How are #chatbots and #ArtificialIntelligence changing the way businesses communicate? http://bit.ly/2zXOJKD #AI

There are a few schools of thought when it comes to chatbot plugins. You either love them or you hate them. #web #chatbots #chillybin

The purpose of chat bots is to support and scale business teams in their
relations with customers. Doing this helps businesses save a lot of money
which is why many business owners are adopting this technology. And given
the fact that these bots can be placed in places like Facebook Messenger, Slack, Telegram, SMS based or on your own
website gives you the potential to reach a bigger audience.

Learn The Top 5 Benefits Of Using Chatbots For Your Business
https://chatbotsmagazine.com/top-5-benefits-with-using-chatbots-for-your-business-159a0cee7d8a

Find out if you need a #Chatbot for your business.
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#Chatbots: Tedious tasks, made easy. #AI #HRTech #Bot https://hubs.ly/H09f6Jh0

‪Chat-Bots vs. Google Search https://medium.com/swlh/chat-bots-vs-google-search-b44d11a53f14

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Learn to Build Your Own Alexa with us on November 30th! #Chatbots #AI #MachineLearning Register here: https://hubs.ly/H09dB0V0

NLP, NLU, NLG and how Chatbots work: Various acronyms and words are thrown around while talking about Chatbots and at first glance it seems they’re all interchangeable with each other. To understand what the future of chatbots holds, let’s familiarize ourselves with three basic acronyms.NLP (Natural Language Processing), NLU (Natural Language Understanding) and NLG (Natural Language Generation). I. NLP, or Natural Language Processing is a blanket term used to describe a machine’s ability to ingest what is said to it, break it down, comprehend its meaning, determine appropriate action, and respond back in language the user will understand. II. NLU, or Natural Language Understanding is a subset of NLP that deals with the much narrower, but equally important facet of how to best handle unstructured inputs and convert them into a structured form that a machine can understand and act upon. While humans are able to effortlessly handle mispronunciations, swapped words, contractions, colloquialisms, and other quirks, machines are less adept at handling unpredictable inputs. III. NLG, or Natural Language Generation, simply put, is what happens when computers write language. NLG processes turn structured data into text. Now imagine for a minute what the process for communication with another human being is like. Your mother asks you to go buy some Tropicana 100% Orange Juice. Your first question is how much of it does she want? 1 litre? 500ml? 200? She tells you she wants a 1 litre Tropicana 100% Orange Juice. Now you know that regular Tropicana is easily available, but 100% is hard to come by, so you call up a few stores beforehand to see where it’s available. You find one store that’s pretty close by, so you go back to your mother and tell her you found what she wanted. It’s $2, maybe $3, and after asking her for the money, you go on your way.A chatbot follows the same process, with two fundamental differences, the channel of communication and what you’re talking to. I’ll give you a step by step breakdown based on the most fundamental principles of AI/Chatbots.Architecture Diagram for Chatbots * You find a product on Facebook’s Messenger and for the sake of consistency, let’s say it’s the same bottle of Tropicana. You only ever see the presentation layer and send the bot a message that is picked up by the backend saying you want some Tropicana. * Using Natural Language Processing (what happens when computers read language. NLP processes turn text into structured data), the machine converts this plain text request into codified commands for itself. * Now the chatbot throw this data into a decision engine, since in the bots mind it has certain criteria to meet to exit the conversational loop, notably, the quantity of Tropicana you want. * Using Natural Language Generation (what happens when computers write language. NLG processes turn structured data into text), much like you did with your mother the bot asks you how much of said Tropicana you wanted. * This array of responses goes back into the messaging backend and is presented to you in the form of a question. You tell the bot you want 1 litre and we go back through NLP into the decision engine. * The bot now analyzes pre-fed data about the product, stores, their locations and their proximity to your location. It identifies the closest store that has this product in stock and tells you what it costs. * It then directs you to a payment portal and after it receives confirmation from gateway, it places your order for you, and voila in one to two business days, you have 1 litre of Tropicana 100% Orange Juice. The biggest difference between chatbots and humans at this point of time though, is what the industry calls empathy understanding. Chatbots simply aren’t as adept as humans at understanding conversational undertones. For example, there’s a very large difference between the statements,“We need to talk baby!” and“we need to talk babe….” Which while immediately apparent to a human being, is difficult for a machine to comprehend. Progress is being made in this field though and soon machines will not only be able to understand what you’re saying, but also how you’re saying it and what you’re feeling while you’re saying it.https://medium.com/media/7078d8ad19192c4c53d3bf199468e4ab/href --- NLP, NLU, NLG and how Chatbots work was originally published in Chatbot’s Life on Medium, where people are continuing the conversation by highlighting and responding to this story. #ai #bots #chatbots

5 Ways #Chatbots and #AI Impact Customer Education
As chatbots were mainly deployed as customer service tools, #customer #education teams can use the chatbot data to influence the education programs and assets they manage.

http://bit.ly/2zNjrFN

Designing chatbots and what we can learn from IVR: Chatbots are a hot topic. Looking at my twitter and LinkedIn feeds, the interest and excitement that is bubbling around this area is vast. Having worked in conversational user experience (UX) for the past 12 years, it’s exciting to see that the industry is experiencing a rapid growth, and delving into emerging technologies like bots and agents. However, the reality is that conversational interfaces aren’t a new thing. At VoxGen we’ve been designing and learning about conversational UX for the last decade. A lot of our early work focused on IVR (and that demand shows no signs of slowing down) but there’s no doubt that bots and agents are the new conversational interfaces that everyone is talking about. While IVR and chatbots may seem like a world apart, our experience has taught us is that the fundamentals of conversational UX for emerging technologies like chatbots are closely linked to the fundamentals of IVR design. There are two key areas where IVR and chatbot design are intrinsically linked: designing for conversation and persona. So, what can our experiences in IVR teach is for the design of these chatbots? Designing for conversation — written Vs. spoken dialog Designing natural conversational interfaces isn’t as easy as it may seem. When we’re designing an IVR for example, it’s important to understand and use the norms of spoken conversation. These norms are very different to the norms of written dialog. Spoken dialog tends to be less formal, relies on contextual cues and, when face to face, a lot can be implied from body language. When creating conversational interfaces, we need to understand and apply those conversational norms. In a recent VoxGen and Forrester webinar on ‘Chatbots or Optimisation?’, Kerry Robinson (CEO at VoxGen) builds on the need to follow conversational norms by reminding us that language is an instinct and something that we learn from a young age. This means that it’s very difficult for us to unlearn. In conversational design, whether that be for an IVR or a chatbot, we need to leverage that instinct for language and design those conversational interactions based on how we talk to each other or how we text each other. And we need to follow the melody of language and to use terminology that is familiar to users. The formality (or register) of the dialog will be dependent on the context and task, but conversational dialog is typically less formal than written content. Chatbot design is an interesting mix. On the one hand, we’re dealing with a visual interface but we’re also designing with ‘chat’ in mind. IVR has shown us that the appropriate use of contractions (e.g. isn’t, can’t, won’t rather than is not, cannot and will not) helps improve the flow of the conversational interface without impacting negatively on brand perception. When we’re having a conversation with someone, context is often implied by what has been said previously as well as non-verbal cues — hesitations, tone of voice, body language. When designing IVRs and chatbots, we need to think about how we can imply context without repetition of being too formal. Take this IVR example from a credit card payment. The first example follows conversational norms while the second example follows written dialog norms: Example 1: Conversational norms Example 2: Written norms When you read this second example in your head, it may sound OK. But now read both versions aloud. Notice the difference? In usability evaluations, the first approach gets better results because it mimics the norms of conversation, matching users’ mental model of what a conversation would sound like if they were interacting with a live person. The second example in comparison is often perceived as long-winded, less efficient and too formal. As with IVR, chatbot interaction is also about designing a two-way conversation so it’s important to design to the same conversational norms that we would follow when designing an IVR. However, there is a balance between being overly conversational in chat while still portraying professionalism. One interesting area of continued investigation related to formality in chatbot design is the use of emojis and ‘text speak’ e.g. LOL, TTFN. From our experience, the decision to use these SMS elements is as much about concerns over brand perception as the actual task at hand. Adding a ‘smiley’ in a response to a complaint for example is clearly not a great idea. But some brands also worry that using these elements reduce the perception of professionalism. The impact and relevance of use needs to be judged on a case by case basis, but essentially the only way to determine how users will react to the use of emojis and text speak is to test with users. The formality of conversation appropriate to chatbots is something that we’re currently researching, so more on that to come. The importance of persona Persona relates to the character portrayed by the interface — not just the voice but also dialog style. In IVR, we know that callers will make an impression based on their perception of the persona (or character) they are dealing with. When we’re designing an IVR persona, there is a formal process that we go through, based on research, to make sure the persona is relevant and applicable to the brand, caller population and reason for call. We therefore start with context research to understand the callers and their tasks and the brand attributes that need to be portrayed by the IVR persona. It’s also important to evaluate the persona with representative users to ensure the persona design matches brand and user expectation. The same is true for chatbots. When we’re designing the conversational dialog for a chatbot, we’re always designing with users, tasks and brand in mind. The same contextual research needs to be carried out in order understand how to portray appropriate persona characteristics through the chatbot dialog. In a recent text messaging chatbot usability evaluation, insight showed us that users liked the conversational dialog style, but their expectations of using SMS to contact a company was very different to that of how they would interact with a friend. Not surprisingly, the chatbot dialog needed to be friendly but also professional. Interestingly, we didn’t initially tell participants that they were interacting with an automated system and the dialog didn’t explicitly state that they were interacting with a bot. The conversational style of the dialog and usability of the system meant that all but one participant felt they were interacting with a live agent. While SMS content from participants started formally, towards the end of an interaction, those responses became increasingly more informal. It would be interesting to investigate whether this changes when participants know upfront that they are dealing with an automated bot. Our recent research has shown us that chatbot persona is just as important as IVR persona. So, when designing dialog for chatbots, it’s essential that you understand the users, tasks and brand attributes and that dialog style is designed accordingly. Usability evaluations will help you understand whether your chatbot persona is working well. In summary… Our research has highlighted that our learnings from IVR design can be applied to chatbot design , particularly in terms of designing conversations and creating the right personas. There are of course differences in terms of context and interaction modes that will impact user expectations and behaviors, but the fundamental principles of conversational design and persona learnt from IVR can be consistently applied to chatbots. To find out more about designing effective chatbots and how they fit within your wider customer experience tool kit, watch our ‘Chatbots or Optimisation?’ webinar. Originally published at www.voxgen.com.https://medium.com/media/7078d8ad19192c4c53d3bf199468e4ab/href --- Designing chatbots and what we can learn from IVR was originally published in Chatbot’s Life on Medium, where people are continuing the conversation by highlighting and responding to this story. #ai #bots #chatbots