Applications and Considerations
This article will provide an overview of how Chatbots cab be used in your business and major considerations for implementing Chatbot.
Application of Chatbots
Chatsbots are not going away, and will be more and more frequent because they are a more natural way for humans to interact with a machine, as a dialog, either written or text. Furthermore, chatbots will have a 99% uptime, and can immidiatly respond to customers 24/7, thereby expanding service hours outside of normal business hours and time zones.
- Recent Studies show that 65% of millennial prefer to communicate through txt.
- Recent Studies have show that speech to text (talking to a computer) is faster that typing. (Aug 2016)
- Recent advances (Sept 2016) in machine learning algorithms have reduced speech to text error rates to 6% that’s a 4% reduction in the last year!
Therefore, any user interaction with an app or webpage can utilize a Chatbot to increase a users experience.
Will websites be completely audible, like talking to Siri on my iPhone? They can, but most likely not. The reason why, is that humans are still very visual. Therefore, website assistants (Chatbots) will become more common. These assistants will be like assistants in an Apple Store. They will be visible to you can browse at your own leisure. Ask them questions, where they will navigate you to the item/answer that your request, and then can be a register and take your payment. The entire time while you were navigating the site.
Business tools that will most like become obsolete with Chatbots: Website Search bar, Contact Us Page, Support Pages and FAQ’s, Web forms / Data Entry, Site filter criteria (search layout with picklist to find products), User Logins (Machine learning algorithms will recognize your voice, which is as distinct as your finger print, to allow premium access to a site.
Chatbots consist of the following elements: Customer Channels, Context, Intents, Dialogs, Actions, Integrations, and Pricing.
Customer Channel identify where and how you communicate with your customers/users, for example: Social Media, Mobile App, Phone, SMS, and/or Chat App. Customer Channels can be split in two major Categories: Text and Speech.
Text Examples: Facebook Messenger, Twillio SMS or Voice, Slack, Web Chat, Twitter, Mobile App, and/or Custom.
Speech (Speech to Text): Siri (Apple), Alexa (Amazon), Cortana (Microsoft), and Custom.
Speech to Text requires an additional layer of Machine Learning to Translate the audio into Text. This can be either realtime, or batched. Once the audio is converted to text, the chat bot interacts the same as Text Chatbots. Also, on the responses, there will usually be a Text to Speech function so the chatbot can reply to the users.
Context is used in natural language processing to the algorithm can understand technical/specific/trade vocabulary. Without proper context an algorithm’s speech to text recognition could drop from 90% to 60%, this is not good. Since most industries have specific vocabulary, a machine learning algorithms might need to be trained to understand these terms. Special training is not hard, it just requires data to train the algorithm. If this is a requirement, switching to a Voip or phone service that can record all phone conversations would be a mandatory first step, so we could gather data to start training the Chatbot on how to recognize your industry specific language.
Intents are the core of the Chatbot’s functionality. Machine Learning algorithms use Natural Language processing to extract meaning from the statement made by the User. then based on the key phrases, the Algorithm will match to pre-defined intents which can lead the user into different dialogs with the Chatbot. This process will continue, until the dialog with the customer is complete.
Dialogs vary between Chatbots, as they can be linear, non-linear, or both. Linear dialogs are types that have a specific start point, and end point with pre-determined steps in-between. An example is Ordering a Pizza would have these specific steps for all orders: 1) Choose Size, 2) Choose Toppings, 3) Choose Sauce, 4) Choose Sides, 5) specify delivery Address, and 6)specify Payment Info. Non-linear dialogs will not have a specific steps, and the steps can be added or removed based on the inform the user provides. Example – Searching for a Home – User will have home criteria, which will change available options, for example: City, Price, #beds, #baths, pets. Then based on the application, Income, Credit, references, can again change the options for the users. Accepting credit card payment is a simple example, as to whether or not the credit card is approved will change the next steps of the Chatbots dialog.
Actions can be performed with Chatbots in realtime to provide record lookups, record creation, or any actions utilizing any available api via webhooks and REST API requests. This means that when a phone number or email address is passed to the Chatbot, it can query salesforce for a contact record that matches, and respond with the Contact Name. Or if no match is found, a new record can be created.
Hybrid Chatbot/Agent Models can be used to escalate customer conversations when there are certain actions the chatbot cannot perform, or when customer’s tone becomes negative/frustrated. Dialogs can be mapped so certain customer requests direct them to an agent. Additional Machine Learning algorithms can be included to monitor the tone of the customer, so when the sentiment becomes negative, the customer can be escalated to a agent.
Customizing and Integrating your Chatbot is easy. There are Chatbot building tools such as Api.ai, Converse.ai, Flow.xo, Microsoft LUIS, that allow you to create Chatbot intents in a drag and drop fashion. Some like api.ai and converse.ai can let you create complex linear/non-linear dialogs. Most allow some form a integration, such as webhooks and http requests. Some only allow a single endpoint, where others allow you to create a many as you wish. Almost all allow for multiple channels too deploy your bot, such as Twillio, Slack, Skype, Facebook Messenger, etc.
Pricing varies tremendously among the different platforms, and the type of channels, volume, and integrations you require. All Chatbot implementations should strongly consider the required usages of the Chatbot, and what the pricing impact will be for the platform of choice. Any aspect of a Chatbot can easily be customized with coded and hosted on your own server if your requirements are beyond the scope of any platform.
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