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First, log into your Gong account using your credentials. Once logged in, navigate to the section of the platform where you can access your data. Gong typically has a data export tool that allows you to export data in various formats such as CSV or Excel.
Identify the specific data you need to transfer to Google Sheets. This might include call logs, analytics reports, or other relevant data. Use any available filters or date ranges to narrow down the data set to exactly what you need.
Once you have identified the data you wish to export, select the option to export the data. Choose CSV as the export format. This format is widely supported and easy to import into Google Sheets. Save the exported CSV file to your computer.
Go to Google Sheets by visiting sheets.google.com. If you are not already logged in, sign in with your Google account. Once logged in, create a new spreadsheet by clicking on the “Blank” option.
In your new Google Sheets document, go to the “File” menu, select “Import,” and then choose “Upload.” Drag and drop your CSV file or select it from your computer. Google Sheets will then open a dialog asking how you want to import the data. Choose “Replace spreadsheet” to start fresh with the imported data or “Append to current sheet” if you want to add to existing data.
After the import is complete, review the data to ensure it appears correctly in Google Sheets. You may need to adjust column widths, change data formats (such as dates or currency), or apply filters and sorting to organize your data effectively.
Once you have reviewed and formatted the data, save your Google Sheet to ensure your changes are not lost. If you need to share the data with others, click on the “Share” button, enter their email addresses, and set the appropriate permissions (view, comment, or edit).
By following these steps, you can effectively move data from Gong 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.
Gong is a sales enablement platform that uses artificial intelligence to analyze sales calls and meetings, providing insights and recommendations to help sales teams improve their performance. The platform records and transcribes conversations, analyzes them for key topics and sentiment, and provides real-time coaching and feedback to sales reps. Gong also offers analytics and reporting tools to help sales managers track team performance and identify areas for improvement. The platform is designed to help sales teams close more deals, improve customer relationships, and increase revenue.
Gong's API provides access to a wide range of data related to sales conversations. The following are the categories of data that Gong's API gives access to:
1. Conversation data: This includes information about the participants, duration, and content of the conversation.
2. Call recordings: Gong's API allows users to access call recordings, which can be used for training and coaching purposes.
3. Transcripts: Gong's API provides access to transcripts of sales conversations, which can be used for analysis and insights.
4. Sales performance data: Gong's API provides data on sales performance, including metrics such as win rates, deal size, and sales cycle length.
5. Customer insights: Gong's API provides insights into customer behavior and preferences, which can be used to improve sales strategies and customer engagement.
6. Sales team performance data: Gong's API provides data on sales team performance, including metrics such as call volume, talk time, and response time.
7. Sales pipeline data: Gong's API provides data on the sales pipeline, including metrics such as pipeline velocity and conversion rates.
Overall, Gong's API provides a comprehensive set of data that can be used to improve sales performance and customer engagement.
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:
