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Log in to your Retently account and navigate to the data or reports section you wish to export. Use the export feature (usually found as an option to export to CSV or Excel format) to download the data to your local device. Ensure you save the file in a location you can easily access later.
Access Google Sheets by logging into your Google account and navigating to the Google Sheets application. You can find it by clicking on the Google Apps icon (nine small squares) in the top right corner of your Google homepage and selecting Sheets.
In Google Sheets, click on the “+”� button to create a new, blank spreadsheet. This will be your destination for the data you're moving from Retently.
Before importing your data, ensure that the spreadsheet is formatted correctly to accommodate your data. You might want to create headers or adjust column widths as necessary to match the structure of your exported file from Retently.
In the Google Sheets menu, click on “File,”� then “Import.”� Choose the “Upload”� tab and click “Select a file from your device.”� Locate and select the CSV or Excel file you exported from Retently. Choose the appropriate import options (such as “Replace spreadsheet”� to overwrite any existing data, or “Append to current sheet”� if adding to existing data).
Once imported, review the data to ensure everything transferred correctly. Check for any discrepancies or errors that may have occurred during the import process. This might include checking for correct formatting, data types, or any missing information.
After confirming that the data is correct, save your work. You can also share the Google Sheet with others by clicking the “Share”� button in the top right corner. Add email addresses of collaborators and adjust permissions as needed to control how others interact with the data.
By following these steps, you can effectively move data from Retently to Google Sheets without the need for third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Retently is a tool for measuring and increasing customer satisfaction and loyalty through Net Promoter Score surveys and collecting feedback and The tool is packed with various robust features to help you segment your audience, create custom polls, and collect multichannel polls. With Retently, businesses can collect customer feedback and analyze the results with advanced analytics and reports for corrective action. Retently's cloud-based platform is designed to help businesses track their Net Promoter Score, collect valuable customer reviews, and build customer loyalty by converting detractors into repeat customers.
Retently's API provides access to various types of data related to customer feedback and satisfaction. The categories of data that can be accessed through Retently's API include:
1. Customer feedback data: This includes data related to customer feedback, such as NPS scores, customer comments, and ratings.
2. Customer satisfaction data: This includes data related to customer satisfaction, such as customer satisfaction scores, customer loyalty, and customer retention rates.
3. Customer behavior data: This includes data related to customer behavior, such as customer purchase history, customer demographics, and customer preferences.
4. Campaign data: This includes data related to Retently's campaigns, such as campaign performance metrics, campaign engagement rates, and campaign conversion rates.
5. User data: This includes data related to Retently's users, such as user activity, user preferences, and user engagement.
Overall, Retently's API provides access to a wide range of data related to customer feedback and satisfaction, which can be used to improve customer experience and drive business growth.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
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