A rapidly growing global wellness and fitness company was struggling with declining conversion rates. It lacked full visibility of the customer journey, which would have enabled it to identify user drop-off points and measure and improve performance at every step and touchpoint with the user, as well as tools for rapid response in the purchasing process.
Project objective
Increase conversion by gaining full visibility into the user journey, identifying drop-off points, and optimizing actions at every stage of the funnel.
Scope of wrok
- Event tracking configuration:
- Our consultants designed and implemented definitions of key customer journey events along with custom parameters to ensure full data granularity,
- Our team of engineers validated the correctness of data transfer to GA4 through detailed testing and the implementation of automatic quality alerts.
- GA4 integration with BigQuery:
- Our team automated data export from GA4 to BigQuery, ensuring stability and continuity of data streaming,
- Analysts optimized table structure and partitioning, while engineers implemented streaming insert for real-time data access.
- Building a data warehouse (BigQuery):
- Our specialists designed the warehouse architecture with raw, staging, and marts layers to facilitate further analysis,
- The team took care of cost monitoring and clustering mechanisms, minimizing expenses and increasing query performance.
- Transformacja danych w dbt (data build tool):
- Data analysts prepared a hierarchy of models from staging through intermediate to marts, ensuring process transparency and eliminating the need to create complex, unmaintainable SQL queries. This allowed us to reduce technical debt and create a foundation that enables collaboration between multiple analytics teams,
- We validated the consistency of metrics through data quality tests and complete lineage documentation prepared in dbt.
- Tableau Dashboard:
- Our BI engineers created an interactive funnel chart showing user drop-off at each stage of the path,
- The team implemented dynamic filters enabling analysis by traffic sources, demographics, and devices,
- Analysts developed a color-coding system for key metrics and a module for comparing different paths in A/B tests.
Stages of implementation
- Defining priorities and business requirements: Together with the client, we established priorities in terms of needs and then translated them into specific business requirements. We mapped the user journey, identified pain points, and analyzed the requirements in terms of usage scenarios to ensure that the solution we were creating would truly support the business and not just be a technical implementation.
- Design and implementation of data pipelines and dbt models: Our data engineers designed a consistent data flow from tracking in GA4, through BigQuery, to models in dbt. We automated transformation processes, prepared DEV and PROD environments that support tool development and ensure the delivery of correct data to business users, and validated the correctness of metrics at every stage of the pipeline.
- Dashboard development and implementation with iterative testing and acceptance: The BI team created an interactive dashboard in Tableau, enabling detailed analysis of the drop-off funnel. We conducted tests with business users, iteratively refined the functionalities, and validated them, additionally analyzing them in terms of specific use cases to ensure that the tool supports real business processes and that the final solution supports and delivers real business and analytical value.
Results
Key results
- A 15–25% increase in conversion rates after implementation of the solution. Key elements that contributed to this effect include:
- Identification and rapid improvement of critical steps in the path (elimination of conversion barriers),
- Segmentation and comparison of A/B paths, which enabled optimization of activities for specific user groups and testing of the effectiveness of changes.
- Reducing the time needed to identify problems from 2 weeks to 2 hours,
- Full visibility of the customer journey and identification of critical churn moments.
Business benefits
- Increased revenue - improved conversion translated into a higher number of transactions with the same traffic volume, which directly increased revenue.
- Faster business decisions - with real-time monitoring in Tableau.
- Development of analytical skills - internal teams gained tools for independent optimization of activities.
Summary
The project shows how the use of a modern data stack (GA4, BigQuery, dbt, Tableau) combined with expert knowledge allows data to be quickly translated into real business results. End-to-end ownership, from tracking configuration to business visualizations, ensured the consistency and quality of the entire solution, while creating a solid foundation for the organization's further development towards becoming data-driven. Importantly, the solution reduced technological debt, enabled effective collaboration between multiple analytical teams, and ensured ease of further development and low maintenance costs.
