Google Ads to Odoo Integration: Complete 2025 Guide

Integration Architecture and Data Flow

A robust Google Ads and Odoo 18 integration relies on a clear server-to-server architecture. The system uses Odoo as the central hub, with custom modules initiating API calls to the Google Ads API. This design avoids brittle client-side solutions and ensures data consistency across both platforms. You will build a dedicated integration service that handles authentication, data retrieval, and synchronization logic within your Odoo instance.

The core data flow operates in two primary directions. The first direction pulls advertising performance data from Google Ads into Odoo. Your custom module will execute queries against the Google Ads API to fetch campaign statistics, including clicks, impressions, costs, and conversions. This data populates custom models in Odoo, linking advertising spend directly to your financial accounting and analytic distribution. The system maps Google Ads campaigns to Odoo’s UTM source and medium tracking.

The second data flow pushes conversion data from Odoo back to Google Ads. When a customer completes a sale in your eCommerce store, the integration captures the transaction details. It then matches these sales to the original Google Ads click using the gclid (Google Click Identifier) stored in the Odoo lead or sale order. The service formats this data and sends it to Google Ads as an offline conversion, enriching your campaign analytics with actual revenue data.

The architecture requires a scheduler, typically using Odoo’s built-in ir.cron model, to manage synchronization intervals. You configure this scheduler to execute data pulls and pushes at regular intervals, such as every four hours or once per day. This cron job triggers your custom Python methods, which contain the business logic for API communication and data transformation. The scheduler ensures your data remains current without manual intervention.

You must design your data models in Odoo to store the imported Google Ads data. We recommend creating new models or extending existing ones like utm.campaign to hold advertising-specific metrics. These models will store historical performance data, allowing for trend analysis and report generation directly inside Odoo. The structure should maintain foreign keys to related Odoo objects like invoices and analytic accounts for comprehensive reporting.

Authentication and API Gateway

The integration uses OAuth 2.0 for server-to-server authentication with the Google Ads API. You create a service account in your Google Cloud Console and generate a JSON key file. Your Odoo module stores this credential securely and uses it to request access tokens for each API call. This method provides a secure, token-based authentication flow that follows Google’s security best practices.

The Google Ads API operates through a gRPC gateway, which your Python code will access using the official Google Ads API client library. You install this library as a dependency for your Odoo module. The client library handles the underlying network communication, while your code focuses on constructing search queries and processing the returned response objects. This abstraction simplifies the integration but requires careful version management.

Step-by-Step Configuration

Begin the configuration process by setting up API access in the Google Cloud Console. Navigate to your project, enable the Google Ads API, and create a new service account. Download the JSON key file for this service account—you will need its contents for the Odoo configuration. Note the unique service account email address, as you will use it later in the Google Ads UI.

Configure OAuth consent for your application, even though this is a server-to-server integration. Specify the application name and user support email. Under scopes, add https://www.googleapis.com/auth/adwords to grant access to the Google Ads API. If you publish the app internally, verify the app status for all users in your organization. This setup ensures proper authorization for API requests.

Install the required Python libraries in your Odoo environment. The Google Ads API client library is essential. Add google-ads to your module’s dependencies in the __manifest__.py file. You also need the google-auth and google-auth-oauthlib libraries for authentication. Use pip to install these packages on your Odoo server, or include them in your Dockerfile if you use containerized deployment.

Create a new Odoo module for the Google Ads integration. Define the module structure with standard files: __init__.py, __manifest__.py, models/, and views/. In your manifest file, declare the module’s dependencies, including website_sale, utm, and account. This ensures access to Odoo’s eCommerce, marketing, and accounting features. Define the data models you will use for storing Google Ads configuration and metrics.

Implement the configuration model in models/google_ads_config.py. This model stores your Google Ads connection settings. Key fields include developer_token, client_id, client_secret, refresh_token, and customer_id. Use Odoo’s fields.Char with password=True for sensitive data. Create a form view that lets administrators input these credentials through the Odoo interface, avoiding hard-coded values in your source code.

Authentication Flow Implementation

Write the authentication method that generates access tokens. Use the google.oauth2.service_account credentials class with your service account JSON key. The method should request a new access token for each API call or implement a secure caching mechanism. Handle token expiration and refresh logic within this method. Store the active token in a temporary field or use Odoo’s cache system to avoid unnecessary token generation.

Establish the Google Ads API client connection. Instantiate the GoogleAdsClient using your developer token and the authenticated credentials. This client object becomes your primary interface for all subsequent API interactions. Configure it with your Google Ads customer ID and the login customer ID if you manage a manager account. Test this connection with a simple request, such as listing accessible customers, to verify your setup works.

Create the core data synchronization method. This method constructs a GAQL (Google Ads Query Language) query to retrieve the desired campaign performance data. A basic query selects campaign.id, campaign.name, metrics.cost_micros, metrics.clicks, and metrics.impressions from the campaign resource. Execute this query using the API client’s search method and iterate through the returned pages of results.

Data Import and Model Mapping

Process the API response and map the data to your Odoo models. Convert cost from micros (millionths of a currency unit) to your base currency using Odoo’s currency handling. Find or create corresponding utm.campaign records based on the Google Ads campaign names. Create new records in your custom Google Ads performance model, linking them to the UTM campaigns and setting the appropriate date range for the statistics.

Implement the conversion export functionality. Create a method that queries Odoo sales orders with a gclid value and without a sent-to-Google flag. Format this data according to the Google Ads offline conversion import specification. Use the ConversionUploadService from the API client to create these conversions in Google Ads. Mark the records as exported in Odoo to prevent duplicate uploads in subsequent sync cycles.

Configure the scheduled actions. In your module’s data file, define an ir.cron record that calls your main synchronization method. Set a reasonable interval, such as every 12 hours, depending on your reporting needs. Consider creating separate cron jobs for data import and conversion export to isolate potential failures. Test the scheduler manually before leaving it to run automatically.

Data Mapping and Transformation

The data mapping process defines how information moves between Google Ads and Odoo’s distinct data structures. For campaign data, you map the Google Ads campaign ID to Odoo’s utm.campaign external identifier. This creates a persistent link between the advertising campaign and its corresponding tracking object in Odoo. The system uses this link to update performance metrics on existing campaigns rather than creating duplicate records.

Cost data requires significant transformation. The Google Ads API returns cost values in micros (millionths of a currency unit), but Odoo accounting uses standard currency precision. Your integration must divide micro values by 1,000,000 and convert them to your company’s base currency using the exchange rate valid for the transaction date. This ensures accurate cost recording in your general ledger and analytic accounting.

Conversion data mapping involves the most complex logic. You must associate Odoo sales with specific Google Ads clicks using the gclid. Store this identifier in Odoo’s crm.lead model when a visitor arrives from a Google Ad, and propagate it to the sale.order upon conversion. The integration then uses this gclid to send the conversion value and time back to Google Ads, attributing the revenue to the correct ad click.

Handling Currency and Time Zone Differences

Address currency conversion challenges. Google Ads often operates in the account’s local currency, while your Odoo instance uses a different base currency. Implement a currency conversion mechanism that uses Odoo’s built-in exchange rate tables or a live feed from a financial data provider. Apply the correct exchange rate for the date of the advertising transaction, not the sync date, for accurate historical cost reporting.

Manage time zone discrepancies. Google Ads reports data in the account’s time zone, while Odoo uses UTC or a company-defined time zone. Your integration must normalize all timestamps to a consistent time zone, preferably UTC, before storing or comparing data. Apply time zone conversions during the data import process to ensure daily aggregates match between both systems. This prevents off-by-one errors in daily performance reports.

Custom Field Mapping and Extension

Extend standard Odoo models to accommodate Google Ads-specific data. Add fields like google_ads_campaign_id, google_ads_ad_group_id, and gclid to the utm.campaign and crm.lead models. These fields preserve the original Google Ads identifiers, enabling precise data matching and more detailed reporting. Use computed fields to create derived metrics, such as cost-per-acquisition, that combine Google Ads cost with Odoo sales data.

Create specialized mapping tables for complex data relationships. When your Odoo product catalog does not align directly with Google Ads product groups, implement a mapping table that links Odoo product categories to Google Ads labels or product partitions. This intermediate table gives your marketing team flexibility in how they structure campaigns without requiring constant changes to the integration logic.

Handle data type conversions between the systems. Google Ads API represents enums as integers, while Odoo uses string values. Create mapping functions that convert these numeric enums to human-readable strings for display in Odoo reports. For example, convert the campaign status enum (2 for ENABLED, 3 for PAUSED) to corresponding status values in Odoo’s campaign management interface.

Error Handling and Resilience

Robust error handling separates a production-ready integration from a fragile prototype. The Google Ads API can throw several specific exceptions that your code must catch and handle. Common errors include GoogleAdsException for API-specific issues and transport errors for network problems. Implement structured exception handling around all API calls, with specific catch blocks for each exception type your integration might encounter.

Handle quota exceeded errors gracefully. The Google Ads API imposes strict quotas on the number of requests per minute and per day. When you hit these limits, the API returns a quota exceeded error. Your code should catch this specific exception, log the incident, and implement an exponential backoff strategy for retries. Consider implementing a request throttling mechanism to stay within quota limits during normal operation.

Manage authentication failures proactively. Token expiration and credential revocation cause authentication errors. Implement a token refresh flow that automatically obtains a new access token when the current one expires. For more permanent authentication failures, such as invalid credentials, escalate the error by sending an email notification to system administrators and disabling the sync cron job until someone fixes the issue.

Data Validation and Integrity Checks

Implement comprehensive data validation before import. Check for null values, data type mismatches, and referential integrity issues. For example, verify that a campaign exists in Odoo before attempting to update its metrics. Reject records that fail validation and move them to a quarantine table for manual review. This prevents corrupt data from polluting your Odoo database and affecting financial reports.

Create reconciliation reports to identify synchronization gaps. Generate daily reports that compare key metrics between Google Ads and Odoo, such as total spend or conversion counts. Significant discrepancies indicate potential sync issues that require investigation. Build an Odoo dashboard that displays these reconciliation metrics, giving administrators immediate visibility into the integration’s health.

Design a retry mechanism for failed conversion uploads. When the Google Ads API rejects a conversion upload, store the failed records in a separate queue with the specific error message. Implement a manual retry process that lets administrators review and resubmit these failed conversions after addressing the underlying issue. This ensures you capture all valuable conversion data despite temporary API issues.

Logging and Monitoring

Implement detailed logging throughout the integration. Use Odoo’s logging framework to record information about each sync operation, including the number of records processed, any errors encountered, and the operation duration. Structure your logs with consistent severity levels (INFO, WARNING, ERROR) to facilitate filtering and analysis. These logs become your primary tool for diagnosing issues in production.

Create automated health checks for the integration. Build a simple monitoring system that verifies the sync process completes within expected timeframes and processes a reasonable volume of data. If a sync job fails entirely or processes zero records, trigger an alert to the system administration team. Consider integrating with Odoo’s built-in monitoring tools or external systems like Prometheus for enterprise-grade observability.

Testing and Validation

Comprehensive testing ensures your Google Ads integration operates reliably with real data. Begin with unit tests that verify individual components, such as data transformation functions and authentication logic. Mock the Google Ads API responses to test your code without making actual API calls. This approach lets you validate business logic in isolation and catch bugs before integration testing.

Progress to integration tests that exercise the full data flow between Odoo and Google Ads. Use a dedicated Google Ads test account with a small amount of live campaign data. Execute your synchronization methods against this test account and verify the data appears correctly in Odoo. Test both directions of the data flow: importing campaign metrics and exporting conversion data.

Validate data accuracy through sample-based reconciliation. Select a specific date range and campaign in your Google Ads test account. Run your integration and compare the imported data in Odoo against the Google Ads UI reports. Verify that costs, clicks, impressions, and conversion counts match exactly. Even small discrepancies indicate problems with your data transformation or filtering logic.

Performance and Load Testing

Assess the integration’s performance with large datasets. If you manage extensive Google Ads accounts with hundreds of campaigns and thousands of keywords, test how your integration handles this volume. Monitor memory usage and execution time during sync operations. Implement pagination and batch processing if you encounter performance issues with large result sets from the Google Ads API.

Test the integration’s behavior under error conditions. Simulate network failures, API quota exhaustion, and invalid data responses to verify your error handling works as designed. Use tools like Docker network manipulation to create temporary connectivity issues. Confirm your integration logs appropriate errors, implements retry logic, and recovers gracefully when normal conditions resume.

Create a validation checklist for deployment. This checklist should include items like OAuth configuration verification, API quota monitoring, data mapping confirmation, and error handling tests. Complete this checklist before moving the integration from a development environment to production. Update the checklist as you discover new potential failure modes during testing.

User Acceptance Testing

Engage business users in the testing process. Marketing managers and financial controllers should review the imported data in Odoo and confirm it meets their reporting needs. Gather feedback on the data presentation, report accuracy, and any missing metrics. This user validation ensures the integration delivers practical business value, not just technical functionality.

Document test scenarios for ongoing quality assurance. Create a repository of test cases that cover normal operation, edge cases, and error conditions. Execute these tests after any significant changes to the integration code or when updating to new versions of the Google Ads API client library. This regression testing prevents new developments from breaking existing functionality.

Security Considerations

The Google Ads integration handles sensitive financial data and API credentials that demand robust security measures. Store all API credentials, including the developer token and OAuth client secrets, in Odoo’s parameter system with appropriate access controls. Never hardcode these values in your module’s source code or commit them to version control. Use Odoo’s encrypted fields for storing sensitive configuration data.

Implement principle of least privilege access for API credentials. Create a dedicated Google service account with only the permissions necessary for the integration—typically just Google Ads API access. Avoid using accounts with broader Google Cloud permissions. Within Odoo, restrict access to the integration configuration to authorized administrators only, preventing unauthorized users from modifying sync settings.

Secure data in transit between Odoo and the Google Ads API. The Google Ads client library uses gRPC with TLS encryption by default, ensuring all communication travels over secure channels. Verify your Odoo instance uses HTTPS for all web traffic, including the backend administration interface. This end-to-end encryption protects your advertising data and credentials from interception.

Access Control and Audit Logging

Apply Odoo’s built-in access control mechanisms to your integration models. Create specific security groups for users who need to view Google Ads data or manage the integration configuration. Implement record rules that restrict data access based on department or responsibility. This prevents unauthorized users from viewing sensitive advertising performance or cost data.

Maintain comprehensive audit logs of integration activities. Record who configures the integration, when sync jobs run, and what data they process. In regulated industries, these logs demonstrate compliance with data handling requirements. The logs also provide valuable forensic information for investigating potential security incidents or data discrepancies.

Establish credential rotation procedures for your Google Ads integration. Schedule regular rotation of OAuth client secrets and service account keys, even if no security incident occurs. Implement a process for updating these credentials in Odoo with minimal service disruption. This proactive security measure limits the potential impact of credential leakage.

Performance Optimization

Optimize your Google Ads API queries to reduce response times and stay within quota limits. Select only the fields you need in your GAQL queries, avoiding the SELECT * pattern that retrieves unnecessary data. Use filtering in your queries to limit the date range and campaign scope, rather than fetching all historical data during each sync. This approach minimizes the data transfer volume and processing time.

Implement strategic caching for relatively static data. Campaign structures and ad group information change infrequently compared to performance metrics. Cache this structural data in Odoo and refresh it on a separate, less frequent schedule than your performance data sync. This reduces API calls and speeds up the main synchronization process by avoiding redundant structural data retrieval.

Use batch processing for large datasets. When processing thousands of campaigns or conversions, break the work into smaller batches to manage memory usage and provide better progress visibility. The Google Ads API supports batching for some operations, which can improve efficiency. In Odoo, use the self.env.cr.commit() sparingly and consider database indexing on frequently queried integration fields.

Database and Network Optimization

Optimize database performance for integration models. Add indexes to fields used for joining tables or filtering data, such as campaign identifiers and date fields. Monitor query performance using Odoo’s database logging and optimize slow queries. Consider partitioning large historical data tables by date to maintain query performance as your data volume grows over time.

Tune the synchronization schedule based on business needs. Balance data freshness against system load by scheduling frequent syncs during business hours and less frequent syncs overnight. For most businesses, syncing campaign performance data every 4-6 hours provides sufficient freshness without overwhelming the API quotas or Odoo instance. Adjust based on your specific advertising spend and conversion volume.

Monitor and optimize memory usage during data processing. Large API responses can consume significant memory when processing in Python. Use generator expressions and streaming processing where possible to handle large datasets without loading everything into memory at once. Profile your integration’s memory usage during peak loads and optimize data structures that show excessive memory consumption.