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Log into your Retently account using your credentials. Ensure that you have the necessary permissions to access the data you intend to export. Navigate to the section of the dashboard where the data you want to export is located. This might be under reports, surveys, feedback, or any other relevant section depending on what data you need.
Identify and select the specific datasets or reports you wish to export. Retently typically allows customization of data views, so ensure you filter and select the exact data set you require. Use built-in filters and date range options to narrow down your data to the specific information you need.
Look for an export or download button within the data view you have selected. This button is often represented by icons like a downward arrow or a disk, or it might be labeled explicitly as "Export" or "Download." Click this button to open the export options.
When prompted, select CSV (Comma-Separated Values) as the export file format. CSV is a universally accepted format for spreadsheets and is compatible with most data analysis tools. Ensure no other formats like Excel or PDF are selected if you specifically need a CSV file.
If there are additional settings or configurations available, such as including headers, choosing delimiter types, or selecting data columns, configure these as per your requirements. Ensure all necessary columns are selected to be included in the export.
After configuring the export, confirm your selections and proceed to download the CSV file. Your browser will typically prompt you to save the file to your local system. Choose an appropriate directory on your computer where you can easily access the file later.
Once the download is complete, open the CSV file using spreadsheet software like Microsoft Excel or Google Sheets to verify the data. Check for accuracy and completeness against what you expected. Organize the data within your local file system in a manner that aligns with your data management practices.
By following these steps, you can successfully export data from Retently into a local CSV file 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.
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