GCP – How PUMA leverages built-in intelligence with BigQuery for greater customer engagement

Leveraging first-party data, and data quality in general, are major priorities for online retailers. While first-party data certainly comes with challenges, it also offers a great opportunity to increase transparency, redefine customer interactions, and create more meaningful user experiences.

Here at PUMA, we’re already taking steps to seize the opportunities presented by signal loss as organizations embrace privacy-preserving technologies. Our motto “Forever.Faster.” isn’t just about athletic performance, it also describes our rapid response to market changes. In that aim, we’re partnering with Google Cloud to leverage the capabilities of machine learning (ML) for greater customer engagement via advanced audience segmentation.

Moving from manual segmentation to advanced audiences

In August 2022 we decided to test Google Cloud’s machine-learning capabilities to create advanced audiences based on high purchase propensity with different data sets in BigQuery. While Google Analytics offers predictive audiences, we used this pilot to build a custom ML model tailored to our specific needs, deepening our expertise and giving us more control over the underlying data. Designing our own machine learning model allows us to analyze and extract valuable insights from first-party data, enable predictive analytics, and attribute conversions and interactions to the right touchpoints.

The core products used in the process included Cloud Shell for framework setup, Instant BQML as the quick start tool for audience configuration, CRMint for orchestration, and BigQuery for advanced analytics capabilities. The modeling and machine-learning occur within BigQuery while CRMint aids in data integration and audience creation within Google Analytics. When Google Analytics is linked to Google Ads, audience segments are shared automatically with Google Ads where they can be activated in a number of strategic ways.

The Google Cloud and gTech Ads teams worked closely with us throughout the set-up and deployment, which was fast and efficient. Generally speaking, we were impressed with the support we received throughout the process, which was highly collaborative from initiation to execution. The Google teams offered guidance and resources throughout, and their support enabled us to leverage the advanced analytics capabilities of BigQuery to build our own predictive audience model and identify the users most likely to make a purchase. We also appreciated the amount of available documentation, which made things much easier for our developers. 

Engaging the right users with advanced analytics

This was one of the first ML marketing analytics use cases at PUMA, and it turned out to be a very positive experience. Within the first six months, the click-through rate (CTR) of our advanced audience segments was significantly higher compared to other website visitor audiences or any other audience. 

Among the 10 designated audiences, the top three showed a 149.8% increase in click-through rate compared to other audiences used for advertising. Additionally, we observed a 4.6% increase in conversion rate and a 6% increase in average order value (AOV) compared to the previous setup. 

In addition to these results, which are helping us take steps towards increasing revenue, the new solutions are also enabling us to optimize and predict costs. Pricing is well structured, flexible, and transparent, and we can easily identify exactly where we’re spending money. 

We’re looking forward to continuing to partner with Google Cloud as we work to adapt our advertising strategy to signal loss, which has been happening for years. 

Our next step is to explore the development of advanced audiences using PUMA’s internal data, such as offline purchase information or other data not captured by Google Ads or Google Analytics. This opens up new opportunities to reach consumers we’re currently missing, while expanding the size of our audiences. At the same time, we’ll be scaling advanced audiences to all of our 20+ international entities. 

We’re also exploring server-side tagging using Tag Manager and in one market, we’re also experimenting with real-time reporting based on server-side data collection, with promising results so far. 

Looking toward an AI and data-driven future

This year, we will be moving much of PUMA’s e-commerce infrastructure over to Google Cloud. This includes hosting for certain markets, migrating from another cloud provider to Google Cloud for improved data distribution, and exploring Google Cloud’s capabilities for streaming data more efficiently. 

We’re looking to implement an event-driven architecture leveraging Google Cloud’s services, which is part of a broader strategy to reorganize and better structure our data-management processes to better support and operationalize AI use cases for both our organization and customers. 

This project has opened our eyes to the possibilities of data-driven, machine learning automated audience creation. Added to this, the fact that it was so easy to deploy has bolstered our confidence when it comes to machine-learning projects in general. We look forward to a long-term partnership with Google Cloud and are excited to see where the future will take us.