LinkedIn Ads to Google BigQuery – Made Easy
If you’ve come here, you are probably looking for a way to transfer data from LinkedIn Ads to Google Bigquery quickly. In this article, we talk about why LinkedIn Ads is essential and how you can get access to this data without having to write any code.
The choice for eCommerce business when it comes to marketing and selling their merchandise is growing every day. eCommerce vendors have to decide on what channels they want to sell on, which channels they want to spend their advertising dollars on, whether the channels include:
- Branded websites
- In some cases branded eCommerce sites per country
- In many instances, marketplaces per country
- Retail stores
- to create an omnichannel presence and to engage buyers where the shop
Complexity increases with the addition of every sales channel. For instance, if we consider marketing channels available to support online business, you will find a choice of:
- Social Media ads – Some platforms include Facebook Ads, Instagram, LinkedIn, Twitter, and others
- Digital ads and remarketing – Criteo, Taboola, Outbrain, and others
- PPC – Google ads, Bing ads, and others
- Email – Mailchimp, Klaviyo, Hubspot, and others
- Affiliate – Refersion, CJ Affiliates
- Influencer marketing
- Offline marketing and more
Choice, while being a great virtue, leads to complexity, and this complexity when not managed properly, can, in turn, impact the efficiency of running an eCommerce business. Most eCommerce businesses grapple with this complexity; some well and many not so well.
In a competitive digital landscape that we live in, it has become imperative that eCommerce businesses of all sizes that aspire to grow and stay profitable have to look into their data deeply and leverage this for growth.
With the increase in competition, eCommerce Companies should strive to be more data-driven for various reasons. Some of these reasons include.
- understanding the balance between demand and supply
- understanding customer lifetime value (LTV)
- Segmenting customer base for effective marketing
- finding opportunities to reduce wasteful spend
- optimizing digital assets to maximize revenue for the same marketing spend,
- improving ROIs on Ad campaigns and
- offering an engaging and seamless experience for customers in every channel that the customer engages with the brand.
Businesses these days need to be efficient in terms of their data analysis. They are struggling to make sense of the data generated from various applications and tools used to manage different processes efficiently.
Due to the reasons highlighted above, any eCommerce business typically operates at least 10-15 different software/platforms to deliver on their customer expectations. As a result, data silos are created, which makes it more difficult to consolidate data and use the data for reporting, operations, analysis, and taking informed forward-looking decisions.
Marketing platforms like LinkedIn Ads generate a substantial amount of data like impressions, user behaviour, clicks, product details, and more. Additionally, eCommerce companies that sell globally often end up having separate ad accounts for each country which in turn creates data silos for each country. Imagine a brand selling on three marketplaces or three countries – They may have three accounts per channel in which they are generating data—consolidation of data from these accounts for effective reporting.
These silos make an analysis of the entire business data comprehensively, challenging. Data Savvy eCommerce businesses try to reduce the effort of reporting and analysis by integrating data from all these channels into a cloud data warehouse like Google Bigquery. By taking this step, the process of reporting and analysis becomes easy, inexpensive, and consequently done more frequently.
In this post, we will be looking at methods to replicate data from LinkedIn Ads to Google Bigquery.
Before we start exploring the process involved in data transfer, let us spend some time looking at these individual platforms.
LinkedIn Ads Overview
Social media marketing is complex. Not only do you have to develop just the right messaging to attract your audience, but you also have to make sure that this message gets to the users who are most likely to engage with your business. While many companies use Facebook as their primary social marketing channel, that might not always be the best choice!
LinkedIn ads have multiple unique benefits that put them in a class of its own. Depending on your business and marketing goals, it might be just the right channel to help your business attract customers and grow. LinkedIn has 600+ million users. Other social networks boast an even larger user base. But in digital advertising quality reigns supreme over quantity. When you advertise on LinkedIn, you seamlessly reach top-of-funnel audiences. And because you’re interacting with them in a professional setting, they’re also more receptive to your marketing message.
Below are the significant advantages of this network over its alternatives.
1. Narrow Your Targeting Through Industry-Specific Variables
b. Job title
c. Company Name
f. Professional interests
2. You can easily connect with individuals in your target audience directly with InMail. Other uses of Sponsored InMail can be listed as:
a. You can invite users to download exclusive content, like an ebook or white paper
b. Famous for encouraging users to register for an in-person or online event
c. It can motivate users to purchase a product or service
3. It can create a variety of ads, from video to text, like :
a. Sponsored Content
b. Sponsored InMail
c. Video ads
d. Text ads
e. Dynamic ads
f. Carousel ads
g. Display ads
You can leverage Lead-Nurturing Possibilities with a feature called Lead Accelerator. It will help you to track your most high-value prospects and offer more targeted ads explicitly directed towards them.
Google BigQuery Overview
Google BigQuery is the first genuinely serverless data warehouse-as-a-service offering in the market. There is no infrastructure to manage, no patches to apply, or any upgrades to be made. The role of a database administrator in a Google BigQuery environment is to architect the schema and optimize the partitions for performance and cost. This cloud service automatically scales to fulfil the demands of any query without the need for intervention by a database administrator. Google BigQuery service also introduced an unusual pricing model that is based not on the storage capacity or the compute capacity needed to process your queries. Instead, the pricing relies on the amount of data processed by incoming queries.
The best part about Google BigQuery is that you can load data to the service and start using the data immediately. Users no longer have to worry about what runs under the hood because the implementation details are hidden from them. All you need is a mechanism to load data into Google BigQuery and the ability to write SQL queries. By making data warehousing so simple, Google BigQuery has revolutionized the cloud data warehousing space and has put the power back in the hands of the analysts.
It is good practice to understand the architecture of Google BigQuery. Understanding the architecture helps in controlling costs, optimizing query performance, and optimizing storage. The factors that govern Google BigQuery Pricing are Storage and Query Data Processed. You can read about it in more detail here.
Why Do Businesses Need to Replicate LinkedIn Ads Data to Google Bigquery?
Let’s take a simple example to illustrate why data consolidation from LinkedIn Ads to Google Bigquery can be helpful for an eCommerce business.
An e-commerce company selling in multiple countries is running campaigns on LinkedIn Ads. They have different selling platforms like Shopify, Amazon, eBay, payment gateways, inventories, logistic channels and target audience in each country. An ad might be running off a product which might no longer be in stock, or might not be deliverable in the location which it is running, rendering these ads as redundant and thus causing a substantial loss for the company. Now when the decision-makers want to rectify this and optimize the LinkedIn Ad campaigns to maximize ROIs, they are faced with the following problems.
- There are separate data silos for inventory data, logistics data, which need to be separately downloaded and compared and updated regularly to optimize the LinkedIn Ads campaign.
- Again if you want to do remarketing effectively, then people who have not completed payments, or have encountered a failed transaction need to be targeted in addition to people who have added products to their cart, wishlists, or favorites. People who have responded to other marketing campaigns like email, SMS, social media marketing also need to be targeted. So again separate data silos from various selling platforms, payment gateways, marketing tools need to be downloaded, analyzed, and compared.
- Audience profiling data from e-commerce platforms, CRMs, customer support systems need to be analyzed to optimize audience targeting. Since LinkedIn ads are dependent on the target audience, rather than their searched keywords or topics, it is essential to have a very accurate target audience to get the optimum ROI.
- While calculating profits/losses of the overall business, it becomes a nearly impossible task to pull all of these data from multiple platforms for each country separately, and then analyze all of this data together with the expense data and calculate profits. It involves a lot of working hours which costs money, and there is usually a time lag involved, which reduces the accuracy of the analysis and its effectiveness as the data is not analyzed in real-time.
- The compilation and processing of data from multiple sources for thorough research is a considerable challenge if carried out manually.
For these reasons, top companies consolidate all of their data from LinkedIn Ads and other apps and tools into a data warehouse like Google Bigquery to analyze the data and generate reports at a rapid pace.
The more data you can gather and use from different sources in your LinkedIn ad campaign, the more your ad delivery is optimized. All these data can not be natively transmitted to LinkedIn. Such data must be collected and analyzed correctly in a data warehouse like Google Bigquery before you use the relevant information to run ad campaigns on LinkedIn.
Replicate data from LinkedIn Ads to Google BigQuery
There are two board ways to pull data from any source to any destination. The decision is always a build vs buy decision. Let us look at both these options to see which option provides the business with a scalable, reliable, and cost-effective solution for reporting and analysis of LinkedIn Ads data. You can also retrieve the data from Google BigQuery any time you want. To know more, click here.
Use a cloud data pipeline
Building support for APIs is not only tedious but it is also extremely time-consuming, difficult, and expensive. Engaging analysts or developers in writing support for these APIs takes away their time from more revenue-generating endeavors. Leveraging a cloud data pipeline like Daton significantly simplifies and accelerates the time it takes to build automated reporting. Daton supports automated extraction and loading of LinkedIn Ads data into cloud data warehouses like Google BigQuery,Snowflake, Amazon Redshift, and Oracle Autonomous DB.
Configuring data replication on Daton on only takes a minute and a few clicks. Analysts do not have to write any code or manage any infrastructure but yet can still get access to their LinkedIn Ads data in a few hours. Any new data is generated is automatically replicated to the data warehouse without any manual intervention.
Daton supports replication from LinkedIn Ads to a cloud data warehouse of your choice, including Google BigQuery. Daton’s simple and easy to use interface allows analysts and developers to use UI elements to configure data replication from LinkedIn Ads data into Google BigQuery. Daton takes care of
- rate limits,
- historical data load,
- incremental data load,
- table creation,
- table deletion,
- table reloads,
- refreshing access tokens,
and many more important functions that are required to enable analysts to focus on analysis rather than worry about the data that is delivered for analysis.
Daton – The Data Replication Superhero
Daton is a fully-managed, cloud data pipeline that seamlessly extracts relevant data from many data sources for consolidation into a data warehouse of your choice for more effective analysis. The best part analysts and developers can put Daton into action without the need to write any code.
Here are more reasons to explore Daton:
- Support for 100+ data sources – In addition to LinkedIn Ads , Daton can extract data from a varied range of sources such as Sales and Marketing applications, Databases, Analytics platforms, Payment platforms, and much more. Daton will ensure that you have a way to bring any data to Google BigQuery and generate relevant insights.
- Robust scheduling options allow users to schedule jobs based on their requirements using simple configuration steps.
- Support for all major cloud data warehouses including Google BigQuery, Snowflake, Amazon Redshift, Oracle Autonomous Data Warehouse, PostgreSQL, and more.
- Low Effort & Zero Maintenance – Daton automatically takes care of all the data replication processes and infrastructure once you sign up for a Daton account and configure the data sources. There is no infrastructure to manage or no code to write.
- Flexible loading options allows you to optimize data loading behaviour to maximize storage utilization and also easy of querying.
- Enterprise-grade encryption gives your peace of mind
- Data consistency guarantee and an incredibly friendly customer support team ensure you can leave the data engineering to Daton and focus instead of analysis and insights!
- Enterprise-grade data pipeline at an unbeatable price to help every business become data-driven. Get started with a single integration today for just $10 and scale up as your demands increase.
Interested in learning more about data warehouses, their architecture, and how they are priced? Check out our other articles.
|Google BigQuery||GoogleBigquery PricingGoogle BigQuery – Architecture and Key Features|
|Snowflake||Pros and Cons of SnowflakeSnowflake Architecture|
|AWS Redshift||Amazon Redshift|
|Oracle Autonomous DB||Oracles Autonomous Data Warehouse|
|For sections where we talk about manual reports and lost productivity||https://sarasanalytics.com/blog/improving-data-analyst-productivity|
|What is a cloud data pipeline||https://sarasanalytics.com/blog/what-is-a-data-pipeline|