← Back to Portfolio
Case Study: Enterprise Data Strategy for E-Commerce
Case Study: Enterprise Data Strategy for E-Commerce
Part 1: The STAR Analysis
Situation
A high-growth D2C brand was scaling rapidly but flying blind. Marketing spend was spread across Meta, Google, and TikTok, while inventory was managed in a separate ERP. No unified view of LTV or CAC existed.Task
Implement a lean, high-impact data strategy to unify disparate data sources and provide real-time visibility into the metrics that matter for scaling a retail business.Action
I designed a "Minimum Viable Data Stack":- Centralized Warehouse: Automated ingestion from Shopify, Meta, and GA4.
- Unified Attribution: Cross-channel engine to identify true conversion drivers.
- Inventory Bridge: Automatically pause ads for low-stock items.
Result
- 25% Improvement in Marketing ROI.
- 18% Reduction in CAC.
- $200k Savings by preventing stockout ad spend.
- 15 hrs/week saved for the finance team.
Part 2: The Story: Simplicity Over Complexity
The client didn't need a massive enterprise data lake; they needed answers to three questions: *Which ads are actually working? Which customers are worth scaling? And do we have enough stock to fulfill the demand?*
We ignored the complex "big data" hype and focused on **Data Connectivity**.
By connecting their warehouse data to their marketing data, we discovered that they were spending $4,000 a week advertising a specific sneaker that was nearly out of stock.
We implemented a simple "Automation Bridge": when stock fell below 10 units, the Meta Ad campaign was automatically suppressed. This one change alone paid for the entire data initiative within three weeks.
Figure 1: ROAS Improvement (Unified Strategy)