In our work at ZipfWorks building and scaling intelligent shopping platforms and applications, we pay close attention to emerging trends impacting digital commerce such as chatbots and mobile commerce. As this nascent trend towards a more conversational commerce ecosystem unfolds at a dizzying pace, we felt it would be useful to take a step back and look at the major initiatives and forces shaping this trend and compiled them here in this report. We’ve applied some of these concepts in our current project Dealspotr, to help more shoppers save more money through intelligent use of technology and social product design.
Several studies accomplished by analytics agencies such as Juniper or Gartner  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.
Dan uses an example of a text to speech bot that a user might operate within a car to turn windscreen wipers on and off, and lights on and off. The users’ natural language query is processed by the conversation service to work out the intent and the entity, and then using the context, replies through the dialog in a way that the user can understand.
…utilizing chat, messaging, or other natural language interfaces (i.e. voice) to interact with people, brands, or services and bots that heretofore have had no real place in the bidirectional, asynchronous messaging context. The net result is that you and I will be talking to brands and companies over Facebook Messenger, WhatsApp, Telegram, Slack, and elsewhere before year’s end, and will find it normal.
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.
“We believe that you don’t need to know how to program to build a bot, that’s what inspired us at Chatfuel a year ago when we started bot builder. We noticed bots becoming hyper-local, i.e. a bot for a soccer team to keep in touch with fans or a small art community bot. Bots are efficient and when you let anyone create them easily magic happens.” — Dmitrii Dumik, Founder of Chatfuel
Can we provide a better way of doing business that transforms an arduous “elephant-in-the-room” process or task into one that allows all involved parties to stay active and engaged? As stated by Grayevsky, “I saw a huge opportunity to design a technology platform for both job seekers and employers that could fill the gaping ‘black hole’ in recruitment and deliver better results to both sides.”
Along with the continued development of our avatars, we are also investigating machine learning and deep learning techniques, and working on the creation of a short term memory for our bots. This will allow humans interacting with our AI to develop genuine human-like relationships with their bot; any personal information that is exchanged will be remembered by the bot and recalled in the correct context at the appropriate time. The bots will get to know their human companion, and utilise this knowledge to form warmer and more personal interactions.
“I believe the dreamers come first, and the builders come second. A lot of the dreamers are science fiction authors, they’re artists…They invent these ideas, and they get catalogued as impossible. And we find out later, well, maybe it’s not impossible. Things that seem impossible if we work them the right way for long enough, sometimes for multiple generations, they become possible.”
The goal of intent-based bots is to solve user queries on a one to one basis. With each question answered it can adapt to the user behavior. The more data the bots receive, the more intelligent they become. Great examples of intent-based bots are Siri, Google Assistant, and Amazon Alexa. The bot has the ability to extract contextual information such as location, and state information like chat history, to suggest appropriate solutions in a specific situation.
According to the Journal of Medical Internet Research, "Chatbots are [...] increasingly used in particular for mental health applications, prevention and behavior change applications (such as smoking cessation or physical activity interventions).". They have been shown to serve as a cost-effective and accessible therapeutic agents for indications such as depression and anxiety. A conversational agent called Woebot has been shown to significantly reduce depression in young adults.
Through Amazon’s developer platform for the Echo (called Alexa Skills), developers can develop “skills” for Alexa which enable her to carry out new types of tasks. Examples of skills include playing music from your Spotify library, adding events to your Google Calendar, or querying your credit card balance with Capital One — you can even ask Alexa to “open Dominoes and place my Easy Order” and have pizza delivered without even picking up your smartphone. Now that’s conversational commerce in action.
There are multiple chatbot development platforms available if you are looking to develop Facebook Messenger bot. While each has their own pros and cons, Dialogflow is one strong contender. Offering one of the best NLU (Natural Language Understanding) and context management, Dialogflow makes it very easy to create Facebook Messenger bot. In this tutorial, we’ll…
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.
Other bots like X.ai can help schedule your meetings for you. Simply add the bot to your email thread, and it will take over back-and-forth conversation needed to schedule a meeting, alert you once it’s been arranged and add it to your calendar. As bot technology improves, the thinking is that bots will be able to automate all kinds of things; perhaps even something as complex as your taxes.
In one particularly striking example of how this rather limited bot has made a major impact, U-Report sent a poll to users in Liberia about whether teachers were coercing students into sex in exchange for better grades. Approximately 86% of the 13,000 Liberian children U-Report polled responded that their teachers were engaged in this despicable practice, which resulted in a collaborative project between UNICEF and Liberia’s Minister of Education to put an end to it.
Pop-culture references to Skynet and a forthcoming “war against the machines” are perhaps a little too common in articles about AI (including this one and Larry’s post about Google’s RankBrain tech), but they do raise somewhat uncomfortable questions about the unexpected side of developing increasingly sophisticated AI constructs – including seemingly harmless chatbots.
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: