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Begin by logging into your Retently account. Navigate to the section where your data is stored, such as survey responses or customer feedback. Utilize Retently's export functionality to download the data. Typically, you can export data in CSV or Excel format, which are commonly supported.
Open the exported file in a spreadsheet application, such as Microsoft Excel or Google Sheets. Review the data and perform any necessary cleaning, such as removing unnecessary columns or correcting data formats. Ensure the data is structured in a way that Convex can accept, typically involving column headers that match Convex's field requirements.
Log into your Convex account and navigate to the database or data entry section where you wish to import the data. Make sure you have the necessary permissions to add or modify data within Convex.
If Convex supports importing data via CSV or another format, download a sample import template if available. This will guide you on how to arrange your data columns to match Convex's database schema. Adjust your prepared data file to fit this template, ensuring that all required fields are included.
If Convex does not support direct imports, prepare to enter data manually. Split the data entry task into manageable chunks. Copy data from your prepared file and paste it or enter it directly into Convex's web interface. Ensure accuracy by validating each entry against the source file.
After the data is imported or entered, perform a thorough check to ensure that all data has been transferred accurately. Compare a sample of entries between Retently and Convex to confirm that the data matches and no information is lost or altered.
Once the data is successfully imported, create a backup of your data in Convex. This can typically be done by exporting from Convex to ensure you have a local copy. Document the process you followed for future reference, including any issues encountered and how they were resolved, to streamline any similar operations in the future.
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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
