virtual assistants are an integral part of a brand’s customer engagement strategy to deliver on the self-service customer promise. Thanks to conversational AI understanding the intricacies of human speech, natural language, and emotions, it is possible to deploy these AI-based systems to improve customer service and support experiences across the board. The best part is it can scale seamlessly without any issue, pick up where the human left, bring in context and maturity with more social interaction.
4. The power of a persistent customer conversations
With too much competition all around and customers getting picky, it is tough to attract your target audience’s attention. But with conversational AI you can train your chatbots and virtual assistants to help not only in customer service, but also in your marketing and sales efforts. Conversational AI can connect with the audience at a deeper level thanks to advanced technologies like understanding customers’ real intent, gauging their emotions, and hidden expectations. Models trained to handle various nuances of human nature can be a better way of persuading customers to sign-up for your webinar or download a white paper or even buy a product or service from you. All these efforts help drive your marketing and sales teams forward.
5. Deliver on the customer promise
Brands that fail to deliver on the requisite customer experience and customer engagement are bound to fail regardless of having a world-class product and approachable pricing. Therefore, conversational AI platforms can help to deliver on this promise of an excellent customer experience. This means your customer engagement and support channels can remain open 24/7 and your customers don’t have to wait in a queue for a live agent. They can connect with the brand through multiple channels and still expect a seamless customer experience. All this adds to delivering on the customer service promise that you have made to your customers.
If you want to maintain a persistent conversation throughout the customer lifecycle, you need a new strategy for managing your communication interfaces and their supporting resources.
Voice has remained pervasive for business communications, and it is especially having an impact in this Age of Digital Transformation. However, voice poses major challenges for Contact Center and CX professionals to keep their voice-based resources up and running — not to mention managing to keep technology fresh and add new capabilities.
Too often they feel they are “damned if they do” when buying into the view that customer care is moving to chat and text. Or they feel “damned if they don’t” to keep the voice channel up to date with the latest and greatest AI-infused technologies.
With companies rapidly evolving and seeking more voice-enabled applications to deliver powerful experiences, LumenVox was pleased to recently discuss the benefits organizations can see when utilizing an Automatic Speech Recognition (ASR) engine with extremely accurate transcription, flexibility, and high availability.
The Power of Speech
ASR’s everyday applications are vast, and it’s transforming how multiple industries do business. For example, media and entertainment companies can produce content faster when hours of audio or video files are converted into searchable transcripts.
Educational institutions can deliver accessible remote learning through real-time captioning in video conferencing software. In addition, researchers can begin analyzing qualitative data in a matter of minutes thanks to asynchronous, machine-generated transcription.
These are just a few examples of how speech-to-text technology is impacting society.
In addition to industry-leading accuracy and speed, LumenVox’s ASR engine utilizes an end-to-end Deep Neural Network (DNN) architecture to accelerate the ability to add new languages and to recognize non-native speaking accents. This enables LumenVox customers to serve a more diverse base of users.
The Value of Artificial Intelligence
With typical Machine Learning (ML) models, there are two fundamental elements: (1) the language model and (2) the creator of the language model.
The language model can ‘learn’ based upon the data it’s given. With a DNN, creators are not required to augment the code base when building or adding data, which is helpful in eliminating inherent biases.
Ultimately, the more robust data sets will provide a highly accurate, broadly applied language mode.
Delivering Enhanced Customer Experiences with Speech
ASR is a programmatic way to turn voice into text. Voices come in different dialects, languages, and with various levels of background noise.
A good ASR can turn the spoken word from a variety of languages and accents into readable, understandable text. Businesses can then use the text to strengthen decision-making and enhance customer experiences by serving a more diverse user base.
Ready to learn more about automatic speech recognition? Join Dan Miller, lead analyst at Opus Research, and Joe Hagan, chief product officer at LumenVox on September 14 at 11:00 a.m. PT / 2:00 p.m. ET as they discuss what is required to deliver meaningful employee and customer experiences through voice channels. Register now for the webinar.