Source URL: https://cloud.google.com/blog/products/data-analytics/puma-bigquery-customer-engagement/
Source: Cloud Blog
Title: How PUMA leverages built-in intelligence with BigQuery for greater customer engagement
Feedly Summary: 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.
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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.
AI Summary and Description: Yes
**Summary:** The text discusses PUMA’s efforts to enhance customer engagement through the use of first-party data and machine learning capabilities provided by Google Cloud. It highlights a successful pilot project for creating advanced audience segments that improved marketing metrics, showcasing a shift towards data-driven marketing strategies while also addressing privacy concerns.
**Detailed Description:**
This text directly pertains to multiple categories, mainly focused on AI, cloud computing, and data analytics. Here are the core aspects covered:
– **Focus on First-Party Data:**
– PUMA emphasizes the importance of first-party data for improving transparency and creating meaningful user experiences.
– The company’s approach is positioned as a response to privacy-preserving technologies and signal loss due to evolving regulations.
– **Partnership with Google Cloud:**
– Collaboration with Google Cloud aims to leverage machine learning for enhanced customer engagement.
– PUMA has used Google Cloud’s capabilities to conduct advanced audience segmentation based on high purchase propensity.
– **Implementation of Machine Learning:**
– Transition from traditional manual segmentation to ML-based advanced audience segmentation utilizing BigQuery.
– Development of a custom ML model specifically tailored to PUMA’s needs, leading to valuable insights and predictive analytics.
– **Results and Metrics:**
– The pilot project yielded significant improvements in marketing performance:
– 149.8% increase in click-through rates for top audience segments.
– 4.6% increase in conversion rates.
– 6% increase in average order value.
– Positive outcomes have encouraged PUMA to scale these ML solutions across its international entities.
– **Future Directions:**
– Plans to further integrate PUMA’s internal data for advanced audience building.
– Exploration of server-side tagging and real-time reporting, indicating a commitment to innovation in marketing automation.
– Transitioning PUMA’s e-commerce infrastructure to Google Cloud for enhanced data management and AI operationalization.
– **Investment in AI and Data-Driven Strategies:**
– PUMA’s strategy is aimed at reorganizing data management to better support AI use cases.
– The ease of deploying machine learning projects has boosted PUMA’s confidence in ongoing and future ventures in this domain.
Overall, this text serves as a case study showcasing how organizations can effectively utilize cloud computing and AI technology to enhance their marketing strategies while navigating the challenges posed by data privacy regulations. It provides important insights for security and compliance professionals regarding the implications of leveraging first-party data and employing advanced technologies within a secure framework.