Integrate Wingify with BigQuery to Export Campaign Data

This article covers the following:

• Overview
• Enable the BigQuery Integration
  • Step 1: Create a Service Account for Your BQ Data
  • Step 2: Create a Custom Role
  • Step 3: Assign the Custom Role to Your Service Account
  • Step 4: Create a Cloud Storage Bucket and HMAC Key for Your BQ Data
  • Step 5: Create BigQuery Datasets and Assign Permissions
  • Step 6: Create a BigQuery Connection in Wingify
• BigQuery Data Structure and Export Overview

This integration is available with the Wingify Enterprise plan for Testing, Feature Experimentation, and Personalize.

Overview

Integrating your Wingify account with BigQuery (BQ) enables seamless streaming of your Wingify experiment data into BigQuery. This integration helps consolidate your data in a robust and scalable data warehouse, allowing you to run complex SQL queries and conduct advanced data analysis efficiently.

For example, if you export GA and Wingify data to BQ, you can combine historical website traffic data from GA with A/B test results from Wingify. This enables you to analyze long-term trends, understand how A/B tests affect visitor behavior over time, and build more sophisticated attribution models to measure the effectiveness of your overall marketing strategy.

Enable the BigQuery Integration

Enabling this integration is a multi-step process:

Step 1: Create a Service Account for Your BQ Data

1. Log in to your Google Cloud account and select the project where you want to store your Wingify experiment data in BigQuery.

2. From the main menu panel on the left, go to IAM & Admin > Service Accounts. 

   

3. On the Service Accounts page, click Create service account, enter the service account name and account ID, and click Create and Continue.
4. You can skip the next step and click on Done to save your changes. Upon successful creation, the new service account is listed on the Service Accounts page.
5. Now, click on the newly created service account and go to the Keys tab.

6. Click Add Key > Create New Key, select JSON as the key type option, and click Create. The service JSON file is downloaded in your browser. Keep this file secure, as you will need it later. 

   

Step 2: Create a Custom Role

1. In the Google Cloud console, go to IAM & Admin > Roles.
2. Click Create Role and enter the following details:
   • Title: VWO Custom Role
   • ID: vwo_custom_role
   • Stage: General Availability (GA)
3. Under Permissions, click Add Permissions and search for the permission bigquery.jobs.create.

4. Click Add and then Create to save the role. Upon successful creation, the custom role looks like this:

   

Step 3: Assign the Custom Role to Your Service Account

1. Go to IAM & Admin > IAM.
2. Click Add and enter the email ID of the service account created earlier.

3. Under Role, select VWO Custom Role (created in Step 2) and click Save. 

   

Step 4: Create a Cloud Storage Bucket and HMAC Key for Your BQ Data

1. In the Google Cloud console, go to Cloud Storage.
2. Click Create Bucket and follow these steps:
   • Select Protection Tools as None or Object Versioning.
   • Ensure the bucket does not have a retention policy.
3. Once the bucket is created, go to the Bucket Permissions section.
4. Click Add Principal and assign the role Storage Object Admin to your service account.

5. Click SAVE.

   

6. Now, go to Cloud Storage Settings > Interoperability.
7. Click Create a Key for a Service Account.

1. Select the service account you created earlier.
2. Click Create Key and copy the Access ID and Secret Key.

Step 5: Create BigQuery Datasets and Assign Permissions

1. In the Google Cloud console, go to BigQuery.
2. Create a new dataset with your preferred name.

3. Go to the dataset permissions, click Add Principal, and assign the following roles to your service account:

   • BigQuery User
   • BigQuery Data Editor 

   

4. Now, create another dataset named vwo_raw_data. This dataset will serve as the staging area for your Wingify data before being processed into your chosen dataset.
5. Assign the same roles (BigQuery Data Editor and BigQuery User) to the service account for the vwo_raw_data dataset as well.

Step 6: Create a BigQuery Connection in Wingify

Wingify supports the following BigQuery connection type:

• Export data to BigQuery (Connector): Allows you to export raw Wingify campaign data to BigQuery.

To create and configure a BigQuery connection in your Wingify account:

1. Log in to your Wingify account.
2. From the left panel, navigate to Configurations > Integrations.
3. Click the BigQuery integration tile and then click Create Connection.
4. Select Export data to BigQuery.
5. In the Create Connection form, enter a name in the Connection Name field (for example, "Push Connector").
6. Enter the details in the following fields and click Create Connection:

1. Once the connection is created, you can enable the integration at the campaign level using the following steps:
2. Navigate to Web Experimentation.
3. Select the desired campaign and go to Configuration > Integrations.
4. Select BigQuery from the list of available integrations.

BigQuery Data Structure and Export Overview

The Wingify-BigQuery integration exports your campaign data to your BigQuery project, allowing you to run advanced SQL queries, combine experimentation data with other business data sources, and build custom reports outside the Wingify UI.

For detailed information on the exported datasets, tables, column definitions, and reporting guidance, see Wingify Campaign Data Export to BigQuery: Schema and Reporting Guide.

Need more help?

For more information or further assistance, contact Wingify Support.