WeChat was created by Chinese holding company Tencent three years ago. The product was created by a special projects team within Tencent (who also owns the dominant desktop messaging software in China, QQ) under the mandate of creating a completely new mobile-first messaging experience for the Chinese market. In three short years, WeChat has exploded in popularity and has become the dominant mobile messaging platform in China, with approximately 700M monthly active users (MAUs).
One of the first stepping stones to this future are AI-powered messaging solutions, or conversational bots. A conversational bot is a computer program that works automatically and is skilled in communicating through various digital media—including intelligent virtual agents, organizations' apps, organizations' websites, social platforms and messenger platforms. Users can interact with such bots, using voice or text, to access information, complete tasks or execute transactions. 
For example, say you want to purchase a pair of shoes online from Nordstrom. You would have to browse their site and look around until you find the pair you wanted. Then you would add the pair to your cart to go through the motions of checking out. But in the case Nordstrom had a conversational bot, you would simply tell the bot what you’re looking for and get an instant answer. You would be able to search within an interface that actually learns what you like, even when you can’t coherently articulate it. And in the not-so-distant future, we’ll even have similar experiences when we visit the retail stores.

Human touch. Chatbots, providing an interface similar to human-to-human interaction, are more intuitive and so less difficult to use than a standard banking mobile application. They doesn't require any additional software installation and are more adaptive as able to be personalized during the exploitation by the means of machine learning. Chatbots are instant and so much faster that phone calls, shown to be considered as tedious in some studies. Then they satisfy both speed and personalization requirement while interacting with a bank.


As ChatbotLifeexplained, developing bots is not the same as building apps. While apps specialise in a number of functions, chatbots have a bigger capacity for inputs. The trick here is to start with a simple objective and focus on doing it really well (i.e., having a minimum viable product or ‘MVP’). From that point onward, businesses can upgrade their bots.

Two trends — the exploding popularity of mobile messaging apps and advances in artificial intelligence — are coinciding to enable a new generation of tools that enable brands to communicate with customers in powerful new ways at reduced cost. Retailers and technology firms are experimenting with chatbots, powered by a combination of machine learning, natural language processing, and live operators, to provide customer service, sales support, and other commerce-related functions.


“I’ve seen a lot of hyperbole around bots as the new apps, but I don’t know if I believe that,” said Prashant Sridharan, Twitter’s global director of developer relations. “I don’t think we’re going to see this mass exodus of people stopping building apps and going to build bots. I think they’re going to build bots in addition to the app that they have or the service they provide.”
Utility bots solve a user's problem, whatever that may be, via a user-prompted transaction. The most obvious example is a shopping bot, such as one that helps you order flowers or buy a new jacket. According to a recent HubSpot Research study, 47% of shoppers are open to buying items from a bot. But utility bots are not limited to making purchases. A utility bot could automatically book meetings by scanning your emails or notify you of the payment subscriptions you forgot you were signed up for.
One of the more talked about integrations has been Taco Bell‘s announcement that it is working on a Slackbot (appropriately named Tacobot) which will not only take your Gordita Supreme order but will do it with the same “witty personality you’d expect from Taco Bell.” Consumer demand for such a service remains to be seen, but it hints at the potential for brands to leverage Slack’s platform and growing audience.
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.
These are just a few of the most inspirational chatbot startups from the last year, with numerous others around the globe currently receiving acclaim for how quickly and innovatively they are using AI to change the world. With development becoming more intuitive and accessible to people all over the world, we can expect to see more startups using new technology to solve old problems.
Using chatbot builder platforms. You can create a chatbot with the help of services providing all the necessary features and integrations. It can be a good choice for an in-house chatbot serving your team. This option is associated with some disadvantages, including the limited configuration and the dependence on the service. Some popular platforms for building chatbots are:
aLVin is built on the foundation of Nuance’s Nina, the intelligent multichannel virtual assistant that leverages natural language understanding (NLU) and cognitive computing capabilities. aLVin interacts with brokers to better understand “intent” and deliver the right information 24/7; the chatbot was built with extensive knowledge of LV=Broker’s products, which accelerated the process of being able to answer more questions and direct brokers to the right products early on
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.
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.
Once your bot is running in production, you will need a DevOps team to keep it that way. Continually monitor the system to ensure the bot operates at peak performance. Use the logs sent to Application Insights or Cosmos DB to create monitoring dashboards, either using Application Insights itself, Power BI, or a custom web app dashboard. Send alerts to the DevOps team if critical errors occur or performance falls below an acceptable threshold.
Being an early adopter of a new channel can provide enormous benefits, but that comes with equally high risks. This is amplified within marketplaces like Amazon. Early adopters within Amazon's marketplace were able to focus on building a solid base of reviews for their products - a primary ranking signal - which meant that they'd create huge barriers to entry for competitors (namely because they were always showing up in the search results before them).
For designing a chatbot conversation, you can refer this blog — “How to design a conversation for chatbots.” Chatbot interactions are segmented into structured and unstructured interactions. As the name suggests, the structured type is more about the logical flow of information, including menus, choices, and forms into account. The unstructured conversation flow includes freestyle plain text. Conversations with family, colleagues, friends and other acquaintances fall into this segment. Developing scripts for these messages will follow suit. While developing the script for messages, it is important to keep the conversation topics close to the purpose served by the chatbot. For the designer, interpreting user answers is important to develop scripts for a conversational user interface. The designer also turns their attention to close-ended conversations that are easy to handle and open-ended conversations that allow customers to communicate naturally.
Reports of political interferences in recent elections, including the 2016 US and 2017 UK general elections,[3] have set the notion of botting being more prevalent because of the ethics that is challenged between the bot’s design and the bot’s designer. According to Emilio Ferrara, a computer scientist from the University of Southern California reporting on Communications of the ACM,[4] the lack of resources available to implement fact-checking and information verification results in the large volumes of false reports and claims made on these bots in social media platforms. In the case of Twitter, most of these bots are programmed with searching filter capabilities that target key words and phrases that reflect in favor and against political agendas and retweet them. While the attention of bots is programmed to spread unverified information throughout the social media platform,[5] it is a challenge that programmers face in the wake of a hostile political climate. Binary functions are designated to the programs and using an Application Program interface embedded in the social media website executes the functions tasked. The Bot Effect is what Ferrera reports as when the socialization of bots and human users creates a vulnerability to the leaking of personal information and polarizing influences outside the ethics of the bot’s code. According to Guillory Kramer in his study, he observes the behavior of emotionally volatile users and the impact the bots have on the users, altering the perception of reality.
It’s not all doom and gloom for chatbots. Chatbots are a stopgap until virtual assistants are able to tackle all of our questions and concerns, regardless of the site or platform. Virtual assistants will eventually connect to everything in your digital life, from websites to IoT-enabled devices. Rather than going through different websites and speaking to various different chatbots, the virtual assistant will be the platform for finding the answers you need. If these assistants are doing such a good job, why would you even bother to use a branded chatbot? Realistically this won’t take place for sometime, due to the fragmentation of the marketplace.
When we open our news feed and find out about yet another AI breakthrough—IBM Watson, driverless cars, AlphaGo — the notion of TODA may feel decidedly anti-climatic. The reality is that the current AI is not quite 100% turnkey-ready for TODA. This will soon change due to two key factors: 1) businesses want it, and 2) businesses have abundant data, the fuel that the current state-of-the-art machine learning techniques need to make AI work.
Today, more than ever, instant availability and approachability matter. Which is why your presence should be dictated by your customer’s preference or the type of message your business wants to convey. Keep in mind that these can overlap or change depending on your demographic you wish to acquire or cater to. There are very few set-in-stone rules when it comes to new customers.

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".
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