Poor user experience. The bottom line: chatbots frustrate your customers if you are viewing them as a replacement for humans. Do not ever, ever try to pass of a chatbot as a human. If your chatbot suffers from any of the issues above, you’re probably creating a poor customer experience overall and an angry phone call to a poor unsuspecting call center rep.
Intents: It is basically the action chatbot should perform when the user say something. For instance, intent can trigger same thing if user types “I want to order a red pair of shoes”, “Do you have red shoes? I want to order them” or “Show me some red pair of shoes”, all of these user’s text show trigger single command giving users options for Red pair of shoes.

One of the key advantages of Roof Ai is that it allows real-estate agents to respond to user queries immediately, regardless of whether a customer service rep or sales agent is available to help. This can have a dramatic impact on conversion rates. It also eliminates potential leads slipping through an agent’s fingers due to missing a Facebook message or failing to respond quickly enough. 
aLVin is built on the foundation of Nuance’s Nina, the intelligent multichannel virtual assistant that leverages natural language understanding (NLU) and cognitive computing capabilities. aLVin interacts with brokers to better understand “intent” and deliver the right information 24/7; the chatbot was built with extensive knowledge of LV=Broker’s products, which accelerated the process of being able to answer more questions and direct brokers to the right products early on
The fact that you can now run ads directly to Messenger is an enormous opportunity for any business. This skips the convoluted and leaky process of trying to acquire someone's email address to nurture them outside of Facebook's platform. Instead, you can retain the connection with someone inside Facebook and improve the overall conversion rates to receiving an engagement.
These are just a few of the most inspirational chatbot startups from the last year, with numerous others around the globe currently receiving acclaim for how quickly and innovatively they are using AI to change the world. With development becoming more intuitive and accessible to people all over the world, we can expect to see more startups using new technology to solve old problems.

The Evie chatbot has had a huge impact on social media over the last few years. She is probably the most popular artificial personality on YouTube. She has appeared in several videos by PewdiePie, the most subscribed YouTuber in the world. This includes a flirting video with over 12 million views! Evie has been filmed speaking many different languages. She chats with Squeezie in French, El Rubius and El Rincón De Giorgio in Spanish, GermanLetsPlay and ConCrafter in German, NDNG - Enes Batur in Turkish, Stuu Games in Polish and jacksepticeye, ComedyShortsGamer and KSIOlajidebtHD in English. And that is a very small selection. Evie shares her database with Cleverbot, which is an internet star in its own right. Cleverbot conversations have long been shared on Twitter, Facebook, websites, forums and bulletin boards. We are currently working to give Evie some more artificial companions, such as the male avatar Boibot.
The bot (which also offers users the opportunity to chat with your friendly neighborhood Spiderman) isn’t a true conversational agent, in the sense that the bot’s responses are currently a little limited; this isn’t a truly “freestyle” chatbot. For example, in the conversation above, the bot didn’t recognize the reply as a valid response – kind of a bummer if you’re hoping for an immersive experience.
One of the first stepping stones to this future are AI-powered messaging solutions, or conversational bots. A conversational bot is a computer program that works automatically and is skilled in communicating through various digital media—including intelligent virtual agents, organizations' apps, organizations' websites, social platforms and messenger platforms. Users can interact with such bots, using voice or text, to access information, complete tasks or execute transactions. 
Simple chatbots, or bots, are easy to build. In fact, many coders have automated bot-building processes and templates. The majority of these processes follow simple code formulas that the designer plans, and the bots provide the responses coded into it—and only those responses. Simplistic bots (built in five minutes or less) typically respond to one or two very specific commands.
Morph.ai is an AI-powered chatbot. It works across messengers, websites, Android apps, and iOS apps. Morph.ai lets you automate up to 70 percent of your customer support. It can also integrate with your existing CRM and support tools. Plus, it can learn new queries and responses over time. You can add cards, carousels, and quick replies to enrich your conversations. It looks like this  

Prashant Sridharan, Twitter’s global director of developer relations says: “I’ve seen a lot of hyperbole around bots as the new apps, but I don’t know if I believe that. I don’t think we’re going to see this mass exodus of people stopping building apps and going to build bots. I think they’re going to build bots in addition to the app that they have or the service they provide,” as reported by re/code.
Shane Mac, CEO of San Francisco-based Assist,warned from challenges businesses face when trying to implement chatbots into their support teams: “Beware though, bots have the illusion of simplicity on the front end but there are many hurdles to overcome to create a great experience. So much work to be done. Analytics, flow optimization, keeping up with ever changing platforms that have no standard.
How far are we from building systems with commonsense? One often-heard answer is: not in the near future, while the realistic answer is: we don’t know. Last year, I spent some time trying to build a system that can do better than an information retrieval baseline in taking fourth-grade science exam (which still has a ways to go to gain a passing score of 65%). I failed hard. Here’s an example to get a sense of the difficulty of these questions.

Es gibt auch Chatbots, die gar nicht erst versuchen, wie ein menschlicher Chatter zu wirken (daher keine Chatterbots), sondern ähnlich wie IRC-Dienste nur auf spezielle Befehle reagieren. Sie können als Schnittstelle zu Diensten außerhalb des Chats dienen, oder auch Funktionen nur innerhalb ihres Chatraums anbieten, z. B. neu hinzugekommene Chatter mit dem Witz des Tages begrüßen.
Web site: From Russia With Love. PDF. 2007-12-09. Psychologist and Scientific American: Mind contributing editor Robert Epstein reports how he was initially fooled by a chatterbot posing as an attractive girl in a personal ad he answered on a dating website. In the ad, the girl portrayed herself as being in Southern California and then soon revealed, in poor English, that she was actually in Russia. He became suspicious after a couple of months of email exchanges, sent her an email test of gibberish, and she still replied in general terms. The dating website is not named. Scientific American: Mind, October–November 2007, page 16–17, "From Russia With Love: How I got fooled (and somewhat humiliated) by a computer". Also available online.

Improve loyalty: By providing a responsive, efficient experience for customers, employees and partners, a chatbot will improve satisfaction and loyalty. Whether your chatbot answers questions about employees’ corporate benefits or provides answers to technical support questions, users can come away with a strengthened connection to your organization.


With the AI future closer to becoming a reality, companies need to begin preparing to join that reality—or risk getting left behind. Bots are a small, manageable first step toward becoming an intelligent enterprise that can make better decisions more quickly, operate more efficiently, and create the experiences that keep customers and employees engaged.
Last, but not least coming in with the bot platform for business is FlowXO, which creates bots for Messenger, Slack, SMS, Telegraph and the web. This platform allows for creating various flexibility in bots by giving you the option to create a fully automated bot, human, or a hybrid of both. ChatBot expert Murray Newlands commented that "Where 10 years ago every company needed a website and five  years ago every company needed an app, now every company needs to embrace messaging with AI and chatbots."
For every question or instruction input to the conversational bot, there must exist a specific pattern in the database to provide a suitable response. Where there are several combinations of patterns available, and a hierarchical pattern is created. In these cases, algorithms are used to reduce the classifiers and generate a structure that is more manageable. This is the “reductionist” approach—or, in other words, to have a simplified solution, it reduces the problem.
User message. Once authenticated, the user sends a message to the bot. The bot reads the message and routes it to a natural language understanding service such as LUIS. This step gets the intents (what the user wants to do) and entities (what things the user is interested in). The bot then builds a query that it passes to a service that serves information, such as Azure Search for document retrieval, QnA Maker for FAQs, or a custom knowledge base. The bot uses these results to construct a response. To give the best result for a given query, the bot might make several back-and-forth calls to these remote services.
“Major shifts on large platforms should be seen as an opportunities for distribution. That said, we need to be careful not to judge the very early prototypes too harshly as the platforms are far from complete. I believe Facebook’s recent launch is the beginning of a new application platform for micro application experiences. The fundamental idea is that customers will interact with just enough UI, whether conversational and/or widgets, to be delighted by a service/brand with immediate access to a rich profile and without the complexities of installing a native app, all fueled by mature advertising products. It’s potentially a massive opportunity.” — Aaron Batalion, Partner at Lightspeed Venture Partners
Chatbots have been used in instant messaging (IM) applications and online interactive games for many years but have recently segued into business-to-consumer (B2C) and business-to-business (B2B) sales and services. Chatbots can be added to a buddy list or provide a single game player with an entity to interact with while awaiting other "live" players. If the bot is sophisticated enough to pass the Turing test, the person may not even know they are interacting with a computer program.
Love them or hate them, chatbots are here to stay. Chatbots have become extraordinarily popular in recent years largely due to dramatic advancements in machine learning and other underlying technologies such as natural language processing. Today’s chatbots are smarter, more responsive, and more useful – and we’re likely to see even more of them in the coming years.
Once your bot is running in production, you will need a DevOps team to keep it that way. Continually monitor the system to ensure the bot operates at peak performance. Use the logs sent to Application Insights or Cosmos DB to create monitoring dashboards, either using Application Insights itself, Power BI, or a custom web app dashboard. Send alerts to the DevOps team if critical errors occur or performance falls below an acceptable threshold.
Training a chatbot happens at much faster and larger scale than you teach a human. Humans Customer Service Representatives are given manuals and have them read it and understand. While the Customer Support Chatbot is fed with thousands of conversation logs and from those logs, the chatbot is able to understand what type of question requires what type of answers.
This means our questions must fit with the programming they have been given.  Using our weather bot as an example once more, the question ‘Will it rain tomorrow’ could be answered easily. However if the programming is not there, the question ‘Will I need a brolly tomorrow’ may cause the chatbot to respond with a ‘I am sorry, I didn’t understand the question’ type response.
AI, blockchain, chatbot, digital identity, etc. — there’s enough emerging technology in financial services to fill a whole alphabet book. And it’s difficult not to get swept off your feet by visions of bionic men, self-executing smart contracts, and virtual assistants that anticipate our every need. Investing in emerging technology is one of the main […]
For designing a chatbot conversation, you can refer this blog — “How to design a conversation for chatbots.” Chatbot interactions are segmented into structured and unstructured interactions. As the name suggests, the structured type is more about the logical flow of information, including menus, choices, and forms into account. The unstructured conversation flow includes freestyle plain text. Conversations with family, colleagues, friends and other acquaintances fall into this segment. Developing scripts for these messages will follow suit. While developing the script for messages, it is important to keep the conversation topics close to the purpose served by the chatbot. For the designer, interpreting user answers is important to develop scripts for a conversational user interface. The designer also turns their attention to close-ended conversations that are easy to handle and open-ended conversations that allow customers to communicate naturally.
Because chatbots are predominantly found on social media messaging platforms, they're able to reach a virtually limitless audience. They can reach a new customer base for your brand by tapping into new demographics, and they can be integrated across multiple messaging applications, thus making you more readily available to help your customers. This, in turn, opens new opportunities for you to increase sales.
Another option is to integrate your own custom AI service. This approach is more complex, but gives you complete flexibility in terms of the machine learning algorithm, training, and model. For example, you could implement your own topic modeling and use algorithm such as LDA to find similar or relevant documents. A good approach is to expose your custom AI solution as a web service endpoint, and call the endpoint from the core bot logic. The web service could be hosted in App Service or in a cluster of VMs. Azure Machine Learning provides a number of services and libraries to assist you in training and deploying your models.
Aside from being practical and time-convenient, chatbots guarantee a huge reduction in support costs. According to IBM, the influence of chatbots on CRM is staggering.  They provide a 99 percent improvement rate in response times, therefore, cutting resolution from 38 hours to five minutes. Also, they caused a massive drop in cost per query from $15-$200 (human agents) to $1 (virtual agents). Finally, virtual agents can take up an average of 30,000+ consumers per month.
Simply put, chatbots are computer programs designed to have conversations with human users. Chances are you’ve interacted with one. They answer questions, guide you through a purchase, provide technical support, and can even teach you a new language. You can find them on devices, websites, text messages, and messaging apps—in other words, they’re everywhere.

Simplified and scripted. Chatbot technology is being tacked on to the broader AI message, and while it’s important to note that machine learning will help chatbots get better at understand and responding to questions, it’s not going to make them the conversationalists we dream them to be. No matter what the marketing says, chatbots are entirely scripted. User says x, chatbot responds y.


Chatbots are unique because they not only engage with your customers, they also retain them. This means that unlike other forms of marketing, chatbots keep your customers entertained for longer. For example, let's say you catch your audience's attention with a video. While this video may be extremely engaging, once it ends, it doesn't have much more to offer.
Like most of the Applications, the Chatbot is also connected to the Database. The knowledge base or the database of information is used to feed the chatbot with the information needed to give a suitable response to the user. Data of user’s activities and whether or not your chatbot was able to match their questions, is captured in the data store. NLP translates human language into information with a combination of patterns and text that can be mapped in the real time to find applicable responses.
Open domain chatbots tends to talk about general topics and give appropriate responses. In other words, the knowledge domain is receptive to a wider pool of knowledge. However, these bots are difficult to perfect because language is so versatile. Conversations on social media sites such as Twitter and Reddit are typically considered open domain — they can go in virtually any direction. Furthermore, the whole context around a query requires common sense to understand many new topics properly, which is even harder for computers to grasp.

One pertinent field of AI research is natural language processing. Usually, weak AI fields employ specialized software or programming languages created specifically for the narrow function required. For example, A.L.I.C.E. utilises a markup language called AIML, which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so called, Alicebots. Nevertheless, A.L.I.C.E. is still purely based on pattern matching techniques without any reasoning capabilities, the same technique ELIZA was using back in 1966. This is not strong AI, which would require sapience and logical reasoning abilities.
“HubSpot's GrowthBot is an all-in-one chatbot which helps marketers and sales people be more productive by providing access to relevant data and services using a conversational interface. With GrowthBot, marketers can get help creating content, researching competitors, and monitoring their analytics. Through Amazon Lex, we're adding sophisticated natural language processing capabilities that helps GrowthBot provide a more intuitive UI for our users. Amazon Lex lets us take advantage of advanced AI and machine learning without having to code the algorithms ourselves.”
The chatbot must rely on spoken or written communications to discover what the shopper or user wants and is limited to the messaging platform’s capabilities when it comes to responding to the shopper or user. This requires a much better understanding of natural language and intent. It also means that developers must write connections to several different platforms, again like Messenger or Slack, if the chatbot is to have the same potential reach as a website.
Designing for conversational interfaces represents a big shift in the way we are used to thinking about interaction. Chatbots have less signifiers and affordances than websites and apps – which means words have to work harder to deliver clarity, cohesion and utility for the user. It is a change of paradigm that requires designers to re-wire their brain, their deliverables and their design process to create successful bot experiences.
Chattypeople is the best chatbot platform for creating an AI chatbot on Facebook with integrated Facebook commerce. With Chattypeople you can create a Facebook message both quickly and easily, no coding required. The platform's simplicity makes it ideal for entrepreneurs and marketers in smaller companies, while its technology makes it suitable for enterprise customers. You can make a simple bot answering customer service questions or integrate it with Shopify to monetize your Facebook fan pages. ChattyPeople is where f-commerce and ai-commerce come together. Chattypeople is 100% free to get started.
Chatbots have come a long way since then. They are built on AI technologies, including deep learning, natural language processing and  machine learning algorithms, and require massive amounts of data. The more an end user interacts with the bot, the better voice recognition becomes at predicting what the appropriate response is when communicating with an end user.
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