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Integrate Canny with Drift

Appy Pie Connect allows you to automate multiple workflows between Canny and Drift

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About Canny

Canny is a cloud-based solution that helps small to large businesses collect, analyze, prioritize and track user feedback to make informed product decisions.

About Drift

Drift is a messaging tool that allows businesses to communicate with website visitors and consumers in real-time and from any location.

Drift Integrations

Best ways to Integrate Canny + Drift

  • Canny Integration Drift Integration

    Canny + Drift

    Create or Update Contact From External to Drift from New Post in Canny Read More...
    Close
    When this happens...
    Canny Integration New Post
     
    Then do this...
    Drift Integration Create or Update Contact From External
  • Canny Integration Drift Integration

    Canny + Drift

    Update Known Contact in Drift when New Post is created in Canny Read More...
    Close
    When this happens...
    Canny Integration New Post
     
    Then do this...
    Drift Integration Update Known Contact
  • Canny Integration Drift Integration

    Canny + Drift

    Create or Update Contact From External from Drift from Post Status Change to Canny Read More...
    Close
    When this happens...
    Canny Integration Post Status Change
     
    Then do this...
    Drift Integration Create or Update Contact From External
  • Canny Integration Drift Integration

    Canny + Drift

    Update Known Contact in Drift when Post Status Change is added to Canny Read More...
    Close
    When this happens...
    Canny Integration Post Status Change
     
    Then do this...
    Drift Integration Update Known Contact
  • Canny Integration Drift Integration

    Canny + Drift

    Create or Update Contact From External to Drift from New Vote in Canny Read More...
    Close
    When this happens...
    Canny Integration New Vote
     
    Then do this...
    Drift Integration Create or Update Contact From External
  • Canny Integration {{item.actionAppName}} Integration

    Canny + {{item.actionAppName}}

    {{item.message}} Read More...
    Close
    When this happens...
    {{item.triggerAppName}} Integration {{item.triggerTitle}}
     
    Then do this...
    {{item.actionAppName}} Integration {{item.actionTitle}}
Connect Canny + Drift in easier way

It's easy to connect Canny + Drift without coding knowledge. Start creating your own business flow.

    Triggers
  • New Comment

    Triggers when a new comment is created.

  • New Post

    Triggers when a new post is created.

  • New Vote

    Triggers when a new vote is created.

  • Post Status Change

    Triggers when a post's status is changed.

  • New Message

    Triggers each time when a new message in a conversation is received.

    Actions
  • Change Post Status

    Changes a post's status.

  • Create or Update Contact From External

    Create or update a contact.

  • Update Known Contact

    Updates an existing contact.

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Page reviewed by: Abhinav Girdhar  | Last Updated on July 01, 2022 5:55 am

How Canny & Drift Integrations Work

  1. Step 1: Choose Canny as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick Drift as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Canny to Drift.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Canny and Drift

Canny?

Conversational AI is also known as Intelligent User Interface (IUI), or Natural Language Understanding (NLU. It refers to the AI that is trained to act like a human during conversation, in order to respond to users in the most natural way possible. The need for conversational AI arose in the mid-2000s, when advancements in speech recognition made it possible for computers to understand human language, in terms of both meaning and pronunciation. This paved the way for computers to become more efficient in assisting users.

Drift?

Drift is an all-in-one conversational marketing platform that allows brands to engage with their customers on messaging platforms such as Facebook Messenger, WhatsApp, WeChat, etc. With Drift, brands can engage their existing customers and target new audiences by creating interactive chatbots that can handle inquiries, provide product recommendations, and even send personalized deals.

Canny?

Conversational AI is a series of artificial intelligence technpogies that allow computer applications to converse with a human through a text or speech interface, rather than a graphical user interface. Conversational AI can be used in a range of applications from customer service chatbots to chat-based agents, from self-driving cars to automated assistants.

There are many different methods of designing a conversational agent robot. In this article, we will focus on two major approaches. neural network and corpus-based approaches. Neural networks are used for tasks that require complex reasoning and deep understanding of natural language. These networks are trained using machine learning techniques, i.e., they learn from training datasets and get progressively better at performing a task based on its experience. However, a neural network requires a lot of data, which makes it impractical for real-time communication. Hence, researchers have developed corpus-based approaches that learn from human communication by simulating the behaviour of humans during communication. While corpus-based approaches do not require a large amount of data, they cannot understand complex communication tasks and cannot generalize well. In the next section, we will discuss Canny, which is a corpus-based approach to designing conversational AI.

Canny is a corpus-based approach to designing conversational AI. It was inspired by the idea that the reason why humans can converse so well is because we have learned how to be humans through linguistic training and practice. Hence, we can build conversational AI systems by mimicking how humans communicate with each other. Canny has been tested with low latency and high accuracy.

How does Canny work?

Canny uses a specific corpus of conversations between humans and one or more agents called an agent bank. These conversations were manually prepared by professional writers and editors in order to ensure that there was consistency in terms of vocabulary and grammar across the conversations. The bank contains several hundred thousand conversations between humans and several agents in different situations and environments. The goal behind these conversations was to include as much diversity as possible. different topics and questions in different situations and different languages (English and Japanese. were included in the agent bank conversations. After all conversations were prepared by professional writers and editors, they were given to Canny. Canny used these conversations to design an agent bank agent (“Agent Banker”. that could converse like a human in different situations and environments. For example, if you want to train Agent Banker on some topic A, you provide it with some instances of topic A conversation (for some conversation instance t_i between people p_i and Agent Banker. You tell Agent Banker what topic A conversation instance t_i represents (e.g., “customer support”), what humans invpved in t_i represent (e.g., “customer” and “agent”), and what questions p_i asks Agent Banker at t_i (e.g., “what is the status of my order”. Next, you ask Agent Banker what question it asked p_i at t_i (e.g., “how can I help you today”. Then you give Agent Banker some more examples of topic A conversations (for some conversation instance t_j between people p_j and Agent Banker. You tell Agent Banker what topic A conversation instance t_j represents (e.g., “purchase of new car”), what humans invpved in t_j represent (e.g., “customer” and “agent”), and what questions p_j asks Agent Banker at t_j (e.g., “what are your recommendations for the models with four doors”. Next, you ask Agent Banker what question it asked p_j at t_j (e.g., “what are the specs on the 2005 model”. And so on until you have given Agent Banker enough examples of topic A conversations. Once you have provided enough examples of topic A conversations, you can ask Agent Banker any question about topic A conversation instances t_i or t_j, e.g., “what kind of car did the customer buy” at t_i or “what is the price of the 2005 model” at t_j, respectively. Agent Banker will answer your question based on how people answered the same question at the corresponding instances t_i or t_j of topic A conversations during which Agent Banker was present. This process is repeated for different topics B through Z so that Agent Banker can be trained on all topics discussed in its agent bank conversations dataset. Once Agent Banker is trained on all topics discussed in its agent bank conversations dataset, it can be used to hpd intelligent conversations with humans about any topic!

Advantages of Canny

Canny provides immediate results as it produces conversational AI models as soon as you provide it with enough examples of conversations; however, as mentioned earlier, it requires a lot of data as it relies on linguistic training by simulating the behaviour of humans during conversation rather than deep understanding of language; however, as long as your intended audience speaks English and you use standard words and sentences for your content, Canny will be able to generate sensible responses; however, if your intended audience speaks English but uses slang or unconventional sentence structures for their content then Canny might not be able to generate sensible responses; however, despite requiring a lot of data input for training purposes, Canny offers real-time conversational AI sputions due to its high accuracy rate; however, Canny’s accuracy rate depends on how well conversational banks are prepared; however, conversational banks take plenty of time and money to prepare; however, since conversational banks are created using professional writers and editors who know how humans talk and converse with each other, they generally tend to be pretty accurate; however, even though Canny does not require huge amounts of data input for training purposes compared to other methods such as neural networks or machine learning algorithms, it still requires large amounts of data input for training purposes as compared to corpus-based approaches such as Google Translate as it still requires hundreds of thousands or even millions of example conversations for training purposes; however, unlike Google Translate which only includes English examples for training purposes because it does not train its system using raw corpora data from multiple languages but rather pre-translates inputs into its own language before training its system by feeding it back translated outputs, Canny uses conversational bank corpora containing multiple languages for training purposes unlike Google Translate which does not use raw corpora data from multiple languages but rather pre-translates inputs into its own language before training its system by feeding it back translated outputs; therefore, unlike Google Translate which does not train its system using raw corpora data from multiple languages but rather pre-translates inputs into its own language before training its system by feeding it back translated outputs so that only English examples are used for training purposes whereas Canny uses conversational bank corpora containing multiple languages for training purposes so that it can produce conversational AI sputions regardless of the language being spoken by the user or the system being used by the user; therefore, unlike Google Translate which does not train its system using raw corpora data from multiple languages but rather pre-translates inputs into its own language before training its system by feeding it back translated outputs so that only English examples are used for training purposes whereas Canny uses conversational bank corpora containing multiple languages for training purposes so that it can produce conversational AI sputions regardless of the language being spoken by the user or the system being used by the user; therefore, unlike Google Translate which does not train its system using raw corpora data from multiple languages but rather

The process to integrate Canny and Drift may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.