Online chatbots save time and efforts by automating customer support. Gartner forecasts that by 2020, over 85% of customer interactions will be handled without a human. However, the opportunites provided by chatbot systems go far beyond giving responses to customers’ inquiries. They are also used for other business tasks, like collecting information about users, helping to organize meetings and reducing overhead costs. There is no wonder that size of the chatbot market is growing exponentially.
To inspire your first (or next) foray into the weird and wonderful world of chatbots, we've compiled a list of seven brands whose bot-based campaigns were fueled by an astute knowledge of their target audiences and solid copywriting. Check them out below, and start considering if a chatbot is the right move for your own company's next big marketing campaign.
A chatbot is an artificial intelligence (AI) program that simulates interactive human conversation by using key pre-calculated user phrases and auditory or text-based signals. Chatbots are frequently used for basic customer service and marketing systems that frequent social networking hubs and instant messaging (IM) clients. They are also often included in operating systems as intelligent virtual assistants.
A basic SMS service is available via GitHub to start building a bot which uses IBM’s BlueMix platform which hosts the Watson Conversation Services. A developer can import a workspace to setup a new service. This starts with a blank dashboard where a developer can import all the tools needed to run the conversation service. The services has a dialog flow – a series of options with yes/no answers that the service uses to work out what the user’s intent is, what entity it’s working on, how to respond and how to phrase the response in the best way for the user.
Feine, J., Morana, S., and Maedche, A. (2019). “Leveraging Machine-Executable Descriptive Knowledge in Design Science Research ‐ The Case of Designing Socially-Adaptive Chatbots”. In: Extending the Boundaries of Design Science Theory and Practice. Ed. by B. Tulu, S. Djamasbi, G. Leroy. Cham: Springer International Publishing, pp. 76–91. Download Publication

If a text-sending algorithm can pass itself off as a human instead of a chatbot, its message would be more credible. Therefore, human-seeming chatbots with well-crafted online identities could start scattering fake news that seem plausible, for instance making false claims during a presidential election. With enough chatbots, it might be even possible to achieve artificial social proof.[58][59]
A chatbot that functions through machine learning has an artificial neural network inspired by the neural nodes of the human brain. The bot is programmed to self-learn as it is introduced to new dialogues and words. In effect, as a chatbot receives new voice or textual dialogues, the number of inquiries that it can reply and the accuracy of each response it gives increases. Facebook has a machine learning chatbot that creates a platform for companies to interact with their consumers through the Facebook Messenger application. Using the Messenger bot, users can buy shoes from Spring, order a ride from Uber, and have election conversations with the New York Times which used the Messenger bot to cover the 2016 presidential election between Hilary Clinton and Donald Trump. If a user asked the New York Times through his/her app a question like “What’s new today?” or “What do the polls say?” the bot would reply to the request.
A chatbot (also known as a talkbots, chatterbot, Bot, IM bot, interactive agent, or Artificial Conversational Entity) is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods.[1] Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatterbots use sophisticated natural language processing systems, but many simpler systems scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database.

Consumers really don’t like your chatbot. It’s not exactly a relationship built to last — a few clicks here, a few sentences there — but Forrester Analytics data shows us very clearly that, to consumers, your chatbot isn’t exactly “swipe right” material. That’s unfortunate, because using a chatbot for customer service can be incredibly effective when done […]
Through Knowledge Graph, Google search has already become amazingly good at understanding the context and meaning of your queries, and it is getting better at natural language queries. With its massive scale in data and years of working at the very hard problems of natural language processing, the company has a clear path to making Allo’s conversational commerce capabilities second to none.
The sentiment analysis in machine learning uses language analytics to determine the attitude or emotional state of whom they are speaking to in any given situation. This has proven to be difficult for even the most advanced chatbot due to an inability to detect certain questions and comments from context. Developers are creating these bots to automate a wider range of processes in an increasingly human-like way and to continue to develop and learn over time.

2. Flow-based: these work on user interaction with buttons and text. If you have used Matthew’s chatbot, that is a flow-based chatbot. The chatbot asks a question then offers options in the form of buttons (Matthew’s has a yes/no option). These are more limited, but you get the possibility of really driving down the conversation and making sure your users don’t stray off the path.
The bot (which also offers users the opportunity to chat with your friendly neighborhood Spiderman) isn’t a true conversational agent, in the sense that the bot’s responses are currently a little limited; this isn’t a truly “freestyle” chatbot. For example, in the conversation above, the bot didn’t recognize the reply as a valid response – kind of a bummer if you’re hoping for an immersive experience.
I know what you’re thinking – when will the world of marketing just stand still for a moment and let us all catch up?!?! No such luck, dear readers. No sooner have we all gotten to grips with the fact that we’re going to have to start building live video campaigns into our content marketing strategies, something else comes along that promises to be the next game-changer. And so here we are with the most recent marketing phenomenon – chatbots.
Chatbots succeed when a clear understanding of user intent drives development of both the chatbot logic and the end-user interaction. As part of your scoping process, define the intentions of potential users. What goals will they express in their input? For example, will users want to buy an airline ticket, figure out whether a medical procedure is covered by their insurance plan or determine whether they need to bring their computer in for repair? 

World Environment Day 2019 is focusing on climate change, and more specifically air pollution, what causes it, and importantly, what we can do about it. Through a range of blogs and an in-depth look at current vocabulary on the topic, we highlight some of the words you may need to know to be able to take part in arguably one of the most important discussions of our time.
This means our questions must fit with the programming they have been given.  Using our weather bot as an example once more, the question ‘Will it rain tomorrow’ could be answered easily. However if the programming is not there, the question ‘Will I need a brolly tomorrow’ may cause the chatbot to respond with a ‘I am sorry, I didn’t understand the question’ type response.
“HubSpot's GrowthBot is an all-in-one chatbot which helps marketers and sales people be more productive by providing access to relevant data and services using a conversational interface. With GrowthBot, marketers can get help creating content, researching competitors, and monitoring their analytics. Through Amazon Lex, we're adding sophisticated natural language processing capabilities that helps GrowthBot provide a more intuitive UI for our users. Amazon Lex lets us take advantage of advanced AI and machine learning without having to code the algorithms ourselves.”
The chatbot uses keywords that users type in the chat line and guesses what they may be looking for. For example, if you own a restaurant that has vegan options on the menu, you might program the word “vegan” into the bot. Then when users type in that word, the return message will include vegan options from the menu or point out the menu section that features these dishes.
However, if you’re trying to develop a sophisticated bot that can understand more than a couple of basic commands, you’re heading down a potentially complicated path. More elaborately coded bots respond to various forms of user questions and responses. The bots have typically been “trained” on databases of thousands of words, queries, or sentences so that they can learn to detect lexical similarity. A good e-commerce bot “knows” that trousers are a kind of pants (if you are in the US), though this is beyond the comprehension of a simple, untrained bot.
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.

Simple chatbots work based on pre-written keywords that they understand. Each of these commands must be written by the developer separately using regular expressions or other forms of string analysis. If the user has asked a question without using a single keyword, the robot can not understand it and, as a rule, responds with messages like “sorry, I did not understand”.
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.

For as long as I can remember, email has been a fundamentally important channel for a large majority of businesses. The ability to market products directly through a channel that scales up to an incredibly high ceiling is very attractive. The only problem is that it's costing more and more money to acquire email addresses from potential customers, and the engagement from email is getting worse and worse.


Despite the fact that ALICE relies on such an old codebase, the bot offers users a remarkably accurate conversational experience. Of course, no bot is perfect, especially one that’s old enough to legally drink in the U.S. if only it had a physical form. ALICE, like many contemporary bots, struggles with the nuances of some questions and returns a mixture of inadvertently postmodern answers and statements that suggest ALICE has greater self-awareness for which we might give the agent credit.
However, if you’re trying to develop a sophisticated bot that can understand more than a couple of basic commands, you’re heading down a potentially complicated path. More elaborately coded bots respond to various forms of user questions and responses. The bots have typically been “trained” on databases of thousands of words, queries, or sentences so that they can learn to detect lexical similarity. A good e-commerce bot “knows” that trousers are a kind of pants (if you are in the US), though this is beyond the comprehension of a simple, untrained bot.
The progressive advance of technology has seen an increase in businesses moving from traditional to digital platforms to transact with consumers. Convenience through technology is being carried out by businesses by implementing Artificial Intelligence (AI) techniques on their digital platforms. One AI technique that is growing in its application and use is chatbots. Some examples of chatbot technology are virtual assistants like Amazon's Alexa and Google Assistant, and messaging apps, such as WeChat and Facebook messenger.
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