Readwonders: Analytics Migration for an EdTech Startup
For Readwonders, we completed a marketing analytics migration and implemented user behavior tracking for product growth.

Analytics stack migrated without reporting downtime.
After standardizing events and validation checks.
Faster campaign and product experiment evaluation.
A layered legacy analytics setup produced inconsistent insights and low trust in metrics.
We delivered a unified tracking taxonomy, standardized handlers, and quality controls.
The tracking layer was implemented with auditability and long-term maintainability in mind.
- Marketing analytics migration completed
- Reliable user behavior tracking implemented
- Data governance model introduced
- TypeScript
- React
- Next.js
- Analytics integrations
Readwonders is a US startup focused on universities and PhD students that was growing quickly and needed a more precise analytics foundation for both marketing and product decisions. Its existing tool stack had evolved over time, was data-inconsistent, and made campaign performance and user behavior harder to interpret. Our goal was to deliver a controlled migration of marketing analytics so the team could get cleaner data, higher trust in metrics, and better inputs for product scaling.
We started with a data audit where we mapped existing events, conversion points, user attributes, and links between marketing channels and product activity. We identified duplicates, inconsistent naming, and places where context was lost between a visit and later user actions. Based on that audit, we prepared a new tracking plan with clear event taxonomy, prioritization of business-critical metrics, and a migration path that preserved reporting continuity.
Implementation stayed within the existing application stack, and we cleaned up duplicates introduced by earlier "vibe coding." We focused on keeping the tracking layer consistent with the rest of the product. A key requirement was ensuring measurement would not impact user experience or the performance of critical pages. We introduced standardized event handlers, unified data validation, and collection quality controls. This significantly reduced data anomalies and helped the team evaluate experiments and acquisition channel performance faster.
Alongside the migration itself, we also established the process layer: documentation, event ownership, and rules for future analytics expansion. This is critical in startups, where the product changes quickly and data quality degrades fast without governance.
The result was a successful migration of marketing analytics tooling and a full user behavior tracking setup that gives Readwonders better control over growth. The collaboration confirmed that well-designed analytics infrastructure is not just reporting, but a direct part of product strategy.
For the next phase, we also set up a framework for experimenting with new growth scenarios so marketing and product can work from the same success metric. This sped up evaluation of changes and reduced conflicts around data interpretation. Readwonders now has an analytics system ready for the next stages of scaling.
The project aligned product and marketing around one trusted source of behavioral data.


