Why export your Google Analytics 4 data to BigQuery

Now you might be wondering what you could possibly do with all that extra data?

In summary, here are five reasons why your business should start exporting its raw GA4 data to the BigQuery warehouse:

1. Unifying your Google Analytics data  with your advertising data (beyond Google Ads), as well as your offline and CRM data will allow you to analyze and understand at a granular level the true journeys and lifecycle of your customers and users ;
2. Be the owner of your raw digital data  and facilitate the acquisition of new customers by benefiting from greater control over the analysis and creation of remarketing or personalization segments in first party data (to then send them to advertising platforms third parties or to a CDP – Customer Data Platform);
3. Expand the ability to get real-time data  using other cloud tools like Pub/Sub, to better orchestrate marketing offensives at key times of the year (Black Friday, for example) when every minute account;
4. Perform deeper analyzes of the content and size of  your customers' average shopping basket to optimize your upsell and cross-sell campaigns and thus maximize the value of each customer;
5. Prepare your data for predictive analytics  to better predict consumer demand or increase your customer retention rate by capturing churn signals better and faster.

Ultimately, all of this activity in BigQuery fulfills three big business goals for your marketing team:

1. More efficient acquisition of new customers (better CAC or CPA)
2. A more efficient average basket increase (better LTV)
3. A better controlled attrition of your customers (better retention)

And all that… faster!

Currently, your standard Google Analytics account is not equipped to adequately fulfill these purposes, or at least not for all of your marketing initiatives. However, with access to deeper data, it is normal that you can go further.

Another great benefit of making the transition to GA4 now (while preserving your current Google Analytics account at the same time) is that by exporting your data to the Google Cloud environment, you facilitate your transition to the emerging world. augmented analytics and artificial intelligence applied to marketing and e-commerce.  

Even if you don't have access to a data scientist on your or your agency's team, BigQuery offers relatively easy machine learning models to build from your Google Analytics data imported into the cloud. These models are available in SQL (Structured Query Language), which makes them much more accessible to a data analyst less familiar with models designed in programming languages ​​like R or Python.

Of course, for those who can count on the expertise of data scientists, it is also possible to go even further and really push your marketing data to the next level with libraries like Tensorflow or PyTorch.

It is often said that time is money . In an increasingly digitized world, this saying takes on its full meaning.

Waiting weeks or months to act on a one-time business or marketing situation can cost your business millions of dollars, especially in a volatile economy.

How do you respond to a pandemic, a new industry trend or an aggressive offer from a new disruptive competitor in the market? Counting on the right data at the right time is no longer a luxury of large corporations. It is now a matter of survival for businesses of all sizes.

Among those who already operate a traditional data warehouse, but constantly struggle with IT to come up with any interesting analytics, another benefit of BigQuery is that it can greatly speed up and simplify this often laborious process. Imagine accessing a report in minutes that normally takes days or weeks to pull from a traditional database or data warehouse.

Additionally, if you need to cross-reference sensitive data from your traditional data warehouse with marketing data in BigQuery, Google Cloud has a service called DLP (Data Loss Prevention). This service will allow you to import sensitive data into BigQuery while making it anonymous: a feature very popular with the IT security team 🤓.

In a way, a cloud data warehouse brings to your marketing data what the tag manager could bring to the integration of your Google Ads, Facebook Ads and programmatic conversion pixels: it reduces the number necessary interventions and to take action more quickly.

That said, the idea is not to replace your current traditional data warehouse if you have one, but rather to create a separate and complementary instance that will be more agile and better suited to marketing needs. If however your business didn't already have a data warehouse, this is the perfect opportunity to create one and do it the right way 😉.

According to a BDC study , barely 19% of Canadian companies are considered mature in terms of digital transformation and data exploitation. It's a very low percentage, and even worrying. 

In an era of great uncertainty, data is our best compass. We can no longer concentrate all marketing investments solely in activating campaigns in 2021. We must find the balance and invest a greater proportion of marketing budgets in the exploitation of the data themselves, especially in your many activation platforms (advertising, CRO, personalization, email, etc.). It is imperative to understand that the time spent in learning or generating insights  from your activation campaigns is as important, if not more, than the marketing activation itself.

Indeed, neglecting the exploitation of your activation data is tantamount to neglecting your marketing strategy. The two are interdependent and inseparable.

Also, let's remember that the advent of the cookie apocalypse in 2022  makes the need to properly manage your marketing data even more urgent. By extension, equipping yourself with richer data from your marketing platforms, including Google Analytics, would be a smart addition to your analytics roadmap for 2021. Additionally, if you have implemented a working instance of GA4, it will cost you US$150,000 less to achieve it with BigQuery.

*

For those who would like to explore the issue of marketing data warehouses and lakes in more depth , I have published in the Journal of Applied Marketing Analytics , which is a London-based academic and professional publication refereed by global experts, an architecture model lake or data warehouse suitable for marketing. You can download a courtesy copy of the article from the following link: AMA Article . If you have any further questions about how you can integrate GA4 and BigQuery into your 2021 analytics roadmap, feel free to reach out to our team of analytics and data science experts .