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Unlocking the full potential of Google Analytics first-party data with Google BigQuery

19 Dec 2023
With the transition to Google Analytics 4 (GA4) from Universal Analytics (UA), Google has provided free native integration between Google Analytics and Google BigQuery. Many life sciences organizations, with the GA4 transition, have begun to use BigQuery to draw in-depth first-party data insights beyond what is facilitated by the vanilla Google Analytics interface, with the intention to use these data to inform more effective downstream orchestrations.
The Google Analytics interface provides behavioral insights at an aggregate level; however, BigQuery offers a new window for custom queries and much deeper insights. BigQuery essentially stores the entire data set that is processed by Google Tag Manager (GTM) and can be used by life sciences organizations to derive combinations of metrics and dimensions that are not possible through the standard GA4 interface or application programming interface (API).
This is particularly useful for gaining insights into the engagement and behaviors of specific user groups and effectively personalize their experience with a brand and nudge them to conversion.
Let us explore how the following systems can effectively leverage with a practical life sciences focus:
Data storage and management for reporting
BigQuery comes with powerful data warehousing capabilities, with most enterprise life science organizations beginning to use BigQuery specifically for this purpose. With Google’s sunset of UA, teams across life sciences organizations are looking for solutions to store the historical data they have previously collected from UA.
With a Google Analytics 360 subscription for UA, BigQuery suddenly becomes the tool of choice owing to its easy native integration with GA4. BigQuery is also the most efficient and viable method to store UA data for future use, and many organizations continue to use BigQuery to derive insights from GA4.
BigQuery offers a scalable solution for storing and processing massive amounts of data. Scheduled queries can be set up to transform these raw data into actionable and insightful reports, which are customized to the requirements of different brands and teams within these organizations.
In-depth user behavior breakdown and orchestrations
As an example, GA4 data in BigQuery can help identify the exact sequence of events/steps taken by healthcare professionals (HCPs) on any brand website, which most frequently leads to a conversion – such as a copay card or brand promotion sign-up. Furthermore, segments of users can be created who are in this conversion pipeline, but are yet to convert once these user segments are created and available for use, targeted marketing tactics such as personalized emails, advertisements, or a representative interaction can be deployed to nudge them toward conversion.
Data correlation
Continuing with our HCP example, conversions with high degrees of correlations can also be identified using BigQuery. For example, one from the set of conversion events could be from an offline source. Using BigQuery, the offline prescription data can be stitched with web analytics data to make observations on HCP behavior, such as a high correlation between drug prescriptions (offline) and the download of efficacy-related content from the website of the brand (online). In addition, the general gap in time between these events can be calculated to tailor different online and offline marketing strategies targeted at those HCPs.
Predictive analytics
The facilitation of predictive analytics is another area where BigQuery enables additional enhancements and insights based on existing GA4 data. For example, an important use case that is relevant for life science organizations is the use of logistic regression to classify HCPs, such as HCPs who are more likely to prescribe and those who are less likely to do so, based on their web interaction data. This would then enable more optimal resource allocation for each segment based on the brand and campaign objectives.
HCP and patient journey mapping
Another key advantage that comes with leveraging BigQuery is in mapping out HCP and patient journeys across multiple touchpoints. Raw data can be analyzed to understand the way interactions across different touchpoints affect HCP and patient journeys.
In a detailed behavioral analysis conducted for a large global life sciences organization using BigQuery data, our team was able to uncover insights such as the combination of channel interactions that are most likely to drive conversions and the time that a user takes to convert after their first interaction with a touchpoint(s).
The analysis was able to identify those HCPs who had initially interacted with brand emails and subsequently with the brand’s paid search campaigns that had one of the highest conversion rates among other varied channel combinations. With this insight in hand, a new segment of HCPs was created who had interacted with mails but have not had any further interactions with any other brand touchpoints. Targeted paid search campaigns were then run only for this segment, thus saving on campaign cost as well as improving conversions.
Looking to the future
The future is consented. It’s modeled. It’s first-party. So that’s what we’re using as our guide for the next gen of our products and solutions.
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Owing to the increasing privacy concerns and the growing significance of first-party data, web analytics data (in general) are becoming increasingly vital. GA4 is built to adapt to an evolving regulatory environment, which will function independent of third-party cookies. BigQuery’s robust data analysis along with GA4’s event-based tracking will open the door to a multitude of use cases in life science industry.
These can then serve as solid building blocks for life science organizations looking to build a long-term first-party data strategy. Indegene has been working with some of the world’s top life sciences organizations, helping them embrace this integration and gain a competitive edge in their digital strategy. Reach out to us to learn more about our work, and how we can help your life sciences business.

Authors

Praveen Radhakrishnan
Praveen Radhakrishnan
Akhil Mahajan
Akhil Mahajan

Insights to build #FutureReadyHealthcare