It’s no surprise that artificial intelligence has been embraced by just about every sector of the digital marketing community. Analytics analyzation, prediction software, intelligent email solutions and the like are all the rage now in marketing departments across the globe. One standout application is chatbots. One of the biggest problems for consumers is getting their voice heard, and chatbots represent an excellent opportunity to mitigate that problem while still preserving the “feeling” of human interaction, with the advantages of being active 24/7 and handling many simultaneous conversations.
Chatbots are becoming more common in the enterprise domain, replacing things like contact forms, surveys, and product information requests. For many customers, chatting is a more natural way of interacting with the web.
Chatbots are a piece of artificial intelligence software (weak AI) that you can interact with by typing text. Such interactions can be simple questions like asking for a hotel booking or for a city’s weather report, or more complex conversations like troubleshooting or giving advice on a service plan.
While some Chatbots simply recognize patterns or words on a phrase and look for an answer in a database, others use complex Natural Language Processing (NLP) and Natural Language Understanding (NLU) systems to identify what the user tried to communicate and then try to determine the “best” answer. Chatbots are used in dialog systems (think: text messages, Social media chats) for practical purposes like information acquisition or customer assistance.
This kind of programs should hopefully pass Turing’s test because they need to convincingly simulate human interaction in a chat environment.
One of the biggest challenges for software of this kind is the ability to process non-structured information (what the user writes in the chat) into formal requirements that can be interpreted by software. For example, if we write something to a bot, such as “order me 2 pizzas”, internally the software will predict a list of possible intents with a probability:
Additionally, the software needs to understand that “pizza” and “2” are objects or entities from the conversation model, and the whole command could be interpreted like
FoodOrder -> Pizza -> 2 Units
After arriving at this conclusion, a well-trained bot would ask, for example, when the user wants his pizzas delivered.
There are a lot of tools that help you to build, train and define a conversational agent. For example, there is IBM Watson, Microsoft’s LUIS, wit.ai or api.ai, just to name a few.
Furthermore, you can also create a bot using existent conversation platforms like Facebook Messenger, Skype, Slack, Telegram or WhatsApp, etc.
Let’s review some of the most common use cases for chatbots.
Instead of customers having to fill out forms, or send emails or call in, companies now can use a bot to retrieve a user’s query and ask for the user’s contact information. Also, well-trained bots could help troubleshoot problems. This is an even better option if the bot is integrated to a messaging platform like Facebook Messenger.
A messaging interaction with a potential client could be more personalized. Customers are offered products and services they are interested in, instead of trying to adjust an advertisement to fit an imaginary customer.
Currently, there are several options available for a company which wants to develop their own chat engine. For example, Microsoft’s LUIS offers a whole Bot Framework for .NET and provides storage in the cloud using Azure. With LUIS, a conversation model can be defined. A conversation model can contain entities, utterances, tags, and intents. After a model is defined, the bot can be trained. This uses statistical learning, NLP and Custom Random Fields to try to predict an intent based on a user utterance. After an intent is recognized, information like user preferences, history, and product rankings could be gathered and then used to provide a custom response to the user based on that information.
There are numerous tools that make chatbot integration easy with existing platforms. Here are the chat services that support creating conversational bots and embedding it into them:
and others like Snapchat, Apple TV & Siri, Kik, Telegram, Skype, Skype for Business, Telegram, WeChat, Amazon Echo.
People expect a business to be available to answer questions 24/7. People would contact a human through messaging rather than on the phone or personally. People don’t want to be put on hold for hours.
For these reasons, chatbots provide a solution that allows business to be available 24 hours a day, 7 days a week to answer any inquiry. Also with the capability of handling numerous conversation simultaneously.
However, while bots can cut out waiting time, you should make sure your customers know they are talking to a bot. Although chatbots are associated with the lack of empathy, there is more progress on creating a certain level of it, making them more human-like.
In order to compete with organizations that are already implementing bots in their marketing strategies with tangible results, others need to jump on the conversational commerce model.
The data gathered by bots allows companies to make amendments to marketing strategies according to consumer needs and desires. But companies should not rely solely on the chatbot, they need to use it with care and should complement it with other marketing tools. Also, all bots should have a sort of “escalation” feature, where a human gets involved.
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