contact center AI

Use Contact Center AI To Close Deals Better and Faster

Next gen Contact Center AI (CC AI) offers an omnichannel solution that helps to significantly reduce friction for customers and agents. By reducing friction, you reduce churn, protect your brand and grow sales.  In order to see how this works, you will have to change the way you think about CC AI.  Contact Center AI was historically viewed as a way to use automation to contain and deflect the customer to avoid delivering calls to a live agent. This is an old paradigm that’s highly limited.  In order to broaden it, we will need to look closely into how CC tools, such as the one offered by Speakeasy AI, really work. Customers in control When a customer has an issue, whether it’s billing or technical, they expect an instant resolution. It’s when they are ready to sign up, engage, pay or even cancel a service that you have an opportunity to show them that you care and are there for them at all times. In the process of engaging a customer with your brand, there are many touch points where you have the power to automatically make their journey smoother, easier and frictionless.  Once they see this, you can guide them to the right solution and person at the right time. This is how you reduce friction and make your brand truly stand out.  It’s through intelligent customer care solutions such as CC AI that you reduce churn, increase conversions, and generate new revenue.  Minimal friction  Businesses today need to be agile and pivot with the customer and their needs. CC AI keeps your customers engaged throughout their journey, guiding them smoothly across key touchpoints, while keeping them hooked and wanting more. CC AI automatically identifies customers that are serious and ready to buy, reaching out at a moment of maximum impact. It collects and analyzes customer data to decide on a product price, when to offer a promotion, coupon or reward to a customer based on prior behaviors, existing marketing initiatives and client profile. New revenue Conversational AI chat bots engage with customers throughout their lifecycle to check on satisfaction and ask for feedback. By engaging in an ongoing conversation with the customer, companies can automate intimate one-on-one conversations. With ongoing engagement, you get to know the customer better, which helps you create new sales opportunities. AI assists agents in suggesting  actions and scripts that warm up leads and support the customer. This provides ample opportunities to cross sell and upsell the during engagements Don’t delay! Conversational CC AI bridges the gap in customer care with its round-the-clock availability and intelligent engagement. It makes the customer feel in control while empowering agents to handle difficult issues at scale in an on-demand environment. This is what generates trust. In today’s demanding environment where everything moves at light speed, you can’t afford to stay behind. Speakeasy AI will instantly make you stand out from the crowd and delight your customers by making them feel heard, understood and satisfied. MarketsandMarkets forecasts the call center AI market size to grow from USD 800 million in 2019 to USD 2,800 million by 2024.   To learn more about how Speakeasy AI can elevate your customer experience, contact us today. 

An AI-powered contact center knows your customer’s intent

The Need for Smart Speed  There is little doubt that the pandemic has slowed things down. But what it also did is accelerate the need for contact center support. Industries such as telco, cable and wireless are experiencing increasing pressure due to steep rise in contact demand. Retail, travel and banking have moved online.  Everyone is overwhelmed. Especially the call center agents.   As more call center workers and customer service agents work from home, leave their high-pressure jobs or move locations due to lockdowns and natural calamities, there’s a need to get new people up to speed quickly and efficiently. Unfortunately, this often results in low satisfaction for the customer, higher operating costs for the company and more stress for the agents.  A Co-Pilot on Your Side  AI-powered contact center messaging, chat and voice, is on the rise. And for the right reason. The solution helps to bridge the service and culture gaps, ensure brand consistency and give your team access to smart tools that boost your agents’ performance by assisting them during live customer interactions. Think of Agent Assist as a smart co-pilot. It helps your agents, especially those freshly onboard, to hit their stride faster and with less friction.  Knowing Your Customer’s Intent Is a Superpower Your agents should not spend their precious time trying to figure out what to do or needing to repeat themselves. Instead, they should have access to intuitive tools that help them do this automatically.  The flows in Agent Assist are programmed based on real conversations with real customers. What’s more, they are updated in real-time depending on the context of the conversation. This way your agents do not need to second guess what the customer wants. Agent Assist will do that for them, impressing your customers and leading to higher sales.  Build Empathy & Rapport Agent Assist uses Speech-to-Intent to instantly prompt agents with answers based on customer questions, reducing average handle time (AHT) and improving quality and consistency of customer communication. Our machine learning models are based on real-life agent interactions and real-time feedback. The system gets the customer going and supports the agent to take the next best action, giving them the ability to focus their soft skills on building deeper rapport and empathy, even while juggling multiple conversations. And it gets better with time.  So Much More with Less Access to smarter replies, up-sell and cross-sell opportunities and flexible adjustment depending on agent’s capacity and true customer intent is what Agent Assist is all about.  In a nutshell, the system helps to optimize your agents’ performance, allowing for a faster resolution of customer requests. It significantly reduces agent training so that much less time can be spent on onboarding. Furthermore, the system builds compliance and adherence to policy with your workforce. And it can work within any desktop and integrate with any existing CRM or knowledge management system.  To improve solution rates and while also optimizing cross-sell opportunities and grow customer satisfaction, book a free demo today!

Omnichannel CX: An Evolution You Can’t Afford to Miss

For many businesses the pandemic changed the game. One of the major shifts that took place relates to digitization, which has become a prerequisite to surviving and thriving in the rapidly shifting economy.  Digitization and/or digital transformation means implementing an omnichannel CX experience. If your business is not yet doesn’t offer authentic omnichannel CX, you may be falling behind.  Don’t delay! Prevent pain and suffering for your brand  by avoiding a lack of continuity. What does omnichannel mean? Omnichannel is a customer contact platform that virtually connects with a brand across multiple and cross-communication channels via flexible and easy-to-use integration points. Within this environment, customers journey along, effortlessly shifting from chat to call without the need to repeat what they’re looking for. As this happens, the system collects data and learns from each customer and agent interaction, which leads to improvements.  The benefits of integrating omnichannel At its core, omnichannel CX means seamless and easy interaction with your brand across multiple channels, such as text messaging and voice. It improves speed since a customer no longer needs to wait for an agent. Many of their queries can be answered effortlessly by interacting with an AI bot, reducing waiting time and contributing to customer’s delight. By spreading your message across multiple channels, an omnichannel solution makes your brand more relevant and visible.  What omnichannel CX looks like in action When your customers want to join a program, quickly find an answer or cancel their order, their experience within omnichannel CX is intuitive, effortless and continuous. For example, when booking a room at a hotel, a lot can be done via text on the hotel’s website. Once the dates are confirmed and preferences set, the system switches to a phone call and the booking is fine-tuned and finalized.  A digital concierge Omnichannel CX offers a white glove approach for customized user journeys to enroll, enjoy, pay, modify, report or fix problems on the go. The customer doesn’t think about channels, so a concierge approach allows one conversational AI engine to orchestrate personalized experiences across all the customer touchpoints. The evolution of CX Many companies face the challenge that their platforms were built on text which can make understanding voice difficult. In other words, we talk differently than we type. A voice first approach means that your CX solution can understand both: a customer speaking on a speakerphone as well as decipher shorthand messaging acronyms. Integrating omnichannel within your brand For a successful integration, you will first need to identify friction points and then find and integrate the tools to fix them. SpeakEasy AI offers an authentic omnichannel conversational AI solution, which is fast, intuitive and easily integrated with the full contact center AI stack to understand where the customer has been, what they’ve done, identify them, and tailor the approach depending on where they are in their journey. Omnichannel CX makes your brand come across as fresh and smart Don’t fall behind! Create an omnichannel, frictionless customer experience today.  SpeakEasy AI – helping you get the right answer at the right place and the right time, everytime.   Go omnichannel today with Speakeasy AI. Contact us today.  Read more: Download this ebook from Frost and Sullivan titled Omnichannel Customer Engagement Has Changed: Has Your Contact Center Platform Evolved With It?  
AI Powered Call Center

4 Ways AI-Powered Contact Centers Create Super Agents

There is no doubt that the pandemic has slowed things down. It also did is accelerate the need for contact center support. Demand for contact across digital, voice and messaging  is surging across the board for industries such as cable, wireless, retail, travel and banking. As customer expectations are rising, maintaining consistency in execution becomes harder.  Everyone is overwhelmed. Especially call center agents.  That’s unless you bring AI-powered Agent Assist on board. Below are 4 ways in which Agent Assist can turn your agents into Super Agents. 

1. AI Accelerates Agent Speed and Reduces Friction

AI-powered contact center messaging, chat and voice, is on the rise. And for the right reason. It helps your agents, especially those freshly onboard, to hit their stride faster and with less friction. This is crucial especially as more call center workers and customer service agents work from home or move locations due to lockdowns and natural calamities. 

2. AI Helps Maintain Brand Consistency

Contact Center AI delivered via Agent Assist helps to bridge the service gaps, ensure brand consistency and give your team access to smart tools that boost your agents’ performance by assisting them during live customer interactions. Think of Agent Assist as a smart co-pilot. It significantly reduces agent training so that much less time can be spent on onboarding. Furthermore, the system builds compliance and adherence to policy with your workforce.  

3. AI Helps Agents Understand Customer Intent Faster

Your agents should not spend their precious time trying to figure out what to do or asking customers to repeat themselves. Instead, they should have access to intuitive tools that help them do this automatically.  The flows in Agent Assist are programmed based on real conversations with real customers. What’s more, they are updated in real-time depending on the context of the conversation. This way your agents do not need to second guess what the customer wants. Interactions by your live agents with Agent Assist also serve to improve answers and performance for future interactions. Agent Assist will make your agents’ jobs easier,, impressing your customers and leading to higher sales and reduced churn. 

4. AI Frees Agents to Focus on Building Rapport and Empathy

Agent Assist uses Speech-to-Intent to instantly prompt agents with answers based on customer questions, reducing average handle time (AHT) and improving quality and consistency of customer communication. Our machine learning models are based on real-life agent interactions and real-time feedback. The system gets the customer going and supports the agent to take the next best action, giving them the ability to focus their soft skills on building deeper rapport and empathy, even while juggling multiple conversations. And it gets better with time.  Access to smarter replies, up-sell and cross-sell opportunities and flexible adjustment depending on agent’s capacity and true customer intent is what Agent Assist is all about.  In a nutshell, the system helps to optimize your agents’ performance, allowing for a faster resolution of customer requests, reducing costs and leading to higher sales. And it can work within any desktop and integrate with any existing CRM or knowledge management system. To improve solution rates and while also optimizing cross-sell opportunities and grow customer satisfaction, contact us today!  Related Article: Read Forrester Analyst Kate Leggett’s recent article: The Future Of Contact Center Work: How To Source New Agent Talent
Conversational AI for Travel

Conversational AI Has a Big Role in Travel This Year

The travel industry has been through the proverbial wringer during the past two years. With the pandemic restrictions easing, travel is undergoing an explosion in demand. Tired of living in isolation, people are eager to reunite with their families and experience life beyond lockdown. Smart call center leaders are turning to conversational AI to elevate their customer and agent experience.

Bracing for Post-Pandemic Travel Surge

Welcoming back travelers won’t occur without hitting a few bumps on the road to freedom. After having laid off a chunk of the workforce, agencies and companies are understaffed.  What this likely means for call centers and travel agents is having to spend hours on live calls processing a surge of inquiries from finding the perfect destination to digging up unused vouchers. Patience will be in short supply while human error and frustration will rise. Hiring more humans to handle thousands of calls from eager travelers around the world and round the clock is an expensive option. The added customer service costs will be steep for an industry already struggling because of the pandemic. But there’s another way. A smarter way. 

Faster, Smoother, and Easier

Just as interest in travel is about to skyrocket, Speakeasy AI can help to conserve your business resources while keeping sanity levels at bay. When processing a surge of customer inquiries, the system minimizes human error while offering white glove customer service. In other words, it makes everything work faster, smoother, and easier than ever.  The world opening is a massive opportunity for travel companies and booking agencies. Millions of people have been waiting for this moment. By implementing Speakeasy AI, you take the pressure off your agents and make your customer’s convenience and delight a priority. 

The Human Side of AI 

Contrary to what people may think, conversational AI makes the interactive experience more, not less human. By making people feel more connected to your brand through personalizing their interactions with recommendations, the system brings empathy into the experience. What’s more, customers can have their questions answered without the need to wait in queues. The system can contact the hotels, airlines, etc., and make inquiries on behalf of the customer, saving everyone a lot of time.  For those not tech-savvy, AI can help with quick decision-making. It’s an added bonus in the post-pandemic era when people may feel a bit more perplexed and indecisive than normal. 

Smarter Travel Planning 

Speakeasy AI taps into data pools created by customers allowing it to customize their responses to better fit their needs and preferences. It’s a technology with the potential to know the customer better than ever. The technology empowers people to direct the messaging process. This allows travelers to communicate on their own terms so that live agents can focus on the soft-touch, high-level customization. In the end, Speakeasy AI helps to move call and messaging traffic more efficiently. Rather than waiting to chat with a live person, customers can receive their answers instantly, getting another step closer to turning their holiday reveries into reality. 

Bracing For Growth

Our friends at Quiq would agree that scaling your customer support strategy takes time and careful planning. We like the tips they provide on their blog that include ways Conversational AI can help you scale your customer support.

We Are Here for You!

Greet, inform, book, delight. That’s our motto at Speakeasy AI! Visit our website today to schedule a demo and join in our mission to make 2021 a year to remember in travel.

A Brief History of ASR: Automatic Speech Recognition

Our friends at Descript begin a series on the evolution of ASR with the piece below. We at Speakeasy AI are excited about how our revolutionary approach to conversational AI via speech-to-intent ™ will mold the ASR landscape and help enable the future of what can be done with voice. – Frank Schneider, CEO, Speakeasy AI   by Jason Kincaid, @ Descript. This moment has been a long time coming. The technology behind speech recognition has been in development for over half a century, going through several periods of intense promise — and disappointment. So what changed to make ASR viable in commercial applications? And what exactly could these systems accomplish, long before any of us had heard of Siri? The story of speech recognition is as much about the application of different approaches as the development of raw technology, though the two are inextricably linked. Over a period of decades, researchers would conceive of myriad ways to dissect language: by sounds, by structure — and with statistics.

Early Days

Human interest in recognizing and synthesizing speech dates back hundreds of years (at least!) — but it wasn’t until the mid-20th century that our forebears built something recognizable as ASR. 1961 — IBM Shoebox Among the earliest projects was a “digit recognizer” called Audrey, created by researchers at Bell Laboratories in 1952. Audrey could recognize spoken numerical digits by looking for audio fingerprints called formants — the distilled essences of sounds. In the 1960s, IBM developed Shoebox — a system that could recognize digits and arithmetic commands like “plus” and “total”. Better yet, Shoebox could pass the math problem to an adding machine, which would calculate and print the answer. Meanwhile researchers in Japan built hardware that could recognize the constituent parts of speech like vowels; other systems could evaluate the structure of speech to figure out where a word might end. And a team at University College in England could recognize 4 vowels and 9 consonants by analyzing phonemes, the discrete sounds of a language. But while the field was taking incremental steps forward, it wasn’t necessarily clear where the path was heading. And then: disaster. October 1969 The Journal of the Acoustical Society of America

A Piercing Freeze

The turning point came in the form of a letter written by John R. Pierce in 1969. Pierce had long since established himself as an engineer of international renown; among other achievements he coined the word transistor (now ubiquitous in engineering) and helped launch Echo I, the first-ever communications satellite. By 1969 he was an executive at Bell Labs, which had invested extensively in the development of speech recognition. In an open letter³ published in The Journal of the Acoustical Societyof America, Pierce laid out his concerns. Citing a “lush” funding environment in the aftermath of World War II and Sputnik, and the lack of accountability thereof, Pierce admonished the field for its lack of scientific rigor, asserting that there was too much wild experimentation going on: “We all believe that a science of speech is possible, despite the scarcity in the field of people who behave like scientists and of results that look like science.” — J.R. Pierce, 1969 Pierce put his employer’s money where his mouth was: he defunded Bell’s ASR programs, which wouldn’t be reinstated until after he resigned in 1971.

Progress Continues

Thankfully there was more optimism elsewhere. In the early 1970s, the U.S. Department of Defense’s ARPA (the agency now known as DARPA) funded a five-year program called Speech Understanding Research. This led to the creation of several new ASR systems, the most successful of which was Carnegie Mellon University’s Harpy, which could recognize just over 1000 words by 1976. Meanwhile efforts from IBM and AT&T’s Bell Laboratories pushed the technology toward possible commercial applications. IBM prioritized speech transcription in the context of office correspondence, and Bell was concerned with ‘command and control’ scenarios: the precursors to the voice dialing and automated phone trees we know today. Despite this progress, by the end of the 1970s ASR was still a long ways from being viable for anything but highly-specific use-cases. This hurts my head, too.

The ‘80s: Markovs and More

A key turning point came with the popularization of Hidden Markov Models(HMMs) in the mid-1980s. This approach represented a significant shift “from simple pattern recognition methods, based on templates and a spectral distance measure, to a statistical method for speech processing”—which translated to a leap forward in accuracy. A large part of the improvement in speech recognition systems since the late 1960s is due to the power of this statistical approach, coupled with the advances in computer technology necessary to implement HMMs. HMMs took the industry by storm — but they were no overnight success. Jim Baker first applied them to speech recognition in the early 1970s at CMU, and the models themselves had been described by Leonard E. Baum in the ‘60s. It wasn’t until 1980, when Jack Ferguson gave a set of illuminating lectures at the Institute for Defense Analyses, that the technique began to disseminate more widely. The success of HMMs validated the work of Frederick Jelinek at IBM’s Watson Research Center, who since the early 1970s had advocated for the use of statistical models to interpret speech, rather than trying to get computers to mimic the way humans digest language: through meaning, syntax, and grammar (a common approach at the time). As Jelinek later put it: “Airplanes don’t flap their wings.” These data-driven approaches also facilitated progress that had as much to do with industry collaboration and accountability as individual eureka moments. With the increasing popularity of statistical models, the ASR field began coalescing around a suite of tests that would provide a standardized benchmark to compare to. This was further encouraged by the release of shared data sets: large corpuses of data that researchers could use to train and test their models on. In other words: finally, there was an (imperfect) way to measure and compare success. November 1990, Infoworld

Consumer Availability — The ‘90s

For better and worse, the 90s introduced consumers to automatic speech recognition in a form we’d recognize today. Dragon Dictate launched in 1990 for a staggering $9,000, touting a dictionary of 80,000 words and features like natural language processing (see the Infoworld article above). These tools were time-consuming (the article claims otherwise, but Dragon became known for prompting users to ‘train’ the dictation software to their own voice). And it required that users speak in a stilted manner: Dragon could initially recognize only 30–40 words a minute; people typically talk around four times faster than that. But it worked well enough for Dragon to grow into a business with hundreds of employees, and customers spanning healthcare, law, and more. By 1997 the company introduced Dragon NaturallySpeaking, which could capture words at a more fluid pace — and, at $150, a much lower price-tag. Even so, there may have been as many grumbles as squeals of delight: to the degree that there is consumer skepticism around ASR today, some of the credit should go to the over-enthusiastic marketing of these early products. But without the efforts of industry pioneers James and Janet Baker (who founded Dragon Systems in 1982), the productization of ASR may have taken much longer. November 1993, IEEE Communications Magazine

Whither Speech Recognition— The Sequel

25 years after J.R. Pierce’s paper was published, the IEEE published a follow-up titled Whither Speech Recognition: the Next 25 Years⁵, authored by two senior employees of Bell Laboratories (the same institution where Pierce worked). The latter article surveys the state of the industry circa 1993, when the paper was published — and serves as a sort of rebuttal to the pessimism of the original. Among its takeaways:
  • The key issue with Pierce’s letter was his assumption that in order for speech recognition to become useful, computers would need to comprehend what words mean. Given the technology of the time, this was completely infeasible.
  • In a sense, Pierce was right: by 1993 computers had meager understanding of language—and in 2018, they’re still notoriously bad at discerning meaning.
  • Pierce’s mistake lay in his failure to anticipate the myriad ways speech recognition can be useful, even when the computer doesn’t know what the words actually mean.
The Whither sequel ends with a prognosis, forecasting where ASR would head in the years after 1993. The section is couched in cheeky hedges (“We confidently predict that at least one of these eight predictions will turn out to have been incorrect”) — but it’s intriguing all the same. Among their eight predictions:
  • “By the year 2000, more people will get remote information via voice dialogues than by typing commands on computer keyboards to access remote databases.”
  • “People will learn to modify their speech habits to use speech recognition devices, just as they have changed their speaking behavior to leave messages on answering machines. Even though they will learn how to use this technology, people will always complain about speech recognizers.”

The Dark Horse

In a forthcoming installment in this series, we’ll be exploring more recent developments and the current state of automatic speech recognition. Spoiler alert: neural networks have played a starring role. But neural networks are actually as old as most of the approaches described here — they were introduced in the 1950s! It wasn’t until the computational power of the modern era (along with much larger data sets) that they changed the landscape. But we’re getting ahead of ourselves. Stay tuned for our next post on Automatic Speech Recognition by following Descript on Medium, Twitter, or Facebook.     This article is originally published at Descript.

Why Speech-to-Intent?

Speech to Intent vs Speech to Text We are often asked “what is the difference between speech to text vs speech to intent?” Our patented speech to intent system utilizes AI to analyze the entire audio file – a complete voice utterance – to get to the right intent. This methodology brings intelligence to the way we speak, which is far different than the way we type. We created our speech to intent solution due to the experience we had in the AI/chatbot space and the exciting opportunity we uncovered. What’s the Opportunity? Over the last five years chat bots have gone from a let’s try tool to a must-have part of a business CX roadmap. It has been proven that digital AI solutions can answer over 30% of the questions that would have ended up at the call center, but many customers never try the chat bot, they just call. In fact, over 70% of customer conversations are still using voice. There is a vast opportunity to use AI to answer these questions within voice channels. What’s the problem? We have all seen speech to text systems vastly improve over the last three years. Microsoft’s research has seen human parity results in transcription. It would seem simple to just use speech to text to connect IVR to AI solutions like chat bots and get all the benefits realized in the digital world. In reality, this approach does not deliver as expected. Transcription can create a type of interaction, but transcription is not actually intelligent. Speakeasy AI’s solution is Speech to Intent™.  
    Speakeasy AI Speech to Intent Benefits
Method of operation The recognition process is divided into a pipeline of different micro-services that is mapped directly onto the corpus used to train the system. Our speech to intent system bypasses the issues of traditional speech to text where accents or poor audio signal affects the outcome greatly. The Speech to Intent system only matches against known content in the AI system, giving a much better match percentage.
Implementation The existing corpus of alternates   in the AI system is used to set up the Speech to Intent system. The system can be setup quickly because the content already exists. All of the special words and products are immediately recognized by the system.
Accuracy   We have seen over 80% accuracy in testing with a well-developed corpus.
Maintenance Within the Speakeasy AI admin console it is easy to see questions that have been misunderstood and instantly assign alternates to improve the accuracy. Additional content can be added and in production within minutes. The matching can be set up specifically to understand what is being said in the context of the business deployment. Maintenance is instant and completely controlled by the company.
Resources The SpeakeasyAI Speech to Intent system requires fewer resources than speech to text systems.
  Our mission is to make it easier for businesses to understand and respond to their customers’ needs in voice with AI. We accomplish this mission by using the world’s first and only Speech-to-Intent™ solution. Combined with our end-to-end reporting, our solution provides real-time insights into understanding customers’ intents, needs and outcomes. And since an AI platform is only as good as it’s improvement cycle, we enable rapid updates to ensure wins are delivered on the day you launch. With our voice AI solutions and our team’s proven expertise, we work tirelessly to provide better voice experiences and deliver understanding as a service.

Introducing Speakeasy AI – Voice Recognition That Actually Understands Customers

From cable companies, to hotel chains to banks – the days of customer calling say and pray are over. The best customer experiences begin with understanding customer needs and tailoring an experience around those needs. For people on the business side of a customer relationship, the recipe for understanding a customer starts with authentic listening. Listening done right doesn’t create a word list, it creates a picture in your head of what the speaker is saying, describing, and what she/he needs. It should be no different for automated voice systems.

Continue reading

© SpeakEasyAI. All rights reserved.

What do you think of my pop up?