Automate Client Reporting and Campaign Optimization

Most 10-person agencies rely on a patchwork of tools for client reporting. They use Supermetrics to pull data into Google Sheets, then connect those sheets to Looker Studio. This works for visualizing data from one source, but it cannot join disconnected datasets. An account manager cannot easily show a client how spend on a specific LinkedIn ad campaign directly influenced a closed deal in that client's Salesforce instance.

In practice, this means an account manager for a B2B SaaS client spends four hours every Monday on manual data labor. They export a CSV from Google Ads, another from LinkedIn Ads, and a third from the client's CRM. They combine these in a spreadsheet, trying to match UTM parameters to lead sources. When the client asks, "Which LinkedIn campaign drove the most signups that converted to paying customers last month?" the data to answer is in three different places. Answering requires manual VLOOKUPs, introduces a high risk of copy-paste errors, and takes hours.

The structural problem is that reporting tools are built for visualization, not data integration. They lack a central, persistent database to unify data from multiple sources. Without a custom data pipeline that feeds a database like Supabase, your team is stuck performing manual joins in spreadsheets. This ad-hoc process is fragile, time-consuming, and does not scale as your agency grows from 20 to 40 active clients.

The engagement begins with mapping every data source for your 20 clients. This involves connecting to the APIs for Google Ads, Facebook Ads, and LinkedIn, plus establishing read-only access to each client's CRM, whether it's HubSpot, Salesforce, or Pipedrive. Syntora audits data consistency, focusing on UTM tagging and lead source tracking. You receive a data flow diagram showing exactly how information will be unified before any code is written.

The technical approach uses a central Supabase (Postgres) database to store all campaign and CRM data. A set of AWS Lambda functions, written in Python, runs on a schedule to pull new data from each platform's API every 12 hours. Using Python libraries like `httpx` for async API calls and Pydantic for data validation ensures the pipeline is reliable. The core logic joins ad spend to lead data, then to closed-deal revenue, creating a true ROI metric for every single campaign.

The delivered system powers a custom dashboard providing a single performance view across all clients. For deeper analysis, the Claude API generates weekly performance summaries in plain English, highlighting which campaigns need attention. This automated report, which takes the system less than 3 minutes to generate per client, can be emailed directly to your account managers. You get the full source code and a runbook detailing how to add new clients or data sources.