Data Debt in D2C Brands: Why Clean Tracking Wins

---

The Platform Illusion

D2C brands today are spoilt for choice when it comes to data and marketing tools. From Customer Engagement Platforms like MoEngage, Braze, and CleverTap to CDPs like Salesforce Data Cloud and Tealium, and analytics solutions like Adobe Analytics, Amplitude, and GA4, the stack seems complete.

But despite this, most brands fail to fully realize the value of these platforms. Why? Because everything hinges on a single foundation: event schema design. And that’s where things start to break.

When Everyone Owns It, No One Owns It

Event schemas are meant to define what user actions get tracked, when they fire, and how the data is structured. But in reality, every team ends up tracking what matters only to them.

Marketing wants campaign events. CRM teams care about WhatsApp and email triggers. Data teams want every click, impression, and scroll captured. Tech teams aim for quick implementation, sometimes proposing a single “next page” event across journeys. Web and app data flows are built separately, even when the journeys are identical. Offline purchases rarely get integrated at all.

The outcome? A bloated schema filled with redundant or conflicting events. Hundreds of properties with unclear usage. No visibility into what's live, what’s outdated, or what’s even needed.

The Real Cost of a Messy Schema

When data isn't well-structured, it becomes hard to use, and even harder to trust. Segmentation fails. Campaigns don’t perform. Reports go ignored. Personalization becomes surface-level. Decisions are delayed or, worse, made using incorrect data.

Eventually, frustration builds. Teams lose faith in the tools, and the company begins hunting for a new platform; assuming the software is the issue, when the real problem was how it was set up in the first place.

Meanwhile, everyone keeps chasing buzzwords - omnichannel journeys, hyper-personalization, automated campaigns - without ever laying the groundwork to support them.

Start with Less. Build What You’ll Use.

Most D2C brands don’t need to track 200+ events. In fact, tracking too much often leads to tracking nothing well. The smarter approach is to start small—focus on the most important user journeys, and build your schema around them.

These are almost universal across D2C:

1)   User sign up journey for app (applicable for brands with app and guest account)

2)  Signed up user – Onboarding journey for first purchase

3)  Cart abandoner journey

4)  Product engagement and browser abandonment journey

5)  Post purchase feedback journey

6)  Loyalty points redemption journey

7)  In-store purchasers to online engagement journey, may

8)  Dormant users – win back journey

9)  Wishlist recovery to purchase journey

10) Cancelled, returned orders to re purchase

These 8–10 journeys alone can support 80–90% of use cases when tracked correctly.

How to Design a Schema That Works

The process starts with alignment. Bring together all business stakeholders - marketing, CRM, product, tech, and analytics - and agree on what matters. Define your key KPIs. Decide which touchpoints really impact growth, and what data you need to support them.

From there, build a lean, reusable schema. Use properties to differentiate between variations (like tracking wishlist adds and removes under one event). Avoid duplicating events across platforms or teams. Every event should serve a reporting, segmentation, or campaign function.

Once implemented, document the schema centrally and maintain it like product infrastructure. Run sanity checks every few months to remove what's outdated and validate what’s in use.

A Clean, Scalable Event Structure

Here’s what a sample schema might look like: concise, efficient, and applicable across a typical D2C funnel.

This structure is lean enough to manage, powerful enough to scale, and flexible enough to adapt.

Maturing Over Time

Once your foundation is in place, scale carefully. Enrich journeys with additional user attributes. Add dynamic campaign triggers. Introduce channel orchestration (like combining email + WhatsApp). Build out predictive segments for churn, upsell, or retention.

But only after the basics are working. Personalization, automation, and performance all depend on clean, consistent tracking.

The Bottom Line

Most D2C brands don’t have a data volume problem, they have a data design problem. Good platforms can’t fix bad schemas. And bad data will always drag down even the best tools.

Start small. Align your teams. Track what matters. Maintain it. And stop romanticizing more data. Fall in love with the right data instead.