case-studies

WellnessCoach: Helped regain trust in data

Ansh Agrawal

Ansh Agrawal

— 1 min read

About Wellness Coach

Wellness Coach is a B2B digital wellbeing platform that offers guided meditation, coaching sessions, habit programs, and enterprise wellness solutions. With millions of users engaging across mobile and web, having clean, reliable analytics is essential for understanding behaviour, improving retention, and optimizing product experiences.

Challenge: Overtracking & Inconsistent Event Structure Led to Data Chaos

The Wellness Coach team had implemented tracking extensively, in fact, almost every single click and action was being logged. However:

  • Event names were inconsistent
  • The structure lacked hierarchy
  • Properties varied unpredictably across events
  • Tracking had grown organically without governance

With millions of events flowing in, this resulted in:

  • Confusion across product and analytics teams
  • Data discrepancies between dashboards
  • A lack of trust in the numbers
  • Difficulty answering even simple behavioural questions

The company needed a clean, scalable analytics foundation.

Solution

To restore clarity and trust, I conducted a comprehensive analytics audit and rebuilt their event taxonomy from the ground up.

1. Full audit of the existing setup

I reviewed:

  • Event names and patterns
  • Property structures and inconsistencies
  • Redundant or duplicated events
  • Gaps in measuring core workflows
  • Misaligned definitions across teams

This helped identify what was useful, what was unnecessary, and what was missing.

2. Created a clean, simplified event taxonomy

The new analytics structure included:

  • Clear event naming conventions
  • Categorized events based on user journeys (onboarding, sessions, habits, content consumption, etc.)
  • Consistent property schemas
  • Removal of noisy, click-based events that offered no strategic value
  • A blueprint that made sense to both technical and non-technical stakeholders

3. Prioritized only meaningful events

Instead of tracking everything, we focused on:

  • Key activation behaviours
  • Engagement loops
  • Revenue-driving moments
  • Retention indicators

This reduced clutter and significantly improved data interpretability.

Result

Once the engineering team implemented the new taxonomy:

  • Internal teams regained confidence in the data
  • Dashboards began reflecting consistent, reliable numbers
  • Product managers and leadership started using analytics actively for decisions
  • Insights, A/B tests, and product planning became smoother and more accurate

The team now benefits from a stable, scalable analytics foundation that supports better decisions, without the confusion and noise that previously held them back.

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