Google Ads to Looker

This page provides you with instructions on how to extract data from Google Ads and analyze it in Looker. (If the mechanics of extracting data from Google Ads seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Google Ads?

Google Ads (formerly AdWords) is a popular paid marketing tool. With Google Ads, you set a budget, select keywords, and publish ads that appear on Google search results pages relevant to your keywords. Google Ads collects data about campaigns that businesses can use to measure their effectiveness.

What is Looker?

Looker is a powerful, modern business intelligence platform that has become the new standard for how modern enterprises analyze their data. From large corporations to agile startups, savvy companies can leverage Looker's analysis capabilities to monitor the health of their businesses and make more data-driven decisions.

Looker is differentiated from other BI and analysis platforms for a number of reasons. Most notable is the use of LookML, a proprietary language for describing dimensions, aggregates, calculations, and data relationships in a SQL database. LookML enables organizations to abstract the query logic behind their analyses from the content of their reports, making their analytics easy to manage, evolve, and scale.

Getting data out of Google Ads

Google provides a SOAP API for Google Ads. The first step of getting your data into your data warehouse is pulling the data off of Google's servers by using the AdWords API's Reporting features. This is a subset of the API's functionality, which also includes the ability to manage ads.

You can also link your Google Analytics and Google Ads accounts to allow the data to cross-pollinate. This can provide richer reporting due to the breadth of knowledge that exists in Google Analytics about the people who may have viewed or clicked your ads.

You can extract granular data from AdWords API reports, allowing you to see things like impressions, clickthrough rates, and CPC broken out by time period.

Loading data into Looker

To perform its analyses, Looker connects to your company's database or data warehouse, where the data you want to analyze is stored. Some popular data warehouses include Amazon Redshift, Google BigQuery, and Snowflake.

Looker's documentation offers instructions on how to configure and connect your data warehouse. In most cases, it's simply a matter of creating and copying access credentials, which may include a username, password, and server information. You can then move data from your various data sources into your data warehouse for Looker to use.

Analyzing data in Looker

Once your data warehouse is connected to Looker, you can build constructs known as explores, each of which is a SQL view containing a specific set of data for analysis. An example might be "orders" or "customers."

Once you've selected any given explore, you can filter data based on any column available in the view, group data based on certain fields in the view (known as dimensions), calculate outputs such as sums and counts (known as measures), and pick a visualization type such as a bar chart, pie chart, map, or bubble chart.

Beyond this simple use case, Looker offers a broad universe of functionality that allows you to conduct analyses and share them with your organization. You can get started with this walkthrough in Looker's documentation.

Keeping Google Ads data up to date

So, now what? You've built a script that pulls data from Google Ads and loads it into your data warehouse, but what happens tomorrow when you have thousands of new impressions?

The key is to build your script in such a way that it can also identify incremental updates to your data. If you can identify some fields that auto-increment, you could use them to give your script the ability to recognize new data. You can then set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

From Google Ads to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Google Ads data in Looker is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Google Ads to Redshift, Google Ads to BigQuery, Google Ads to Azure SQL Data Warehouse, Google Ads to PostgreSQL, Google Ads to Panoply, and Google Ads to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data from Google Ads to Looker automatically. With just a few clicks, Stitch starts extracting your Google Ads data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Looker.