Case Study

Ecommerce Data Foundation for Digital-Native Brand

Data stack powering omnichannel growth.

Digital-Native eCommerce BrandFounder's Track Record

Services:Unified Data Foundations, Automated Reporting & Dashboards

Clarivant partnered with a fast-growing eCommerce brand to centralize Shopify, Ads, and Odoo into Snowflake + dbt for a scalable data foundation.

Key Results

Margin Definitions Reconciled

Data audit surfaced 4 different margin calculations across the organization

Margin-Negative SKUs Found

Top-10 revenue SKUs that were actually losing money after landed costs

2 Days

New Channel Integration

Adding a new sales channel took 2 days instead of rebuilding from scratch

The Transformation

Before

After

Before

After

CSVs from 3 platforms pasted into spreadsheets

Unified Snowflake + dbt data stack

CSVs from 3 platforms pasted into spreadsheets

Unified Snowflake + dbt data stack

Full day of manual weekly reporting

Opening a dashboard

Full day of manual weekly reporting

Opening a dashboard

Inconsistent margin calculations

SKU-level profitability with landed costs

Inconsistent margin calculations

SKU-level profitability with landed costs

Weeks to integrate new data source

2 daysfollowing established dbt pattern

Weeks to integrate new data source

2 daysfollowing established dbt pattern

New hires ask COO to pull numbers

Self-service dashboards for all roles

New hires ask COO to pull numbers

Self-service dashboards for all roles

The Challenge

This fast-growing eCommerce brand had data everywhere and answers nowhere. Shopify held order and product data, Google and Meta Ads tracked marketing spend, and Odoo ERP managed inventory and fulfillment — but none of these systems talked to each other. The COO was building weekly reports by exporting CSVs from three platforms, pasting them into spreadsheets, and manually matching order IDs to ad spend. Margin calculations were inconsistent because Shopify revenue didn't account for Odoo's landed costs. Marketing couldn't tell which channels actually drove profitable orders versus just revenue. Every strategic decision — whether to scale ad spend, adjust pricing, or expand to a new channel — required days of manual data wrangling before anyone could even frame the question.

Our Approach

We started with a data audit: mapping every metric the team used back to its source system and documenting where calculations diverged. That audit alone surfaced 4 different definitions of 'margin' across the organization.

We built the foundation using Fivetran connectors to pull Shopify, Google Ads, Meta Ads, and Odoo data into Snowflake on automated daily syncs. The dbt transformation layer was where the real work happened — we built staging models for each source, then intermediate models that reconciled order-level data across platforms. The key challenge was matching Shopify orders to Odoo fulfillment records and ad platform attribution data into a single unified order model.

We created mart-level models for three core domains: sales performance (revenue, AOV, repeat rates by channel and cohort), marketing ROI (true ROAS with landed costs, not just Shopify revenue), and inventory health (stock levels, turnover rates, reorder signals). Each mart fed directly into Tableau dashboards designed for specific roles — the COO got a daily P&L view, marketing got channel-level ROAS, and operations got inventory alerts.

We also built a margin reconciliation model that tied Shopify gross revenue to Odoo COGS at the SKU level, giving the team their first accurate view of product-level profitability. The entire dbt project included automated tests on key joins and metric calculations — if Shopify order counts didn't match Odoo fulfillment records within tolerance, the pipeline flagged the discrepancy before it reached a dashboard. This was critical because the team had been making decisions based on numbers they assumed were correct but had never validated.

The Outcome

The unified data stack gave the team something they'd never had: a single place to understand the business. The COO's weekly reporting process dropped from a full day of spreadsheet work to opening a dashboard. Marketing could see within 24 hours whether a campaign was driving profitable orders or just inflating top-line revenue.

The margin reconciliation model was the biggest unlock — it revealed that 3 of their top-10 revenue SKUs were actually margin-negative after accounting for landed costs and returns. That insight alone reshaped their pricing and promotion strategy. The inventory health dashboard caught slow-moving SKUs weeks earlier than the manual process, reducing overstock write-offs and improving cash flow.

The foundation also scales cleanly: when they added a new sales channel 6 months later, it took 2 days to integrate instead of rebuilding from scratch. The dbt architecture means new data sources follow an established pattern — staging, intermediate, mart — rather than requiring custom integration logic each time. The automated data quality tests we built catch integration issues early, before bad data reaches dashboards and erodes trust.

The team now runs their weekly business review entirely from the dashboards, and new hires can find answers independently instead of asking the COO to pull numbers from spreadsheets. What started as a data consolidation project became the analytical backbone the brand needed to grow confidently — knowing that every decision is backed by reconciled, tested numbers rather than spreadsheet approximations.

What the Client Says

Clarivant built our data foundation across Shopify, Ads, and Odoo. For the first time, we had one version of the numbers — and dashboards we could actually trust. It accelerated how we make decisions day-to-day.

E-commerce Client (Founder/COO)

COO, Fast-growing digital-native eCommerce brand (anonymized)

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