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How to upload raw data from Google Ads to Google BigQuery

If you’re like many marketers, you’re probably running campaigns using a variety of digital marketing channels — including not only Google Ads but also Facebook, Instagram, Twitter, LinkedIn, and many more. With no money to waste, you aim to get the most out of each marketing investment. And to do that, you need a clear, holistic view of your customers’ journeys across all channels and devices.

In this article, we’ll examine how you can get everything together and upload all marketing data into one data storage (e. g. Google BigQuery) to paint a good picture of campaign performance and improve the ROI across all your multiple ad channels.

Omnichannel marketing analytics has become the key tool for organizations of any size and industry. Yet, most marketers are wasting time on routine activities such as organizing data manually. OWOX BI provides a solution to your time-consuming marketing routines. With OWOX BI, the advertising data from your paid campaigns will be automatically pulled into Google BigQuery. You’ll be able to store it for as long as you want, create detailed reports for what you need, understand the true value of your paid marketing initiatives, and make better use of your advertising spend.

The challenge of today’s digital marketing

Today’s customer journeys are more complex than ever. The numbers speak for themselves: according to the Salesforce’s 2020 State of the Connected Customer report, 72% of consumers have used multiple methods of communication to start and complete a single transaction. 63% of consumers across generations — from Gen Z to the Silent Generation — have used multiple devices to start and complete a single transaction. And on top of that, 62% of consumers have said that they prefer personalized products or services over standard, ’one-size-fits-all’ offers.


A single customer journey can take anywhere from a few minutes to several months. It may occur in one channel or span multiple touchpoints and devices. Take for example: a user can first visit your website via your Facebook ad on their smartphone, only to sign up and make a purchase via a search ad on their laptop, days later after the initial interaction. In today’s multi-channel, multi-device landscape, the consumer is one click or tap away from leaving your brand for a competitor.

If you’re like most marketers, you’re probably struggling to stay connected with your customers and keep them engaged across the internet. You need data — that goes without saying. Like most marketers, you’re probably already sifting through countless campaign reports in an effort to spot trends and patterns in customer behavior, identify winning tactics, and optimize your marketing strategy. You keep an eye out for some chosen metrics and the rest is just piling up in the ad platform, never really being analyzed or used.

But here’s the caveat: If you’re not focused on value, you’re falling behind.

According to Gartner, most marketing analytics teams are still spending most of their time organizing data, running ad-hoc queries, and generating reports rather than actually using their data to inform their marketing decisions. The result? Gartner reports that more than half of senior marketing leaders are disappointed in the results of their analytics investments.

Automating routine activities will enable you to focus on what really matters for your organization, such as extracting hidden insights and using them to make better, smarter marketing decisions.

Still, before looking for insights in your data, you have to collect and merge it in one place. The most widely used solution for this is Google BigQuery — a fully managed serverless data warehouse that’s part of Google’s infrastructure.

What’s so special about BigQuery?

One of the major stumbling blocks to omnichannel marketing is that data is stored in silos that don’t readily communicate with each other. If you run paid marketing campaigns using many different platforms, you already know the struggle of keeping a close eye on each campaign and trying to piece the data together across all your marketing initiatives.

Here’s where Google BigQuery comes to help.

Google BigQuery is a serverless, scalable data warehouse with a built-in query service. It provides marketers with a single centralized location for advanced data-driven marketing, without the need for extensive technical skills, reliance on IT, or significant money investments.

There is, however, one big problem with BigQuery — it doesn’t natively integrate with non-Google services.

You need third-party connectors to incorporate touchpoints from non-Google data sources. That is, if you want to pull data from your website, CRM, ESP, or ad platforms such as Facebook Ads to BigQuery, you need a service like OWOX BI to do the heavy lifting for you.

Challenge accepted: 6 ways to automate routine data uploads

You can upload your ad data to Google BigQuery in a number of ways. The main task is to find a way that will fit your business, requiring minimum effort, saving maximum time, and providing reliable and accurate results.

Let’s take a look at the methods you can use to upload your data to Google BigQuery:

Upload data from CSV or JSON files. You can manually upload a CSV or JSON file with ad data directly to Google BigQuery from Google Cloud Storage, Google Drive, or your computer.

Upload data with Google Sheets (OWOX BI BigQuery Reports Add-on). You can use the free OWOX BI BigQuery Reports Add-on to upload data from Google Sheets. You can install this add-on directly from Google Sheets or from the Chrome Web Store.

Upload data from Google Cloud Storage. Google Cloud Storage allows you to securely store and transfer data online. You can find out about data download limits and Cloud Storage permissions in the Google Cloud help center.

Upload data from other Google services such as Google Ads and Google Ad Manager. To upload data from various Google services, you first need to configure the BigQuery Data Transfer Service. Before you can use it, you must select or create a data project and, in most cases, enable billing for it.

Download data with the BigQuery API. With Cloud Client Libraries, you can use your favorite programming language to work with the Google BigQuery API.

Upload data using third-party tools. For example, with OWOX BI Pipeline, you can automatically collect data from advertising services as well as call tracking and CRM systems. This allows you to quickly and easily get ready-made sets of complete data from the sources you select.

Each of these methods has pros and cons, as well as business challenges where they work best. Still, to keep up with the pace of the modern world, it is preferable to save your time and effort for tasks you cannot delegate, by automating and simplifying processes as much as possible.

For most businesses, the most optimal decision is to choose a single service that satisfies their needs in the fastest and easiest way. So, let’s take a look at one of the most optimal ways to upload your ads data to Google BigQuery.

BigQuery and OWOX BI: The right combination to unlock data value

OWOX BI is a comprehensive Business Intelligence system designed to facilitate marketing analytics and decision-making. With OWOX BI, marketers can automatically centralize disparate data from various sources and use this consolidated data to:

analyze their marketing efforts

create automated reports

reveal new insights into customer interactions with the organization

optimize your marketing investments for better ROI.

You won’t have to write a single line of code, look around for connectors, or prepare the data manually. OWOX BI will handle it all.

Now, what exactly can OWOX BI and Google BigQuery help you do?

Bring together data from multiple systems

It’s only after that data is consolidated in one centralized location that it becomes actionable. The first thing you need to do is to consolidate your data effectively so you can easily process, analyze, and extract insights from all those numerous customer touch-points. You need to know that you trust your data, and that you’re comparing apples to apples.

OWOX BI provides a set of connectors to automatically pull raw data from all your marketing platforms, including LinkedIn, Twitter, Instagram and Facebook, to BigQuery. The result is one ready-to-use dataset with automated daily updates. Moreover, you’ll be able to connect this data with the data from other touchpoints, including raw real-time data from your website and the data from your CRM, call tracking, and email marketing systems. The only thing left for you to do is to buckle down and focus on putting the data to action.

Crunch mountains of data

BigQuery is part of the Google Cloud Platform, which means you get access to Google’s cloud computing and storage capabilities. You can have terabytes of data stored here, including all your historical data, and run queries against billions of rows in a matter of seconds.

Cut down on repetitive tasks

In traditional scenarios, marketers have to manually switch back and forth between advertising services to extract data snapshots and organize them into reports and dashboards. Automating these processes with OWOX BI and BigQuery removes the headaches of performing regular data updates and ensures that you’ll have the most recent data at your fingertips. OWOX BI will also retrospectively refresh cost data in Google BigQuery if it changes in your ad account.

Never lose control over your historical data

Most marketing platforms have limits on how much data you can store or how long you can access the data. With Google BigQuery, you can preserve data from all your marketing platforms for as long as you want, and analyze it to capture not-so-obvious trends that only become visible over extended periods of time. OWOX BI can backfill your BigQuery dataset with historical data from your ad account so you can start working right away.

Conduct comprehensive data analysis

With data in BigQuery, you’ll be able to create custom metrics and dimensions that are unavailable in the original data, create arbitrary parameter combinations, apply custom filters to your reports.

And if you’re not into writing SQL queries, OWOX BI has got you covered. You can use the Report Builder in OWOX BI Smart Data to ask questions of your data in natural English and get answers in the form of neatly organized reports with specific metrics you need based on your data.

Visualize data

Create actionable data visualizations by connecting BigQuery to your favorite visualization tools, or exporting your reports to data visualization services such as Google Data Studio. To do this, you won’t have to use multiple data sources: a single BigQuery connector is enough.

How to upload ad data to Google BigQuery with OWOX BI Pipeline

To set up data collection, you must have BigQuery Data Editor and BigQuery User roles in the project in which you want to collect data. Once these roles are present in your project, follow the steps below to upload your ad data to Google BigQuery:

1 In your OWOX BI dashboard, click Create

2. Select the data source you want to connect.

3. Select Google BigQuery as the destination.

4. Provide access to your advertising account.

5. Provide access to the Google BigQuery account where you want to store the exported data.

6. Select a Google BigQuery project and create a dataset you want to upload your data to (or choose an existing dataset).

7. Specify the date that marks the beginning of the period for which you want to upload cost data and select the currency you want your cost data to be converted to.

8. Click Create pipeline.

Done! Data will be collected automatically in Google BigQuery, and you’ll get ready-made sets of complete data from your selected sources.

Final thoughts

Data needs to be collected, processed, analyzed, and put to action before it brings value. With Google BigQuery and OWOX BI, marketers can easily step up away from manual tasks and disparate spreadsheets towards a fully-fledged business intelligence system, the one where you can automatically aggregate data from multiple touch-points and transform it into valuable insights.

After all, the value of the data you collect depends entirely on your ability to harness this data and make decisions from it.

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