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Begin by logging into your Freshcaller account. Navigate to the section where the data you wish to export is located, such as call logs or contact lists. Use the export functionality provided by Freshcaller to download the data as a CSV file. This option is typically found in the settings or under specific report sections.
Once the CSV file is downloaded, open it in a program like Microsoft Excel or Google Sheets to inspect the data. Ensure that the data is formatted correctly, with appropriate headers and no corrupt entries. Clean up any unnecessary columns or adjust data formats as needed to ensure consistency.
Log into your Google account and navigate to Google Sheets. Open an existing spreadsheet where you want to import the Freshcaller data, or create a new spreadsheet if needed by clicking on “Blank”� to start with a fresh sheet.
In Google Sheets, click on “File”� from the top menu, then select “Import.”� Choose the “Upload”� tab, and drag your CSV file into the designated area or click “Select a file from your device”� to upload your file. Once uploaded, choose the appropriate import options, such as “Replace spreadsheet”� or “Append to current sheet,”� and make sure the delimiter is set correctly (usually a comma for CSV files).
After importing the CSV data, review the spreadsheet to ensure all the data fields have been imported correctly. Check for any formatting issues, such as date or number formats, and adjust columns as necessary. Use Google Sheets' data cleaning tools if required to ensure data integrity.
Organize the data in a way that suits your needs. You can create new sheets within the same Google Sheets file for different types of data or to separate raw data from processed data. Use Google Sheets functions to format headers, create filters, or apply conditional formatting to enhance readability and usability.
To keep your Google Sheets data updated with new Freshcaller exports, establish a regular process. Decide on a schedule for exporting data from Freshcaller and importing it into Google Sheets. Document this process for consistency and consider using Google Sheets scripting capabilities for automation, although this requires some scripting knowledge and does not involve third-party 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.
Setup a connection to your Freshcaller site in minutes, and select the Freshcaller collections you want to replicate.
Freshcaller's API provides access to a wide range of data related to call center operations. The following are the categories of data that can be accessed through Freshcaller's API:
1. Call data: This includes information about incoming and outgoing calls, call duration, call recordings, and call transcripts.
2. Agent data: This includes information about agents, such as their availability, status, and performance metrics.
3. Queue data: This includes information about call queues, such as the number of calls waiting, the average wait time, and the number of agents available.
4. IVR data: This includes information about Interactive Voice Response (IVR) systems, such as the number of calls handled by the IVR, the number of calls transferred to agents, and the success rate of the IVR.
5. Ticket data: This includes information about tickets created from calls, such as the status of the ticket, the agent assigned to the ticket, and the resolution time.
6. Analytics data: This includes information about call center performance metrics, such as call volume, call abandonment rate, and average handle time.
Overall, Freshcaller's API provides a comprehensive set of data that can be used to monitor and optimize call center operations.
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:
