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Voice user interfaces are filled to the brim with human language. Human languages are beautiful; they are geographically diverse and filled to the brim with uncertainties. Some languages have words that derive their semantic meaning from their context; for others, meaning derives from the tone in which the speaker says it or the syntactical placement of the word in the sentence. In linguistics, there are five main categories to human language that help linguists understand how each language harmonizes into a unified whole: syntax, semantics, pragmatics, morphology and phonology. Because of how ambiguous human languages are and how uniquely innate they are, it may be evident that they would not translate smoothly into a computer. While partly true, it is an inevitably complex subject. Computational linguistics is the academic study of computing natural language. On the theoretical and artificially intelligent level, this is referred to as machine language learning. All of the above is used in voice user interfaces.

Machine language learning is a component of deep learning. Deep learning is a core part of artificial intelligence and is critical to an artificially intelligent system’s performance – whether text-based, voice-based, or neither. Machine language learning possesses many layers, with its main one being natural language processing – also commonly referred to as NLP. Like deep learning at the core of artificial intelligence, natural language processing is at the core of machine language learning. It is an essential component of the fundamentals of any computational linguistic algorithm. Natural language processing often goes together with natural language understanding (NLU), and together, they make up the most successful chatbots and other text and voice-based artificial intelligence systems.

The use of computation to store text-based data is not a new concept. Any simple computer programming language will have a text-based database. It thus makes sense to want to apply language analysis, language translation, conversational design, and language databasing to computation. From a computational perspective, computational linguists can draw from their interdisciplinary background and apply it to artificial intelligence in the form of conversational artificial intelligence within chatbots, virtual assistants, and even virtual reality.

Voice User Interfaces

Voice user interfaces such as those seen from Google (Google Home) or those as technologically iconic as Amazon’s Alexa have become commonplace within many people’s day-to-day lives. For example, FitBit Sense now allows one to talk to Amazon’s Alexa right from the watch itself with the press of a button. As visual interfaces begin to intertwine themselves with voice user interfaces, it is critical that we not only understand why they work but how this will impact our lives as humans who already use language to connect on many levels with other people. How this phenomenon will translate into chatbots is an entirely different narrative.

person reaching out to a robot
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As mentioned previously, machine language learning is at the heart of computational linguistics. Within artificial intelligence, cognitive scientists aim to create artificial intelligence that is ‘strong’ – ‘strong’ in that it does not rely on a user for its input. ‘Strong’ in that it can use past inputs and outputs to create new algorithms. Essentially, computational linguists and cognitive scientists want artificially intelligent systems and voice user interfaces that can learn and speak without the help of a human. They want a piece of artificial intelligence that can evolve with time, independent of any individual behind the scenes working on simulating the learning experience of the chatbot’s experiences with the user. We want the artificial intelligence system to be phenomenally conscious, not just simulate it.

Strong artificial intelligence systems such as those mentioned previously are indeed the future. With self-driving cars on the way and mobile GPS apps with voice user interfaces that are almost entirely voice-based, the push for complex voice user interfaces and conversational artificial intelligence will continue to grow. With the success of social media and social communication apps, it only makes sense that cognitive scientists and computational linguists would want to have chatbots and voice user interfaces that mirror human-to-human communication and interaction. Humans are social creatures, and we crave social interaction. Whether or not an artificially intelligent robot can satisfy that craving is up to our ability to make conversational AI and voice user interface systems naturalized.

READ MORE: Machine Learning 101: The Revolutionary Side of Artificial Intelligence

Unfortunately, an area with such high potential results in high expectations. Much like the expectation of strong artificial intelligence to understand us and evolve and learn from their experiences with the user, this would ultimately translate into a piece of conversational artificial intelligence or voice user interface that is more human-like than its predecessor. The study of cognitive science is, after all, to naturalize and formalize the human mind into a machine.

Why Personalization Requires Strong Conversational Artificial Intelligence

With the expectations of users and those creating the robot to be more human-like and therefore more personalized, it becomes apparent that conversational artificial intelligence branches out into a much more complex area of technology and technological development. To make voice user interfaces that are as engaging and realistic as a human at the other end of a telephone would be, we would need it to function as a human. It would need to be personalized to each and every user, not your stereotypical cookie-cutter chatbot. A significant problem with the rise of conversational AI is its lack of personalization and user-friendliness. At this point in time, it is still too robotic, and therefore, unfriendly.

Presently, chatbots and other voice user interfaces are believed by the average user to not handle complex inquiries and tasks. Synthesizing language and natural language processing to allow artificial intelligence systems to process and understand natural language is one thing. Providing personalized implementations of conversational AI to the user that tailors to them and their experiences in a human-like manner is an entirely different story – one that must technologically evolve as the digital era continues.

While hard to imagine, this is crucial to keep in mind whilst thinking about the future of artificial intelligence, chatbots, virtual assistants, mobile apps, and other viable pieces of technology and their future in marketing. Going beyond artificial intelligence that manually simulates learning from a human and actually learns from the user, their prompts and experiences are the next steps in creating artificial intelligence eco-systems that would have consumers be open to the idea that chatbots can, indeed, handle complex tasks in an empathetic manner. Communication automation is the goal, yes, but we must consider that having communication automation be personalized to the individual is the ultimate goal regarding a voice user interface’s strength as an artificially intelligent system.

Personalization
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This is not to say that we have not already reached artificial intelligence that is strong and, thus, formalized and personalized. We are far from completing such a task with all the natural human languages that exist – especially for voice user interfaces. Unfortunately for machines, human language is not as innate to machines as they are to humans.

The Applications of Conversational AI

Fortunately, natural language processing and conversational artificial intelligence as a scientific discipline of computer science have uses that go beyond user experiences on mobile apps or other hyper-personalized experiences with technology. Because machine intelligence weaves itself into computational linguistics and all its subfields, it is important to note its uses beyond futuristic speech-based interaction with humans. 

Statistical natural language processing is a massive component of this aspect of conversational artificial intelligence. It involves complex computer algorithms combined with machine language learning, deep learning, and neural networks to analyze and categorize voice and/or text data. There are many aspects of technology used in – presumably, types of technology you are familiar with and have encountered. It is an important aspect of voice user interfaces, specifically.

For example, many websites want to check that you are not a robot. What is so ironic about this is that it is a robot making sure you are not a robot. They do this by asking you questions that are typically beyond most standardly automated robot capabilities. To put it more simply, they ask you questions that are more ‘human’ – questions that often require critical thinking, common sense, and a lack of repetition, all of which are core components to the ‘humanity’ of humans. Said subjects are also very complex and have yet to be fully formalized within pieces of artificial intelligence machinery.

Therefore, it is easy to see why conversational artificial intelligence combines with natural language processing and understanding to form a subcategory of artificial intelligence that does not revolve around a user. Instead, it relies upon its own unique machine language learning algorithms to help with spam detection (especially within the financial sector), thorough machine translation, social media rule-analysis and even summarizations of voices or texts.

Just like any digital solution, chatbot solutions are available in varying levels of complexity and customization. Chatbots range from simple decision-tree-based architectures to fully customized conversational AI-powered solutions. And while there are many inexpensive options in the marketplace, you might be wondering why enterprise is investing upfront in a custom chatbot solution.

5 Reasons Enterprises Are Choosing Custom Chatbots

Let’s address the elephant in the room first. Custom chatbots aren’t the cheapest option. To get targeted, use case-specific technology to address a key business need, developers and consultants are going to spend some serious time and effort building you the best solution. But in the long run, even the inexpensive chatbot solutions are going to end up costing money to upgrade, modify or shelf altogether. So getting it right, the first time, is always the best way forward.

The ChatC Group has worked with dozens of enterprise clients globally over the last few years. Each custom AI chatbot brought with it a number of learnings, including providing evidence as to why the custom solution was the only right choice for the customer. Here are five reasons why custom chatbots are best for enterprise.

  1. Front Loaded Design Produced the Best Long Term Solution

A key feature of a custom chatbot solution is that the bulk of the work for all parties involved in the project is at the outset. Gathering the stakeholders, discussing what need the chatbot should meet, and carefully outlining the specific use case are essential to getting things right, the first time. 

In addition to that, a custom solution should always be using KPIs to measure and track its progress against your specific requirements. Custom solutions nail down these KPIs early on and set up dashboards inside the chatbot so that it automatically gathers data, analyzes it and reports what you need to know. 

The upfront burden of work can seem tiresome, but when enterprise clients see the data collected after their efforts, there was no question as to whether it was worth the hard work. The AI-powered chatbot continues to analyze data, which is then used to guide the business, providing accurate insights contributing to the bottom line, long after the chatbot project has concluded. 

  1. Enterprise Needs More than a Pop-up Solution

For some very simple use cases, the decision tree chatbot approach may work. Examples include capturing leads after a webinar, recording sign-ups for an email list or answering very basic FAQs on your website. The benefit of the decision tree chatbot here is ease of use, and keeping users in the same window to collect their information.

For larger businesses though, this solution won’t always work. Enterprise clients need a chatbot that is more involved and solves a larger business issue for them, in a way that more people will benefit. An entrepreneur or small business could use the decision tree chatbot for sign-ups, but SMBs or enterprises need a more polished solution that can do more than reply with pre-canned answers. Understanding language and intent are key.

  1. Recognizing Intent in Conversations Was Crucial

Understanding a person’s intent when they type a message or question into a chatbot window is only possible when it’s run on a conversational AI platform. A simple decision-tree chatbot cannot tell what the person is getting at in their query; they can only identify if a keyword has been entered, and then look-up how to respond to that keyword in their decision tree framework.

When handling customer service or answering more involved questions, in the case of a dedicated internal chatbot, the person’s intent matters. Understanding lingo, undertones of emotion, and getting to the root of what’s being asked when different phrases are used is a tricky thing for a program. But when you want a solution that is more efficient at retrieving information and answering questions correctly, conversational AI is required, and all of our large enterprise clients have agreed and invested in this technology.

  1. Improvement over Time Made the Best Business Case

One additional benefit of custom chatbot solutions: they actually improve over time. As their database of conversations grows with each passing day in operation, they learn to deliver better service, all on their own. 

With other forms of software and technology, improvements are often at the cost of a new round of development or diverting current IT professionals to instead focus on bringing an older technology back to today’s standards. Enterprise clients are thrilled to hear that once they’ve invested in the conversational AI and spent the time training the code on their own data, it will continue to be relevant and useful without any other significant expenditures.

  1. Custom Chatbot Solutions Meet Individualized Use Case Requirements Best

Custom solutions to meet very specific use cases may seem like an obvious answer, but the complexities associated with a modern-day enterprise and their tech stacks are not trivial. Big businesses have already invested heavily in their infrastructure and myriad technical solutions and software.

A custom AI chatbot can be developed on a platform that integrates seamlessly with a business’s current tech stack. Building a custom chatbot means it will fit into your existing architecture and can be deployed onto the server of your choice (from Azure and AWS to Google Cloud and more). All of this work upfront is again saving you time and frustration in dealing with a new system. Chatbots and consultants that cater to your needs make your life easier, not yet another thing to learn and add to your to-do list. 

Chatbot Consultants Deliver Personalized Chatbot Solutions

While a custom chatbot solution is the most versatile offering for enterprise, the only way to achieve that end is by working with a chatbot consultant. These are experts versed in all things conversational AI, who don’t just have the tech background, but also the ability to work well with all parties, managing a project for big business, and ensuring seamless execution.
If you’re curious about our process at The ChatC Group, take a look at our webpage, or reach out to one of our experts to discuss what we do. Talking tech and helping your business reach its goals are two things we can’t get enough of. Book a call; it’s easy to chat with us.

Chatbots are here to stay. And while we can all see the benefits of using chatbots in our various enterprises, there are some concerns about design issues. Ultimately your main concern should be your customer and how they feel about using your chatbot. So keep your customer experience elevated by avoiding these common pitfalls in design.

3 Common Chatbot Design Issues

Chatbot design requires a skilled team of experts

You may have had one or more of these issues with your current chatbot or one that you’ve used as a consumer. But, if you are just starting out, here are some problems to avoid early on in the design process. 

  1. Broken Script: You may have had the experience of being stuck in the endless loop of dead-ends in an interaction with a chatbot. Even the best chatbots cannot predict the thousands of potential word combinations that customers may put together. But some bots are better than others when it comes to getting conversations right.
  2. Transparency: Many people are leery of interacting with a chatbot for fear of exposing personal information. Some people aren’t comfortable putting things in writing, because they don’t know where that data will end up. How can they be sure that the chatbot is part of the company’s website and not some third-party?
  3. Impersonal Interactions: Now more than ever customers are looking for empathy and connection. Chatbots are designed to increase productivity, but the interaction it has with your customers can sometimes feel robotic (even though it is a bot, we want it to feel like a person).
chatbot design

7 Best Practices For Chatbot Design

To avoid as many pitfalls as possible, here are some helpful tips to keep in mind while you are creating your chatbot (or boosting your current chatbot to the next level):

  1. DO use a suitable chatbot platform. Determine the types of features you want your chatbot to have. Take the time to study the market and the options available for your business. Complexity and quality vary greatly; be aware.
  2. DO leverage deep learning on artificial intelligence. Your goal should be to develop a chatbot that is dynamic, flexible and constantly improving
  3. DO aim for human-like conversation. A strong chatbot should be able to understand small talk and the common slang used in our language. If you already have a chatbot developed, take a look back through your chat logs to see if there are any common phrases that your chatbot is not understanding or responding to improperly. Then re-train your bot to better understand your customers.
  4. DO build trust. Whether you are transparent about using a chatbot or not, you want your customers to feel safe in the conversations they are having. This could involve including a message from your bot that outlines the security measures in place to protect their information.
  5. DON’T make your chatbot too “chatty.” While you want to mimic human conversation, ensure that your chatbot is direct and to the point. The benefit of a chatbot is that it solves the problems your customers are having. But customers don’t want to spend time answering unnecessary questions about how their day is going.
  6. DON’T overwhelm your customers with too much information. While you may be proud of the chatbot you have developed, keep in mind that it doesn’t need every bell and whistle to be effective. Also, remember that most users will be using a smartphone to access your chatbot so they will have minimal screen space.
  7. DON’T set expectations that are too high. You are not going to solve every problem that your customers will ever have. So don’t be discouraged if there are things your chatbot can’t do. Be realistic when you set your use case and accept that even if half of the problems are dealt with, you have done a great job.
group of multiethnic colleagues reading contract details standing on street
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You Need This Advice if You Are Designing a Chatbot

You may feel overwhelmed at the thought of all the things you have to keep in mind while designing a chatbot. In order to avoid the potential pitfalls, consider reaching out to a highly trained Chatbot Consultant. These consultants have tried and true processes in place to ensure quality the first time. 

Chatbot consultants can save you time and money.

If you want to connect with a Chatbot Consultant to avoid problems with your chatbot, reach out. One of our highly trained Chatbot Consultants can answer all of your questions in a discovery call.

Chatbot development is becoming more popular as the demand for smart automation increases. The chatbot market is projected to grow from $2.6 billion in 2019 to $9.4 billion by 2024. There are a variety of benefits that chatbots offer, depending on your company’s specific use case. Chatbots can deliver many different results; they:

The 5 Best Chatbot Companies

  • Are accessible 24/7
  • Allow for work automation
  • Are cost-effective
  • Allow for faster onboarding
  • Are a virtual personal assistant
  • Allow for increased customer satisfaction
  • Are an alternate sales channel
person using a laptop on a table
Photo by Ron Lach on Pexels.com

There are hundreds of chatbot companies, so it can be hard to find the best one for the needs of your business. We’ve highlighted 5 of the best chatbot companies to save you time.

1. BotsCrew

About: BotsCrew is a global leader in chatbot development with offices in London, UK, and Lviv, Ukraine. Founded in 2016, it designs and develops custom AI chatbots to help small and medium enterprises. BotsCrew prides itself on how effectively it brings unparalleled customer experience to the market. The chatbots ensure workplace routine automation, 24/7 customer support, and high user engagement.  

Specialty: BotsCrew specializes in developing custom conversational AI chatbots for startups and Fortune 500 companies.

Recognitions: 

  • Top chatbot development company by Clutch in 2017 & 2018.
  • Top 30 Artificial Intelligence Companies in 2019 by Techreviewer.
  • Top 50 Chatbot Development Companies in 2019 by Manifest
  • Listed amongst 15 of the Best Whatsapp Chatbot Tools to Use in 2020 by InfluencerMarketingHub

2. Chatbots.Studio

About: Chatbots.Studio is a leading conversational design and UiPath company. It helps to automate businesses and build new communication channels in messenger apps (e.g. Viber, WhatsApp, Facebook Messenger, etc.).
Specialty: Chatbots.Studio specializes in Robotic Process Automation, which delivers high-performance business tasks and processes. Custom AI chatbots help businesses to move their customer experience to the next level.

Recognitions: 

  • #1 Chatbot development company according to Recovendor
  • #2 Chatbot development company according to Clutch 
  • #1 AI developer in Lviv
  • Visa partner
  • Viber partner
  • PrivatBank partner

3. Masters of Code

About: Founded in 2004, Masters of Code combines technology, people and partners to help organizations transform their customer experience to create a competitive edge. They make customer interaction easy and deliver a great customer experience, which translates into their clients achieving their business goals. 
Speciality: Masters of Code specializes in AI-powered chatbots, as well as conversational solutions. 

4. MobiDev

About: Founded in 2009, MobiDev helps businesses create new software products so they can focus on their business and clients.
Specialty: Web development, mobile development (iOS/Android and cross-platform)
and machine learning/AI applications.
Recognitions:

  • MobiDev was awarded #1 place in the spheres of Web, Mobile & Software Development for 4 years in a row.

5. PixelPlex

About: Founded 2007, PixelPlex is a company that transforms cherished ideas into products that perform well, inspire, and win international recognition.
Specialty: IT consulting, customer engagement via UI/UX, and enterprise application development, including customized AI chatbots on multiple platforms.
Recognitions:

  • 350 successful projects
  • $500m raised via solutions they’ve built
  • 2 unicorns captured (Projects valued over $1 billion)
  • 150M end users engaged
  • 100+ qualified specialists
  • 120 countries using our products
best chatbot companies

Chatbots Can Transform Your Business

There are a variety of chatbot developers that can help your company improve customer service and streamline processes. But to fully capitalize on your investment, you need to ensure that the specialty of the developer matches your business’s use case. The fastest way to source the correct development team is by partnering with an experienced chatbot consultant. 

Are you interested in learning how to outsource your chatbot development and project management to save yourself time and money? Get in touch with a Chatbot Consultant today to have your questions answered!

In order to improve the interaction process between restaurant holders and their clients, Fetchh knew they needed to create something unique and innovative.

fetchh
fetchh

Fetchh is an English chatbot startup, who’s Facebook Messenger chatbot was created for café lovers who did not want to wait in line or miss out on the freshly baked, first come first serve dishes.

fetchh

Most days you are rushing through life, the modern world demands it. The faster a transaction can happen, the happier everyone is. Which is why Fetchh knew they needed to solve the proble of people wasting time waiting in resturant and cafe queues. Waiting to order their favorite foods or even having to download multiple apps is unnecessary for ordering food.

fetchh

The goal was to simplify the process of managing restaurants, making it easier for the cafe owners, employees and even the market stall owners.

The goal was to solve the problem of clients wasting their valuable time waiting in queues.

fetchh

Fetchh started looking for a company that could help them create a product that would fix this growing problem. After doing their research, they discovered BotsCrew and knew their knowledge of the restaurant industry was a perfect match. Fetchh trusted the team and knew they could fully rely on them during the development process.

fetchh

BotsCrew is the global leader in Chatbot Development with offices in London, UK, and Lviv, Ukraine. Founded in 2016, BotsCrew designs and develops custom AI chatbots to help small and medium enterprises bring stellar customer experience to their markets.

fetchh

With a relationship built on trust, BotsCrew and Fetchh collaborated as teams and discussed the different possibilties for the future of the chatbot. After many discussions and ideas from both Fetchh and BotsCrew, the teams where about to create a product that brings true value to the restuarant industry.

Fetchh Facebook Messenger Chatbot: a mashup of social, mobile, and instant messaging power – which is exactly the direction that marketing is going in.

A great solution for people who have a busy life and very little time, it is a cashless process and is a quick and easy pickup experience for anyone who uses it. It also makes it easier to follow café and market trends, so you always know what is fresh, new, or straight out of the oven. Automation and optimization of the whole working process.

fetchh

Chatbots are equally popular among millennials and baby boomers.

Mobile Markers
fetchh

Originally, BotsCrew started the development process with the solution being one simple functionality: one chatbot for one restaurant, where you can view the menu, place your order and pay for your food. However, once the project got started and ideas began to flow, they decided to go further and make a platform for multiple restaurants. BotsCrew and Fetchh had the vision for this bot to be useful for both the customers and the owners of the restaurant, and so they divided Fetchh into a user bot and an admin bot.

Fetchh had no doubts and trusted BotsCrew to create a UX design that they knew would be the best for this chatbot and that Fetchh would love.

The result is a unique and innovative chatbot that is now starting to become popular. In fact, if you want to chat with Fetchh, you can do so here.

Fetchh may be connected to one Facebook page, but in a single chatbot it unites a lot of restaurants. It is without a doubt one of the most prospective startups because it reduces and takes away routine work and due to this, it helps improve the interaction process between a customer and their favorite café.

Read more case studies here.