Marketing Analytics SaaS
Engineering advisor and senior contributor to a marketing analytics SaaS during a multi-year scaling phase. Pipeline architecture, attribution modeling, integrations across the major ad platforms.
- Client
- Anonymous (martech SaaS)
- Engagement
- Engineering advisory + scaling support
The product
A marketing analytics SaaS that ingests campaign data from the major ad platforms, attributes conversions across them, and gives marketers a single dashboard for ROI by channel. The complexity isn't the dashboard — it's the data pipeline behind it, and it's the attribution model.
What we contributed
Engineering advisory across pipeline architecture, attribution model design, and integration patterns for Meta, Google, TikTok, and other ad platforms. Helped scale the team and the system through a multi-year growth phase.
What this case study illustrates
Marketing data is messy. The same conversion shows up three times under different IDs in three different platforms, and pretending otherwise is how attribution models lie to founders. Cleaning that up properly — at the pipeline level, not the dashboard level — is the difference between a tool you trust and a tool you ignore.
The discipline around building trustworthy data pipelines is what underwrites the AI brief work we now do — an executive brief that hallucinates one number is worse than no brief at all.