IMPORT CRM DATA INTO GOOGLE SHEETS

Introduction to Importing CRM Data

CRM Data Harvester helps you to streamline the extraction of contacts and leads from Salesforce, HubSpot, Pipedrive, Zoho or Freshsales into Google Sheets.
Configure date filters, select specific fields and let Flookup handle pagination, authentication and error management for seamless CRM integration.
This Apps Script project pulls raw contact or lead data from multiple CRMs and writes each fetch to its own sheet, labelled with the CRM name and fetch date, for unified analysis and reporting.

To get started, simply head to Extensions > Flookup Data Wrangler > CRM Data Harvester in your spreadsheet menu.

Function Highlights

• Intelligent API pagination for complete data retrieval.
• Optional start_date and end_date filters to limit by creation date.
• Customisable selected_fields to retrieve only required properties.
• Token-based or instance-based authentication per CRM.
• Graceful handling of common API errors.
• Automatic sheet creation and header formatting.

How to Import CRM Data

1. Open the sidebar
   Go to Extensions > Flookup Data Wrangler > CRM Data Harvester in your spreadsheet menu.
2. Select the CRM to connect
   Choose your CRM e.g. Salesforce, HubSpot from the drop-down menu.
3. Authenticate your account
   Follow the prompts to log in and authorize Flookup to access your CRM data.
4. Configure your data fetch
   Set any desired filters e.g. date range, specific fields to customize the data retrieval.
5. Specify the columns to compare
   Enter the Left_column and Right_column indices for the two ranges.
6. Specify the output location
   Click an empty cell to mark where the results should appear.
7. Return similarities
   Click the Get percentage similarities button.

Limitations and Best Practices

• Extracted data is raw. In order to use it effectively, add a post-import data wrangling layer for cleaning and transformation.
• The Apps Script timeout of 6 minutes may affect very large datasets. Consider date-range batching.
• Ensure valid tokens and instance details; schedule periodic runs via time-driven triggers for incremental updates.
• Monitor error logs and implement retry logic where needed.

Further Reading

• Fuzzy Match Data by Text Similarity
• Removing Duplicates by Text Similarity