A data warehouse like BigQuery is an excellent choice for analyzing ad and marketing performance. However, it doesn’t usually have native connectors for PPC platforms, except for Google Ads.
Luckily, there are other no-code options to transfer data from Pinterest Ads to BigQuery. For example, you can use a no-code data automation tool or load data manually. Let’s now have a look at how to do this.
Option 1. Load data from Pinterest Ads to BigQuery with a no-code tool
Using a data automation tool is the most simple and convenient way to transfer data between these apps. This option doesn’t require any tech skills, and it allows you to transfer data in just a few minutes.
You can also activate automatic updates, which will keep your Pinterest Ads information in the data warehouse always fresh. Thanks to this, you can build live dashboards and self-refreshing reports, once you connect BigQuery to a data viz tool or a spreadsheet app.
1. Collect your Pinterest Ads data
To start sending your ad data to BigQuery, click the Proceed button below.
You will be asked to create a Coupler.io account (that’s free) and connect your Pinterest Ads account. Once this is done, select the preferred date range and report type.
In particular, you can export reports such as:
- Campaign performance
- Ad group performance
- Ad (promoted pin) performance
- Ad account performance
- Product group performance
- Product item performance
- Keywords performance
- Ad account targeting analysis
- Campaign targeting analysis
- Ad group targeting analysis
- Ad (promoted pin) targeting analysis
- Product group targeting analysis
Apart from that, you can also export lists of campaigns, keywords, ad accounts, and so on. In addition, you can select metrics and dimensions to include.
2. Organize and transform data
In the next step, preview and organize your data, if needed.
For example, here you can:
- Sort and filter data
- Manage columns (hide, rearrange, rename, split, or merge)
- Add formula-based columns and perform calculations
- Blend data from different accounts or PPC platforms into one dataset
As a result, the data that you send from Pinterest Ads to BigQuery will be clean and analysis-ready.
After this, follow the wizard instructions to connect your BigQuery account.
To do so, you will need to upload the .json key file for your account – check these instructions on how to get it. Finally, specify the dataset name and table name where you want to import your data.
3. Schedule updates
Toggle on Automatic data refresh and specify your preferences for automatic updates. Coupler.io will keep transferring your Pinterest Ads data to BigQuery according to your schedule. So, basically, you get a forever-green dataset that is always analysis-ready.
Follow the wizard’s hints to finalize the setup. Then, click View results.
Here’s a campaign performance report transferred from Pinterest Ads to BigQuery:
As already mentioned, this is the most simple and convenient way to connect Pinterest Ads to BigQuery automatically. The alternative would be establishing the connection via the API. This would require much more time and effort, as well as some technical knowledge.
Apart from Pinterest Ads, Coupler.io extracts data from many other PPC platforms and marketing apps. For example, Facebook Ads, Microsoft (Bing) Ads, Instagram Ads, LinkedIn Ads, and many others. This allows you to easily automate reporting and manage all your ad data sources with just one user-friendly tool.
Option 2. How to move data from Pinterest Ads to BigQuery manually
This option is a good fit for one-time data export, but it’s not so convenient for continuous analysis or regular monitoring. The reason is simple – every time your data changes, you will need to extract it and manually upload it to BigQuery again. If you gather data from several PPC platforms, then it’s even less feasible to do this manually. Still, this method can be useful in some cases.
Here’s how it works.
In your Pinterest Ads account, go to the list of your campaigns. Select the campaign you need and click Export -> Report: Selected Campaigns. Alternatively, you can export other types of reports, depending on what you need. You can also select multiple campaigns.
Once you select the type, the CSV file with the data will be downloaded to your computer.
Now, you need to import this data into the data warehouse. Go to your Google BigQuery console. Click +Add in the upper left corner.
In the next step, choose Local file.
Then, click Browse in the Select file section to find the CSV file with Pinterest Ads data on your computer. Provide the names of the project, dataset, and table where you want to import your data. In the Schema section, select Auto-detect.
Then, click the Create table button in the lower left corner. After this, data will be uploaded. Here’s what the result looks like:
As you can see, strictly speaking, this method doesn’t actually connect Pinterest Ads to BigQuery. Nevertheless, it still can transfer data between the two apps.
Analyzing Pinterest Ads data in BigQuery
BigQuery gives you a versatile toolset for data analysis. It allows you to query data, perform advanced calculations, and use machine learning algorithms to build projections for the future. Here are some examples of how you can use Pinterest Ads data in BigQuery for analyzing your ad metrics in a broader context.
- Cross-channel PPC analytics
Querying your Pinterest Ads data gives you many benefits. But you probably run ads on other platforms as well. To gain deeper insights, you can combine data from all your PPC sources in a data warehouse and analyze it all together. This can be especially useful if you run the same campaign across multiple platforms. By performing cross-channel analysis in BigQuery, you can identify the most efficient channels and approaches. This will help you redistribute your ad budget to maximize the result while keeping your cost per conversion in check.
Apart from channel and campaign performance analysis, analyzing data from different platforms together allows you to better understand your audience. You can identify overlapping segments, define new audience groups, and so on.
Channeling data from your other PPC platforms is just as easy as transferring ad information from Pinterest Ads to BigQuery. If you use Coupler.io, you can follow the same steps that we described at the beginning of the article. The tool can export data from all popular PPC apps and other marketing sources.
In Coupler.io, you can set a separate importer for each data source. Depending on your needs, you can also add several sources to the same importer and even combine this information into one dataset before sending it to BigQuery.
Using automation for cross-channel analysis is very efficient. You won’t need to check and download data from each PPC platform separately. Instead, the latest data from all your PPC sources will be moved to the data warehouse without any manual effort from your side.
- Closed-loop analysis
Apart from analyzing data across several PPC platforms, you can also combine Pinterest Ads data with information from other business apps. For example, from Google Analytics 4, your CRM app, email campaign platform, and other sales and marketing data sources. Once you bring all this together in BigQuery, you can conduct closed-loop analytics. It can help you understand how your marketing efforts translate into financial results and what brings you the most conversions.
For instance, you can combine such data types:
- Pinterest Ads, Google Ads, and other PPC apps – clicks, CTR, CPC, ad spend, and conversions.
- Google Analytics 4 – information on user engagement, page views, website conversions, and session duration.
- CRM tools (f.e., HubSpot) – customer and company details, lead status, and sales pipeline metrics.
- Social media data – information about reach, impressions, views, and followers.
This will give you a more meaningful picture compared to analyzing Pinterest Ads data alone. You will be able to refine audience segmentation, see how campaign results change depending on geographical location, and more.
You can also use Coupler.io to collect all this information and send it to BigQuery automatically on a schedule. In this case, you will need to set up a separate importer for each data source.
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Get started for freeSending data from Pinterest Ads to BigQuery: what to keep in mind
Depending on your goals, you can select exporting data manually, using a data integration tool, or even writing custom scripts and using the API. Our recommendation is to opt for a data automation solution. This will allow you to connect Pinterest Ads to BigQuery quickly and easily via a user-friendly interface. You can also clean and organize data before loading it to the data warehouse, as well as schedule updates. In addition, you can use Coupler.io to manage all your data sources with just one solution, as it supports multiple apps.