Use Python and AI to Analyze and Optimize Your Ad Spend

Most small businesses rely on the native dashboards in Google Ads and Facebook Ads Manager. These tools are good for launching campaigns but poor for analysis. They cannot join ad spend data with actual sales data from your payment processor, making it impossible to calculate true return on ad spend (ROAS) without manual exports.

To solve this, teams adopt data connectors that pull everything into Google Sheets. A marketing manager for a direct-to-consumer brand spends every Monday morning trying to map Facebook campaign IDs to Shopify order data using VLOOKUP. The process is slow and fragile; a single copy-paste error last month caused them to misattribute over 10% of their revenue, leading to a poor budget allocation for the following week.

This spreadsheet-based approach fundamentally cannot scale. It provides a snapshot of the past but offers no predictive insight or real-time alerts. It cannot answer critical questions like, "Which of my video ads are generating the highest lifetime value customers, not just the most first-time purchases?" without a data engineer's help.

Syntora's approach for an ad campaign automation system would start with a discovery phase to understand the client's specific KPIs, ad platforms, and data sources. Following this, we would design an architecture that begins by connecting to official APIs like the Facebook Marketing API and Google Ads API using their Python SDKs. A scheduled script would then pull campaign performance data, including impressions, clicks, and spend. We would ingest corresponding order data from the Shopify API, storing all information in a Supabase Postgres database to establish a single source of truth.

An AWS Lambda function, written in Python and leveraging libraries like Polars for high-speed data processing, would be implemented to trigger daily or on a custom schedule. This function would join ad spend with sales data to calculate critical metrics such as cohort-based ROAS and customer LTV, even across large datasets. This automated process is designed to replace time-consuming manual spreadsheet work, providing fast and reliable data aggregation.

The cleaned and unified data would then be fed into the Claude API with specific analytical prompts, tailored to the client's strategic questions. For instance, prompts could identify top-performing ad creatives based on ROAS or describe common visual elements across successful campaigns. Syntora has experience building similar Claude API-powered document processing pipelines for financial documents, and the same pattern applies here for structured marketing data. The API's plain-English analysis would be formatted for delivery, potentially as daily Slack messages, allowing marketing teams to receive actionable insights directly.

For interactive analysis, Syntora would propose deploying a lightweight dashboard, potentially using Streamlit on Vercel. This front-end would allow the marketing team to ask natural language questions about their performance data on-demand, providing immediate access to insights beyond the automated daily reports. The deliverables for such an engagement would include the deployed cloud infrastructure, source code, documentation, and training for the client's team. Clients would need to provide API access credentials and define their key analytical objectives.