In business-to-business environments, chatbots are commonly scripted and used to respond to frequently asked questions or perform simple, repetitive calls to action. In sales, for example, a chatbot may be a quick way for sales reps to get phone numbers. Chatbots can also be used in service departments, assisting service agents in answering repetitive requests. For example, a service rep might provide the chatbot with an order number and ask when the order was shipped. Generally, once a conversation gets too complex for a chatbot, the call or text window will be transferred to a human service agent.
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.
In 1950, Alan Turing's famous article "Computing Machinery and Intelligence" was published, which proposed what is now called the Turing test as a criterion of intelligence. This criterion depends on the ability of a computer program to impersonate a human in a real-time written conversation with a human judge, sufficiently well that the judge is unable to distinguish reliably—on the basis of the conversational content alone—between the program and a real human. The notoriety of Turing's proposed test stimulated great interest in Joseph Weizenbaum's program ELIZA, published in 1966, which seemed to be able to fool users into believing that they were conversing with a real human. However Weizenbaum himself did not claim that ELIZA was genuinely intelligent, and the Introduction to his paper presented it more as a debunking exercise:
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…
Ultimately, only time will tell how effective the likes of Facebook Messenger will become in the long term. As more and more companies look to use chatbots within the platform, the greater the frequency of messages that individual users will receive. This could result in Facebook (and other messaging platforms) placing stricter restrictions on usage, but until then I'd recommend testing as much as possible.
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.
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.
Chatbots can direct customers to a live agent if the AI can’t settle the matter. This lets human agents focus their efforts on the heavy lifting. AI chatbots also increase employee productivity. Globe Telecom automated their customer service via Messenger and saw impressive results. The company increased employee productivity by 3.5 times. And their customer satisfaction increased by 22 percent.
Just last month, Google launched its latest Google Assistant. To help readers get a better glimpse of the redesign, Google’s Scott Huffman explained: “Since the Assistant can do so many things, we’re introducing a new way to talk about them. We’re them Actions. Actions include features built by Google—like directions on Google Maps—and those that come from developers, publishers, and other third parties, like working out with Fitbit Coach.”
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 .
Companies most likely to be supporting bots operate in the health, communications and banking industries, with informational bots garnering the majority of attention. However, challenges still abound, even among bot supporters, with lack of skilled talent to develop and work with bots cited as a challenge in implementing solutions, followed by deployment and acquisition costs, as well as data privacy and security.
This kind of thinking has lead me to develop a bot where the focus is as a medium for content rather than a subsitute for intelligence. So users create content much as conventional author, (but with text stored in spreadsheets rather than anywhere else). Very little is expected from the bot in terms of human behavious such as “learning”, “empathy”, “memory” and character”. Does it work?
Chatbots are a great way to answer customer questions. According to a case study, Amtrak uses chatbots to answer roughly 5,000,000 questions a year. Not only are the questions answered promptly, but Amtrak saved $1,000,000 in customer service expenses in the year the study was conducted. It also experienced a 25 percent increase in travel bookings.
In the early 90’s, the Turing test, which allows determining the possibility of thinking by computers, was developed. It consists in the following. A person talks to both the person and the computer. The goal is to find out who his interlocutor is — a person or a machine. This test is carried out in our days and many conversational programs have coped with it successfully.
This is a lot less complicated than it appears. Given a set of sentences, each belonging to a class, and a new input sentence, we can count the occurrence of each word in each class, account for its commonality and assign each class a score. Factoring for commonality is important: matching the word “it” is considerably less meaningful than a match for the word “cheese”. The class with the highest score is the one most likely to belong to the input sentence. This is a slight oversimplification as words need to be reduced to their stems, but you get the basic idea.
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.
Let’s take a weather chat bot as an example to examine the capabilities of Scripted and Structured chatbots. The question “Will it rain on Sunday?” can be easily answered. However, if there is no programming for the question “Will I need an umbrella on Sunday?” then the query will not be understood by the chat bot. This is the common limitation with scripted and structured chatbots. However, in all cases, a conversational bot can only be as intelligent as the programming it has been given.
“It’s hard to balance that urge to just dogpile the latest thing when you’re feeling like there’s a land grab or gold rush about to happen all around you and that you might get left behind. But in the end quality wins out. Everyone will be better off if there’s laser focus on building great bot products that are meaningfully differentiated.” — Ryan Block, Cofounder of Begin.com
“Utility gets something done following a prompt. At a higher level the more entertainment-related chatbots are able to answer all questions and get things done. Siri and Cortana you can have small talk with, as well as getting things done, so they are much harder to build. They took years and years of giant company’s efforts. Different companies that don’t have those resources, like Facebook, will build more constrained utility bots.”
Kik is one of the most popular chat apps among teens with 275M MAUs and 40% of those are in the 13–24 year old demographic. In April, Kik launched its own bot store with 16 launch partners including Sephora, H&M, Vine, the Weather Channel, and Funny or Die. Using Kik’s bots currently feel like using the internet in 1994, very rough around the edges and limited functionality / usefulness. However, we’ll see how their API and bots progress over time, Kik’s popularity among an attractive demographic might convince some brands to invest in the platform.
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.

Several studies accomplished by analytics agencies such as Juniper or Gartner [34] report significant reduction of cost of customer services, leading to billions of dollars of economy in the next 10 years. Gartner predicts an integration by 2020 of chatbots in at least 85% of all client's applications to customer service. Juniper's study announces an impressive amount of $8 billion retained annually by 2022 due to the use of chatbots.
×