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How PUMA leverages built-in intelligence with BigQuery for greater customer engagement
- Puma is partnering with Google Cloud to create advanced audiences using machine learning, in an aim for greater customer engagement through advanced audience segmentation.
- Traditional segmentation methods are manual, but Puma wants to use ML as a method to create audience models tailored to their specific needs and allowing them to analyze and extract valuable insights from first-party data, enabling predictive analytics and attribution of conversions to the right touchpoints.
- The Core products used in the process include Cloud Shell for framework setups, Instant BQML for audience configuration, CRMint for orchestration, and BigQuery for advanced analytics.
- The new approach enables Puma to optimize and predict costs in a flexible, transparent way while identifying exactly where spend is going.
- Puma hopes to migrate most of its e-commerce infrastructure to Google Cloud by leveraging Google's event-driven architecture; this is part of a broader corporate strategy to reorganize and optimize data-management processes.
- One of the benefits of the pilot project is that it enabled the creation of an ML model to predict audience behaviours, in the first instance indicating that click-through rates of our advanced audience segments were better by a significant degree compared to other designated audiences.
- Puma is also exploring server-side tagging using Tag Manager and real-time reporting based on server-side data collection in one market, with promising initial results.
- Puma plans to scale advanced audiences across its 20+ international entities, using its own data such as offline purchase information or other non-Google Ads or Analytics collected information to further identify and expand its customer audience.
- Puma and Google Cloud will continue to partner as Puma adapts its advertising strategies in response to signal loss across the industry, increasingly protecting users' privacy.
- The pilot program deepened Puma's understanding of machine learning, and encouraged confidence in investment in the technology.
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