Cracking the Code: How to Calculate Total Cost for an Ad in Google Ads Data in BigQuery

Welcome to the world of Google Ads data analysis, where the quest for accurate cost calculations can be a daunting task, especially when dealing with massive datasets in BigQuery! Fear not, dear reader, for today we’re about to embark on a thrilling adventure to uncover the secrets of calculating total cost for an ad in Google Ads data using BigQuery.

What You’ll Need

• A Google Ads account with data flowing into BigQuery
• A basic understanding of SQL and BigQuery
• A willingness to learn and conquer the world of cost calculations

Understanding the Google Ads Data Structure in BigQuery

Before we dive into the calculation, it’s essential to understand the Google Ads data structure in BigQuery. The data is organized into several tables, including:

• `ads` table: contains ad-level data, such as ad ID, ad group ID, and ad status
• `ad_groups` table: contains ad group-level data, such as ad group ID, campaign ID, and ad group status
• `campaigns` table: contains campaign-level data, such as campaign ID, campaign name, and campaign status
• `cost` table: contains cost data, such as cost, currency, and date

The Formula for Calculating Total Cost

The total cost for an ad in Google Ads can be calculated using the following formula:

Total Cost = SUM(Cost) WHERE Ad ID = [ad_id] AND Date = [date]

This formula sums up the cost for a specific ad ID on a specific date.

Breaking Down the Calculation in BigQuery

In BigQuery, we can use the following query to calculate the total cost for an ad:

WITH total_cost AS (
  SELECT 
    ad_id,
    DATE_TRUNC(date, DAY) AS date,
    SUM(cost) AS total_cost
  FROM 
    `project_id.dataset_id.cost`
  WHERE 
    ad_id = [ad_id]
  GROUP BY 
    ad_id, date
)

SELECT 
  *
FROM 
  total_cost

This query uses a Common Table Expression (CTE) to simplify the calculation. The `DATE_TRUNC` function is used to truncate the date to the day level, and the `SUM` function is used to calculate the total cost.

Handling Multiple Ad Groups and Campaigns

In a real-world scenario, you may have multiple ad groups and campaigns associated with an ad. To handle this, we can modify the query to join the `ads`, `ad_groups`, and `campaigns` tables:

WITH total_cost AS (
  SELECT 
    a.ad_id,
    a.ad_group_id,
    ag.campaign_id,
    DATE_TRUNC(date, DAY) AS date,
    SUM(cost) AS total_cost
  FROM 
    `project_id.dataset_id.cost` c
  JOIN 
    `project_id.dataset_id.ads` a ON c.ad_id = a.ad_id
  JOIN 
    `project_id.dataset_id.ad_groups` ag ON a.ad_group_id = ag.ad_group_id
  WHERE 
    a.ad_id = [ad_id]
  GROUP BY 
    a.ad_id, a.ad_group_id, ag.campaign_id, date
)

SELECT 
  *
FROM 
  total_cost

This query joins the `cost` table with the `ads` and `ad_groups` tables to include ad group and campaign-level data in the calculation.

Advanced Calculations: Handling Conversions and Conversion Values

In addition to calculating the total cost, you may also want to calculate the total conversion value and cost per conversion. To do this, we can add two more columns to our query:

WITH total_cost AS (
  SELECT 
    a.ad_id,
    a.ad_group_id,
    ag.campaign_id,
    DATE_TRUNC(date, DAY) AS date,
    SUM(cost) AS total_cost,
    SUM(conversions) AS total_conversions,
    SUM(conversion_value) AS total_conversion_value
  FROM 
    `project_id.dataset_id.cost` c
  JOIN 
    `project_id.dataset_id.ads` a ON c.ad_id = a.ad_id
  JOIN 
    `project_id.dataset_id.ad_groups` ag ON a.ad_group_id = ag.ad_group_id
  WHERE 
    a.ad_id = [ad_id]
  GROUP BY 
    a.ad_id, a.ad_group_id, ag.campaign_id, date
)

SELECT 
  *,
  total_cost / total_conversions AS cost_per_conversion,
  total_conversion_value / total_cost AS return_on_ad_spend
FROM 
  total_cost

This query adds two new columns: `total_conversions` and `total_conversion_value`. The `cost_per_conversion` column is calculated by dividing the total cost by the total conversions, and the `return_on_ad_spend` column is calculated by dividing the total conversion value by the total cost.

Visualizing the Data with BigQuery Data Studio

Now that we have our calculation, let’s visualize the data using BigQuery Data Studio! We can create a dashboard with the following components:

Component	Description
Table	Display the total cost, total conversions, and cost per conversion data
Line Chart	Show the trend of total cost and total conversions over time
Bar Chart	Compare the cost per conversion across different ad groups and campaigns

With BigQuery Data Studio, we can easily create a dashboard that provides actionable insights into our Google Ads data.

Conclusion

In this article, we’ve cracked the code of calculating total cost for an ad in Google Ads data using BigQuery. We’ve covered the basics of the Google Ads data structure, the formula for calculating total cost, and how to break down the calculation in BigQuery. We’ve also touched on advanced calculations, such as handling conversions and conversion values, and visualizing the data with BigQuery Data Studio.

With this knowledge, you’re now empowered to conquer the world of Google Ads data analysis and make data-driven decisions to optimize your campaigns. Happy querying!

Keywords: Google Ads, BigQuery, cost calculation, data analysis, SQL

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