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How to load data from LinkedIn Ads to Google BigQuery

Access your data on LinkedIn Ads

The first step in loading your LinkedIn Ads data to any data warehouse solution is to access them and start extracting them.

Using the REST API offered from LinkedIn Ads, you can programmatically interact with your account to access your digital advertising data. By doing so, you can get aggregated metrics that, among others, include the following:

  • Counts of the clicks on the action button and the ad unit
  • The number of impressions or clicks for each card of the carousel ad and creative landing page clicks.
  • Count of comments and likes on each comment
  • Value of conversions and cost in the account’s local currency.

All the available aggregated metrics can be retrieved for any user-defined time period.

In addition to the above, the things that you have to keep in mind when dealing with the LinkedIn Ads API are:

  • Rate limits. There are daily request Quotas per application, per user, and per-application developer as described in the documentation. These vary depending on the Application tier. Current day’s usage and limits can be found in the application (choose application → Application Settings → Usage & Limits)
  • Authentication. Linkedin uses OAuth for authentication. An access token is valid for 60 days. Adding the access token to the requests’ header (bearer authorization) is sufficient to get the reports.
  • Pagination. API endpoints that return a collection of items are always paginated.

About LinkedIn Ads

LinkedIn Ads is an advertising platform that enables businesses to reach their ideal customers on the world’s largest professional network, consisting of more than 560 million users. By using LinkedIn Ads, you can effectively:

  1. Increase awareness of your products and services
  2. Lure potential users to click on a new page on your website
  3. Attract new followers to your company’s company page, thus increasing its visibility

Additionally, LinkedIn allows getting pretty smart about targeting the right audiences by targeting specific company size, job title, industry, and geographic location. This granularity enables you to reach people and companies that matter to your business easily.

Regarding the different types of ads that LinkedIn currently supports, the options are quite many:

  1. Sponsored Content: used for promoting content from your company’s page
  2. Direct Sponsored Content: used for promoting personalized content without publishing it on your company’s page.
  3. Sponsored InMail: This allows you to delivered personalized private messages to potential customers.
  4. Text Ads: Like with other advertising platforms like Google and Bing, these ads are placed on the right rail of the news feed page and available only on the desktop version.
  5. Dynamic Ads: refers to dynamically generated, highly personalized ads that drive users to your company page or apply to a job post.
  6. Lead Gen Forms: refers to prefilled forms with user’s LinkedIn information that aim to generate quality leads.

Transform and prepare your LinkedIn Ads data

After you have accessed data from LinkedIn Ads, you will have to transform it based on two main factors,

  1. The limitations of the database that is going to be used
  2. The type of analysis that you plan to perform

Each system has specific limitations on data types and data structures that it supports. If you want to push data into Google BigQuery, you can send nested data like JSON directly.

Additionally, you have to choose the right data types. Again, depending on the system you send data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database.

Also, you have to consider that the reports you’ll get from LinkedIn Ads are like CSV files in terms of their structure, and you need to identify somehow what and how to map to a table into a database.

Load data from LinkedIn Ads to Google BigQuery

If you want data to be loaded from LinkedIn Ads to BigQuery, you must use one of the following supported data sources.

  1. Google Cloud Storage
  2. Sent data directly to BigQuery with a POST request
  3. Google Cloud Datastore Backup
  4. Streaming insert
  5. App Engine log files
  6. Cloud Storage logs

From the above list of sources, 5 and 6 are not applicable in our case.

For Google Cloud Storage, you first must load your data to it. There are a few options on how to do this. For example, you can use the console directly as described here and do not forget to follow the best practices.

Another option is to post data through the JSON API. As we see again, APIs play an important role in both the extraction and the loading of data to our data warehouse. In its simplest case, it’s just a matter of one HTTP POST request using a tool like CURL or Postman.

After you have loaded your data in Google Cloud Storage, you have to create a Load Job for BigQuery to load any data into it. This Job should point to the source data in Cloud Storage that have to be imported. This happens by providing source URIs that point to the appropriate objects.

The best way to load data from LinkedIn Ads to BigQuery

So far, we just scraped the surface of what you can do with BigQuery and how to load data into it. Things can get even more complicated if you want to integrate data coming from different sources.

Are you striving to achieve results right now?

Instead of writing, hosting, and maintaining a flexible data infrastructure, use RudderStack to handle everything automatically for you.

RudderStack, with one click, integrates with sources or services, creates analytics-ready data, and syncs your LinkedIn Ads to BigQuery right away.

Don't want to go through the pain of direct integration? RudderStack's LinkedIn Ads integration makes it easy to send data from LinkedIn Ads to Google BigQuery.

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