Google Campaign Manager to Looker

This page provides you with instructions on how to extract data from Google Campaign Manager and analyze it in Looker. (If the mechanics of extracting data from Google Campaign Manager 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 Campaign Manager?

Campaign Manager (formerly DoubleClick Campaign Manager) is a web-based ad management system that advertisers and agencies use to manage creative assets and run ad campaigns.

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 Campaign Manager

Campaign Manager has an API that you can use to get information about advertisers, campaigns, creative assets, and more. For example, to get information about a campaign for a given profile, you would call GET /userprofiles/{profileId}/campaigns/{id}.

Sample Campaign Manager data

Here's an example of the kind of response you might see with a query like the one above.

{
  "kind": "dfareporting#campaign",
  "id": long,
  "idDimensionValue": dimensionValues Resource,
  "accountId": long,
  "subaccountId": long,
  "advertiserId": long,
  "advertiserIdDimensionValue": dimensionValues Resource,
  "advertiserGroupId": long,
  "name": string,
  "archived": boolean,
  "startDate": date,
  "endDate": date,
  "comment": string,
  "billingInvoiceCode": string,
  "audienceSegmentGroups": [
    {
      "id": long,
      "name": string,
      "audienceSegments": [
        {
          "id": long,
          "name": string,
          "allocation": integer
        }
      ]
    }
  ],
  "eventTagOverrides": [
    {
      "id": long,
      "enabled": boolean
    }
  ],
  "clickThroughUrlSuffixProperties": {
    "overrideInheritedSuffix": boolean,
    "clickThroughUrlSuffix": string
  },
  "defaultClickThroughEventTagProperties": {
    "overrideInheritedEventTag": boolean,
    "defaultClickThroughEventTagId": long
  },
  "creativeGroupIds": [
    long
  ],
  "creativeOptimizationConfiguration": {
    "optimizationModel": string,
    "optimizationActivitys": [
      {
        "floodlightActivityId": long,
        "floodlightActivityIdDimensionValue": dimensionValues Resource,
        "weight": integer
      }
    ],
    "id": long,
    "name": string
  },
  "additionalCreativeOptimizationConfigurations": [
    {
      "optimizationModel": string,
      "optimizationActivitys": [
        {
          "floodlightActivityId": long,
          "floodlightActivityIdDimensionValue": dimensionValues Resource,
          "weight": integer
        }
      ],
      "id": long,
      "name": string
    }
  ],
  "lookbackConfiguration": {
    "clickDuration": integer,
    "postImpressionActivitiesDuration": integer
  },
  "createInfo": {
    "time": long
  },
  "lastModifiedInfo": {
    "time": long
  },
  "traffickerEmails": [
    string
  ],
  "externalId": string,
  "nielsenOcrEnabled": boolean,
  "adBlockingConfiguration": {
    "enabled": boolean,
    "overrideClickThroughUrl": boolean,
    "clickThroughUrl": string,
    "creativeBundleId": long
  },
  "defaultLandingPageId": long
}

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 Campaign Manager data up to date

Now what? You've built a script that pulls data from the Campaign Manager API and loads it into your data warehouse, but what happens tomorrow when you have new data?

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, many of the API results include fields like createInfo that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

From Google Campaign Manager to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Google Campaign Manager 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 Campaign Manager to Redshift, Google Campaign Manager to BigQuery, Google Campaign Manager to Azure SQL Data Warehouse, Google Campaign Manager to PostgreSQL, Google Campaign Manager to Panoply, and Google Campaign Manager 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 Campaign Manager to Looker automatically. With just a few clicks, Stitch starts extracting your Google Campaign Manager data via the API, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Looker.