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Request User Data node

What is the Request User Data node?

Collecting information from engaging users is a key function of most chatbots. Engati makes it easy to do the same by providing a number of options to collect data using nodes like Request User Data, Identity Node & more. 

The Request User Data data node enables the chatbot to read input from the user and save them as an attribute or entity. Therefore, facilitating use cases like Lead Generation, personalizing user experience, and much more.

Why use a chatbot to request data?

People, especially the younger generation, find it easy and natural to solve their problems by communicating with a chatbot. That is why a chatbot is a conversation-catcher, acting two-fold. First, it addresses the client’s problem or transfers them to a human assistant. Secondly, recording all the interactions creates a repository of real data that can be used to create better versions. For example, by looking at a chatbot’s unanswered questions and the words utilized by the clients, developers can create new triggers and assign existing answers to develop the bot’s capabilities.

Coupled with natural language processing (NLP), collecting data can help us learn more about how people communicate. By looking at the words they use and analyzing their demographics, it is easy to detect trends and personalize messages. An organization using a chatbot can use the new insights to talk their clients’ language in e-mail, commercials, or even through the chatbot.

Also, by looking at the frequency of some issues, a company can get more ideas about which products are not clearly understood by customers or constitute central attraction points and redirect their marketing efforts accordingly.

Currently, The primary data sources were emails and the users’ social media activity, but this opens the door to new personalization opportunities.

Every time personal information is the subject, privacy and security matters should be thoroughly addressed to avoid concerns related to identity theft. Let clients know about the ways you protect them. This increases trust and makes them more prone to fully use the chatbot’s features like payment options.

 

How to set up Request User Data?

There are two types of inputs that you can collect from users using the Request User Data Node.

The first, “Standard,” allows you to capture user input as an attribute, and the other is “Entity,” which helps capture the user input as an entity.

Both of these can be used subsequently in your workflows in a very similar manner in other nodes, including “Send Message,” “Send Message with Options,” “Send Carousel,” “Randomize Message,” “Script,” and any other nodes that allow you to refer Attributes. 

1. Standard

With standard mode, you save user inputs as attributes. 

The user prompt is the message that gets displayed to prompt the user to provide their information. 

The validation type is the type of data that gets collected.

Engati currently offers these types of validation in the request user data node:

  • Text:  Valid for all input.
  • Password: To take password inputs. Data collected over this validation type is not persistent and can only be used over the next immediate flow step.
  • Number validation: To validate numeric input only.
  • NPS (1-10): For user feedback. It allows the user to choose a number in the Net Promoter Score of 1 to 10.
  • NPS (1-5): For user feedback. It allows the user to choose a number in the Net Promoter Score of 1 to 5.
  • NPS (1-3): For user feedback. It allows the user to choose a number in the Net Promoter Score of 1 to 3.
  • Email validation: To check if user input is a valid email address.
  • Mobile validation: To check if user input is a valid phone number
  • Date: To validate user input is a valid date. It can also be set up to accept a date from a date picker.
  • Regex: To store and set the validation type of the attribute into any regex format of your choice.

 

2. Entity

Saving user input as an entity enables you to custom verify them. You can use values and their metrics in the flows just like you would use an attribute by referring to it within a double braces notation. For using entity values in the flow though, you also have to add the prefix ‘context.’ to the entity name. 

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Request User Data node

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is the Request User Data node?

Collecting information from engaging users is a key function of most chatbots. Engati makes it easy to do the same by providing a number of options to collect data using nodes like Request User Data, Identity Node & more. 

The Request User Data data node enables the chatbot to read input from the user and save them as an attribute or entity. Therefore, facilitating use cases like Lead Generation, personalizing user experience, and much more.

Why use a chatbot to request data?

People, especially the younger generation, find it easy and natural to solve their problems by communicating with a chatbot. That is why a chatbot is a conversation-catcher, acting two-fold. First, it addresses the client’s problem or transfers them to a human assistant. Secondly, recording all the interactions creates a repository of real data that can be used to create better versions. For example, by looking at a chatbot’s unanswered questions and the words utilized by the clients, developers can create new triggers and assign existing answers to develop the bot’s capabilities.

Coupled with natural language processing (NLP), collecting data can help us learn more about how people communicate. By looking at the words they use and analyzing their demographics, it is easy to detect trends and personalize messages. An organization using a chatbot can use the new insights to talk their clients’ language in e-mail, commercials, or even through the chatbot.

Also, by looking at the frequency of some issues, a company can get more ideas about which products are not clearly understood by customers or constitute central attraction points and redirect their marketing efforts accordingly.

Currently, The primary data sources were emails and the users’ social media activity, but this opens the door to new personalization opportunities.

Every time personal information is the subject, privacy and security matters should be thoroughly addressed to avoid concerns related to identity theft. Let clients know about the ways you protect them. This increases trust and makes them more prone to fully use the chatbot’s features like payment options.

 

How to set up Request User Data?

There are two types of inputs that you can collect from users using the Request User Data Node.

The first, “Standard,” allows you to capture user input as an attribute, and the other is “Entity,” which helps capture the user input as an entity.

Both of these can be used subsequently in your workflows in a very similar manner in other nodes, including “Send Message,” “Send Message with Options,” “Send Carousel,” “Randomize Message,” “Script,” and any other nodes that allow you to refer Attributes. 

1. Standard

With standard mode, you save user inputs as attributes. 

The user prompt is the message that gets displayed to prompt the user to provide their information. 

The validation type is the type of data that gets collected.

Engati currently offers these types of validation in the request user data node:

  • Text:  Valid for all input.
  • Password: To take password inputs. Data collected over this validation type is not persistent and can only be used over the next immediate flow step.
  • Number validation: To validate numeric input only.
  • NPS (1-10): For user feedback. It allows the user to choose a number in the Net Promoter Score of 1 to 10.
  • NPS (1-5): For user feedback. It allows the user to choose a number in the Net Promoter Score of 1 to 5.
  • NPS (1-3): For user feedback. It allows the user to choose a number in the Net Promoter Score of 1 to 3.
  • Email validation: To check if user input is a valid email address.
  • Mobile validation: To check if user input is a valid phone number
  • Date: To validate user input is a valid date. It can also be set up to accept a date from a date picker.
  • Regex: To store and set the validation type of the attribute into any regex format of your choice.

 

2. Entity

Saving user input as an entity enables you to custom verify them. You can use values and their metrics in the flows just like you would use an attribute by referring to it within a double braces notation. For using entity values in the flow though, you also have to add the prefix ‘context.’ to the entity name. 

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