LinkedIn Ads
Google BigQuery
Tutorial
In this tutorial, we’ll show you how to send your LinkedIn Ads data to Google BigQuery with Porter. We’ll send campaign performance data including fields like Campaign Name, Impressions, Clicks, and custom fields like CPA and campaign segmentation by funnel stage.
Log in to portermetrics.com with Google. Click “Create” and select ” Google BigQuery ” as destination. Name your connection (e.g., “LinkedIn Ads Campaign Performance”). Select LinkedIn Ads as data source.
Data blending : optionally, add Google Ads, HubSpot, Salesforce in the same connection for cross-channel reports.
Connect your LinkedIn profile and grant access to your Campaign Manager accounts. Select the Ad Accounts you want to pull data from. Select the Ad Accounts you want to connect.
Consolidate multiple Campaign Manager accounts in a single report.
Editor, Analyst, or Admin on the Ad Account.
LinkedIn tokens refresh automatically. Re-auth only needed if you revoke access.
Authenticate with Google login or Service Account. Select Project ID, Dataset location, Dataset, and Table name.
Google login (recommended) : Porter lists your projects in a dropdown. Easiest option.
Service Account JSON : for companies with strict permissions management on Google Workspace. Copy a JSON text from your project details to connect.
Dataset location : US, EU, or your preferred region.
Auto-update schema : if you change your query later, Porter updates the schema automatically and rewrites it in your BigQuery table, unlike other tools.
New to BigQuery? Create your first project:
Go to console.cloud.google.com . In the Navigation Menu (top left), select BigQuery → Studio. On the left panel, you’ll see your projects.
Create a Project : select or create a new project (e.g., “Marketing Data”). Choose a name, type, and organization. BigQuery creates a folder for it.
Create a Dataset : expand your project folder, click the ellipsis, and select “Create Dataset.” Name it (e.g., “ppc_data”) and select a location (US or EU).
Create a Table : inside your dataset, you can create a table (e.g., “meta_ads”). Or let Porter create it automatically when you send your first query.
The Project ID, Dataset name, and Table name you set here are the same values you’ll enter in Porter’s BigQuery configuration.
Verify your data in BigQuery:
When you select a table, BigQuery shows the Schema view first. This is the metadata: field names, field types, and modes. To see your exported data, go to the Preview tab. Once your query executes, you’ll see the complete table with your data.
In the metrics dropdown, search and select: e.g., Amount Spent, Purchases, Leads, Custom Conversions, Impressions, Link Clicks.
Porter brings most fields and constantly updates from the LinkedIn Marketing API. Expect the exact same data you see in Campaign Manager.
Compare conversions across 1-day click, 7-day click, 28-day click, 1-day view, 7-day view in one query.
Bring form fields and submitted information from users directly to your data warehouse.
To segment your data, in the dimensions dropdown, search and select: e.g., Campaign Name, Date.
Other dimensions : Ad Account, Objective, Ad Set Name, Ad Name, Creative (image URL, video URL, headline, primary text, ad preview link), Hour, Week, Month, Year.
Company targeting data : Industry, Company Size, Job Function, Seniority available as dimensions.
Lead Gen Forms included : Lead Form Completions and Lead Form Opens are available as metrics.
Daily granularity recommended : LinkedIn Ads API provides best data accuracy at daily level. Hourly breakdowns not available.
For custom metrics , add a new metric, prompt your formula in natural language, and check the formula generated and a preview of the query. Choose the format of your metric (number, currency, percentage). For this example: CPC = “spend / clicks”, CPA = “spend / conversions”.
For custom dimensions , prompt your formula to segment data based on naming conventions. If your naming conventions include objective, funnel stage, or products, prompt a formula like: “If campaign name contains ‘awareness’, tag as ‘Awareness’. If contains ‘consideration’, tag as ‘Consideration’. If contains ‘conversion’, tag as ‘Conversion’. Else ‘Other’.” In the preview, see how Porter transforms conditionals into regex for custom segmentations.
Create your own metrics or dimensions so no SQL or transformation is needed in BigQuery. Your data is ready to be connected to Looker Studio. Supported operations: math (sum, subtract, divide, multiply), conditionals (if/then/else), regex (pattern matching). Same capabilities as Looker Studio calculated fields.
Select a date range from the dropdown. For this example: last 30 days.
Dynamic ranges : today, yesterday, last 7/14/28/30/90 days, this week/month/quarter/year to date, last week/month/quarter/year.
Fixed ranges : specific start and end dates.
Auto-update : data refreshes automatically based on dynamic range.
The Meta API recently started returning campaigns with no activity. When you run a query, you get campaigns with spend and campaigns with zero spend, which is noise to your tables. We’ll create a filter to exclude them.
For this example:
Condition : Exclude
Field : Amount Spent
Operator : equals
Value : 0
This excludes all campaigns without activity, so your query only returns campaigns with spend.
Available operators : equals, contains, not contains, greater than, less than, starts with, ends with.
Value detection : Porter detects if the field is a number or text automatically.
Combine filters : add AND/OR logic within the same condition or create multiple filters in one query.
Prompt your schedule in natural language. For this example: “every day at 8am”.
Examples : “Every Monday at 5am”, “Weekdays at 7pm”, “Every hour”, “Every Tuesday and Friday at 9am”.
Auto-convert : Porter converts prompts into cron expressions.
Timezone : detected automatically from your browser.
Minimum frequency : every minute. No extra cost for frequent refreshes.
Select how Porter writes data to BigQuery. For this example: Overwrite.
Overwrite (recommended) : deletes existing table and writes fresh data. No duplicates.
Append : adds new rows below existing data. Risk of duplicates if same date range runs twice.
Update : matches rows by dimension and updates values. For CRM data with changing values.
Click “Save” to save your query and click “Send” to deliver the data to Google BigQuery. The transfer takes a few seconds depending on the volume of data. Once finished, you can refresh it or create more queries.
To create more queries: go back to the query manager inside your connection, or go to Porter Metrics → Account → Reports → Connections. In the Queries tab, you’ll see all queries running from your account with their associated connection, name, data sources, last run time, latest status, and option to run manually.
To monitor executions: click the ellipses icon and select “History.” You’ll see logs with exact date and time, execution type (manual or scheduled), and status. If an error occurs, you’ll see the specific error message.
To organize your data: manage connections and queries within them. Name connections by campaign (e.g., “Black Friday”), by client, or by data source. Within each connection, create as many queries or tables as needed and rename them. You can enable/disable queries or connections, and update any query anytime—Porter refreshes and updates the schema on BigQuery automatically.
First, verify your data in BigQuery:
Go to console.cloud.google.com/bigquery . In the left menu, under Products, find BigQuery → Studio. This is where you manage your tables.
BigQuery hierarchy:
Project (e.g., “Marketing Data”): your top-level container.
Dataset (e.g., “PPC Data”): a collection of tables within a project.
Table (e.g., “LinkedIn Ads”): your actual data.
In BigQuery Studio, go to “Classic Explorer” and select your project. Click the ellipsis to create a new dataset if needed (set a name and location, e.g., US or Europe). Navigate to your dataset and table. In “Schema,” see the list of fields and their types. In “Preview,” see your actual data. To refresh data, go back to Porter and resend—Porter overwrites the table.
Connect BigQuery to Looker Studio:
Go to Looker Studio . Click “Create” and select “Report” to start a blank report. Looker Studio will prompt you to add a data source. Search for “BigQuery” and connect your Google account.
You’ll see options:
Select “My Projects” and navigate to your project, dataset, and table. In this example: Project “Marketing Data” → Dataset “PPC Data” → Table “LinkedIn Ads”. Click “Add” to connect.
Once connected, Looker Studio loads the fields from your table. Create a chart, add your dimensions (e.g., date) and metrics (e.g., spend). Make sure to set a date range that matches your query in Porter.
Rename fields for readability:
BigQuery field names use underscores (e.g., “facebook_ads_spend”). To rename them, go to Resource → Manage added data sources → Actions → Edit. Double-click on each field name to change it (e.g., “facebook_ads_spend” → “Amount Spent”). This makes fields more readable for end users.
Your BigQuery data is now connected to Looker Studio.
Now that your LinkedIn Ads data is in BigQuery:
Connect to BigQuery: learn Google BigQuery for marketers and read tutorials to connect other data sources.
Connect to Looker Studio: Build dashboards that load in seconds. Use Porter templates or create your own.
Connect to Google Sheets: Export BigQuery data to Sheets for quick analysis, sharing with clients, or custom calculations.
Blend data from multiple sources: Add Google Ads , GA4, Shopify , CRM to the same connection. Porter auto-maps equivalent fields. Create cross-channel reports without SQL joins.
Create AI workflows: Automate alerts and reports with natural language. Example: “Every Monday at 9am, get LinkedIn Ads spend for last 7 days, analyze performance with AI, send summary to Slack.”
Use templates: Start with pre-built Looker Studio templates. Campaign performance, creative analysis, audience breakdowns—ready to connect.
Explore other destinations: Send LinkedIn Ads data to Google Sheets, PostgreSQL, or other warehouses. Same setup process.
