Integrate Your Agency's CRM and Analytics Data

Most agencies start with HubSpot's native Google Analytics connector. This works for top-level metrics but breaks when you need to track custom conversion events or join data across multiple client sub-domains. The next step is often an off-the-shelf data connector like Supermetrics, which pulls data into Google Sheets or a BI tool. These tools are rigid and present a fixed data model. They cannot handle the custom objects in your CRM that contain the most valuable data.

Consider an agency managing 10 clients, each with HubSpot, Google Ads, and LinkedIn Ads accounts. On the first of the month, an analyst spends three full days exporting CSVs from 30 different platforms. They use VLOOKUPs in a massive spreadsheet to join HubSpot deal stages with ad spend, but the data is never perfectly aligned. A single typo in a UTM parameter means a $50,000 deal gets misattributed to the wrong campaign, making true ROAS calculation impossible.

The structural problem is that these connectors are built for the 80% use case. They assume standard fields and common data models. A high-performance agency's value comes from its unique insights, often stored in custom CRM fields that track lead quality, offline conversions, or industry-specific segments. Off-the-shelf tools cannot read, interpret, or join this custom data. Their architecture is designed for mass-market compatibility, not for the specific data that drives your agency's performance.

An engagement would start with a full audit of your data sources: HubSpot, Google Ads, LinkedIn Ads, and any client-specific platforms. Syntora maps every API endpoint and documents the custom fields and objects you use for reporting. The outcome is a detailed data flow diagram and a technical specification that you approve before any code is written.

We built an automated Google Ads management system for an agency using Python and AWS Lambda. A CRM integration would use a similar pattern. A central FastAPI service would act as a data orchestrator, pulling data from each source API on a schedule using async httpx calls. The data is then normalized using Pydantic models to enforce data types and structure before being loaded into a Supabase Postgres database. This architecture provides a single source of truth for all marketing and sales data.

The delivered system feeds clean, unified data directly to your BI tool or a custom-built dashboard. Instead of manual exports, your team gets a live view of performance, updated every 15 minutes. You receive the full Python source code, a runbook for managing the data pipelines, and a system hosted on your own AWS account. The entire process is transparent.