Stop Manually Building Marketing Reports

Many marketing teams rely on tools like Google Looker Studio or HubSpot's reporting add-ons. Looker Studio is great for visualization, but its data blending feature is brittle. Joining Google Ads cost data with HubSpot deal data on a campaign name fails if naming conventions are not perfectly consistent across platforms, requiring manual cleanup before every import.

For example, a marketing manager spends Monday morning pulling CSVs from Google Ads, LinkedIn Ads, and HubSpot. She merges them in a master Google Sheet, manually fixes mismatched campaign names, and calculates metrics like Cost Per MQL. The process takes 4-5 hours. One week, a platform export changes a column header. The VLOOKUP formula breaks, the entire report shows #N/A, and she spends another two hours debugging a spreadsheet.

Data connectors like Supermetrics solve the export problem but introduce a new one. The transformation logic now lives in complex spreadsheet formulas or BI tool settings. If an API changes a field name, the fragile connection breaks and troubleshooting is difficult. These tools are fundamentally data movers, not data processors. They push the core work of cleaning, joining, and analysis onto the user, inside a spreadsheet that becomes a single point of failure.

The structural problem is that off-the-shelf tools are built for mass-market use cases and assume clean, standardized inputs. They cannot handle the custom business logic, messy data, and unique attribution models that define how a real marketing team operates. They offer a rigid data model that forces you to adapt your process to the tool, not the other way around.

An engagement starts with a discovery call to map every data source: Google Analytics, your ad platforms, your CRM. Syntora audits the APIs for each, identifying rate limits and data schemas. We define the exact metrics and report format you need, creating a specification document. This audit confirms the project is feasible and provides a fixed-price quote before work begins.

The technical approach is a Python service running on a schedule using AWS Lambda. The service uses libraries like httpx to pull data from each source API concurrently. The raw data is then cleaned and normalized into a consistent format using Pydantic models and loaded into a Supabase Postgres database. This creates a durable, clean data warehouse that serves as a single source of truth for all marketing activities.

A separate script queries this database to build the final reports, performing complex calculations that are impossible in standard BI tools. Syntora used this exact pattern to automate Google Ads performance reporting for a marketing agency. The final system can deliver reports as a PDF, a Slack message, or a feed into a custom dashboard. You receive the full source code, a runbook for maintenance, and a system built with production-grade tools for reliable logging and monitoring.