OnTimeStack | Terpli E-Commerce Tracking Platform

Project overview

Terpli E-Commerce Tracking Platform captured, standardized, and delivered reliable commerce analytics across hundreds of storefront implementations. The system combined a lightweight browser plugin, monkey patch interceptors, canonical event mapping, resilient delivery, and downstream ETL processing so attribution, conversion reporting, recommendation performance, loyalty insights, and executive dashboards could rely on a trusted dataset.

Terpli e-commerce tracking platform architecture showing storefronts, browser plugin interceptors, event transport, collection API, storage, ETL, data lake, warehouse, and analytics destinations — The tracking platform captures browser-level commerce signals, normalizes inconsistent storefront events, and delivers trusted datasets for attribution, recommendation analytics, loyalty reporting, and business intelligence.

Challenge

Retailers used different platforms and analytics implementations, with inconsistent event names, broken GA4 setups, irregular dataLayer structures, iframe storefronts, SPA behavior, third-party script conflicts, incomplete purchase payloads, and platform-specific tracking bugs. Conversion reports often disagreed with actual business outcomes.

Solution

Designed browser-level instrumentation around fetch, XMLHttpRequest, postMessage, gtag, dataLayer, and Meta Pixel, then normalized platform-specific events into a canonical schema. Reliable transport, retries, batching, idempotency controls, session synchronization, cross-origin iframe communication, and ETL validation reduced event loss and turned noisy signals into curated analytics datasets.

Tech Stack

JavaScript
Data Engineering
Analytics Engineering
Event Tracking
E-commerce

Technical scope

Browser-level event collection across Dutchie, Jane, Tymber, Dispense, LeafBridge, RankReallyHigh, and custom storefronts
Monkey patch interceptors for fetch, XHR, postMessage, GA4, dataLayer, and Meta Pixel
Canonical event schema for view_item, add_to_cart, begin_checkout, purchase, and related commerce actions
ETL validation, enrichment, deduplication, quality checks, and curated datasets for BI and attribution

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