Affiliate Transaction Analytics Platform

Revenue from affiliate marketing arrives scattered across many networks — Commission Junction, Partnerize, Awin, Rakuten, Impact Radius and others — each with its own API, data format and reporting cadence. Pulling these together by hand to understand sales, commissions and validation rates is slow and unreliable, and discrepancies between networks are easy to miss. The client needed a single source of truth for affiliate transactions, kept continuously up to date and easy to analyse.

We built a platform that automatically fetches transaction data from every major affiliate network, normalises it into one consistent model, and stores it in a single database. A background worker pulls transactions on a schedule and handles the full lifecycle — new, updated and deleted records — while tracking sync status and alerting on failures via Slack. The platform calculates and monitors validation-rate parity across networks so discrepancies surface automatically. A React dashboard lets the team browse transactions with rich filtering, view summaries by retailer and status, monitor sync health, and export data to CSV.

Affiliate revenue from many networks is now consolidated into one continuously-updated view, replacing manual report-wrangling. The team can filter and summarise transactions, track validation rates, and catch sync failures the moment they happen through automated alerts. Validation-rate parity monitoring surfaces network discrepancies that would otherwise go unnoticed.

• Multi-Network Aggregation: Pulls transactions from CJ, Partnerize, Awin, Rakuten, Impact Radius and more into one model.
• Continuous Sync: A scheduled worker keeps data current and handles new, updated and deleted transactions.
• Validation-Rate Monitoring: Tracks parity across networks so discrepancies are flagged automatically.
• Failure Alerts: Slack notifications surface sync problems immediately.
• Filter, Summarise & Export: A React dashboard for browsing, summarising by retailer/status, and CSV export.

• Go: Backend service and scheduled worker for multi-network ingestion.
• PostgreSQL: Unified store for normalised transaction data.
• Redis: Caching for fast queries and dashboards.
• React (Vite): Dashboard with filtering, charts and CSV export.

We started by mapping each affiliate network's API and data quirks, then designed a single normalised transaction model so everything downstream sees consistent data. A scheduled worker handles ingestion and the full transaction lifecycle, with sync-status tracking and Slack alerts for reliability. We added validation-rate parity monitoring to automatically catch discrepancies between networks. The React dashboard was built around the team's real workflows — filtering, retailer/status summaries, sync monitoring and export — to make the consolidated data genuinely useful.