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Blog

Sales and Marketing Insights for a Packaging Machine Manufacturer

A leading European machine manufacturer lacked a clear, unified view of marketing and sales performance. Data sat across PR campaigns, digital ads, CRM, and web analytics platforms. Teams worked with fragments, not facts. Valanor was brought in to design a reporting layer that consolidated these sources—delivering consistent, region-specific insights and sharper alignment between strategy and execution.

Client

Ruess Group

Sector

Mechanical engineering

Market

Germany

Date

2023 - 2025

Technology

Airbyte, Google BigQuery, dbt, Metabase

Objective

Unify scattered marketing and sales data into one reporting layer. The client required reliable, cross-channel visibility across regions to track performance, allocate spend with confidence, and adjust campaigns before they deviated from course.

Challenges

Information was spread across PR logs, lead databases, Google Analytics 4, Google Search Console, HubSpot, LinkedIn (organic and paid), and Google Ads. No one owned the data. No standard model existed. Regional performance couldn’t be tracked with consistency. Manual reporting slowed decisions and eroded trust in the numbers.

Approach

We focused on repeatability and scale. We established clear accountability for each source system, leveraged open-source tools to avoid vendor lock-in, and centered the design around a cloud-native warehouse. Every component was carefully selected to minimize friction and ensure seamless updates.

Solution

We built an automated pipeline with Airbyte to ingest from all source systems. The data was harmonized in BigQuery and then shaped into a semantic model, translating raw inputs into meaningful KPIs. Metabase dashboards exposed these KPIs to regional marketing and sales leads in a way that allowed them to act on them.

Result

The client moved from slow, manual monthly reporting to near real-time visibility. Regional campaigns could now be measured weekly, allowing faster course corrections. Teams gained confidence in both data quality and attribution. The unified model also cut down duplicated effort and reporting overhead.

Conclusion

Centralized, structured data gave the client a faster and more accurate view of marketing effectiveness. The system brought teams into alignment and sharpened responses to regional shifts, building a foundation for scalable, data-driven growth.

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