Ulv AI
Case Study

Data Warehouse for First Party Data

We’ve built a state of the art Data Warehouse to ensure true first party data activation.

Mille Notti

  • CategoryE-commerce
  • ServicesData, Advisory
  • PlatformsGoogle Cloud, BigQuery, Meta, Centra, Rule
  • First-Party Data Collection Rate
  • First-Party Data Utilization in Campaigns
  • Improvement in Customer Segmentation Accuracy

Project overview

Mille Notti, a premier Swedish bedding and accessories company, embarked on a transformative journey to consolidate their diverse data streams into a state-of-the-art Data Warehouse (DWH). The primary objective was to harness the power of first-party data across multiple platforms, including Google Ads, Meta Ads, Google Analytics 4, Centra, Triggerbee, and Rule, to drive growth and optimize their marketing strategies. By leveraging Google’s robust cloud infrastructure, the project aimed to create a future-proof repository that would facilitate advanced data analysis and actionable insights, and ultimately drive stronger revenue through Mille Notti’s E-Commerce.

The integration involved multiple stages, starting with the collection of data from various advertising platforms like Google Ads and Meta Ads, as well as from e-commerce and customer interaction tools such as Centra and Triggerbee. The central challenge was to ensure seamless data flow and integration, maintaining high data quality and consistency across sources. Google Cloud Platform (GCP) tools, particularly Google BigQuery, were utilized to build a scalable and efficient DWH. The process included:

  1. Data Collection: Automated extraction of data from each platform using APIs and webhooks.
  2. Data Transformation: Cleaning, transforming, and normalizing data to fit a unified schema, ensuring compatibility and maximizing utility.
  3. Data Integration: Consolidating all transformed data into Google BigQuery, utilizing its powerful data warehousing capabilities to handle large volumes of data efficiently.
  4. Data Analysis and Reporting: Implementing advanced analytics and machine learning models to derive insights and facilitate decision-making.

To summarize

Mille Notti, a leading Swedish bedding and accessories firm, has developed a sophisticated Data Warehouse (DWH) by integrating data from various platforms such as Google Ads, Meta Ads, Google Analytics 4, Centra, Triggerbee, and Rule. Utilizing Google Cloud Platform’s tools like Google BigQuery, the company aimed to centralize and optimize the use of first-party data to enhance marketing strategies and boost e-commerce revenue.

The project involved several key stages: automated data collection, data transformation to ensure uniformity and compatibility, data integration into BigQuery for efficient large-scale data handling, and advanced data analysis to generate actionable business insights.

Project execution

The execution of the DWH project for Mille Notti was meticulously planned and executed in phases:

  1. Phase 1: Planning and Design
    • Mapping out data sources and defining the data architecture.
    • Establishing data governance and security protocols.
  2. Phase 2: Development
    • Setting up ETL (Extract, Transform, Load) pipelines using Google Cloud’s Dataflow and Pub/Sub for real-time data processing.
    • Integrating APIs from Google Ads, Meta Ads, and other tools to feed data directly into the DWH.
  3. Phase 3: Integration and Testing
    • Merging data streams into BigQuery, ensuring integrity and consistency across data sets.
    • Conducting thorough testing to validate the data and its utility for analytics.
  4. Phase 4: Deployment and Optimization
    • Deploying the DWH into production with continuous monitoring and optimization.
    • Implementing user dashboards and reporting tools using Google Data Studio for accessible insights.
  5. Phase 5: Maintenance and Scaling
    • Regular updates and maintenance to adapt to new data sources and changing business requirements.
    • Scaling the infrastructure as needed to accommodate growing data volumes and complexity.


The establishment of the DWH for Mille Notti has brought about significant improvements and capabilities:

  • Unified View: A single source of truth for all marketing, sales, and customer data.
  • Enhanced Decision Making: Data-driven insights helping to refine marketing strategies and customer engagement.
  • Cost Efficiency: Reduced overheads by eliminating siloed systems and improving campaign effectiveness.
  • Scalability: A robust infrastructure that grows with the company’s needs.
  • Future Proofing: Preparedness for evolving data privacy laws and reliance on first-party data.

This comprehensive DWH solution not only positions Mille Notti at the forefront of data-driven business practices in the luxury goods sector but also provides a robust platform for sustainable growth and innovation.

  • First-Party Data Collection RateMeasures the increase in the volume of first-party data collected through the e-commerce platform and integrated tools like Triggerbee and Rule. This KPI reflects the effectiveness of data collection strategies in expanding the company's proprietary data assets.
  • First-Party Data Utilization in CampaignsTracks the percentage of marketing campaigns utilizing first-party data for targeting and personalization. This KPI highlights the shift towards more privacy-compliant, data-driven marketing practices that leverage internally sourced data.
  • Improvement in Customer Segmentation Accuracy Quantifies enhancements in the precision of customer segmentation based on first-party data. This metric assesses how well the DWH enables the business to understand and segment its customer base using proprietary data, leading to more tailored marketing strategies and potentially higher engagement rates.