Feedier
Listen, analyse and convert feedback data into actionable insights that drive satisfaction, retention and quantifiable business results.
Integrate the Feedier API with the Python API
Setup the Feedier API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Feedier and Python remarkably fast. Free for developers.
Popular Feedier and Python Actions#
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Overview of Feedier#
The Feedier API lets you harness the power of customer feedback by automating the collection and analysis process. With this API, you can create, retrieve, update, and delete feedback, along with managing carriers and rewards. It enables you to streamline the feedback loop, integrate with your CRM, and trigger actions based on customer responses. Pipedream's serverless platform opens up a world of possibilities for integrating Feedier with hundreds of other apps to automate workflows, analyze data, and respond in real-time to customer insights.
Connect Feedier#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
feedier: {
type: "app",
app: "feedier",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.feedier.com/v1/carriers`,
headers: {
Authorization: `Bearer ${this.feedier.$auth.api_private_key}`,
"Accept": `application/json`,
"Cache-Control": `no-cache`,
},
})
},
})
Overview of Python#
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
Connect Python#
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}