Facebook has jumped fully on the conversational commerce bandwagon and is betting big that it can turn its popular Messenger app into a business messaging powerhouse. The company first integrated peer-to-peer payments into Messenger in 2015, and then launched a full chatbot API so businesses can create interactions for customers to occur within the Facebook Messenger app. You can order flowers from 1–800-Flowers, browse the latest fashion and make purchases from Spring, and order an Uber, all from within a Messenger chat.

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.


Chatbots are gaining popularity. Numerous chatbots are being developed and launched on different chat platforms. There are multiple chatbot development platforms like Dialogflow, Chatfuel, Manychat, IBM Watson, Amazon Lex, Mircrosft Bot framework, etc are available using which you can easily create your chatbots. If you are new to chatbot development field and want to jump…
Closed domain chatbots focus on a specific knowledge domain, and these bots may fail to answer questions in other knowledge domains. For example, a restaurant booking conversational bot will be able to take your reservation, but may not respond to a question about the price of an air ticket. A user could hypothetically attempt to take the conversation elsewhere, however, closed domain chatbots are not required, nor often programmed to handle such cases.
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.

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

Endurance is a companion chatbot that uses neurolinguistics programming (better known as NLP) to have friendly conversations with suspected patients with Alzheimer’s and other forms of dementia. It uses AI technology to maintain a lucid conversation while simultaneously testing the human user’s ability to remember information in different ways. The chatbot encourages the user to talk about their favorite activities, memories, music, etc. This doesn’t just test the person’s memory but actively promotes their ability to recall.
2010 SIRI: Though Siri is considered colloquially to be a virtual assistant rather than a conversational bot, it was built off the same technologies and paved the way for all later AI bots and PAs. Siri is an intelligent personal assistant with a natural language UI to respond to questions and perform web-based service requests. Siri was part of apples IOS.

In sales, chatbots are being used to assist consumers shopping online, either by answering noncomplex product questions or providing helpful information that the consumer could later search for, including shipping price and availability. Chatbots are also used in service departments, assisting service agents in answering repetitive requests. Once a conversation gets too complex for a chatbot, it will be transferred to a human service agent .
Tay, an AI chatbot that learns from previous interaction, caused major controversy due to it being targeted by internet trolls on Twitter. The bot was exploited, and after 16 hours began to send extremely offensive Tweets to users. This suggests that although the bot learnt effectively from experience, adequate protection was not put in place to prevent misuse.[56]
Since Facebook Messenger, WhatsApp, Kik, Slack, and a growing number of bot-creation platforms came online, developers have been churning out chatbots across industries, with Facebook’s most recent bot count at over 33,000. At a CRM technologies conference in 2011, Gartner predicted that 85 percent of customer engagement would be fielded without human intervention. Though a seeming natural fit for retail and purchasing-related decisions, it doesn’t appear that chatbot technology will play favorites in the coming few years, with uses cases being promoted in finance, human resources, and even legal services.
Not integrated. This goes hand-in-hand with the contextual knowledge, but chatbots often suffer from “death by data silo” where their access to data is limited. If a chatbot is “chatting with” a customer, they not only need to access the contextual data of their customer but also have access to every place where the answer to the customer’s question may reside. Product documentation site, customer community, different websites are all places where that answer can be.
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.
1. Define the goals. What should your chatbot do? Clearly indicate the list of functions your chatbot needs to perform. 2. Choose a channel to interact with your customers. Be where your clients prefer to communicate — your website, mobile app, Facebook Messenger, WhatsApp or other messaging platform. 3. Choose the way of creation. There are two of them: using readymade chat bot software or building a custom bot from scratch. 4. Create, customize and launch. Describe the algorithm of its actions, develop a database of answers and test the work of the chatbot. Double check everything before showing your creation to potential customers.
Natural Language Processing (NLP) is the technological process in which computers derive meaning from natural human inputs. NLP-Based Conversational Bots are machine learning bots that exploit the power of artificial intelligence, which gives them a “learning brain.” These types of conversational bots have the ability to understand natural language, and do not require specific instructions to respond to questions as observed in types of chatbots such as Scripted and Structured Conversational Bots.
The chatbot design is the process that defines the interaction between the user and the chatbot.[31] The chatbot designer will define the chatbot personality, the questions that will be asked to the users, and the overall interaction.[32] [33] It can be viewed as a subset of the conversational design.In order to speed up this process, designers can use dedicated chatbot design tools, that allow for immediate preview, team collaboration and video export.[34] An important part of the chatbot design is also centered around user testing. User testing can be performed following the same principles that guide the user testing of graphical interfaces.[35]
Now, with the rise of website chatbots, this trend of two-way conversations can be taken to a whole new level. Conversational marketing can be done across many channels, such as over the phone or via SMS. However, an increasing number of companies are leveraging social media to drive their conversational marketing strategy to distinguish their brand and solidify their brand’s voice and values. When most people refer to conversational marketing, they’re talking about interactions started using chatbots and live chat – that move to personal conversations.
As retrieved from Forbes, Salesforce’s chief scientist, Richard Socher talked in a conference about his revelations of NLP and machine translation: “I can’t speak for all chatbot deployments in the world – there are some that aren’t done very well…but in our case we’ve heard very positive feedback because when a bot correctly answers questions or fills your requirements it does it very, very fast.
A chatbot works in a couple of ways: set guidelines and machine learning. A chatbot that functions with a set of guidelines in place is limited in its conversation. It can only respond to a set number of requests and vocabulary, and is only as intelligent as its programming code. An example of a limited bot is an automated banking bot that asks the caller some questions to understand what the caller wants done. The bot would make a command like “Please tell me what I can do for you by saying account balances, account transfer, or bill payment.” If the customer responds with "credit card balance," the bot would not understand the request and would proceed to either repeat the command or transfer the caller to a human assistant.
In a bot, everything begins with the root dialog. The root dialog invokes the new order dialog. At that point, the new order dialog takes control of the conversation and remains in control until it either closes or invokes other dialogs, such as the product search dialog. If the new order dialog closes, control of the conversation is returned back to the root dialog.
One of the most thriving eLearning innovations is the chatbot technology. Chatbots work on the principle of interacting with users in a human-like manner. These intelligent bots are often deployed as virtual assistants. The best example would be Google Allo - an intelligent messaging app packed with Google Assistant that interacts with the user by texting back and replying to queries. This app supports both voice and text queries.
To keep chatbots up to speed with changing company products and services, traditional chatbot development platforms require ongoing maintenance. This can either be in the form of an ongoing service provider or for larger enterprises in the form of an in-house chatbot training team.[38] To eliminate these costs, some startups are experimenting with Artificial Intelligence to develop self-learning chatbots, particularly in Customer Service applications.
ALICE – which stands for Artificial Linguistic Internet Computer Entity, an acronym that could have been lifted straight out of an episode of The X-Files – was developed and launched by creator Dr. Richard Wallace way back in the dark days of the early Internet in 1995. (As you can see in the image above, the website’s aesthetic remains virtually unchanged since that time, a powerful reminder of how far web design has come.) 

Die meisten Chatbots greifen auf eine vorgefertigte Datenbank, die sog. Wissensdatenbank mit Antworten und Erkennungsmustern, zurück. Das Programm zerlegt die eingegebene Frage zuerst in Einzelteile und verarbeitet diese nach vorgegebenen Regeln. Dabei können Schreibweisen harmonisiert (Groß- und Kleinschreibung, Umlaute etc.), Satzzeichen interpretiert und Tippfehler ausgeglichen werden (Preprocessing). Im zweiten Schritt erfolgt dann die eigentliche Erkennung der Frage. Diese wird üblicherweise über Erkennungsmuster gelöst, manche Chatbots erlauben darüber hinaus die Verschachtelung verschiedener Mustererkennungen über sogenannte Makros. Wird eine zur Frage passende Antwort erkannt, kann diese noch angepasst werden (beispielsweise können skriptgesteuert berechnete Daten eingefügt werden – „In Ulm sind es heute 37 °C.“). Diesen Vorgang nennt man Postprocessing. Die daraus entstandene Antwort wird dann ausgegeben. Moderne kommerzielle Chatbot-Programme erlauben darüber hinaus den direkten Zugriff auf die gesamte Verarbeitung über eingebaute Skriptsprachen und Programmierschnittstellen.
Perhaps the most important aspect of implementing a chatbot is selecting the right natural language processing (NLP) engine. If the user interacts with the bot through voice, for example, then the chatbot requires a speech recognition engine. Business owners also have to decide whether they want structured or unstructured conversations. Chatbots built for structured conversations are highly scripted, which simplifies programming but restricts the kinds of things that the users can ask.
In sales, chatbots are being used to assist consumers shopping online, either by answering noncomplex product questions or providing helpful information that the consumer could later search for, including shipping price and availability. Chatbots are also used in service departments, assisting service agents in answering repetitive requests. Once a conversation gets too complex for a chatbot, it will be transferred to a human service agent .
Expecting your customer care team to be able to answer every single inquiry on your social media profiles is not only unrealistic, but also extremely time-consuming, and therefore, expensive. With a chatbot, you're making yourself available to consumers 24 hours a day, seven days a week. Aside from saving you money, chatbots will help you keep your social media presence fresh and active.

1. AI-based: these ones really rely on training and are fairly complicated to set up. You train the chatbot to understand specific topics and tell your users which topics your chatbot can engage with. AI chatbots require all sorts of fall back and intent training. For example, let’s say you built a doctor chatbot (off the top of my head because I am working on one at the moment), it would have to understand that “i have a headache” and “got a headache” and “my head hurts” are the same intent. The user is free to engage and the chatbot has to pick things up.


These are hardly ideas of Hollywood’s science fiction. Even when the Starbucks bot can sound like Scarlett Johansson’s Samantha, the public will be unimpressed — we would prefer a real human interaction. Yet the public won’t have a choice; efficient task-oriented dialog agents will be the automatic vending machines and airport check-in kiosks of the near future.
With competitor Venmo already established, peer-to-peer payments is not in and of itself a compelling feature for Snapchat. However, adding wallet functionality and payment methods to the app does lay the groundwork for Snapchat to delve directly into commerce. The messaging app’s commerce strategy became more clear in April 2016 with its launch of shoppable stories with select partners in its Discover section. For the first time, while viewing video stories from Target and Lancome, users were able to “swipe up” to visit an e-commerce page embedded within the Snapchat app where they could purchase products from those partners.
Previous generations of chatbots were present on company websites, e.g. Ask Jenn from Alaska Airlines which debuted in 2008[27] or Expedia's virtual customer service agent which launched in 2011.[27][28] The newer generation of chatbots includes IBM Watson-powered "Rocky", introduced in February 2017 by the New York City-based e-commerce company Rare Carat to provide information to prospective diamond buyers.[29][30]
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