If you're building a chatbot to support a customer base, Einstein from Salesforce might be an option to consider. Chris Ward dives in to see what's possible.Chatbots have a variety of use cases. One of the more common uses is to help reduce repetitive customer service work, enabling human agents to focus on more complex and personal tasks. In this tutorial, I create a basic bot for a small company that assists the customer support team. The bot can answer a selection of common questions about a fictional software application.
Chatbots have a variety of use cases. One of the more common uses is to help reduce repetitive customer service work, enabling human agents to focus on more complex and personal tasks. In this tutorial, I create a basic bot for a small company that assists the customer support team. The bot can answer a selection of common questions about a fictional software application. The bot uses natural language processing (NLP) to recognize certain questions and respond appropriately, directing the user to a human support agent if they ask, or the bot is unable to understand or answer.
There are a lot of platforms available for creating bots, but I decided to try Einstein from Salesforce, as it can integrate with Salesforce data and workflows, which are commonly used by customer service teams.
Einstein is AI for the Salesforce Platform, providing infrastructure for creating predictive models to interact with Salesforce data. This includes analytics, text, and image analysis, as well as a bot platform that combines text analysis and Salesforce workflows.
I wanted to add a bot to an external site I had set up with Heroku, but following the steps for creating and adding it to a Salesforce Community seemed to be the easiest and fastest way to see what was possible as I didn’t need to set up a custom server or whitelisting.
The first step is to create the community and add the chat capabilities that my bot will use to talk to the customers. I used this Trailhead module as a guide. For my specific case, I called my community “Customer Support,” and chose a domain that suited, https://acme-users-developer-edition.um6.force.com/support. I also changed some of the settings to “Acme Support” to suit my use case, and added my domain to the Website URL step.
When you add the embedded chat to your community components, make sure you select the correct Chat Deployment and configure its look to suit your use case.
If you want to add the bot to a web page of your own, instead of creating a community for the bot, create a “web chat” button (following the same steps mentioned in the Trailhead module above), then follow this Trailhead module to get started.
At the end of the webchat flow in the trailhead, the module is a code snippet that you can paste into your web page (including Apex pages) to add your bot. The flow for creating an Einstein-powered bot is the same, regardless if you are implementing the bot on a Salesforce community or your custom site.
embedded_svc.settings.extraPrechatInfo parameters. Use
extraPrechatFormDetails to send additional information to the chat transcripts, and
extraPrechatInfo to map those values to new or existing records in Salesforce. Find more details in the documentation.
Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge.NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition
Natural language processing (NLP) is a specialized field for analysis and generation of human languages. Human languages, rightly called natural language, are highly context-sensitive and often ambiguous in order to produce a distinct meaning. (Remember the joke where the wife asks the husband to "get a carton of milk and if they have eggs, get six," so he gets six cartons of milk because they had eggs.) NLP provides the ability to comprehend natural language input and produce natural language output appropriately.
In this video we are going to learn about Python Natural Language Processing (NLP) in 2 Hours. there are different topics that we are going to cover in this video like tokenization, stemming, lemmatization, parts of speech tagging, named entity recognition, sentiment analysis, language translation and many more. Python Natural Language Processing (NLP) in 2 Hours
An Introduction to Natural Language Processing (NLP) Terms. I gave an introduction to NLP, how it works, and some beginning terms. In this blog, I’ll add more terms.
With computational algorithms and sentiment examination, Artificial Intelligence and Natural Language Processing (NLP) can help chatbots decipher the raw content, process it, and convey enhanced data to clients.