The educators or class organizers can opt for chatbots to simplify daily routine tasks. Chatbots may serve as a helping hand to the teacher in dealing with the daily queries by allowing bots to answer the questions of students on a daily basis, or perhaps even check their homework. Eventually, they offer teachers more time to work with their students on a one-by-one basis.

For starters, he was the former president of PayPal. And he once founded a mobile media monetization firm. And he also founded a company that facilitated mobile phone payments. And then he helped Facebook acquire Braintree, which invented Venmo. And then he invented Messenger’s P2P payment platform. And then he was appointed to the board of directors at Coinbase.
It may be tempting to assume that users will perform procedural tasks one by one in a neat and orderly way. For example, in a procedural conversation flow using dialogs, the user will start at root dialog, invoke the new order dialog from there, and then invoke the product search dialog. Then the user will select a product and confirm, exiting the product search dialog, complete the order, exiting the new order dialog, and arrive back at the root dialog.
Bots are also used to buy up good seats for concerts, particularly by ticket brokers who resell the tickets.[12] Bots are employed against entertainment event-ticketing sites. The bots are used by ticket brokers to unfairly obtain the best seats for themselves while depriving the general public of also having a chance to obtain the good seats. The bot runs through the purchase process and obtains better seats by pulling as many seats back as it can.
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.
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.

The biggest benefit of having a conversational AI solution is the instant response rate. Answering queries within an hour translates into 7X increase in the likelihood of converting a lead. Customers are more likely to talk about a negative experience than a positive one. So nipping a negative review right in the bud is going to help improve your product’s brand standing.
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.
Interface designers have come to appreciate that humans' readiness to interpret computer output as genuinely conversational—even when it is actually based on rather simple pattern-matching—can be exploited for useful purposes. Most people prefer to engage with programs that are human-like, and this gives chatbot-style techniques a potentially useful role in interactive systems that need to elicit information from users, as long as that information is relatively straightforward and falls into predictable categories. Thus, for example, online help systems can usefully employ chatbot techniques to identify the area of help that users require, potentially providing a "friendlier" interface than a more formal search or menu system. This sort of usage holds the prospect of moving chatbot technology from Weizenbaum's "shelf ... reserved for curios" to that marked "genuinely useful computational methods".