Import Data From CRM into Google Analytics 4: Detailed Guide

Integrating Customer Relationship Management (CRM) data into Google Analytics 4 (GA4) can provide valuable insights into your customer's journey and behavior. Your company’s internal database, such as an ERP or CRM system, may store a lot of valuable information that you can use to increase sales. 

In this blog post, we cover the process of importing CRM data into GA4, and how you can benefit from the revenue (internal CRM) data integration into Google Analytics 4 which helps in identifying the user behavior data from your website and match with final transactions.

Note: This article on integrating CRM with Google Analytics was originally published in 2017 and is updated with new context to suit the current state of marketing analytics and trends in 2023

Firstly, CRM systems can be used to store the detailed information about your customers: their gender, age, interests, marital and parental statuses, pets, car ownerships, and a lot more. One could argue that Google Analytics 4 also provides information about the age, gender and interests of website visitors. 

That’s true, but this information isn’t bound to a particular user, their Client ID or User ID. In addition, CRM data can be used for RFM analysis and customer segmentation based on purchase recency, frequency, and spending.

The additional data about the customers and the results of RFM analysis can be sent into Google Analytics 4 and used to create new custom reports, segments, and remarketing audiences. 

For example: you can offer a loyalty program or special discounts to your best customers who buy often and spend a lot; send out some interesting promo emails to return those who haven’t bought from you for a while; and offer accessory products to those who often make small purchases. 

Speaking of segments, take a look at how boodmo, India’s largest online marketplace for auto components, managed to optimize advertising spend and improve the LTV with cohort analysis.

Secondly, A CRM system contains detailed product information, including internal classifications and supplier data. CRM integration with Google Analytics 4 allows you to track purchases for specific products from different suppliers and traffic sources. It helps analyze profitability by comparing profits to revenues across various channels.

However, Google Analytics 4 data may not match your ERP system due to missing information on order cancellations, returns, offline transactions, and phone orders. It may also miss tracking some website purchase data due to JavaScript issues. Importing data directly from CRM to Google Analytics can lead to distorted data as it cannot be modified once processed, including adjusting numbers or adding past transactions.

Sending data about customers and margins into Google Analytics directly from your website is not the best option either, and here’s why:

• Business information from your internal system may be disclosed to website visitors in the page code.

• Google Analytics prohibits collecting any personally identifiable information of website visitors.

• It may take lots of time and effort to tell your IT specialists exactly what data, and where to send.

How can you tackle this problem and use for analysis all the data that is stored in your CRM? Here’s the solution: upload the data from your internal system into Google BigQuery, and transfer the data from Google BigQuery to Google Analytics using OWOX BI Pipeline.

1. Cost Data: Cost data import to GA4 includes various non-Google advertising, such as Facebook Ads, TikTok Ads, Bing Ads, and other ad platforms. This data helps you understand the performance and costs associated with your advertising efforts.

2. Item Data (or Product Data): This pertains to details about your products. For example, data include attributes like color, variants, brands, and other product-specific details. This data is handy for e-commerce businesses to track product performance.

3. User Data by User ID: This is stored data in a CRM or ERP System. For example, user data is the status of a loyalty program for a specific user. It helps in understanding user behavior and segmenting users based on specific attributes.

4. User Data by Client ID: Data concerns your prospects or potential customers. These individuals aren't yet your clients, and you might not have a specific user ID for them. For example, data related to phone calls or initial inquiries. This helps in understanding the behavior of potential customers.

5. Offline Event Data: Data from sources that aren't connected to the internet and can't send data to GA4 in real time. For example, purchase event in a physical store. By importing this data, businesses can get a holistic view of online and offline customer interactions.

Google Analytics 4 (GA4) offers a more flexible and integrated data collection and analysis approach than its predecessor, Universal Analytics. When it comes to integrating CRM data with other data in GA4, here's how it can be done:

1. Event-based Data Model: Unlike Universal Analytics, which was session-based, GA4 uses an event-based data model. This allows for more granular tracking of user interactions. CRM data, such as user purchases or lead generation, can be sent as events to GA4.
2. BigQuery Integration: GA4 offers a tighter integration with Google BigQuery. You can export your GA4 data to BigQuery and combine it with your CRM data for more advanced analysis. This helps businesses that want to merge online behavior data with offline CRM data.
3. Offline Data Import: GA4 provides a more flexible data import feature. You can import data from your CRM using a CSV file and match it with existing user data in GA4. This is done using identifiers like User ID or any other unique identifier.
4. Audience Building and Activation: Once you've integrated your CRM data, you can use it to build audiences in GA4. For example, you can create an audience of users who have purchased in the past but have yet to visit your site. You can then use these audiences for remarketing campaigns.
5. API Integrations: GA4 offers various APIs that allow for more custom integrations with CRMs and other platforms. This means you can programmatically send data from your CRM to GA4 or pull data from GA4 into your CRM.

GA4 primarily focuses on tracking user interactions on your website or app. However, you can use Google Analytics 4 with other Google tools and services to import and analyze CRM/ERP data indirectly. Here's a  step-by-step procedure for this process:

Step 1: Prepare Your CRM/ERP Data

• Export the relevant data from your CRM or ERP system into a CSV file.

• Ensure that the CSV file is structured correctly and includes all necessary user identifiers (e.g., email addresses, user IDs) that can be used to match the data to GA4 user data.

Step 2: Set Up Data Import in Google Analytics 4

• In your GA4 property, go to the Admin section.

• Under the "Property" column, click "Data Import."

• Click on "Create Data Source."

• Under "Data Source Details, give your data source a name and description.

• Select the data type that best represents the data you're importing (e.g., Cost data, User data, Event data, etc.,).

• Under “Upload data for import,” choose Import source “Manual CSV upload.”

Step 3: Map Identifiers

• Select the Analytics field for mapping.

• If you want to associate CRM/ERP data with user data in GA4, you'll need to map identifiers (e.g., user IDs, email addresses) between your CSV file and GA4 data. This helps GA4 recognize which user the imported data belongs to.

Step 4: Review & Save

• Review your data import configuration to ensure it's accurate.

• Click “Import”.

Step 5: Trigger Data Import

• After setting up the data import, you can manually trigger it.

Step 6: Analyze Data

• After importing data, you can create custom reports and analyze them alongside your GA4 web/app tracking data.

Step 1: Data Preparation

• Ensure CRM/ERP data is correctly formatted (CSV/TSV).

• Align data with GA4 schema; create custom dimensions/metrics if needed.

Step 2: SFTP Server Setup

• Set up or use an SFTP server like OpenSSH, FileZilla, or cloud options (AWS, Azure, Google). You can integrate your data in SFTP without coding with OWOX BI.

• Configure for incoming connections; create a data file directory.

Step 3: Export CRM/ERP Data

• Export the data you want to import into GA4 from your CRM/ERP system in CSV or TSV format.

• Save these data files to the directory on your SFTP server created in Step 2.

Step 4: Create a Data Import Schema in GA4

• Log in to your Google Analytics 4 account.

• Go to the Admin section in the GA4 property.

• Select the appropriate data stream Under the "Data Streams" section.

Step 5: Set Up Data Import in GA4

• Click "Data Import".

• Click "Create Data Source" to define the schema for your imported data. You must specify custom dimensions and metrics matching your CRM/ERP data.

• Under "Data source details," give your data source a name.

• Under "Upload data for import," select "SFTP Server".

• Configure server details, "SFTP server username," "SFTP server URL," and "SFTP key comment".

• Schedule and click “Next”.

• Select the Analytics field for mapping and click “Import”.

• Test the connection.

There’s a number of ready-made libraries and applications you can use to send the data from your CRM into Google BigQuery (see the article in our Help Center for more details). The upload can be automated, i.e. the data in Google BigQuery will always be timely and relevant. 

OWOX BI also has a connector for Salesforce → Google BigQuery, as well as for most of the other popular internal systems like: Hubspot, Fibery, Pipedrive, Zoho, SAP and so much more.

Another benefit is that your company’s IT specialists won’t have to make changes to the website, as there are ready-made integrations. In addition, you can retrieve all the necessary information, instead of having to choose what you need now and redo the settings every time you need something else.

1. Google Analytics 360 (Universal Analytics): Create user-level custom dimensions for the information you need. To do this, navigate to Admin — Property — Custom Definitions — Custom Dimensions, and click +New Custom Dimension. Make sure to select the User scope in the dropdown list. To learn more about setting up Google Analytics, take a look at our tutorial blog post. To see how you can import the RFM analysis results into Google Analytics, see our documentation.

2. Google Analytics 4 (GA4): In GA4 (Google Analytics 4), to create a user-level custom dimension, access the Admin section, select your property, and navigate to Data Streams > Event Tracking > Custom Definitions. Create a new dimension with a meaningful name and select "User" as the scope. Configure any advanced options as needed and activate the dimension. Finally, update your tracking implementation to start collecting and sending data to this custom dimension for in-depth user analysis.

The query will retrieve the data you need in the "key—value" format. For example, user number 2346 — owns a car. Save this query in your OWOX BI project — you’ll be able to simply select it when automating the data transfer into Google Analytics 4.

OWOX BI Pipeline allows you to automatically import the data retrieved by the query into Google Analytics 4. You only configure the data transfer once, and all future data uploads will be performed without your direct participation (see our documentation for more details). Detailed information about the data transfer will be available on the pipeline page in the OWOX BI interface.

As a result, your data will be integrated as shown on the flowchart below:

• Data Format Issues: Ensure proper CSV/TSV format and encoding.

• Incomplete Data: Check for missing or incomplete fields.

• SFTP Configuration Errors: Verify server settings and permissions.

• Data Schema Mismatch: Align CSV structure with GA4 dimensions/metrics.

• Data Overwrite/Duplication: Avoid unintentional overwrites or duplications.

• Scheduling Issues: Set appropriate import frequency.

• Testing and Monitoring Neglect: Test connections, monitor logs, and review reports.

• Data Privacy and Security: Comply with privacy regulations and implement security measures.

• Documentation and Communication: Maintain clear documentation and communicate effectively.

• Data Cleanup: Address data quality problems before importing.

• Backup Strategy: Always have backup copies of data and configurations or store them in the cloud.

1. The data can be uploaded to Google BigQuery in the structure you need, saving the time of your IT specialists.

2. You can pre-validate the data before sending it to Google Analytics 4, which makes it quicker and easier to pinpoint possible errors.

3. Should anything go wrong during the data transfer, OWOX BI will notify you and provide recommendations on fixing the issue.

4. Upon uploading the data from your internal system into Google BigQuery, you can not only import it into Google Analytics 4, but also create any custom reports you need, based on this and other data you have in Google BigQuery.

5. You can control the data you send, without ever having to involve the IT department. For example, you can easily add a new field to the table in case you only uploaded color specifications for a product, and now also need to upload materials. Or, you can add another Google Analytics property if you decide to collect data simultaneously in multiple properties.

Collect your marketing data into Google BigQuery with no code

As a result, you’ll obtain additional data by CRM integration with Google Analytics 4 , which will allow you to create custom reports and segments of your visitors. You’ll also be able to create remarketing audiences, using detailed information about your customers, and never offer irrelevant products to your customers. Showcase diapers to families with babies, and cat food to cat lovers. Just don’t get those two mixed up :)

How do you create remarketing audiences? Comment and share your experience and thoughts about this post!