vertbaudet Identifies Its Key Revenue Drivers With Meridian
2026-02-24 | Case study | Insights
Meridian has given us a much clearer picture of our core revenue drivers. By accounting for variables that were previously hard to quantify - such as seasonality and promotional discounts - we’ve moved toward a more informed approach to our budget planning. These insights provide a solid foundation for future experiments and budget allocation.
Lorena Unger | Team Lead Online Marketing, vertbaudet
vertbaudet identifies its key revenue drivers with Meridian
vertbaudet identifies its key revenue drivers with Meridian
As a Google Meridian Partner, Digitl is an independent specialist in analytics, ad-tech, and data science. Digitl supports clients with the setup and utilization of their entire marketing stack.
of data and 12 channels being analyzed with Meridian
of net revenue explained
potential incremental net revenue with optimized allocation
The Challenge
The Challenge
vertbaudet, a French pure-player for children's fashion and furniture, wanted to measure the true impact of its multi-channel marketing. Within the mix of Google Shopping, mailings, and social media, visibility into the strongest revenue drivers was lacking. The goal was to establish a data-driven method to separate organic baseline sales from incremental uplift and to optimize budget allocation.
The Approach
The Approach
With the support of Digitl, vertbaudet developed a Marketing Mix Model (MMM) based on three years of historical data, using net revenue as the primary KPI. To ensure model stability, major channels were segmented by category and intent, while smaller channels were strategically grouped. The model also integrates external control variables, such as discounts and weather, as these were identified as critical business drivers.
The Result
The Result
The model served as a successful proof of concept: it accounted for 96% of net revenue and provided a foundation for analyzing 12 marketing channels. The results confirmed Search, high-reach social media channels, and discount promotions as the primary drivers of incremental revenue. For grouped channels, insights were generated at the group level; individual evaluations will require further attribution and incrementality testing.
Despite this limitation, the model provides clear guardrails: it identified channels nearing saturation as well as untapped potential in social media marketing. A budget reallocation based on these insights increased incremental net revenue by 6.3% in the optimization scenario and now serves as the benchmark for future budget decisions.
Goals
- Data-driven approach: Implementing a systematic method for marketing evaluation.
- Separating baseline from uplift: Distinguishing organic baseline sales from incremental marketing uplift.
- Budget optimization: Maximizing the efficiency of marketing spend.
Solution
- MMM with Meridian: Developed a Marketing Mix Model using the Google Meridian framework.
- Comprehensive data foundation: Built on extensive historical data with net revenue as the primary KPI.
- External control variables: Integration of external factors, such as discounts and weather patterns, into the model.
Results
- Successful PoC: The model accounted for 96% of net revenue.
- Saturation insights: Identified specific marketing channels nearing their point of diminishing returns (saturation).
- +6.3% revenue potential: Demonstrated a 6.3% increase in potential incremental net revenue through optimized budget allocation.