Chatbots and voice assistants weren’t created with marketers first in mind. Both are task-oriented products at their cores, serving users when actual human beings can’t, which is quite often. So it should come as no surprise that both support an estimated 5 billion global users combined.

And now marketers are showing up in droves. 

Rise of Chatbot Marketing

Online stores turned to chatbots to fight cart abandonment. Their automated service around-the-clock provided a safety net late into user journeys. Unlike traditional channels broadcasting one-way messages, chatbots (like Drift and Qualified) fostered two-way interactions between websites and users. Granting consumers a voice boosted engagement. As it turned out, messaging a robot became much more popular than calling customer service.

Successful marketing chatbots rely on three things: 1) Scope, 2) Alignment, and 3) KPIs. 

Conversational A.I. needs to get specific. Defining a chatbot’s scope — or how well it solves a finite number of problems — makes or breaks its marketing potential. A chief marketing officer (CMO) at a B2C retail brand probably would not benefit from a B2B chatbot that displays SaaS terminology in its UX. Spotting mutual areas of expertise is quite simple. The hard part requires evaluating how well the chatbot aligns with the strategies you and your team deploy. If there is synergy between conversational A.I. and a marketer, then they must choose KPIs that best measure the successes and failures yet to come. Some of the most common include the amounts of active users, sessions per user, and bot sessions initiated.

Chatbots vs. Voice Assistants

Chatbots and voice assistants have a few things in common. Both are constantly learning more about their respective users, relying on newer data to improve the quality of interactions. And both use automation to communicate instantly and make the user experience (UX) convenient.

Chatbots carry out fewer tasks repetitively. Despite their extensive experience, they require a bit more supervision. Voice assistants, meanwhile, oversee entire user journeys. If needed, they can solve problems independently with a more versatile skill set. 

Voice assistants are just getting started when it comes to marketing. Upon taking the global market by storm for the last decade, there is still room for voice products to reach new customers. But the B2B space presents a larger addressable market. The same way voice assistants gained traction rather quickly with millions of users is how they may pop up across organizations. 

Rise of Voice Assistant Marketing

Many marketers considered voice a “low priority” back in 2018. Lately, the tides have changed: 28 percent of marketers in a Voicebot.ai survey find voice assistants “extremely important.” Why? Because they enjoy immediate access to a mass audience. Once voice products earn more public trust, they can leverage bubbling user relationships to introduce products and services. 

Voice assistants are stepping beyond their usual duties and tapping into their marketing powers. Amazon Alexa enables brands to develop product skills that alleviate user and customer pains. Retail rival Wal-Mart launched Walmart Stories, an Alexa skill showcasing customer and employee satisfaction initiatives. 

Amazon created a dashboard to gauge individual Alexa skill performance in 2017. For example, marketers can see how many unique customers, plays, sessions, and utterances a skill has. Multiple metrics can be further broken down by type of user action, thus indicating which moments are best suited for engagement.

Google Assistant also amplifies brands through an “Actions” feature similar to Alexa’s skills. TD Ameritrade launched an action letting users view their financial portfolios via voice command. 

The Bottom Line

Chatbots aren’t going anywhere. According to GlobeNewswire, they form a $2.9B market predicted to be worth $10.5B in 2026. Automation’s strong tailwinds almost guarantee chatbots won’t face extinction anytime soon. They are likely staying in their lane, building upon their current capabilities instead of adding drastically different ones. 
Meanwhile, voice e-commerce will become a $40B business by 2022. Over 4 billion voice assistants are in use worldwide. By 2024, that number will surpass 8 billion. It’s hard to bet against voice assistants dominating this decade’s MarTech landscape. Their future ubiquity, easy access to hands-free searches, and the increased likelihood A.I. improves its effectiveness will leave plenty of room for growth. If future voice products address recurring user pains, they will innovate with improved personalization and privacy features. 

If you’re looking for a voice platform to bring your application, get started with the Alan AI platform today.

References

1. The Future is Now – 37 Fascinating Chatbot Statistics (smallbizgenius)
2. 2020’s Voice Search Statistics – Is Voice Search Growing? (Review 42)
3. 10 Ways to Measure Chatbot Program Success (CMSWire)
4. Virtual assistants vs Chatbots: What’s the Difference & How to Choose the Right One? (FreshDesk) 
5. Digiday Research: Voice is a low priority for marketers (Digiday)
6. Marketers Assign Higher Importance to Voice Assistants as a Marketing Channel in 2021 – New Report (Voicebot.ai)
7. Use Alexa Skill Metrics Dashboard to Improve Smart Home and Flash Briefing Skill Engagement (Amazon.com)
8. The global Chatbot market size to grow from USD 2.9 billion in 2020 to USD 10.5 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 23.5% (GlobeNewsWire)
9. Chatbot Market by Component, Type, Application, Channel Integration, Business Function, Vertical And Region – Global Forecast to 2026 (Report Linker)
10. Voice Shopping Set to Jump to $40 Billion By 2022, Rising From $2 Billion Today (Compare Hare)
11. Number of digital voice assistants in use worldwide 2019-2024 (Statista)
12. The Future of Voice Technology (OTO Systems, Inc.)

After a decade of taking the commercial market by storm, it’s official: voice technology is no longer the loudest secret in Silicon Valley. The speech and voice recognition market is currently worth over $10 billion. By 2025, it is projected to surpass $30 billion. No longer is it monumental or unconventional to simply speak to a robot not named WALL-E and hold basic conversations. Voice products already oversee our personal tech ecosystems at home while organizing our daily lives. And they bring similar skill sets to enterprises in hopes of optimizing project management efforts. 

The future arrived a long time ago. Problems at work and in life are now more complex. Settling for linear solutions will not suffice. So how do we know what separates a high-quality modern voice user experience (UX) from the rest? 

Navigating an ever-changing voice technology landscape does not require a fancy manual or a DeLorean (although we at Alan proudly believe DeLoreans are pretty cool). A basic understanding of which qualities make the biggest difference on a modern voice product’s UX can catch us up to warp speed and help anyone better understand this market. Here are four features each user experience must have to create win-win scenarios for users and developers. 

• Multiple Contexts
• Workflow Capabilities
• Seamless Integration
• Privacy

MULTIPLE CONTEXTS

Pitfalls in communication between people and voice technology exist because limited insights were available until a decade ago. For instance, voice assistants were forced to play a guessing game upon finally rolling out to market and predict user behavior in their first-ever interactions. They lacked experience engaging back and forth with human beings. Even when we communicate verbally, we still rely on subtle cues to shape how we say what we mean. Without any prior context, voice products struggled to grasp the world around us. 

By gathering Visual, Dialog, and Workflow contexts, it becomes easier to understand user intent, respond to inquiries, and engage in multi-stage conversations. Visual contexts are developed to spark nonverbal communication through physical tools like screens. This does not include scenarios where a voice product collects data from a disengaged user. Dialog contexts process long conversations requiring a more advanced understanding. And Workflow contexts improve accuracy for predictions made by data models. Overall, user dialogue can be understood by voice products more often. 

When two or more contexts work together, they are more likely to help support multiple user interfaces. Multimodal UX unites two or more interfaces into a voice product’s UX. Rather than take a one-track-minded approach and place all bets on a single UX that may fail alone, this strategy aims to maximize user engagement. Together, different interfaces — such as a visual and voice — can flex their best qualities while covering each of their weaknesses. In turn, more human senses are interacted with. Product accessibility and functionality improve vastly. And higher-quality product-user relationships are produced. 

WORKFLOW CAPABILITIES

Developers want to design a convenient and accurate voice UX. This is why using multiple workflows matters — it empowers voice technology to keep up with faster conversations. In turn, optimizing personalization feels less like a chore. The more user scenarios a voice product is prepared to resolve quickly, the better chance it has to cater to a diverse set of user needs across large markets. 

There is no single workflow matrix that works best for every UX. Typically, voice assistants combine two types: task-oriented and knowledge-oriented. Task-oriented workflows complete almost anything a user asks their device to do, such as setting alarms. Knowledge-oriented workflows lean on secondary sources like the internet to complete a task, such as searching for a question about Mt. Everest’s height.

SEAMLESS INTEGRATION

Hard work that goes into product development can be wasted if the experience curated cannot be shared with the world. This mantra applies to the notion of developing realistic contexts and refining workflows without ensuring the voice product will seamlessly integrate. While app integrations can result in system dependencies, having an API connect the dots between a wide variety of systems saves stress, time, and money during development and on future projects. Doing so allows for speedier and more interactive builds to bring cutting-edge voice UX to life. 

PRIVACY 

Voice tech has notoriously failed to respect user privacy — especially when products have collected too much data at unnecessary times. One Adobe survey reported 81% of users were concerned about their privacy when relying on voice recognition tools. Since there is little to no trust, an underlying paranoia defines these negative user experiences far too often.

Enterprises often believe their platforms are designed well-enough to side-step past these user sentiments. Forward-thinking approaches to user privacy must promote transparency on matters regarding who owns user data, where that data is accessible, whether it is encrypted, and for how long. A good UX platform will take care of computer infrastructure and provide each customer with separate containers and their own customized AI model.

If you’re looking for a voice platform to bring your application, get started with the Alan AI platform today.

It’s no secret voice tech performs everyday magic for users. By now, basic voice product capabilities and features are well-known to the public. Common knowledge is enough to tell us what this technology does. Yet we fail to consider what factors and mechanisms behind the scenes enable these products to work. Multiple frameworks oversee different methods that people and products communicate with. But as a concept, the frequent lifeblood of the user experience — Spoken Language Understanding (SLU) — is quite concrete.  

As the name hints, SLU takes what someone tells a voice product and tries to understand it. Doing so involves detecting signs in speech, coding inferences correctly, and navigating complexities as an intermediary between human voices and scripts featuring calligraphy. Since typed and spoken language form sentences differently, self-corrections and hesitations recur. SLUs leverage different tools to navigate user messages through traffic. 

The most established is Automatic Speech Recognition (ASR), a technology that transcribes user speech at the system’s front end. By tracking audio signals, spoken words convert to text. Similar to the first listener in an elementary school game of telephone, ASR is most likely to precisely understand what the original caller whispered. Conversely, Natural Language Understanding (NLU) determines user intent at the back end. Both ASR and NLU are used in tandem since they typically complement each other well. Meanwhile, an End-to-End SLU cuts corners by deciphering utterances without transcripts. 

This Law & Order: SLU series gets juicier once you know all that Spoken Language Understanding is up against. One big challenge SLU systems face is the fact ASR has a complicated past. The number of transcription errors ASRs have committed is borderline criminal — not in a court of law — but the product repairs and false starts that resulted left a polarizing effect on users. ASRs usually operate at than the speed of sound or slower. And the icing on the cake? The limited scope of domain knowledge across early SLU systems hampered their appeal across targeted audiences. Relating to different niches was difficult as jargon was scarce. 

Let’s say you are planning a vacation. After deciding your destination will be New York, you are ready to book a flight. You tell your voice assistant, “I want to fly from San Francisco to New York.” 

That request is sliced and diced into three pieces: Domain, Intent, and Slot Labels. 

1. DOMAIN (“Flight”)

Before accurately determining what a user said, SLU systems figure out what subject they talked about. A domain is the predetermined area of expertise that a program specializes in. Since many voice products are designed to appeal broadly, learning algorithms can classify various query subjects by categorizing incoming user data. In the example above, the domain is just “Flight.” No hotels were mentioned. Nor did the user ask to book a cruise. They simply preferred to fly to the Big Apple.    

Domain classification is a double-edged sword SLU must use wisely. Without it, these systems can miss the mark, steering someone in need of one application into another. 

The SLU has to guess if the user referred to flights or not. Otherwise, the system could produce the wrong list of travel options. Nobody should prompt the user to accidentally book a rental car for a cross-country road trip they never wanted. 

2. INTENT (“Departure”)

Tracking down the subject a speaker talks about matters. However, if a voice product cannot pin down why that person spoke, how could it solve their problem? Carrying out a task would then become unnecessary. 

Once the domain is selected, the SLU identifies user intent. Doing so goes one step further and traces why that person communicated with the system. In the example above, “Departure” is the intent. When someone asks about flying, the SLU has enough information to believe the user is likely interested in leaving town. 

3. SLOT LABELS (Departure: “San Francisco”, Arrival: “New York”)

Enabling an SLU system to set domains and grasp intent is often not enough. Sure, we already know the user is vouching to leave on a flight. But the system is yet to officially document where they want to go.   

Slots capture the specifics of a query once the subject matter and end goal are both determined. Unlike their high-stakes Vegas counterparts, these slots do not rack up casino winnings. Instead, they take the domain and intent and apply labels to them. Within the same example, departure and arrival locations must be accounted for. The original query includes both: “San Francisco” (departure) and “New York” (arrival). 

IMPACT

Spoken Language Understanding (SLU) provides a structure that establishes, stores, and processes nomenclature that allows voice products to find their identity. When taking the needed leap to classify and categorize queries, SLU systems collect better data and personalize voice experiences. Products then become smarter and channel more empathy. And they are empowered to anticipate user needs and solve problems quickly. Therefore, SLU facilitates efficient workflow design and raises the ceiling on how well people can share and receive information to accomplish more.

If you’re looking for a voice platform to bring your application, get started with the Alan AI platform today.

The development of conversational AI is a huge step forward for how people interact with computers. The menu, touchscreen, and mouse are all still useful, but it is only a matter of time before the voice-operated interface becomes indispensable to our daily lives.

Conversational AI is arguably the most natural way we can engage with computers because that is how we engage with one another, with regular speech. Moreover, it is equipped to take on increasingly complex tasks. Now, let’s breakdown the technology that makes applications even easier to use and more accessible to more people.

Table of Contents

• Defining Conversational AI
• How Does Conversational AI Work?
• What Constitutes Conversational Intelligence
• How Businesses Can Use Conversational AI
• Benefits of Conversational AI
• Key Considerations about Conversational AI

   

  

   Defining Conversational AI

  Conversational Artificial Intelligence or conversational AI is a set of technologies that produce natural and seamless conversations between humans and computers. It simulates human-like interactions, using speech and text recognition, and by mimicking human conversational behavior. It understands the meaning or intent behind sentences and produces responses as if it was a real person.

  Conversational interfaces and chatbots have a long history, and chatbots, in particular, have been making headlines. However, conversational AI systems offer an even more diversified usage, as they can employ both text and voice modalities. Therefore, it can be integrated into a user interface (UI) or voice user interface (VUI) through various channels – from web chats to smart homes.

  AI-driven solutions need to incorporate intelligence, sustained contextual understanding, personalization, and the ability to detect user intent clearly. However, it takes a lot of work and dedication to develop an AI-driven interface properly. Conversational design, which identifies the rules that govern natural conversation flow, is key for creating and maintaining such applications.

  Users are presented with an experience that is indistinguishable from human interaction. It also allows them to skip multiple steps when completing certain tasks, like ordering a service through an app. If a task can be completed with less effort, it’s a bonus for both businesses and consumers.

   

  

  How Does Conversational AI Work?

  Conversational AI utilizes a combination of multiple disciplines and technologies, such as natural language processing (NLP), machine learning (ML), natural language understanding (NLU), and others. By working together, these technologies enable applications to interpret human speech and generate appropriate responses and actions.

  Natural Language Processing

  Conversational AI breaks down words, phrases, and sentences to their root form because people don’t always speak in a straightforward manner. Then, it can recognize the information or requested action behind these statements.

  The underlying process behind the way computer systems and humans can interact is called natural language processing (NLP). It draws intents and entities by evaluating statistically important patterns and taking into account speech peculiarities (common mistakes, synonyms slang, etc.). Before being employed, it is trained to identify said patterns using machine learning algorithms.

  User intent refers to what a user is trying to accomplish. It can be expressed by typing out a request or articulating it through speech. In terms of complexity, it can take any form – a single word or something more complicated. The system’s goal then is to match what the user is saying to a specific intent. The challenge is to identify it from a large number of possibilities.

  Intent contains entities referring to elements, which describe what needs to be done. For example, conversational AI can recognize entities like locations, numbers, names, dates, etc. The task can be fulfilled as long as the system accurately recognizes these entities from user input.

  Training Models

  Machine learning and other forms of training models make it possible for a computer to acknowledge and fulfill user intent. Not only does the system identify specific word combinations, but it is continuously learning and improving from experience.

  Such methods imply that a computer can perform actions that were not explicitly programmed by a human. In terms of how exactly ML can be trained, there are two major recognized categories:

  • Supervised ML: In the beginning, the system receives input data as well as output data. Based on a training dataset and labeled sample data, it learns how to create rules that map the input to the output. Over time, it becomes capable of performing the tasks on examples it did not encounter during training.
  • Unsupervised ML: There are no outcome variables to predict. Instead, the system receives a lot of data and tools to understand its properties. It can be done to expand a voice assistant or bot’s language model with new utterances.

  The key objective is to feed and teach the conversational AI solution different semantic rules, word match position, context-specific questions, and their alternatives and other language elements.

   

  

  What Constitutes Conversational Intelligence

  People interact with conceptual and emotional complexity. The exact words are not the only part of conversations that convey meaning – it is also about how we say these words. Normally, computers are unable to grasp these nuances. A well-designed conversational AI, on the other hand, takes it to the next level.

  Here are four key elements that ensure conversational intelligence and that voice-operated solutions should include.

  Context

  A response can be obtained solely based on the input query. However, conversational intelligence takes into account that a typical conversation lasts for multiple turns, which creates context. Previous utterances usually affect how an interaction unfolds.

  So, a realistic and engaging natural language interface does not only recognize the user input but also uses contextual understanding. Before queries can be turned into actionable information, conversational AI needs to match it with other data – why, when, and where.

  Memory

  Conversational systems based on machine learning, by their nature, learn from patterns that occurred in the past. It is a huge improvement from task-oriented interfaces. Now, users can accomplish tasks in a more concise and simple way.

  When appropriate, voice-first experiences should utilize predictive intelligence. Whether it’s something the user said 10 minutes ago or a week ago, the system can refer back to it and change the course of the conversation.

   Tone

  Depending on what you’re trying to achieve with your conversational AI solution, you can make your bot’s “persona” formal and precise, informal and peppy, or something in between. You can achieve this by tweaking the tone and incorporating some quirks to mimic a real conversation. Make sure it’s consistent and complements your brand’s message.

  Engagement

  This requirement for conversational intelligence is a natural progression from the previous points. By using context, memory, and appropriate tone, our AI-driven tool should create a feeling of genuine two-way dialogue.

  Conversations are dynamic. Naturally, you want to generate coherent and engaging responses unless you want users to feel they are talking to a rigid, predefined script.

   

  

  How Businesses Can Use Conversational AI

  Artificial intelligence and automation can make a practical impact on different business functions and industries. We look at industries that can benefit from this technology and how exactly this transformation takes shape for the better.

  Online Customer Support

  The automation of the customer service process helps you deliver results in real-time. When users have to search for answers themselves or call customer service agents, it increases the waiting time. If you want to reduce user frustration and delegate some tasks to an automated system, you can configure the bot to provide:

  • Product information and recommendations
  • Orders and Shipping
  • Technical Support
  • FAQ-style Queries

  Banking

  A large portion of requests that banks receive does not require humans. Users can just say what they need, and a bot will be capable of collecting the necessary data to deliver it. Here are a few examples of what conversational AI can easily handle in this sector:

  • Bill payments
  • Money Transfers
  • Credit Applications
  • Security notifications

  Healthcare

  Conversational AI can make a big difference in an industry where that relies on fast response times. You can customize and train language models specifically for healthcare and medical terms. While technology will never replace real doctors and other medical professionals, it ensures easy access to better care for some specific areas of healthcare:

  • Patient registration
  • Appointment scheduling
  • Post-op instructions
  • Feedback collection
  • Contract management

  Retail & e-commerce

  Even with the digitization of shopping, customers enjoy the social aspects of retail. Implementation of conversational commerce into your website or application opens up more possibilities. Engage customers with interactive content and offer conversational control of:

  • Product search
  • Checkout
  • Promotions
  • Set price alerts
  • Reservations

  Travel

  Voice assistants can do everything from booking flights to hotel selection. Travel can be frustrating, however, bots can make it a more pleasant experience. It can be used for:

  • Vacation planning
  • Reservations/cancellations
  • Queries and complaints

  Media

  Algorithms are able to provide news fast and on a large scale, while also providing convenient access for users. Conversational AI can create an engaging news experience for busy individuals. Applications include:

  • News Delivery
  • Opinion Polling

  Real Estate

  B2C businesses like real estate rely on personal contact. Conversational AI algorithms can greet potential clients, gauge their level of interest, and qualify them as potential leads. As a result, human agents can address customers, depending on their priority.

   

  

  Benefits of Conversational AI

  While innovations like conversational AI are new and exciting, they should not be disregarded as something trivial or inconsequential for business. This technology has actual revenue-driving benefits and the ability to enhance a variety of operations.

  Provides Efficiency

  Conversational AI delivers responses in seconds and eliminates wait times. It also operates with unmatched accuracy. So, whether your employees use it to complete workflows or customers to track their purchases or order statuses, it will be done quickly and error-free.

  Increases Revenue

  It is not a surprise that optimized workflows are good for business. The advantages of conversational AI solutions are consistently effective, which translates to better revenue. Plus, when you create better experiences for customers, they will be more likely to stay loyal and purchase from you.

  Reduces Cost

  This benefit is the logical aftermath of enhancing productivity within your company. The technology leads to better task management and quickly reduces customer support costs. Also, implementing conversational AI requires minimal upfront investment and deploys rapidly.

  Generates Insights

  AI is a great way to collect data on your users. It helps you track customer behavior, communication styles, and engagement. Overall, when you introduce new ways of interacting with your existing application or website, you can use it to learn more about your users.

  Makes Businesses Accessible and Inclusive

  If there are no tools to ensure a seamless user experience for everyone, businesses are essentially alienating some of their users. Conversational AI keeps those with impaired hearing and other disabilities, in mind. Accessibility to all is something employers in the modern workplace need to adhere to.

  Scales Infinitely

  As companies evolve, so do their needs, and it might get too overwhelming for humans and traditional technologies to handle. Conversational AI scales up in response to high demand without losing efficiency. Alternatively, when usage rates are reduced, there are no financial ramifications (unlike maintaining a call center, for example).

   

  

  Key Considerations about Conversational AI

  Like any other technology, AI is not without its flaws. At this point, conversational AI faces certain challenges that specific solutions need to overcome. Even though we’ve come a long way since less-advanced applications, let’s look at several areas, which have room for improvement.

  Security and Privacy

  New technology deals with an immediate need for cybersecurity defense. Since your business and associated data could be at risk, your solution needs to be designed with robust security policies. Users often share sensitive personal information with conversational AI applications. If someone gained authorized access to this data, it could potentially lead to devastating consequences.

  Changing, Evolving and Developing Communications

  Considering the number of languages, dialects, and accents, it is already a complex task to include them into conversational AI. There are many other factors that further complicate this process. Developers also have to account for slang and any other developments. Thus, language models have to be massive in scope, complexity and backed by substantial computing power.

  Discovery and Adoption

  Conversational AI applications do not always catch on with the general consumer. Although technology is becoming easier to use, it can take some time for users to get accustomed to new forms of interaction. It’s important to evaluate the technological literacy of your users and come up with ways to make AI-powered advancements create better experiences so they are better received.

  Technologies mature once weaknesses have been identified then resolved. We are working to address challenges caused by changes in language and cyber-security threats. It’s not an easy task or a fast one, but it’s essential in order to make sure AI-powered interactions run smoothly.

   

  

  What to Expect from Conversational AI in the Future

  Our smartphones already allow us to do things hands and vision-free. But as more and more companies use conversational technology, it gets us thinking about how it can be improved. Here are some trends aimed at creating seamless conversations with technologies.

  The Elimination of Bias

  As the application of AI expands, regulatory institutions will be making factual findings of how this technology impacts society and whether it holds ramifications affecting individual wellbeing.

  The European Union has introduced guidelines on the ethics of AI. Along with covering human oversight, technical robustness, and transparency, they touched on discriminatory cognitive biases. We might expect more regulatory requirements with legal repercussions. If the technology is found to have negative implications, it will not be deemed trustworthy.

  The key is to use fair training data. Since the machine learning algorithms are impartial by themselves, the focus will be shifted toward eliminating prejudice and discrimination from the initial data.

  Collaboration of Conversational Bots with Different Tools

  As new devices emerge, e.g., drones, robots, and self-driving cars, we are facing challenges of how we can simplify interactions with them. Conversing with these new technologies requires collaboration and input from across different platforms.

  Disparate bots will need to learn how to collaborate through an intelligent layer. Thus, solutions will be able to bridge the gap between collaborative conversational design and the implementation. For example, if there are multiple stand-alone conversational bots within the organization, it will be easier to combine them for a more consistent experience.

  New Skill Sets

  Building conversational AI systems isn’t exclusive to developers and researchers. It also involves scriptwriters that map conversation workflows, draw up follow-up questions, and match them to brand values. Team leaders will shed more light on internal business processes. Understanding visual perception will enable designers to create more effective user interfaces. Together, this team effort will enable new skills that conversational AI will put to use.

  The New Norm for Personalized Conversations

  Many industries are using big data and advanced analytics to personalize their offerings. This unspoken requirement has become so ubiquitous that no service industry can afford to ignore it. Conversational AI can be a driving force for crafting a relationship-based approach and personalizing your application or website.

   

  

  Employing Conversational AI with Alan

  Now that you understand the potential of conversational AI, you need to be thinking about how you can properly implement it within your organization. However, designing synchronous conversations across different channels requires a systematic approach. Here are some principles that help us meet this objective:

  1. Determine areas with the greatest conversational impact. 

  Not all business processes will benefit from the conversational interface. Consider high-friction interactions that can be enhanced with context-aware dialogues. Then, assign a relative value to each opportunity to prioritize them.

  2. Understand your audience.

  Use the knowledge gleaned from your current audience to reach a bigger audience. Do you want to transform the way your employees accomplish tasks or are looking into expanding your international customer base? You can also target your audience by demographics, product engagement levels, platforms they already use, etc.

  3. Build the right connections for an end-to-end conversation. 

  Identify all service integrations required for future conversations and make sure the bot has access to the full range of services that it needs. For example, if you need a sales chatbot, it should not only provide information about services and products but also locate them on your website and guide users there.

  4. Make sure all your content is ready

  If you want the conversational AI system to respond appropriately, refine, and expand the data it receives. It should include call transcripts, interactions via web chats and emails, social media posts, etc. After you provide existing conversational content, the mechanism will learn how to build on it without your involvement.

   5. Generate truly dynamic responses.

  Your goal is to transition from using structured menu-like interactions to natural language dialogues. In order to do that, you need to generate responses based on applied linguistics and human communication.

  6. Create a persona for your business.

  Identify what characteristics and values you want to enhance with conversational AI. The “personality” of your bot should adopt key traits that support your brand strategy. Make it recognizable and unique so that your users can form a real, human-like connection with it.

  7. Prioritize Privacy.

  Comprehensive privacy policies are imperative for handling any data. Since users can share personally identifiable information, you need to create a product they can trust. In some cases, users even provide more information than necessary. Overall, you need to implement security control proactively and prevent data leaks as much as possible.

  As you can see, there are no shortcuts for creating a good conversational system. The checklist above requires iteration and analysis along the way. However, you can still easily embed a conversational voice AI platform, into your existing application, with Alan. We do all the heavy lifting, breaking down the process into logical steps and making bespoke AI strategies for you. Our solutions are quick, hassle-free and so both you and your customers will see the results in no time at all.

  ]]> https://alan.app/blog/what-is-conversational-ai/feed/ 0 2952 https://alan.app/blog/what-is-conversational-user-interface-cui/ https://alan.app/blog/what-is-conversational-user-interface-cui/#respond Tue, 18 Feb 2020 06:01:56 +0000 https://alan.app/blog/?p=2814 The Evolution of the CUI In many industries, customers and employees need access to relevant, contextual information that is quick and convenient. Conversational User Interfaces (CUIs) enable direct, human-like engagement with computers. It completely transforms the way we interact with systems and applications. CUIs are becoming an increasingly popular tool,...]]>

  The Evolution of the CUI

  In many industries, customers and employees need access to relevant, contextual information that is quick and convenient. Conversational User Interfaces (CUIs) enable direct, human-like engagement with computers. It completely transforms the way we interact with systems and applications.

  CUIs are becoming an increasingly popular tool, which the likes of Amazon, Google Facebook, and Apple, have incorporated into their platforms. With the right approach, you can do the same.

  Table of contents

  • What is a Conversational User Interface?
  • How Do CUIs Work?
  • Types of Conversational User Interfaces
  • Business Use Cases
  • Benefits of Conversational UI
  • Conversational UI Challenges
  • The Future of Conversational UI
  • Checklist for Making a Great Conversational UI for your Applications 
  • Conclusion

  

  What is Conversational User Interface?

  A Conversational User Interface (CUI) is an interface that enables computers to interact with people using voice or text, and mimics real-life human communication. With the help of Natural-Language Understanding (NLU), the technology can  recognize and analyze conversational patterns to interpret human speech. The most widely known examples are voice assistants  like Siri and Alexa. 

  Voice interactions can take place via the web, mobile, desktop applications,  depending on the device. A unifying factor between the different mediums used to facilitate voice interactions is that they should be easy to use and understand, without a learning curve for the user. It should be as easy as making a call to customer service or asking your colleague to do a task for you. CUIs are essentially a built-in personal assistant within existing digital products and services.

  In the past, users didn’t have the option to simply tell a bot what to do. Instead, they had to search for information in the graphical user interface (GUI) – writing specific commands or clicking icons. Past versions of CUI consisted of messenger-like conversations, for example, where bots responded to customers in real-time with rigidly spelled-out scripts.

  But now it has evolved into a more versatile, adaptive product that is getting hard to distinguish from actual human interaction.

  The technology behind the conversational interface can both learn and self-teach, which makes it a continually evolving, intelligent mechanism.

  

  How Do CUIs Work?

  CUI is capable of generating complex, insightful responses. It has long outgrown the binary nature of previous platforms and can articulate messages, ask questions, and even demonstrate curiosity. 

  Previously, command line interfaces required users to input precise commands using exact syntax, which was then improved with graphical interfaces. Instead of having people learn how to communicate with UI, Conversational UI has been taught how to understand people. 

  The core technology is based on:

  • Natural Language Processing – NLP combines linguistics, computer science, information engineering, and artificial intelligence to create meaning from user input. It can process the structure of natural human language and handle complex requests.
  • Natural Language Understanding – NLU is considered a subtopic of natural language processing and is narrower in purpose. But the line between them is not distinct, and they are mutually beneficial. By combining their efforts, they reinterpret user intent or continue a line of questioning to gather more context.

  For example, let’s take a simple request, such as:

  “I need to book a hotel room in New York from January 10th to the 15th.”

  In order to act on this request, the machine needs to dissect the phrase into smaller subsets of information: book a hotel room (intent) – New York (city) – January 10 (date) – January 15 (date) – overall neutral sentiment.

  Conversational UI has to remember and apply previously given context to the subsequent requests. For example, a person may ask about the population of France. CUI provides an answer to that question. Then, if the next phrase is “Who is the president?”, the bot should not require more clarification since it assigns the context from the new request. 

  In modern software and web development, interactive conversational user interface applications typically consist of the following components:

  • Voice recognition (also referred to as speech-to-text) – A computer or mobile device captures what a person says with a microphone and transcribes it into text. Then the mechanism combines knowledge of grammar, language structure, and the composition of audio signals to extract information for further processing. To achieve the best level of accuracy possible, it should be continuously updated and refined.
  • NLU – The complexity of human speech makes it harder for the computer to decipher the request. NLU handles unstructured data and converts it into a structured format so that the input can be understood and acted upon. It connects various requests to specific intent and translates them into a clear set of steps.  
  • Dictionary/samples – People are not as straightforward as computers and often use a variety of ways to communicate the same message. For this reason, CUI needs to have a comprehensive set of examples for each intent. For example, the request “Book Flight”, the dictionary should contain “I need a flight”, “I want to book my travel”, along with all other variants.
  • Context – An example with the French president above showed that in a series of questions and answers, CUI needs to make a connection between them. These days, UIs tend to implement an event-driven contextual approach, which accommodates an unstructured conversational flow.
  • Business logic – Lastly, the CUI business logic connects to specific use cases to define the rules and limitations of a particular tool.

  

  Types of Conversational User Interfaces

  We can distinguish two distinct types of Conversational UI designs. There are bots that you interact with in the text form, and there are voice assistants that you talk to. Bear in mind that there are so-called “chatbots” that merely use this term as a buzzword. These fake chatbots are a regular point-and-click graphical user interface disguising and advertising itself as a CUI. What we’ll be looking at are two categories of conversational interfaces that don’t rely on syntax specific commands.

  Chatbots

  Chatbots have been in existence for a long time. For example, there was a computer program ELIZA that dates back to the 1960s. But only with recent advancements in machine learning, artificial intelligence and NLP, have chatbots started to make a real contribution in solving user problems.

  Since most people are already used to messaging, it takes little effort to send a message to a bot. A chatbot usually takes the form of a messenger inside an app or a specialized window on a web browser. The user describes whatever problem they have or asks questions in written form. The chatbots ask follow-up questions or meaningful answers even without exact commands.

  Voice recognition systems

  Voice User Interfaces (VUI) operate similarly to chatbots but communicate with users through audio. They are hitting the mainstream at a similar pace as chatbots and are becoming a staple in how people use smartphones, TVs, smart homes, and a range of other products.

  Users can ask a voice assistant for any information that can be found on their smartphones, the internet, or in compatible apps. Depending on the type of voice system and how advanced it is, it may require specific actions, prompts or keywords to activate. The more products and services are connected to the system, the more complex and versatile the assistant becomes. 

  

  Business Use Cases

  Chatbots and Voice UIs are gaining a foothold in many important industries. These industries are finding new ways to include conversational UI solutions. Its abilities extend far beyond what now dated, in-dialog systems, could do. Here are several areas where these solutions can make an impressive impact.

  Retail and e-commerce

  A CUI can provide updates on purchases, billing, shipping, address customer questions, navigate through the websites or apps, offer products or service information, along with many other use cases. This is an automated way of personalizing communication with your customers without involving your employees.

  Construction sector

  Architects, engineers, and construction workers often need to review manuals and other text chunks, which can be assisted by CUI. Applications are diverse:  contractor, warehouse, and material details, team performance, machinery management, among others.

  First Responders

  Increasing response speed is essential for first responders. CUI can never replace live operators, but it can help improve outcomes in crises by assessing incidents by location, urgency level, and other parameters.

  Healthcare

  Medical professionals have a limited amount of time and a lot of patients. Chatbots and voice assistants can facilitate the health monitoring of patients, management of medical institutes and outpatient centers, self-service scheduling, and public awareness announcements.

  Banking, financial services, and insurance

  Conversational interfaces can assist users in account management, reporting lost cards, and other simple tasks and financial operations. It can also help with customer support queries in real-time; plus, it facilitates back-office operations.

  Smart homes and IoT

  People are starting to increasingly use smart-home connected devices more often. The easiest way to operate them is through vocal commands. Additionally, you can simplify user access to smart vehicles (open the car, plan routes, adjust the temperature).

  

  Benefits of Conversational UI

  The primary advantage of Conversational UI is that it helps fully leverage the inherent efficiency of spoken language. In other words, it facilitates communication requiring less effort from users. Below are some of the benefits that attract so many companies to CUI implementations.

  Convenience

  Communicating with technology using human language is easier than learning and recalling other methods of interaction. Users can accomplish a task through the channel that’s most convenient to them at the time, which often happens to be through voice. CUI is a perfect option when users are driving or operating equipment.

  Productivity

  Voice is an incredibly efficient tool – in almost every case, it is easier and faster to speak rather than to use touch or type. Voice is designed to streamline certain operations and make them less time consuming. For example, CUI can increase productivity by taking over the following tasks:

  • Create, assign, and update tasks
  • Facilitate communication between enterprises and customers, enterprises and employees, users and devices
  • Make appointments, schedule events, manage bookings
  • Deliver search results
  • Retrieve reports

  Intuitiveness

  Many existing applications are already designed to have an intuitive interface. However, conversational interfaces require even less effort to get familiar with because speaking is something everyone does naturally. Voice-operated technologies become a seamless part of a users’ daily life and work.

  Since these tools have multiple variations of voice requests, users can communicate with their device as they would with a person. Obviously, it’s something everyone is accustomed to. As a result, it improves human-computer interactivity.

  Personalization

  When integrating CUI into your existing product, service, or application, you can decide how to present information to users. You can create unique experiences with questions or statements, use input and context in different ways to fit your objectives.

  Additionally, people are hard-wired to equate the sound of human speech with personality. Businesses get the opportunity to demonstrate the human side of their brand. They can tweak the pace, tone, and other voice attributes, which affect how consumers perceive the brand.

  Better use of resources

  You can maximize your staff skills by directing some tasks to CUI. Since employees are no longer needed for some routine tasks (e.g., customer support or lead qualification), they can focus on higher-value customer engagements.

  As for end-users, this technology allows them to make the most out of their time. When used correctly, CUI allows users to invoke a shortcut with their voice instead of typing it out or engaging in a lengthy conversation with a human operator.

  Available 24/7

  There are restrictions no when you can use CUI. Whether it’s first responders looking for the highest priority incidents or customers experiencing common issues, their inquiry can be quickly resolved.

  No matter what industry the bot or voice assistant is implemented in, most likely, businesses would rather avoid delayed responses from sales or customer service. It also eliminates the need to have around-the-clock operators for certain tasks.

  

  Conversational UI Challenges

  Designing a coherent conversational experience between humans and computers is complex. There are inherent drawbacks in how well a machine can maintain a conversation. Moreover, the lack of awareness of computer behavior by some users might make conversational interactions harder.

  Here are some major challenges that need to be solved in design, as well as less evident considerations:

  • Accuracy level – The CUI translates sentences into virtual actions. In order to accurately understand a single request and a single intent, it has to recognize multiple variations of it. With more complex requests, there are many parameters involved, and it becomes a very time-consuming part of building the tool.
  • Implicit requests – If users don’t say their request explicitly, they might not get the expected results. For example, you could say, “Do the math” to a travel agent, but a conversational UI will not be able to unpack the phrase. It is not necessarily a major flaw, but it is one of the unavoidable obstacles.
  • Specific use cases – There are many use cases you need to predefine. Even if you break them down into subcategories, the interface will be somewhat limited to a particular context. It works perfectly well for some applications, whereas in other cases, it will pose a challenge.
  • Cognitive load – Users may find it difficult to receive and remember long pieces of information if a voice is all they have as an output. At the very least, it will require a decent degree of concentration to comprehend a lot of new information by ear.
  • Discomfort of talking in public – Some people prefer not to share information when everyone within earshot can hear them. So, there should be other options for user input in case they don’t want to do it through voice.
  • Language restrictions – If you want the solution to support international users, you will need a CUI capable of conversing in different languages. Some assets may not be suitable for reuse, so it might require complete rebuilds to make sure different versions coexist seamlessly.
  • Regulations protecting data – Making sure interactions are personalized, you may need to retrieve and store data about your users. There are concerns about how organizations can make it comply with regulation and legislation. It is not impossible but demands attention. 

  These challenges are important to understand when developing a specific conversational UI design. A lot can be learned from past experiences, which makes it possible to prevent these gaps from reaching their full potential.

  

  The Future of Conversational UI

  The chatbot and voice assistant market is expected to grow, both in the frequency of use and complexity of the technology. Some predictions for the coming years show that more and more users and enterprises are going to adopt them, which will unravel opportunities for even more advanced voice technology.

  Going into more specific forecasts, the chatbots market is estimated to display a high growth continuing its trajectory since 2016. This expected growth is attributed to the increased use of mobile devices and the adoption of cloud infrastructure and related technologies.

  As for the future of voice assistants, the global interest is also expected to rise. The rise of voice control in the internet of things, adoption of smart home technologies, voice search mobile queries, and demand for self-service applications might become key drivers for this development. Plus, the awareness of voice technologies is growing, as is the number of people who would choose a voice over the old ways of communicating.

  Naturally, increased consumption goes hand-in-hand with the need for more advanced technologies. Currently, users should be relatively precise when interacting with CUI and keep their requests unambiguous. However, future UIs might head toward the principle of teaching the technology to conform to user requirements rather than the other way around. It would mean that users will be able to operate applications in ways that suits them most with no learning curve.

  If the CUI platform finds the user’s request vague and can’t convert it into an actionable parameter, it will ask follow-up questions. It will drastically widen the scope of conversational technologies, making it more adaptable to different channels and enterprises. Less effort required for CUI will result in better convenience for users, which is perhaps the ultimate goal.

  The reuse of conversational data will also help to get inside the minds of customers and users. That information can be used to further improve the conversational system as part of the closed-loop machine learning environment.

  

  Checklist for Making a Great Conversational UI for your Applications

  There are plenty of reasons to add conversational interfaces to websites, applications, and marketing strategies. Voice AI platforms like Alan, makes adding a CUI to your existing application or service simple. However, even if you are certain that installing CUI will improve the way your service works, you need to plan ahead and follow a few guidelines.

  Here are steps to adopt our conversational interface with proper configuration:

  1. Define your goals for CUI – The key factor in building a useful tool is to decide which user problem it is going to address.
  2. Design the flow of a conversation – Think of how communication with the bot should go – greeting, how it’s going to determine user needs, what options it’s going to suggest, and possible conversation outcomes.
  3. Provide alternative statements – As you know, users frame their requests in different ways, sometimes using slang, so you need to include different types of words that the bot will use to recognize the intent.
  4. Set statements to trigger some sort of action so that it can make a corresponding API call. What does a user have to say to make the bot respond appropriately for the situation? It is useful to use word tokenization to assign meaning at this point.
  5. Add visual and textual clues and hints – If a user feels lost, guide them through other mediums (other than voice). This way, you will improve the discoverability of your service.
  6. Make sure there are no conversational dead ends – Bot misunderstandings should trigger a fallback message like “Sorry, I didn’t get that”. Essentially, don’t leave the user waiting without providing any feedback.

  Overall, you should research how CUI can support users, which will help you decide on a type of CUI and map their journey through the application. It will help you efficiently fulfill the user’s needs,

  keep them loyal to the product or service, and simplify their daily tasks.

  Additionally, create a personality for your bot or assistant to make it natural and authentic. It can be a fictional character or even something that is now trying to mimic a human – let it be the personality that will make the right impression for your specific users.

  

  Conclusion

  A good, adaptable conversational bot or voice assistant should have a sound, well-thought-out personality, which can significantly improve the user experience. The quality of UX affects how efficiently users can carry out routine operations within the website, service, or application.

  In fact, any bot can make a vital contribution to different areas of business. For many tasks, just the availability of a voice-operated interface can increase productivity and drive more users to your product. Many people can’t stand interacting over the phone – whether it’s to report a technical issue, make a doctor’s appointment, or call a taxi.

  A significant portion of everyday responsibilities, such as call center operations, are inevitably going to be taken over by technology – partially or fully. The question is not if but when your business will adopt Conversational User Interfaces.

   

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