Pinterest is a visual discovery engine that can be used to find ideas such as recipes, inspiration for home and style projects, and much more. It is unique among social media platforms and the majority of its users go there with the intention of learning about new products, and these users have a positive reaction to advertisements. Pinterest provides users with both free and paid advertising tools; however, using both together can result in three times as many conversions and twice as much Return on Investment (ROI) as using paid ads alone.
By establishing a connection to a Cloud Data Warehouse such as Google BigQuery, you will be able to conduct advanced analytics on massive amounts of data obtained from a variety of e-commerce platforms using queries that are similar to SQL.
In this article, you will learn two methods to connect Pinterest Ads to BigQuery and their key features.
What are Pinterest Ads?
Pinterest is a search engine and social media platform rolled into one, with the goal of assisting its users in finding new ideas, saving them, and sharing them with other people. Brands and businesses can promote their products and services using Pinterest by purchasing advertisements on the platform.
Promoted Pins are another name for Pinterest advertisements. As part of its retargeting advertising strategy, Pinterest displays Promoted Pins to users based on their interests, previous activity, and actions taken on other websites.
Pinterest is a powerful marketing tool because it understands its audience, for example, by curating taste or by spotting trends before they happen. This is made possible by combining a search engine and social media.
Pinterest stands out among other social media platforms as an exceptional choice for use by businesses in their social media marketing efforts. Pinterest also serves as an alternative search engine. Businesses can use the platform’s optimization features to further promote themselves, thereby increasing the volume of website visitors, sales, and overall awareness of their brand.
Key Features of Pinterest Ads
- Gain Brand Awareness: Advertisements on Pinterest are an effective method for raising online brand awareness and recognition. When compared to other social media platforms, Pinterest is the most appropriate location for branded content because users are more tolerant of it, and the content itself does not interfere with the user experience.
- Increase Your Reach, Traffic, and Sales: If you use images of high quality within Pinterest search, you’ll be able to attract more visitors to your websites and landing pages, which will ultimately lead to an increase in sales, and brand awareness, and traffic. One of the advantages of using Pinterest is that it has the potential to drive more online traffic to your company. This is true regardless of whether the traffic is paid for or obtained naturally through the platform.
- Pinterest Search: The fact that content that has been shared on Pinterest can be located again and again through keywords, interests, and boards raises the possibility that it will drive traffic to your company over the long term. Users of Pinterest can save content they enjoy for later use by “pinning” it, and the fact that this feature exists alone makes Pinterest a viable method for achieving long-term visibility. When more of your posts are pinned, and when users remind themselves to look for your company when they need to, it becomes easier to find it again.
- Pinterest Benefits to Content Marketing: You will have more options for your content distribution strategy as a result of the expansion afforded to you by the addition of Pinterest ads to your content marketing strategy. This is because Pinterest provides a visual platform on which you can post your content. If you use Pinterest as part of your content strategy, you can easily adapt the content you’ve created for other platforms so that it conforms to Pinterest’s guidelines and establishes a connection with your audience there as well. Increasing your online reach can be accomplished in an efficient manner by developing a social media strategy that works across multiple platforms.
What is Google BigQuery?
Google BigQuery is a Data Warehouse hosted on the Google Cloud Platform that helps enterprises with their analytics activities. This Software as a Service (SaaS) platform is serverless and has outstanding data management, access control, and Machine Learning features (Google BigQuery ML). Google BigQuery excels in analyzing enormous amounts of data and quickly meets your Big Data processing needs with capabilities like exabyte-scale storage and petabyte-scale SQL queries.
Google BigQuery’s columnar storage makes data searching more manageable and effective. On the other hand, the Colossus File System of BigQuery processes queries using the Dremel Query Engine via REST. The storage and processing engines rely on Google’s Jupiter Network to quickly transport data from one location to another.
Key Features of Google BigQuery
- Fully Managed: An in-house setup is not required since Google BigQuery is a fully managed Data Warehouse. To use Google BigQuery, you only need a web browser to log in to the Google Cloud project. By offering serverless execution, Google BigQuery takes care of complicated setup and maintenance processes, including Server/VM Administration, Server/VM Sizing, Memory Management, etc.
- Exceptional Performance: Due to the column-based design, Google BigQuery provides several advantages over traditional row-based storage, like higher storage efficiency and quicker ability to scan data. These features minimize slot consumption, querying time, and data use by supporting nested tables for practical data storage and retrieval.
- Security: Google BigQuery offers Column-level protection, verifies identity and access status, and establishes security policies as all data is encrypted and in transit by default. Since it is a component of the Google Cloud ecosystem, it complies with security standards like HIPAA, FedRAMP, PCI DSS, ISO/IEC, SOC 1, 2, and 3.
- Partitioning: Google BigQuery’s decoupled Storage and Computation architecture employs column-based segmentation to lower the quantity of data retrieved from discs by slot workers. Once the slot workers have finished reading their data from the disc, Google BigQuery automatically finds the most optimum data sharing method and instantly repartition data using its in-memory shuffle function.
Benefits of Connecting Pinterest to BigQuery
Here are a few benefits of Pinterest Ads BigQuery Connection:
- The Boards on Pinterest: You have the ability to create, delete, and edit boards for users who have been authenticated. You should also retrieve boards and pins on a board and make use of the information you obtain to improve your efforts.
- Pinterest Pins: Create, edit, and delete Pins for authenticated users by using the Application Programming Interface (API). You also have the ability to retrieve a user’s pins, boards, and the Pins that are displayed on a board. You will gain a better understanding of user preference, UX, and other concepts with this.
- Pinterest Users: You can obtain a significant amount of user information by utilizing the Pinterest API. This information includes profile information, boards, suggested boards, and following relationships. Make use of this information to gain a deeper understanding of the users and to send communication that is more specific and accurate.
Advertising on Pinterest is an efficient method that can be incorporated into your overall digital marketing strategy. Also, BigQuery is a data warehouse known for ingesting data instantaneously and performing almost real-time analysis. When integrated together, moving data from Pinterest Ads to Bigquery could solve some of the biggest data problems for businesses. In this article, we have described two methods to achieve this:
Method 1: Connect Pinterest Ads to BigQuery using Hevo
Hevo, an Automated Data Pipeline, provides you a hassle-free solution to connect Pinterest Ads to BigQuery within minutes with an easy-to-use no-code interface. Hevo is fully managed and completely automates the process of not only loading data from Pinterest Ads but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code.
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Method 2: Connect Pinterest Ads to BigQuery using API Connector Add-on
This method would be time-consuming and somewhat tedious to implement. Users will have to write custom codes to enable two processes, streaming data from Pinterest Ads and ingesting data into BigQuery. This method is suitable for users with a technical background.
Both the methods are explained below.
Methods to Connect Pinterest Ads to BigQuery
Many organizations need to load their data from Pinterest Ads to BigQuery service to access raw customer data, like shipment types, item checkpoints, etc. Take advantage of Google BigQuery’s capability to efficiently run complex analytical queries across petabytes of data. Connecting Pinterest Ads to BigQuery provides a more comprehensive insight into your customer interaction and company’s performance.
Furthermore, with the Pinterest Ads to BigQuery connector, you can perform effective real-time automated processes, saving you time when working on repetitive tasks. This integration is the ideal value addition for an e-commerce company or business owner who wants to improve operations, increase efficiency, and sync data throughout their workspace.
To perform Pinterest Ads to BigQuery Migration, you can use two methods.
These two methods are explained below:
Method 1: Connect Pinterest Ads to BigQuery using Hevo
Hevo provides Google Bigquery as a Destination for loading/transferring data from any Source system, which also includes Pinterest Ads. You can refer to Hevo’s documentation for Permissions, User Authentication, and Prerequisites for Google BigQuery as a destination here.
Configure Pinterest Ads as a Source
Carry out the procedures listed below in order to configure Pinterest Ads as the Source in your Pipeline in Pinterest Ads to BigQuery Integration:
- Step 1: In the Asset Palette, select the PIPELINES option.
- Step 2: In the Pipelines List View, select the +CREATE button.
- Step 3: Choose Pinterest Ads from the drop-down menu on the Select Source Type page.
- Step 4: Click the + ADD PINTEREST ADS ACCOUNT button on the page that allows you to configure your Pinterest Ads account.
- Step 5: Sign in with the Pinterest Business account you normally use.
- Step 6: To give Hevo permission to access the data from your Pinterest Ads account, click the Continue button to connect Pinterest Ads to BigQuery.
- Step 7: On the page where you configure your Pinterest ads source, enter the following information to connect Pinterest Ads to BigQuery:
- Name of Pipeline: A one-of-a-kind name for the Pipeline, with a maximum of 255 characters.
- Select Accounts: Choose the Pinterest Ads account (or accounts) whose data you want to import into your account. There is support for selecting multiple Ad accounts.
- Historical Sync Duration: The amount of time over which the historical data must be ingested. Also known as “duration.” Default value: 6 Months.
- Breakdowns: The amount of temporal detail that you want to see in your report data is referred to as the “breakdown.” You could, for instance, separate the total number of clicks into weekly or monthly totals.
- Conversion Report Time: The time period for which you want to generate a conversion report is referred to as the “Conversion Report Time.” Time of Ad Action has been set as the default value. You have the option of choosing either of the following:
- Time of Ad Action: Displays the conversion report according to the time at which a user action such as engagement with Pins, clicks, reactions, or comments took place. Time of Ad Action
- Time of Conversion: Displays the conversion report according to the time at which a conversion event, such as sign-up or adding an item to a cart, took place.
- Step 8: Just hit the TEST & CONTINUE button.
- Step 9: Move on to the next step, which is to configure the data ingestion and set up the Destination in Pinterest Ads to BigQuery Integration.
Configure BigQuery as a Destination
To configure BigQuery as a Destination in Pinterest Ads to BigQuery Integration, follow these steps:
- In the Asset Palette, choose DESTINATIONS.
- In the Destinations List View, click + CREATE.
- Select Google BigQuery as the Destination type on the Add Destination page to connect Pinterest Ads to BigQuery.
- Select the authentication method for connecting to BigQuery on the Configure your Google BigQuery Account page to connect Pinterest Ads to BigQuery.
- Perform one of the following:
- To connect with a Service Account, follow these steps:
- Attach the Service Account Key file.
- Click on CONFIGURE GOOGLE BIGQUERY ACCOUNT.
- To join using a User Account, follow these steps:
- Click on + ADD A GOOGLE BIGQUERY ACCOUNT.
- Sign in as a user with BigQuery Admin and Storage Admin permissions.
- Provide Hevo access to your data by clicking Allow to connect Pinterest Ads to BigQuery.
- Configure your Google BigQuery Warehouse page with the following information to connect Pinterest Ads to BigQuery:
- Destination Name: Give your Destination a distinctive name.
- Project ID: The BigQuery instance’s Project ID.
- Dataset ID: The dataset’s name.
- GCS Bucket: A cloud storage bucket where files must be staged before being transferred to BigQuery.
- Enable Streaming Inserts: Select this option to stream data to your BigQuery Destination as it arrives from the Source, rather than loading it via a task according to a Pipeline schedule.
- Sanitize Table/Column Names: Select this option to replace any non-alphanumeric characters and spaces in table and column names with an underscore (_).
- Populate Loaded Timestamp: Enabling this option adds the __hevo_loaded_at_ column to the Destination Database, indicating the time when the Event was loaded to the Destination.
- To test the connection, click TEST CONNECTION and then SAVE DESTINATION to finish setting up Pinterest Ads to BigQuery Integration.
Method 2: Connect Pinterest Ads to BigQuery using API Connector Add-on
To get started with Pinterest Ads to BigQuery Integration, install the API Connector add-on from the Google Marketplace. Then follow the steps below to connect the API and make a simple request:
- Step 1: Launch Google Sheets and select Extensions > API Connector > Open > Add New from the menu that appears. You should now be looking at the primary Create screen for API Connector.
- Step 2: In the field designated for the URL, type https://api.pinterest.com/v5/ad accounts to connect Pinterest Ads to BigQuery.
- Step 3: Make your selection from the dropdown menu under Pinterest Ads. You will need to click the Connect button in order to validate your identity if this is your first time connecting to the server.
- Step 4: You will then be taken to Pinterest where you will be asked to grant permission for the connection.
- Step 5: When you have successfully authenticated yourself, you will see a Connected badge that appears on your screen once you have successfully connected.
- Step 6: Since we do not require any headers in this section, you can just leave it blank.
- Step 7: After giving your destination sheet a name, naming and saving the request, and clicking the Run button, you can get started. You should see a list of your advertising accounts at this point (s). Be sure to pay close attention to the ad ID, as you will need that for any requests that come after this one.
In this article, you understood the main features of Pinterest Ads and Google BigQuery and learned two methods to integrate Pinterest Ads to BigQuery. Pinterest Ads is Pinterest’s advertising platform for Business Accounts. It enables users to retrieve statistics about their ads, ad accounts, ad groups, and campaigns running on Pinterest. Google BigQuery allows you to analyze this data to find meaningful insights to improve user experience.
However, as a Developer, extracting complex data from a diverse set of data sources like Databases, CRMs, Project management Tools, Streaming Services, and Marketing Platforms to your Database can seem to be quite challenging. If you are from non-technical background or are new in the game of data warehouse and analytics, Hevo Data can help!
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