How to Select Metrics and Dimensions for BigQuery With Porter Metrics

When you send marketing data to BigQuery, you choose which metrics and dimensions to include. This determines what columns appear in your BigQuery table and what you can analyze once the data lands.

Porter Metrics gives you two advantages here that you do not get when loading data manually: pre-calculated blended metrics and automatic custom calculations.

Metrics vs Dimensions: The Difference

Before selecting your data, it helps to understand the difference between metrics and dimensions.

Metrics are numbers. They measure something: spend, impressions, clicks, conversions, ROAS, cost per click. Metrics are what you aggregate and calculate.

Dimensions are attributes. They describe context: date, campaign name, ad set name, country, platform, device type. Dimensions are what you group by and filter on.

In BigQuery, your table has columns for both. Each row represents one combination of dimensions (for example, one campaign on one date), and the metric columns hold the numbers for that combination.

Platform-Specific Metrics

When you select a data source in Porter, you see all the metrics available from that platform. For Meta Ads, this includes spend, impressions, reach, clicks, link clicks, conversions, cost per result, and more. For Google Ads, it includes clicks, impressions, cost, conversions, conversion value, quality score, and more.

You select only the metrics you need. Selecting fewer metrics keeps your table smaller and your queries faster. You do not need to include every available metric if you only use a subset for reporting.

Blended Metrics: The Main Advantage

When you select more than one data source, Porter offers blended metrics in addition to platform-specific metrics. Blended metrics are pre-calculated across all your selected sources.

Examples of blended metrics:

• Blended spend: Meta Ads spend + Google Ads spend + TikTok Ads spend in one column.
• Blended impressions: total impressions across all platforms.
• Blended ROAS: total conversion value divided by total spend across all platforms.
• Blended conversions: total conversions across all platforms.

Without Porter, calculating blended ROAS requires writing a SQL query that joins multiple tables, handles different metric definitions from each platform, and applies the correct formula. Porter calculates it for you and loads the result as a column in your BigQuery table.

Custom Calculations Without SQL

Porter also lets you create custom metric formulas in the interface. You define a formula using the metrics available from your selected sources. For example:

Cost per lead = spend / conversions

Click-through rate = clicks / impressions

Revenue per session = revenue / sessions

You define these formulas in Porter, and the result appears as a column in your BigQuery table. You do not write the formula in SQL after the fact. The calculated value is already there when you query the table.

This is especially useful for teams that need consistent metric definitions across reports. Every report that queries that table uses the same formula, calculated the same way.

Dimensions to Always Include

Certain dimensions are essential for any marketing data table. Always include:

• Date: without a date dimension, you cannot filter by time period or compare periods.
• Campaign name: lets you filter and group by campaign.
• Account name or account ID: essential when you have multiple ad accounts in one table.
• Platform: when blending multiple sources, a platform column tells you which row came from which platform.

Additional dimensions depend on your reporting needs. If you report by country, include country. If you report by device type, include device. If you need ad-level granularity, include ad name and ad ID.

How Granularity Affects Table Size

The dimensions you select determine the granularity of your data. More dimensions mean more rows.

If you select date + campaign name, you get one row per day per campaign. A table with 10 campaigns over 90 days has 900 rows.

If you add ad set name and ad name, you get one row per day per ad. If each campaign has 5 ad sets with 3 ads each, that same 10-campaign table now has 13,500 rows over 90 days.

Choose the granularity that matches your reporting needs. If you only report at the campaign level, you do not need ad-level data. Keeping granularity appropriate to your use case keeps your table smaller and your queries faster.

Saving Your Metric and Dimension Selection

Porter saves your metric and dimension selection as part of your query configuration. When your pipeline runs on its daily schedule, it loads the same metrics and dimensions every time. You change the selection at any time by editing your query in Porter, and the next sync reflects the updated configuration.